SURP 2025 Project Abstracts
Summer Undergraduate Research Program
Faculty: Kira Abercromby, AERO
Email: kabercro@calpoly.edu
Accepted Projects Modes: Hybrid
Traditional orbital mechanics courses typically start with simpler problems that can be solved on paper, gradually progressing to more complex problems that require computational tools. In the AERO department, we have traditionally used MATLAB for these computational tasks. However, the industry is increasingly shifting toward platforms like Python for coding these types of problems. Additionally, with the rapid rise of AI, tools like ChatGPT are now at the forefront of code generation. To stay ahead, students need to learn how to generate code using AI and assess whether the generated code is functioning as expected. This SURP will have students solve problems in both MATLAB and Python, then compare their solutions with AI-generated code. As part of the project, a database of best practices will be developed, and a paper will be written for ASEE.
Faculty: Kira Abercromby, AERO
Email: kabercro@calpoly.edu
Accepted Projects Modes: In-person
Thermal Vacuum Chamber (TVAC) testing is a staple in testing for spacecraft components and parts. Prior to flying in space, each spacecraft must show that their system will be able to perform while experiencing a large temperature swing from -100 to 100 C. In the fall of 2023, the AERO department’s new Thermal Vacuum Chamber (TVAC) was completed and ready to begin system checkout and testing. Over the past 1.5 years, the TVAC system has undergone short-duration tests to refine operational procedures and build general knowledge of the system. Starting this summer, the system will begin its first long-duration tests. Students in this SURP will learn how to operate the vacuum chamber, set it up for long-duration tests, and create a one-page synopsis outlining the system and its capabilities. After completing the necessary training, the tests will include an 8-hour test, a 12-hour test, and potentially a 24-hour test, depending on the results of the initial tests.
**Due to current restrictions, the students applying must be US Persons**
Faculty: Puneet Agarwal, IME
Email: pagarw05@calpoly.edu
Accepted Projects Modes: Fully Remote, Hybrid
This research project will investigate the ability of advanced Large Language Models (LLMs) to identify and assess misinformation across diverse forms of media, including text, images, and video. In an age where misleading content spreads rapidly across digital platforms, evaluating the reliability and integrity of AI systems tasked with fact-checking is critical. We will develop a comprehensive dataset composed of factual and misleading examples drawn from various well-known and reliable fact-checking organizations. Each item will be independently reviewed and transparently labeled to ensure reproducibility. We will then prompt a curated group of state-of-the-art LLMs—including GPT-4, Claude, Gemini, Perplexity, Grok, and Meta’s LLaMA—to return a “misleadingness score” and a self-reported “confidence score” for each item. These responses will be evaluated against ground-truth labels to assess each model’s accuracy and consistency. Ultimately, we will develop a tool that allows users to input various types of media. The tool will then collect responses from multiple LLMs and use a statistical model to determine its own score for how misleading the information is and the confidence in that statement. This tool will use LLMs to fact-check other LLMs, avoiding bias or hallucination from a single inaccurate answer/model. The project emphasizes reproducibility, open-source tooling, and ethical safeguards to ensure that all findings are verifiable and responsibly communicated.
Faculty: Duha Ali, IME
Email: duali@calpoly.edu
Co-Advisor: Javier Gonzalez Sanchez, CSSE
Co-Advisor: Rafael Guerra-Silva, OCOB
Accepted Projects Modes: In-person
As artificial intelligence tools become integral to everyday tasks, understanding how users interact with these systems is essential for improving user experience and system design. This research aims to investigate and compare the interface usability and emotional responses elicited by three prominent AI tools—ChatGPT, DeepSeek, and Google Gemini—using the Emotiv Insight EEG headset. By combining usability testing with emotional biometrics, this study offers a novel approach to evaluating conversational AI systems. The study will capture both subjective usability metrics and objective emotional markers such as arousal, valence, and engagement. Participants will complete standardized tasks using each AI tool, while their EEG data is recorded. A follow-up usability questionnaire (e.g., SUS or UEQ+) will assess perceived interface quality. The goal is to identify which AI system provides the most user-friendly and emotionally positive interaction, contributing to the development of emotionally adaptive AI interfaces.
Faculty: Brendon Anderson, ME
Email: bga@calpoly.edu
Accepted Projects Modes: In-person, Hybrid
Modern artificial intelligence (AI) systems exhibit highly sensitive and unsafe behavior when subjected to undetectable cyberattacks. For instance, human-imperceptible manipulations of the pixels in image data can cause traffic sign classifiers to mispredict stop signs as yield signs. In this project, we will design and analyze new methods to robustify machine learning (ML) models against these adversarial threats. Specifically, we will explore randomization techniques that “smooth out” the ML model’s decision making process by intentionally corrupting input data with small amounts of noise. Optimizing this noise to enhance resilience against attacks while maintaining the system’s accuracy poses a major open problem that we will pursue. The end goal of this project is not only to design and implement such a randomized smoothing method that increases the state-of-the-art robustness of ML systems, but also to develop safety certificates of the proposed method using mathematical analysis. This project is inherently cross-disciplinary, drawing on tools from mechatronics and autonomous systems, computer science and programming, and mathematics. As such, all students with backgrounds and interests in any of these areas are encouraged to apply.
Faculty: John Bellardo, CSSE
Email: bellardo@calpoly.edu
Accepted Projects Modes: In-person
The main objective of the SAL-E mission is to grow the Cal Poly CubeSat Lab’s (PolySat) capabilities in rapid spacecraft design and testing, as well as to provide the current generation of students with the opportunity to design, test, integrate, and communicate with a satellite. This summer we will mainly be focusing on aspects of spacecraft test and integration, as we prepare for our satellite handoff in the Fall. Specifically, we will be conducting satellite assembly, Thermal Vacuum Chamber (TVAC) Testing, Vibrations Testing, and software validation and testing on the flight unit satellite. Students participating in this multidisciplinary SURP will gain hands-on experience with many aspects of the spacecraft testing and integration cycle while ensuring SAL-E will be ready for launch.
Faculty: Iian Black, BMED
Email: iiblack@calpoly.edu
Accepted Projects Modes:
Cuff electrodes and microchannel electrode arrays are chronically stable neural recording devices that record neural activity in peripheral nerves. Their traditional design with a recording site situated at mid-channel prevents these devices from being used to discriminate between efferent (motor) and afferent (sensory) neural activity. Recent studies ([1]–[5]) have demonstrated that off-center or “offset” electrodes – i.e. electrodes situated to the right or left of mid-channel – record signals having different shapes for neural signals traveling in opposite directions (Figure 1). Offset electrodes, therefore, have the potential to discriminate between efferent and afferent neural activity. Electrical noise arising from interna Johnson noise and externals sources, such as EMG or ECG artifacts, obscures the fidelity of the neural recording and makes it more difficult to distinguish shape differences. This proposed research will simulate neural recordings and noise artifacts obtained at different offset positions inside a microchannel and develop a machine learning algorithm to differentiate between left- and right-bound neural signals recorded at various levels of noise. The goal is to identify the minimum offset distance at a given signal-to-noise ratio that would enable the algorithm to correctly differentiate between efferent and afferent neural activity 90% of the time.
Faculty: Aditya Chivate, IME
Email: achivate@calpoly.edu
Accepted Projects Modes: In-person
This project investigates the use of acoustic signals captured during Fused Deposition Modeling (FDM) 3D printing to predict part quality and detect process anomalies. Traditional quality monitoring in FDM often relies on visual inspection or post-process evaluation, which can be slow and inconsistent. This research explores a low-cost, non-contact alternative using microphones and accelerometers to capture real-time audio and vibration signatures of the printing process. By applying signal processing and machine learning techniques to these acoustic signals, the project aims to classify part quality and identify defects such as under-extrusion, layer misalignment, or nozzle clogging. The outcomes have potential applications in smart manufacturing, predictive maintenance, and low-cost quality assurance systems.
Faculty: Giovanni De Francesco, CEEN
Email: gidefran@calpoly.edu
Accepted Projects Modes: Hybrid, In-person
Wood houses dominate the construction landscape in the United States (U.S.), with over 94% of new homes constructed in 2022 being wood framed. New Zealand shares a similar construction landscape, with building practices for wood houses like those in California. Nevertheless, the 2010-2011 Canterbury earthquake sequence damaged nearly three quarters of the housing stock in the region. A total of 150,000 homes were damaged, with about one fifth exceeding NZ$100,000 in damage. To assess the likelihood of economic losses of California’s timber houses, a shake table testing campaign is underway at the California Polytechnic State University Advanced Technology Laboratory in San Luis Obispo. A full-scale, two-story timber specimen—designed with typical light-frame construction details—is being tested with representative nonstructural components, including windows, doors, and sliding doors. This project will provide experimental evidence of earthquake-induced economic losses in timber houses and serve as a foundation for developing mitigation strategies to reduce the economic impact on our society of future earthquake events.
Faculty: Dennis Derickson, EE
Email: ddericks@calpoly.edu
Accepted Projects Modes: Hybrid, In-person, Fully Remote
The Sal-E cube sat mission that is planned for launch in 2026 includes a 902-928 MHz receiving module called the Space Quacker Advanced Development (SQUAD) module. The SQUAD module uses the LoRa modulation format for unlicensed uplink to the satellite using the 902-928 MHz Industrial, Scientific, and Medical (ISM) band. The goal of the SQUAD module is to demonstrate the link robustness of the LoRa communication standard to a low earth orbit (LEO) satellite using this ISM band. To demonstrate this communication link, a 902-928 MHz uplink ground station needs to be established at Cal Poly. The goal of this SURP project is to demonstrate that a student-designed and built circularly polarized “Egg Beater” antenna with a power amplifier is capable of making the LEO link using LoRa modulation with adequate signal-to-noise ratio margin. The use of the “Egg Beater” type of antenna, LoRa Modulation, and a power amplifier greatly simplifies the uplink design by eliminating the need for an expensive Az-El rotator and complicated antenna tracking needs while still providing a reliable satellite uplink signal.
Faculty: Dianne DeTurris, AERO
Email: ddeturri@calpoly.edu
Accepted Projects Modes: In-person
This research focuses on creating an experiment to measure performance of an aircraft propeller on a specially designed stand. Results from the wind tunnel experiments measuring thrust, rotation speed and torque will enable students to create a database of propeller characteristics for use in aircraft design projects. Students in aerospace engineering are regularly assigned design projects powered by turboprop or propeller driven engines. There is not an easy way to verify design performance other than simplistic calculations or extrapolation of open-source wind tunnel data, and not a lot of data is available. Being able to correlate actual test data from different propeller sections will be a great help in verifying the design predictions. A paper will be completed with a goal of documenting and publishing test results at an AIAA conference for use as an unclassified propeller performance database.
Faculty: Dianne DeTurris, AERO
Email: ddeturri@calpoly.edu
Accepted Projects Modes: In-person
This research focuses on optimizing the configuration of a 13cm ion thruster for long term efficiency. Ion thrusters produce thrust for spacecraft when they are in orbit. The thruster needs to be efficient enough to last for the lifetime of the spacecraft as well as through its deorbit procedure. Thruster efficiency is achieved through optimizing the distribution of the high velocity beam of charged particles at the exit that impacts fatigue patterns on voltage grids. The shape of the beam is determined by the placement of magnets on the thruster walls. An experiment to test configurations for increasing efficiency through beam uniformity will be completed with a goal of documenting and publishing test results at an AIAA conference for use in the electric propulsion industry.
Faculty: Dale Dolan, EE
Email: dsdolan@calpoly.edu
Accepted Projects Modes: Hybrid
The Cal Poly Solar farm has been built as a single axis tracking facility with two different types of panels. Both conventional single cell solar panels and twin cell solar cells have been used in its construction. Twin Cell panels typically perform better than their conventional counterparts when shaded by other panels in the row in front of them in a fixed tilt system. However neither module performs well when even a small portion is shaded. This project will access the system API to access data for the field and process to determine areas of concern and different power profiles will be identified that locate and identify inefficiencies in the operation of the system. Backtracking is a technique where the single axis tracker avoids shading other rows by sub-optimally tracking the sun until it is high enough in the sky that this is not necessary. This is relatively straightforward in a field that is flat or at least has all controlled modules in the same array. However the Cal Poly Solar farm installation has significant variation in terrain and this was transferred to the modules in adjacent rows such that they are not in the same plane nor are they parallel. As a result there is significant error in the backtracking algorithm that was not designed for this case which is seen by significant power losses in both of the types of panels. The subject of this research project will be to investigate and identify the areas where there is significant power loss from row to row shading. We will then determine how to adjust the limited input parameters into the existing algorithm to adjust such that performance is improved. The parameters will need to be adjusted for all 12 trackers and they will need to be determined every day. This is too time intensive for a plant operator to do every day manually and through a process of trial and error as they have been doing. As a result the parameters are only adjusted a few times per year and thus great quantities of energy are lost. We will develop an automated process for determining the parameters and through remote control validate the model used. I have obtained access to allow control of the tracking system at the Cal Poly Goldtree Solar Farm to allow this project to be completed 100% remotely if necessary. All control and data collection can be accomplished via a web based dashboard that allows us to monitor the performance of the farm in real time and to collect data that can be analyzed for optimization. The angle that the modules are tracking to can be monitored in real time and it can be determined through power monitoring if any modules are being shaded or not. It is desired to determine an optimal strategy such that the competing interests of operational time invested and energy gain achieved can be optimized.
Faculty: Dale Dolan, EE
Email: dsdolan@calpoly.edu
Accepted Projects Modes: Hybrid
The Cal Poly Solar farm has been built as a single axis tracking facility with two different types of panels. Both conventional single cell solar panels and twin cell solar cells have been used in its construction. Twin Cell panels typically perform better than their conventional counterparts when shaded by other panels in the row in front of them in a fixed tilt system. However neither module performs well when even a small portion is shaded. This project will access the system API to access data for the field and process to determine the energy yield improvements that can be made by optimizing backtracking settings for each array in the Cal Poly Solar Farm. Backtracking is a technique where the single axis tracker avoids shading other rows by sub-optimally tracking the sun until it is high enough in the sky that this is not necessary. This is relatively straightforward in a field that is flat or at least has all controlled modules in the same array. However the Cal Poly Solar farm installation has significant variation in terrain and this was transferred to the modules in adjacent rows such that they are not in the same plane nor are they parallel. As a result there is significant error in the backtracking algorithm that was not designed for this case which is seen by significant power losses in both of the types of panels. The subject of this research project is to determine the improvements that can be made in energy yield by adjustment of the backtracking settings for each array. The parameters have been adjusted and we will need to determine the change in daily energy yield that is achieved for all 12 trackers. This is extremely time intensive and we will be using automation to implement the calculations and comparisons, likely through use of Python. I have obtained access to allow data acquisition for power and energy data and operation of the tracking system at the Cal Poly Goldtree Solar Farm to allow this project to be completed 100% remotely if necessary. All control and data collection can be accomplished via a web based dashboard that allows us to monitor the performance of the farm in real time and to collect data that can be analyzed to determine the improvements.
Faculty: Eltahry Elghandour, ME
Email: eelghand@calpoly.edu
Accepted Projects Modes: Hybrid
This study explores the bulletproof capabilities of Kevlar composites fabricated using advanced additive manufacturing techniques. Continuous fiber reinforced 3D printing (CFRP) is employed to produce Kevlar-reinforced polymer matrix composites (PMCs) with optimized structural configurations for ballistic protection. The primary objective is to evaluate the impact resistance of these composites against 9mm projectiles through systematic experimental testing. A series of ballistics tests are conducted using a high-velocity gas-gun system to fire 9mm projectiles at varying speeds angles. The tests will be conducted at the San Luis Obispo Range, with projectiles fired at various angles. The effects of fiber volume fraction, reinforcement strategy, and infill patterns on energy absorption and failure modes are analyzed. The experimental data is analyzed to assess penetration resistance, energy dissipation, and damage propagation across different composite configurations. This research contributes to the development of lightweight, high-strength ballistic armor by integrating advanced manufacturing techniques with rigorous impact testing, offering potential advancements in protective gear and defense applications.
Faculty: Amanda Emberley, ME
Email: acemberl@calpoly.edu
Accepted Projects Modes: Hybrid, Fully Remote
Learning from failure is an essential component of both learning and practicing engineering. However, failure is often stigmatized and avoided in engineering education. This project aims to better understand how to support students throughout their engineering education to help them learn from their failures, rather than become frustrated or discouraged by them. The project will build on prior research in students’ responses to failure experiences to specifically analyze students who respond to failure in different ways and build on these experiences to help students in similar situations. During the SURP project, the student will use qualitative methods to analyze interview data from students at Cal Poly who have experienced failure and persisted in engineering. The interview questions focus on students’ experiences both in and out of the classroom that have supported them through learning from failure and we will use qualitative data analysis methods to analyze students’ experiences. The aim is to compile the results of the analysis to present to students and instructors at Cal Poly, as well as prepare a conference paper to report the findings to a larger audience. These results have the potential to support students to persist through failure experiences and to develop ways for instructors and institutions to support learning from failure.
Faculty: Eric Espinoza-Wade, ME
Email: erwade@calpoly.edu
Accepted Projects Modes: Hybrid, In-person
An ongoing study funded by the National Science Foundation (NSF) is focused on developing computational models of rehabilitation for people post-stroke. Stroke affects around 800,000 people per year in the US, and often leaves people with limited motor (movement) and neurological (brain-based) capability. Wearable sensors may allow clinicians to better understand how these individuals recover, and to better design therapies and interventions. We are seeking students who can use existing software, and perhaps adapt the software to extract, analyze, and visualize detail from study participants. This project will involve with the Cal Poly PI, and collaborators from physical therapy. This project will involve coding, signal processing, machine learning, and human-subjects research methodologies.
Faculty: Dongfeng Fang, CSSE
Email: dofang@calpoly.edu
Accepted Projects Modes: Hybrid
The objective of this SURP proposal is to investigate machine unlearning as a viable approach to support the right to be forgotten in artificial intelligence (AI) systems, many of which rely heavily on personal data. The capability to selectively remove user-specific information upon request—without necessitating full model retraining—is critical for safeguarding individual privacy and enhancing computational and energy efficiency. This project will undertake a systematic review of state-of-the-art unlearning techniques, evaluate their performance using standard benchmark datasets, and assess their feasibility in real-world applications. In addition, the project will provide participating students with practical experience in implementing and analyzing machine unlearning methods. The outcomes will include a comparative analysis of different approaches and the development of recommendations for improving the effectiveness and efficiency of unlearning in privacy-sensitive AI systems.
Faculty: Daniel Frishberg, CSSE
Email: dfrishbe@calpoly.edu
Accepted Projects Modes: Hybrid, In-person
Faculty: Daniel Frishberg, CSSE
Email: dfrishbe@calpoly.edu
Accepted Projects Modes: In-person, Hybrid
In this project we investigate approximation algorithms for the maximum weighted independent set (MWIS) problem in random graphs that approximate real-world graphs, such as social networks. The problem involves finding a large collection of nodes in a network (vertices in a graph) such that no two nodes are directly connected to one another. Applications of the MWIS problem are broad and include clique-finding algorithms as well as the use of large independent sets in distributed algorithms. We build on prior work by the mentor with a SURP 2024 and senior project, in which preliminary findings suggested that a standard greedy algorithm for finding large independent sets, when applied to certain random graphs—can be sped up without any loss in the quality of the solution found. In fact, we found that in the average case the faster version of the algorithm finds a better solution, avoiding the speed-quality tradeoff that is common in algorithm design. We seek to verify and explain this behavior through theory and experiments. This is an opportunity for the mentee to engage in theoretical research and reinforce their algorithmic fundamentals—and to gain experience in experimental design. The mentee will also gain practical, hands-on experience running experiments with standard graph packages for Python, as well as porting the code to C++, parallelizing it, and running it on a computing cluster.
Faculty: Joel Galos, MATE
Email: jgalos@calpoly.edu
Accepted Projects Modes: In-person, Hybrid
This SURP proposal is centered around the exploration of the application of 3D printing composite part processing techniques to produce multi-material sandwich structures for high performance applications such as civilian and military aircraft. These processing techniques have the potential to significantly reduce the costs and the environmental impact of existing conventional autoclave composite processing techniques. The exploration of new ‘out-of-autoclave’ composite part processing techniques may help to reduce processing costs and the environmental impact of processing, as well as broaden the engineering applications of cellular core materials beyond their existing engineering applications. 3D printing directly onto sandwich core materials will eliminate the need to evacuate air from the core, as well as the corresponding process complications of conventional vacuum bag autoclave co-cure processes. In this project composite sandwich structures will be designed, fabricated and characterized with the Fused Deposition Modelling (FDM) 3D printing process, combined with conventional cellular core materials such as Nomex honeycomb and PET polymer foam.
Faculty: Behnam Ghalamchi, ME
Email: bghalamc@calpoly.edu
Accepted Projects Modes: Hybrid
This project aims to integrate OpenCap, a markerless motion capture system, with augmented reality (AR) to optimize real-time human movement. By leveraging biomechanics principles, AR visualization, and machine learning (ML), the system will provide instant feedback to users, improving movement efficiency while minimizing joint and muscle stress. Previous research in 2024-2025 has successfully demonstrated 2D motion tracking and AR-based mapping onto another person for interactive comparison. This project will build upon that foundation by enhancing real-time 3D motion tracking and developing an advanced AR interface to guide users in sports training, rehabilitation, and workplace ergonomics. The integration of ML will further personalize movement recommendations, making the system adaptable to individual needs. This research contributes to advancements in AR-based biomechanics, human motion analysis, and real-time feedback technologies, fostering safer and more efficient movement practices.
Faculty: Javier Gonzalez Sanchez, CSSE
Email: javiergs@calpoly.edu
Co-Advisors: Duha Ali, IME and Rafael Guerra-Silva, OCOB
Accepted Projects Modes: In-person
This project explores the use of Extended Reality (XR) technologies to enhance human- robot interaction in industrial contexts. Building upon prior research in affective and cognitive state recognition during human-cobot collaboration, this study investigates how natural hand and head gestures, captured through Meta Quest passthrough mode, can be used to communicate human intent to a Universal Robotics e-Series collaborative robot. The XR system provides users with an immersive, real-world visual interface while tracking motion and position in real time. The captured gestures are interpreted through a custom software pipeline that integrates machine learning models and rule-based logic to trigger adaptive robot behaviors. This hands-free, intuitive control mechanism has the potential to lower barriers to cobot use, improve safety, and support both skilled and unskilled workers in manufacturing tasks. The project will prototype and evaluate an XR-based interface for gesture recognition, assess its usability and responsiveness in collaborative tasks, and explore its potential for integration with affective data gathered from complementary sensors. Ultimately, this work aims to contribute to the development of human-centric, adaptive robot systems that respond not only to physical commands but also to the human’s cognitive and emotional context.
Faculty: April Grow, CSSE
Email: amgrow@calpoly.edu
Accepted Projects Modes: In-person, Hybrid, Fully Remote
“Romance” is a common genre of books, especially among women, but in games it has a complex reputation: often relegated to visual novels or side mechanics in life simulations or role-playing games. These games rely on heavily scripted interactions, pre-written scenes, or represent relationships as a simple binary in code. This project aims to use cutting-edge embodied agent research and drama management research to build more inclusive, robust, satisfying, and interactive romance game mechanics. The project will likely be in 2D and use the Godot game engine, though the students involved will have the freedom to influence the design decisions of the underlying game and systems. This project advances the area of game studies research and aims to entice more people, especially women, to become interested in STEM technologies.
Faculty: Nandeesh Hiremath, AERO
Email: nhiremat@calpoly.edu
Accepted Projects Modes: In-person, Hybrid
Radiative heat transfer and the fundamental understanding of the electromagnetic spectrum, ranging from infrared to ultraviolet, are crucial topics for space environments and aerodynamic re-entry systems. The advancement of the aerospace curriculum in these areas will lay the cornerstone for undergraduate students focusing on experiential learning. A specific laboratory experiment involving black body radiation and modes of heat transfer, with a particular emphasis on radiation, will be scoped, designed, and fabricated during the SURP performance period. The outcomes of this work will standardize the introductory courses on heat transfer specific to aeronautics and astronautics concentrations, which is mainly lacking outside of the Cal Poly Aerospace curriculum in an undergraduate setting. The work will also address the pedagogical methods that adhere to Bloom’s Taxonomy, meeting learning outcomes, grading, and assessment. The student researcher will gain hands-on experience with conducting the specified activities, developing an operational and safety manual, and potentially co-authoring an education research-based paper.
Faculty: Borislav Hristov, CSSE
Email: bhristov@calpoly.edu
Accepted Projects Modes: In-person, Hybrid
Malaria, a mosquito-borne infectious disease caused by the Plasmodium parasite, is responsible for more than a half a million deaths per year, the vast majority of which occur in central Africa. The parasite undergoes an incredibly complex cell molecular transformation as it transitions from living in mosquitoes to living in humans with different sets of genes being activated or silenced in order to evade the immune system of the host. Understanding how its genome guides this transition is critical for developing adequate treatments. In this project, we aim to develop a computational framework for investigating the role of the three dimensional (3D) DNA architecture in enabling the expression of virulence genes. We use recently and newly acquired data of DNA-DNA and long non-coding RNA-DNA interactions to create a genome interaction map. We plan to employ a diffusion kernel algorithm to highlight clusters of interacting virulence genes and computationally build a background model to statistically assess the importance of lncRNAs. Our work lays the foundations for further systematic analyses of changes in 3D parasite genome conformation during its complex lifecycle.
Faculty: Silas Hsu, CSSE
Email: sihsu@calpoly.edu
Accepted Projects Modes: In-person
Large video platforms like YouTube and Twitch rely extensively on advertising for revenue. Often, they deploy mid-roll advertising: ads that play in the middle of video content, interrupting it. Such interruptions can irritate consumers significantly. This project primarily involves conducting user testing of an already-developed YouTube browser extension that that gives users some control over when mid-roll ads play. We hope to ascertain how effectively the design mitigates user irritation from mid-roll ads. Tasks will involve recruiting people into a laboratory setting where they will use the browser extension, interviewing them about their experience, and manually extracting themes from the interview data. Good people and socialization skills required. As a stretch goal, the student will create a webpage with video player functionality that matches the web extension’s functionality, enabling larger-scale user evaluation.
Faculty: Kun Hua, EE
Email: kuhua@calpoly.edu
Accepted Projects Modes: Hybrid
This proposed SURP project aims to design and evaluate a smart transportation network capable of preventing multiple-vehicle collisions due to sudden slowdowns in traffic. This project will simulate abrupt braking scenarios and implement adaptive vehicle-to-vehicle (V2V) communication protocols. By enhancing real-time awareness and responsiveness among vehicles, the system will reduce pileup risks and improve road safety.
Faculty: Irene Humer, CSSE
Email: ihumer@calpoly.edu
Co-Advisors: Christian Eckhardt, CSSE and Christian Eckhardt, CSSE
Accepted Projects Modes: Hybrid, In-person
In this research project we will investigate the effect of augmented reality aided dashboard information projected onto the windshield on car driving performance such as reaction time reduction. As augmented reality technology becomes more popular and accessible, essential driving information such as the current speed can be projected to where the traffic happens in terms of location and distance. In comparison to direction ones look at the dashboard, augmented reality devices offer the possibility to place the data in the current view direction. Because of this, the eyes of the driver do not need to constantly focus on the dashboard, when most of the imminent attention needs to be set to the space in front of the vehicle. We plan to test the impact of using augmented reality in a safe environment such as a free parking area: a car parkour with different speed requirements will be set up for our user study. Splitting our participants into two equally big groups, one with a conservative dashboard and the other with the speed displayed in augmented reality using hololens 2. We will be assessing the participant’s performance in areas such as maintaining the speed limit and time to match up with the given speed limit.
Faculty: Shreeshan Jena, BMED
Email: shjena@calpoly.edu
Accepted Projects Modes: Hybrid
The idea of the present proposal is to develop a biomimetic foot model and validate the model accuracy in predicting plantar stresses on the foot, using insole pressure sensors. The incidences of foot pain can often be coupled with uncomfortable footwear and prolonged usage of a shoe may be associated with long-term musculoskeletal disorders in the lower limb. While multiple studies have tracked the progression of knee osteoarthritis (OA) or midfoot OA and have studied the effect of orthotic treatment on the foot pain across individuals [1], there are few biomimetic models of the foot that can study and associate the complex distribution of foot-stresses to the development or progression of such foot or foot-associated pathologies. Further, the model can be tested by simulating different combinations of shoe-wear geometries and therefore get an estimate of the development of stresses within the foot, and how it relates to foot pain.
Faculty: Nicole Johnson-Glauch, MATE
Email: njohns66@calpoly.edu
Co-Advisor: Joel Galos, MATE
Accepted Projects Modes: In-person, Hybrid
This SURP proposal is centered on the design and development of a Materials Marbles Game, an educational tool designed to introduce students to materials science and materials selection through tactile interaction and hands-on experimentation. The game will incorporate measurement of key material properties—such as mechanical, thermal, electrical, and mass density—using spherical marbles made from different materials. These measurements will then be used to construct a physical Material Property Chart (Ashby-Cebon plot) on a game board, fostering a hands-on, embodied understanding of how different materials behave. The game will also illustrate the thought process behind materials selection, a critical aspect of engineering design. The Materials Marbles Game is aimed at being adaptable for both classroom use and outreach initiatives, making it an effective tool for high school seniors, first-year engineering students, and public engagement events. This project will also develop corresponding teaching resources (guides and videos) to support instructors so that the game can be implemented in multiple Cal Poly courses and/or in local K-12 schools. To measure the game’s educational efficacy, we will develop methods to evaluate the impact of the game on students’ self-efficacy, interest in engineering, and design thinking. We will also create ways to illicit feedback from students and instructors in future courses that use the game. Both will form the basis for future educational studies.
Faculty: Seamus Jones, MATE
Email: sjones97@calpoly.edu
Accepted Projects Modes: In-person
Progress toward durable and energy-dense lithium-ion batteries has been hindered by instabilities at electrolyte–electrode interfaces, leading to poor cycling stability, and by safety concerns associated with energy-dense lithium metal anodes. Organic Solid electrolytes (OSEs) can help mitigate these issues; however, OSE conductivity is often limited by sluggish dynamics through rubbery domains. Recent work has suggested that zwitterionic OSEs can self-assemble into superionically conductive domains, permitting decoupling of ion motion and liquid rearrangement timesscales. Although crystalline domains are conventionally detrimental to ion conduction in SPEs, we this work suggests that properly designed semicrystalline OSEs with labile ion–ion interactions and tailored ion sizes can exhibit excellent lithium conductivity (1.6 mS/cm) and selectivity (t+ ≈ 0.6–0.8). In this summer research, I will seek to further understand how to optimize zwitterionic OSEs towards this goal. Firstly, we will characterize the phase diagram of zwitterion/salt blends to understand the self-assembled structures that are present in these blends. Secondly we will utilize impendence spectroscopy to understand the transport properties of the resulting blends. Finally we will integrate the best electrolytes in full-stack LIB designs to demonstrate the ability of these electrolytes to enable stable lithium plating/stripping reactions necessary for battery operation.
Faculty: Paris Kalathas, CSSE
Email: pkalatha@calpoly.edu
Accepted Projects Modes: Fully Remote
This is a proposal for an activity to initiate an effort to create a series of X+CS integrated curricula for learning Computer Science (CS) in K-12. As a start, we examine Mathematics and CS standards, to find the cross-cutting concepts between the two fields. By leveraging these concepts, we bring to the foreground the ways CS can be used in the mathematical context. The goal for this research is to create a 15-week teacher training curriculum that will expose teachers to the CS concepts of Abstraction, Data Representation, Problem Comprehension and Decomposition, Control Structures, Functions and Generalization. Historically, Mathematics and CS have been linked together to support learning concepts from both fields (Papert, S. 1980). However, technological innovations during the last decade, such as social networks, cloud computing and block-based interface for programming languages, changed the way users interact with computers to create digital artifacts (Grover, S. 2021). In addition, research shows that these innovations have a profound impact on teachers’ professional development as well as on how young children conceptualize CS concepts, creating the opportunity to develop their computation literacy earlier in their career (Mishra, P. & Koehler, M. 2006, Yadav, A & Berges, M 2019).
Faculty: Ria Kanjilal, CPE
Email: rkanjila@calpoly.edu
Accepted Projects Modes: Hybrid, Fully Remote
This research project proposes the development of an adaptive reinforcement learning (RL)-based parking system designed to handle complex parking maneuvers that are often unaddressed in existing research. Unlike previous works that focus on isolated maneuvers such as reverse parking or parallel parking, this work presents a flexible framework where multiple parking types (reverse, parallel, diagonal) are addressed through independent agents and then integrated into a cohesive system. The primary gap addressed by this work is the integration of diverse parking maneuvers into a unified RL framework with adaptability to dynamic and varied parking environments. Additionally, this project introduces curriculum learning to enhance training efficiency and domain randomization to improve robustness against environmental variations. This multi-maneuver capability allows for a broader, more scalable application in smart parking systems that can handle different vehicle types and parking scenarios without requiring retraining from scratch. Building on the foundational work of RL-based reverse parking, this project aims to generalize the approach to multiple parking tasks while enhancing robustness through novel training methodologies. The proposed system is trained using the Proximal Policy Optimization (PPO) algorithm within customized simulation environments using OpenAI Gymnasium and HighwayEnv frameworks. A key contribution of this research is the development of an adaptive control architecture that dynamically selects the appropriate maneuver type based on the parking scenario. The project will culminate in a comprehensive simulation-based evaluation demonstrating the system’s ability to effectively handle varied parking tasks with high success rates. The results will inform future extensions to real-world scenarios and contribute valuable insights for integrating diverse control tasks within a single RL framework.
Faculty: Ria Kanjilal, CPE
Email: rkanjila@calpoly.edu
Accepted Projects Modes: Hybrid, Fully Remote
This research project proposes the development of a novel, data-driven framework for detecting concussions using machine learning (ML) and deep learning (DL) models applied to high-resolution eye-tracking data. Unlike traditional concussion assessments that rely on subjective evaluations, this work seeks to identify objective, quantifiable biomarkers derived from ocular dynamics—such as saccadic velocity, smooth pursuit accuracy, and pupillary response—captured through advanced eye-tracking technology. The project will explore state-of-the-art feature extraction techniques and predictive modeling approaches to uncover subtle neuro-ocular signatures associated with mild traumatic brain injury. By combining principles from biomedical signal processing, artificial intelligence, and neurophysiology, this work advances current understanding of how concussions manifest in eye movement patterns and contributes to the interdisciplinary development of intelligent diagnostic systems. The outcomes have the potential to transform clinical and sideline concussion screening by enabling more accurate, timely, and scalable detection methods grounded in computational neuroscience.
Faculty: Fahim Khan, CSSE
Email: fkhan19@calpoly.edu
Accepted Projects Modes: Hybrid, In-person
This research project will develop and evaluate a smartphone-based, AI-powered system to crowdsource and analyze accessibility features and barriers in public spaces. Using computer vision and geospatial mapping, the system will identify and categorize issues such as uneven sidewalks, missing or inadequate curb ramps, damaged tactile paving, obstructive overhangs, and the absence of visual or auditory wayfinding cues. The overarching goal is to generate a dynamic, real-time accessibility map that empowers individuals with diverse mobility, sensory, and cognitive needs to navigate public spaces more safely and confidently. The project will integrate technologies and methods from applied machine learning, mobile computer vision, human-centered interface design, and participatory citizen science. The system will support multiple accessibility dimensions, including needs associated with wheelchair and stroller users, people with vision or hearing impairments, neurodiverse individuals, and older adults with endurance or balance limitations. Building on prior published works such as SmartCS (2024), a platform enabling codeless development of ML-powered apps for citizen science, this project will apply similar frameworks for data collection, annotation, and model deployment in an inclusive, community-driven context. The resulting application will feature an interactive, filterable map interface that visualizes both AI-detected and human-reported barriers, enabling both public users and civic planners to understand and prioritize accessibility needs. The student researcher will work closely with the faculty mentor on training and fine-tuning computer vision models based on deep neural networks, collecting and annotating accessibility-related datasets, prototyping and refining the mobile app, and coordinating a pilot study on the Cal Poly campus and in the City of San Luis Obispo (if time permits). Through this experience, the student will gain hands-on skills in inclusive technology development, interdisciplinary research, and ethical community engagement.
Faculty: Fahim Khan, CSSE
Email: fkhan19@calpoly.edu
Accepted Projects Modes: In-person, Hybrid
This project aims to develop an Augmented Reality (AR) application that overlays real-time coastal environmental data onto physical landscapes when viewed through AR devices such as smartphones and mixed reality headsets. By integrating data from machine learning models, computer vision-based event detection, coastal sensors, and geospatial mapping technologies, the application will provide users with an immersive and interactive experience, enhancing their understanding of coastal dynamics and environmental changes. The application will focus on observing coastal phenomena such as rip currents, tracking endangered coastal species and marine mammals, monitoring crowd levels on beaches, etc. Data sources will include NOAA’s National Data Buoy Center, the Coastal Data Information Program, and live webcam feeds from NOAA’s WebCOOS, incorporating parameters such as tide levels, wave heights, and weather conditions. Geospatial mapping will be achieved through GPS and GIS technologies to accurately align data overlays with the user’s physical environment. Potential user groups for this application include lifeguards, coastal scientists, swimmers, and surfers, with the potential to serve as both a research and educational tool. The student researcher will be responsible for developing the AR interface using platforms like Unity3D and WebXR, integrating real-time data APIs, and ensuring accurate geospatial mapping. The real-time event detection component involves training machine learning models for computer vision capable of running on the targeted AR devices. Additionally, the student will design and implement user interaction features that allow for the selection and visualization of specific data layers and timeframes. This interdisciplinary approach combines computer science, environmental science, and user experience design to advance knowledge in AR applications for environmental monitoring. The project will build upon my previously published works such as RipScout (2025), RipFinder (2025), which focused on utilizing technology for coastal hazard detection and monitoring. Future expansions of this project may explore the potential application of AR devices for citizen science data collection.
Faculty: Franz Kurfess, CSSE
Email: fkurfess@calpoly.edu
Accepted Projects Modes: Fully Remote
The goal of this project is to build a computational model to identify cell towers in satellite images. For a satellite image of a given location, the model should respond with Yes, No, or Maybe to indicate if there is a cell tower in the image. The sponsor of this project, CostQuest Associates, will provide images, existing code for one model (Mask RCNN), and computational resources to refine the model. The student is expected to improve the performance of the model and compare it against another model, CNN VGG-16. This second model is based on open-source libraries but has not yet been adapted for the CostQuest use case.
Faculty: Franz Kurfess, CSSE
Email: fkurfess@calpoly.edu
Co-Advisor: Paris Kalathas, CSSE
Accepted Projects Modes: In-person, Hybrid, Fully Remote
The goal of this summer research activity is to expand the capabilities of a system developed in the larger AI in Search and Rescue project. This project supports volunteer organizations participating in the search for missing persons by providing a basic framework for the collection and organization of relevant information, augmented with specific components that utilize a variety of AI methods. The focus of the summer research will be on the development of agent components for selected roles and tasks in a search and rescue mission. The agents utilize generative AI methods such as Large Language Models (LLMs) to provide insights for the people involved in the search effort. A “Clue Meister,” for example, is responsible for collecting, organizing, evaluating, and distributing all the clues that come in during a search mission. This task and many others can be very challenging for humans, and computer support can significantly speed up and improve the process.
Faculty: Franz Kurfess, CSSE
Email: fkurfess@calpoly.edu
Accepted Projects Modes: Hybrid
This summer research project offers students a unique opportunity to explore the field of sustainable artificial intelligence (AI). The project will involve a comprehensive examination of the environmental impact of AI technologies and the development of AI agents with a focus on sustainability. A key component of this project includes participation in the Munich Summer School of Applied Sciences 2025 course, Sustainable AI Agents, for two weeks. In exchange for participation in the course, the student is expected to help other participants with programming and implementation issues. Building upon the knowledge gained at the summer school, the student will engage in the hands-on development of AI agents using pre-trained models and critically assess their ecological footprint, possibly continuing the work of Bella Boulais’ Senior Project on the ecological footprint of ChatGPT. They will explore techniques for fine-tuning and optimizing these models to minimize their environmental impact. Particular topics of interest will be the energy consumption of AI models, the carbon footprint of AI hardware, green AI algorithms, energy-efficient AI hardware, and the ethical considerations surrounding sustainable AI development. They will examine the dual potential for AI to contribute to resource depletion on one hand, but also to identify potential approaches to reduce resource consumption. This could involve measuring the energy consumption and carbon emissions associated with the development and deployment of AI agents, as well as investigating strategies for reducing these environmental impacts.
Faculty: Stephen Kwok Choon, AERO
Email: skwokcho@calpoly.edu
Accepted Projects Modes: In-person
This SURP project is focused on the preparation of the lab, experiments, and operation procedures necessary for the proposed equipment in the Space Robotics Lab. As such, the primary goal is to create safety and test documentation that shall be used in the lab. Students selected for this project shall gain a hands-on learn-by-doing with equipment that is intended for teaching and research of a simulated planar space test environment for an air-bearing vehicle test bed to be able to test sensors, actuation and deployment of mechanisms, spacecraft collaboration through closed loop control, as well as experimental propulsion systems.
Faculty: Ben Lutz, ME
Email: blutz@calpoly.edu
Accepted Projects Modes: In-person, Hybrid, Fully Remote
Sociotechnical thinking is a critical skill in engineering education and practice, yet relatively little is understood about how students and professionals develop these skills or how they change over time with more experience. Our project is motivated by the following research question: What are the relevant sociotechnical dimensions for engineers (e.g., environmental, cultural, economic, political), and how does their thinking and reasoning about those dimensions change over time? Our guiding hypothesis is that as engineers progress through curricular and professional experiences, they develop sociotechnical thinking skills, and that this progression can be characterized in ways that can inform engineering education and practice. The purpose of this project is to chart the development of sociotechnical thinking from student to professional. To accomplish this goal, we will use an existing dataset comprised of transcripts of collaborative, scenario-based design activities and follow up group interviews with engineers of varying levels of academic and professional experience. We will use this data to examine differences across groups of students and professionals in terms of both the range of factors they consider and the depth and complexity with which they consider them. The result will be a developmental model of sociotechnical thinking in engineering that can be used to inform educational and professional development decisions. We will also use these findings to refine our protocols for subsequent rounds of data collection from students and professionals.
Faculty: Leily Majidi, ME
Email: lmajidi@calpoly.edu
Accepted Projects Modes: In-person, Hybrid
Shape memory polymers (SMPs) are a class of stimuli-responsive materials capable of undergoing programmable shape transformations upon thermal activation. Among various heating methods, light-induced activation provides a contact-free, spatially selective, and controllable approach for triggering self-folding behavior, making it particularly valuable for applications in soft robotics, aerospace structures, and biomedical devices. Despite the growing interest in SMPs, a systematic investigation into the influence of photothermal patterning materials, light intensity, and heat distribution on self-folding behavior remains limited. This research aims to explore the self-folding response of a commercially available SMP by employing photothermal materials such as carbon black and laser-induced graphene to enhance light absorption and localized heating. The study will involve precise pattern deposition, targeted light irradiation, and real-time image analysis to quantify folding characteristics such as bending angles, response times, and deformation kinetics. Additionally, heat transfer and temperature distribution will be analyzed using a thermal resistance network model and finite element simulations to provide deeper insight into the underlying mechanisms of light-induced actuation. The outcomes of this study will advance the fundamental understanding of light-responsive SMPs and contribute to the development of remotely controlled, programmable actuation systems for next-generation engineering applications.
Faculty: Derek Manheim, CEEN
Email: dmanheim@calpoly.edu
Accepted Projects Modes: In-person
Plastics are now ubiquitous in consumer products and their global environmental prevalence is rapidly increasing due to an ongoing reliance on solid waste landfill disposal practices. Through combined physical, chemical, and biological processes encountered at product end of life during solid waste landfilling, plastics are degraded into small particles (< 5 mm), commonly referred to as microplastics (MPs). Once generated, MPs are associated with a variety of known human health impacts, ranging from cancers to cardiovascular issues, as they are readily ingested, accumulated, and encapsulated with other harmful micropollutants. Sustainable waste treatment technologies, such as dry thermophilic anaerobic digestion (DTAD) systems, have the capability to biodegrade MPs into less harmful end products. However, MP biodegradative pathways within these engineered systems are complex, consisting of multiple steps catalyzed by a diversity of competing microorganisms. The potential for synergistic biodegradation of MPs within these systems can offset many of the challenges often reported regarding anaerobic MP biodegradation efficiency and kinetics. Therefore, this experimental research aims to: i) investigate the enrichment of MP degrading bacterial populations within the greater fermenting communities of DTADs; ii) identify potential synergistic MP degradative pathways within the greater microbial community of DTADs; and iii) evaluate the impact of these synergistic MP degradative interactions on the treatment performance and renewable energy production of DTADs. In collaboration with the SLO Kompogas Digester facility, samples will be acquired, and bench scale, batch anaerobic biodegradation experiments will be performed. Metagenomic and metatranscriptomic data, as acquired through shotgun next generation sequencing of total genomic DNA/RNA, will be applied to track the microbial community composition and expression of functional genes encoding MP degradative enzymes during simulated DTAT in the presence and absence of synthetic MPs. In addition, treatment performance and energy production will be evaluated through quantification of volatile solids reduction and methane generation potential, respectively. Gene abundance and co-expression data, coupled with treatment performance observations, will provide valuable insight into the identification of viable MP anaerobic biodegradation pathways and development of synergistic microbial community relations to inform the optimization of future operational practices at the Kompogas digester facility.
Faculty: David Marshall, AERO
Email: ddmarsha@calpoly.edu
Accepted Projects Modes: In-person, Hybrid
As the use of computational fluid dynamics (CFD) grows in the aerospace (and other engineering disciplines), the need for coursework, and in particular labs, that develop the appropriate CFD skills is growing. In the semester transition, the Aerospace Engineering Department will be offering an applied computational aerodynamics class that will have a heavy emphasis on practical CFD applications to realistic problems to be encountered by our students. This research activity will help develop labs that will be used to teach students how to use StarCCM+ (the department’s current commercial CFD tool) starting from simple geometries going up to complete airframes. The student(s) participating in this effort will not only gain insight on how to perform these types of analyses, they will also develop skills on how to systematically grow the complexity of an instructional activity so that the end product is a curriculum that provides a practical skill-set in applied CFD.
Faculty: David Marshall, AERO
Email: ddmarsha@calpoly.edu
Accepted Projects Modes: In-person
With the exponential growth in the use of computing, there is a growing need for undergraduate students to enter the workforce with experience with more complicated computing architectures. The CFD research group in the Aerospace Engineering Department received an HPC system and related computing hardware through the Air Force Research Lab. This system consists of three components: (1) a cluster compute engine with 256 CPU cores, 3.2 TB of RAM, 4 Tesla A100 GPUs, and 200 Gbps InfiniBand network backplane; (2) a high performance storage platform with 540 TB of raw storage, 200 Gbps InfiniBand network, and BeeGFS parallel cluster filesystem, and (3) a long term file storage server. These three systems need to be stood-up, tested, and upgraded as needed to the latest versions of their operating systems. There also needs to be system administration plans developed along with a coordinated suite of automated system administration utilities to monitor and maintain these systems. Finally, there a number of research specific software applications that need to be installed on this system, including commercial computational fluid dynamics applications and custom libraries for future research activities.
Faculty: Eric Mehiel, AERO
Email: emehiel@calpoly.edu
Accepted Projects Modes: In-person
There are three typical methods used to control the attitude (or orientation) of spacecraft in orbit. They are momentum exchange devices (reaction wheels and control moment gyroscopes), magnetic torquers, and thrusters (both cold and hot gas). The PolySim research group is currently developing a spacecraft attitude dynamics simulator utilizing reaction wheels. The simulator floats on a spherical air bearing and is balanced to accurately simulate the rotational motion of a spacecraft in orbit (not including the orbital motion). As a continuation of that project, we have completed the development of spacecraft attitude simulator using cold gas thrusters to control the altitude of the simulator. With the completion of the Cold Gas Thruster platform, we now have a single axis low friction test bead that can be used to develop other spacecraft attitude control technologies. The goal of this project is to design, develop, manufacture, and test a reaction wheel that can be assembled and characterized by other upper division undergraduate students. The project will require completion of several tasks. First, the mechanical and the electrical systems must be developed and designed. This includes the microcontroller and power system, including motors and batteries. Next, the student will design a feedback control including simulations in Matlab and Simulink. Next, the development of the microcontroller used to implement the control law and all code must be completed. Finally, the full system will be tested with hardware results compared to simulations developed in Matlab and Simulink.
Faculty: Joydeep Mukherjee, CSSE
Email: jmukherj@calpoly.edu
Accepted Projects Modes: In-person, Hybrid, Fully Remote
The Fourth Industrial Revolution, or Industry 4.0, integrates a range of advanced technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), Cloud Computing, and Data Analytics to improve the efficiency, productivity, scalability, and security of modern manufacturing systems. Smart manufacturing leverages these technologies across various domains to enhance data-driven decision-making and quality control in production processes. In a smart manufacturing environment, heterogeneous IoT sensors interact with each other and control systems to automate real-time monitoring, analysis, and decision-making. This integration enables manufacturers to optimize production, quickly detect and address anomalies, reduce environmental impact, and adapt rapidly to changing conditions. Given the reliance on IoT sensors and the traffic they generate, smart manufacturing environments require a scalable and efficient IoT pipeline capable of ingesting, processing, and analyzing large volumes of sensor data in real time. This project aims to develop an automated IoT pipeline for modern smart manufacturing, ensuring low latency and strong fault tolerance when processing data from diverse sensors. The pipeline will utilize cutting-edge IoT technologies, including Apache Kafka and Apache Spark, to manage and process the sensor data effectively.
Faculty: Sumona Mukhopadhyay, CSSE
Email: mukhopad@calpoly.edu
Accepted Projects Modes: In-person, Hybrid
This project focuses on the use of Natural Language Processing (NLP) techniques to analyze text stimuli used in cognitive research. Specifically, the project involves analyzing text that presents different types of mindsets, such as growth and fixed mindsets, to understand their impact on cognitive state. Students will apply various NLP methods, such as tokenization, text classification, and sentiment analysis, to analyze the language used in different types of mindset stimuli. The goal is to understand how text-based stimuli can influence cognitive responses and to extract meaningful features from the text that can be used to predict outcomes like engagement or cognitive load. Students will gain experience in working with unstructured text data and applying machine learning techniques for text classification, making this an ideal project for students with a background in data science who are interested in the intersection of language and cognition.
Faculty: Eric Ocegueda, ME
Email: ocegueda@calpoly.edu
Accepted Projects Modes: In-person
Assessment of human balance provides valuable metrics to track the health, development, and fall risk of individuals. The prominent method for objective balance assessments has used force plates to track the center of pressure (COP) position as a participant attempts to balance during varying tasks. However, the use of force plates is limited by the cost of equipment and expertise required, leading to recent interest in using embedded inertial measurement units (IMUs) in mobile devices instead. Many researchers have explored placing mobile devices close to the subject’s center of mass (COM) to approximate the subject’s COM acceleration and using the COM acceleration as an alternative to COP measurements for balance assessment. Recently, as part of Cal Poly’s Mobile Balance Lab (MBL) Master’s project, we have developed a methodology to use cameras to aid in tracking both COM acceleration and COP position with a smartphone placed anywhere on a participant. The proposed project aims to build off the current methodology and code to improve the accuracy and accessibility of the model for use as a mobile balance lab. The goals include (1) removing the need for the camera system by using anatomical measurements and dynamics to track relative positions and (2) using the double inverted pendulum model to improve the COP position estimate using smartphones. One SURP student (from ME, AERO, BMED, or other related disciplines) will work on familiarizing themselves with the developed method and MATLAB code, explore the literature on the statistical variance of anatomical measurements, develop equations of motion to approximate human balance as a double inverted pendulum, modify the existing MATLAB code to implement subroutines for goals (1) and (2). Students should indicate in their application if they are interested in continuing work on the project during the academic year for independent study course credit.
Faculty: Rebekah Oulton, CEEN
Email: roulton@calpoly.edu
Co-Advisor: Priya Verma, ENVM
Accepted Projects Modes: In-person, Hybrid, Fully Remote
Higher education institutions are increasingly incorporating the concept of sustainability into the curricula, but the evaluation of students’ level of sustainability knowledge is underdeveloped. The proposed research focuses on Cal Poly’s efforts to integrate sustainability principles within the engineering curricula and the impact on students’ knowledge. By analyzing existing data collected on the sustainability literacy of Cal Poly’s engineering students, this project aims to enhance teaching strategies and align them with the sustainability goals of both the College and the University. This initiative builds on work initiated last summer and seeks to better prepare engineering students to become future contributors to sustainable development by effectively embedding sustainability competencies within their educational programs.
Faculty: Jason Poon, EE
Email: jasonp@calpoly.edu
Accepted Projects Modes: Hybrid
This project will develop an analog computing circuit that can accelerate power system simulations used for grid interconnection studies. The project will leverage analog computing to create a specialized circuit capable of simulating large-scale power networks with detailed models of power electronics-based loads, such as those found in data centers and manufacturing plants. A software application programming interface will be developed to integrate this circuit with a desktop computer, where simulations can be run by the user. The project team will also work with industry partners and utilities to evaluate the feasibility of the proposed technology for conducting real-world grid interconnection studies. Students will take part in tasks including designing, simulating, and fabricating the proposed analog computing circuits at the printed circuit board level, as well as performing lab tests to verify their functionality. Additionally, students will assist in developing software in Python to integrate the circuit with simulation tools, such as MATLAB Simulink.
Faculty: Amuthan Ramabathiran, AERO
Email: aramabat@calpoly.edu
Accepted Projects Modes: In-person
The aim of this project is to study experimentally the structural stability of highly flexible wings under low subsonic flow conditions. The experiments will be carried out at the low-speed wind tunnel in the Aerospace Engineering department. As part of this project, the student will design and build a variety of different flexible wing structures and test their performance in the wind tunnel–the experiments will involve pressure, deflection, and strain measurements. Both additively manufactured and built-up structures will be explored as part of this project. The project will also require the student to perform finite element simulations for the wing structures. The eventual goal of this study is to understand the pros and cons associated with flexible wings, primarily from a structural viewpoint, and understand what design choices are essential to develop stable and controllable flexible wings.
Faculty: Amuthan Ramabathiran, AERO
Email: aramabat@calpoly.edu
Accepted Projects Modes: In-person, Hybrid
The goal of this project is to develop a new class of hybrid solvers for partial differential equations (PDEs) encountered in solid and fluid mechanics that blend traditional finite element methods (FEM) with modern machine learning algorithms. While FEM solvers are well developed, they can be computationally expensive for realistic problems. Machine learning algorithms have emerged as possible new solutions to cut down the computational cost associated with expensive FEM simulations, but these are typically not interpretable. Previous work on this topic resulted in a class of fully interpretable machine learning solvers for PDEs that had two primary drawbacks: (i) they were not competitive with FEM solvers, and (ii) the enforcement of boundary conditions is both inaccurate and inefficient. The present project aims to overcome both these limitations by developing hybrid solvers that extend a coarse FEM solver with an adaptive machine learning model building on previous research by the PI. These hybrid solvers will be applied to solve a variety of problems in solid and fluid mechanics, and specifically for fluid-structure interaction problems. The latter are of significant importance to aerospace applications.
Faculty: Paul Schmitt, CSSE
Email: prs@calpoly.edu
Accepted Projects Modes: In-person, Hybrid
Nearly everything we do on the Internet leaves a trace, and in recent decades the value of user data has proven to be highly profitable and become a fundamental business strategy of the Internet. The only recourse users have in this situation is seeking increased privacy, yet privacy is uniquely challenging on the Internet because we inherently rely on others (e.g., ISPs, content providers, CDNs) to carry and serve our traffic. Recent systems have sought to enhance user privacy without sacrificing performance by adopting Multi-Party Relay (MPR) architectures, including Apple’s iCloud Private Relay. These architectures mask user IP addresses by tunneling through cloud infrastructure, yet Private Relay attempts to maintain accurate location information for users by intentionally falsifying location metadata for cloud-hosted IP prefixes. This project proposes a comprehensive measurement study to analyze the inherent tradeoffs between privacy and usability in the Private Relay (PR) architecture. The study will gather geolocation data from multiple IP databases (e.g., MaxMind and IPInfo) across several countries, evaluating the accuracy of geolocation when using PR services. Specifically, the research will examine: (1) geolocation performance inequalities; (2) the relationship between geolocation errors and proximity to cloud infrastructure; (3) quantification of privacy benefits; and (4) correlation of these metrics with underlying population characteristics (e.g., average household income, etc.). This research aims to illuminate the fundamental tension between privacy preservation and accurate geolocation in PR systems, potentially revealing disparities affecting users based on geographic region, and provide a foundation for improved PR system design. Students with an interest in Internet measurement, privacy, GIS, and computing for social good would likely find this project most suited to them.
Faculty: John Seng, CPE
Email: jseng@calpoly.edu
Accepted Projects Modes: In-person
In this project, we will collaborate with Caltrans District 5 (covering San Luis Obispo County and surrounding regions) to develop a small, battery-powered, camera-based embedded system utilizing an NVIDIA Jetson board and neural networks to detect highway traffic anomalies. The device will analyze real-time traffic flow patterns and send alerts regarding detected anomalies. Unlike the stationary commercial camera systems currently in use, the proposed system offers increased mobility, affordability, and will give Caltrans engineers improved access over system outputs.
Faculty: Dev Sisodia, CSSE
Email: dsisodia@calpoly.edu
Accepted Projects Modes: In-person, Hybrid, Fully Remote
The underrepresentation of Latinx students in computer science highlights the need for innovative and inclusive educational approaches. This project addresses challenges such as limited access to educational resources and the demand for multilingual learning tools by developing a co-located, collaborative, game-based programming environment. Designed for use on phones, tablets, and laptops, this tool supports English, Spanish, and Mixtec, facilitating broader engagement. By promoting peer collaboration and interactive learning, our approach challenges traditional notions of solitary programming and reinforces the idea that expertise is shared, fostering an inclusive and equitable learning environment.
Faculty: Thale Smith, MATE
Email: tsmith50@calpoly.edu
Co-Advisor: Joel Galos, MATE
Accepted Projects Modes: In-person, Hybrid
Material Extrusion (MEX) is a low-cost approach to metal additive manufacturing that involves extruding a metal-polymer composite filament to create a part comprised of polymer-bound metal powder, which is subsequently debound and sintered to produce a fully metal component. A major barrier to broader implementation of metal MEX is the time-intensive process for determining printing and sintering process parameters. To consistently produce sintered parts that satisfy geometric and materials properties requirements, trial-and-error experimentation is required to develop process parameters, correct shrinkage variability, and mitigate defects. This project aims to leverage Citrine Informatics’ machine learning (ML) tools to accelerate development of key aspects of the metal MEX process, including defect mitigation, shrinkage behavior, and sintering conditions, while still achieving materials properties that meet metal injection molding (MIM) standards. Successfully integrating ML with metallographic characterization and mechanical testing will enhance industrial viability of metal MEX by using predictive modeling to accelerate process parameter optimization, thereby reducing the number of printing and sintering hours required to produce a component that meets design requirements.
Faculty: Hyeonik Song, ME
Email: hsong13@calpoly.edu
Accepted Projects Modes: Hybrid
Design-by-Analogy (DbA) is a powerful design tool that uses analogical reasoning to help engineers develop groundbreaking innovations, such as cyclonic separator inspired bagless vacuum cleaner or gecko inspired adhesives. DbA leverages the natural, human process of analogical reasoning in a systematic manner in three phases namely: retrieval (what prior knowledge/experience is relevant to a design problem?), mapping (what elements of the design problem align with the retrieved knowledge?), and evaluation (how applicable is the retrieved knowledge to solving a design problem?). To date, most DbA techniques support one or two phases of analogical reasoning and therefore overlook important opportunities for more innovative engineering solutions. In this summer project, we aim to develop an intelligent DbA tool called VISION+ that provides a comprehensive guidance to designers during all phases of the analogical reasoning process, helping them generate innovative and successful design concepts. We propose to develop the tool by integrating a cognitive assistant (CA) into an existing DbA tool, VISION developed by the PI. VISION+, will function as a design collaborator, offering suggestions and examples that help designers expand the solution space and explore a wider range of possibilities. The CA will support designers during the analogical reasoning process by analyzing information from diverse sources and feature a natural language interface to facilitate communication between human designers and CA. This project will expand our efforts to enhance modern engineering design through human-AI collaboration.
Faculty: Jill Speece, IME
Email: jespeece@calpoly.edu
Accepted Projects Modes: Hybrid
TurboRad has developed a system with proven results that streamlines radiology report creation and improves radiologist productivity. A study performed in 2023 found that “the advanced macro-button toolbar system has great correlation for both lower report completion times and increased wRVU productivity. The benefits of the toolbar system are most evident in the simpler XR reports. Though they are simpler to read and complete reports, the greater quantity of XR reports contributes to a greater overall wRVU completion rate throughout the day as radiologists can spend more time on more complex examinations and reports” (Chow, Speece, & Holtzman, 2024). Since 2023, the system has been moved to a new platform that can integrate with various practices’ picture archiving and communication system (PACS). With the industry-wide launch of this new reporting tool coming soon, TurboRad is planning to offer their customers a total solution by understanding the performance metrics they care about the most and offering a tailored dashboard to track progress to these metrics that ties with results of using the new reporting tool. This SURP project has two parts – develop the process for collecting the voice of the customer and develop an interface that tailors a metric dashboard to the needs of the radiologist. This research project will advance student understanding of the process of continuous improvement in healthcare and provide them with an opportunity to gain experience and develop expertise in healthcare data analytics.
Faculty: Ramanan Sritharan, ME
Email: rsrithar@calpoly.edu
Accepted Projects Modes: In-person
Nanomaterials have found widespread applications, particularly carbon nanotubes (CNTs) in structural applications, which have gained significant traction over the past few decades [1] [2]. Numerous experimental [3] [4] [5] [6] [7] [8] [9] [10] and analytical studies [11] have demonstrated that incorporating CNTs into an epoxy matrix can significantly enhance the mechanical properties of the composite. This study evaluates and compares the effectiveness of nanocomposite panels fabricated using two different fabrication techniques: ultra-sonication combined with a hot plate process, and the FlackTek mixer. The primary objective of the research is to investigate the impact of these processing methods on the dispersion of CNTs, uniformity of the composite matrix, and overall performance characteristics of the nanocomposite panels. The ultra-sonication and hot plate method uses high-frequency sound waves to disperse CNTs into ethyl alcohol, followed by heat-induced magnetic stirring to evenly mix the CNTs with epoxy. On the other hand, the FlackTek mixer employs high-shear forces to achieve CNTs dispersion in epoxy in a more controlled manner. The samples fabricated using two different methods will be assessed and compared on important mechanical properties such as tensile strength, tensile modulus, and strain. The study aims to provide a comprehensive understanding of how each processing technique influences the mechanical properties of nanocomposite panels. The results will offer valuable insights for optimizing the fabrication of nanocomposite panels.
Faculty: Lubomir Stanchev, CSSE
Email: lstanche@calpoly.edu
Accepted Projects Modes: In-person
We would like to build a compiler for relational algebra that converts it to SQL. This compiler will be used in database labs to give the students hands-on experience to write relational algebra code. Current relational algebra compilers that are open source are not very good and are hard for the students to use.
Faculty: Stefan Talke, CEEN
Email: stalke@calpoly.edu
Accepted Projects Modes: In-person
In this SURP project, a student will help catalog, refurbish, test, and then deploy oceanographic equipment into Morro Bay and nearby coastal regions. In 2024, the Talke lab inherited approximately $300-400k of oceanographic equipment (sticker price) that was originally bought between 2000-2020. Instruments include multiple pressure gauges, velocity instruments, salinity-temperature-turbidity gauges, particle sizers, and a sonar for making bathymetric measurements. Help is needed to check (“talk to”) these instruments, determine which still work, and then field deploy them into the central coast., For some instruments, additional hardware (batteries, cables) and communication adapters may be needed. Because instrument software is often old, emulation of older operating systems or DOS may be required to establish communication. Testing will first take place in a lab, then in Morro Bay. Measurements will be post-processed using software such as Matlab and compared to independent measurements to make sure they are accurate. If these instruments can be made to work again, it greatly increases the ability of Cal-Poly to make physical oceanographic measurements in the coastal zone, both for teaching and research purposes. Desired qualifications include experience with instrumentation, field work, and computer programming, and a willingness to learn-by-doing, to come up with solutions, and to implement them.
Faculty: Taufik Taufik, EE
Email: taufik@calpoly.edu
Accepted Projects Modes: In-person, Hybrid
In pursuit of supporting the global efforts in reducing carbon footprint and reliance on fossil fuels, this project seeks to continue the development of a hybrid AC/DC house prototype at Cal Poly State University. To enhance the power flow to DC loads, a dedicated 48 V DC bus will be constructed to replace the impractical multiple DC buses in the previous system. This iteration will also add a key feature that enables users to monitor real-time AC and DC powers. Another new functionality will involve the provision of a mix of latching and non-latching relays to switch between sources, thus ensuring that both AC and DC loads receive power. This new feature will further enable users to conveniently observe the system status and select which sources are utilized from a touchscreen. Other proposed improvements entail the integration of a control system with a Raspberry Pi and Arduino Nano and a touchscreen human-machine interface to control main components in the system. When fully completed, the hybrid AC/DC house will not only serve as a living laboratory to educate students and the community on residential electrical systems but will also serve as a demonstration site of a future Net-Zero Energy Home.
Faculty: Allison Theobold, Noyce
Email: atheobol@calpoly.edu
Accepted Projects Modes: In-person, Hybrid
Group work has been widely recognized as a “high-impact practice” and is commonly integrated into statistics and data science courses. However, implementing collaborative coding—a critical aspect of “doing” statistics—in these settings presents challenges. This project examines the landscape of collaborative programming tools, focusing on how both students and instructors engage with them. Specifically, we explore two commonly used tools—Posit Cloud and Google Colab—exploring each tool from the perspective of the instructor and the student. Based on these insights, we provide recommendations on the optimal use of each tool and strategies for how they can be used within these contexts to facilitate more productive group collaborations.
Faculty: Jonathan Ventura, CSSE
Email: jventu09@calpoly.edu
Accepted Projects Modes: In-person, Fully Remote, Hybrid
Previous work from our group utilized drone-based images and a novel machine learning classification model to quickly and accurately quantify spatial variability in eelgrass in Morro Bay. In this SURP project we aim to develop and evaluate quantifiable estimates of the model’s prediction uncertainty. Our model produces a binary labeling indicating the presence or absence of eelgrass at each pixel. However, internally the model produces a probabilistic output indicating the likelihood of detection at each pixel that we propose to use to produce uncertainty estimates and a confidence interval for the cumulative extent of eelgrass detected. This would allow for more credible communication of measurements when measuring the area of eelgrass across a study area or analyzing the change in the amount of eelgrass over time. To evaluate and calibrate our uncertainty predictions, we will compare them to the empirical uncertainty observed in our ground truth annotations.
Faculty: Madhusudan Vijayakumar, AERO
Email: msudan@calpoly.edu
Accepted Projects Modes: Hybrid, Fully Remote, In-person
This project will establish comprehensive guidelines and a strategic action plan for developing an inclusive and accessible computing platform that integrates NASA’s Evolutionary Mission Trajectory Generator (EMTG) software with PolySpace, Cal Poly’s in-house space mission design toolkit. Employing Universal Design for Learning (UDL) principles, the project will ensure equitable access and participation for diverse undergraduate aerospace engineering students, emphasizing inclusion for women and underrepresented groups in aerospace. Inclusivity will be promoted by creating detailed guidelines for remote software interfaces and visualization layers specifically designed for diverse learning styles and accessibility needs. A blueprint for an inclusive curriculum module will also be developed to facilitate equitable learning outcomes. To measure the effectiveness of the tools in advancing justice, inclusion, diversity, and equity (JEDI) in STEM education, a student engagement survey will be developed stressing on using metrics such as performance assessments across different demographic groups, and feedback on accessibility and usability. The guidelines produced will directly support complimentary research initiative to implement API/Web-based interfaces for PolySpace. The interface will be universally accessible to students’ campus-wide, suitable for senior design projects, classroom activities, and research applications. Further, the web-based calls will be leveraged to deliver outreach activities at local high schools broadly enhancing equity and inclusive STEM education opportunities within the community.
Faculty: Madhusudan Vijayakumar, AERO
Email: msudan@calpoly.edu
Accepted Projects Modes: In-person, Hybrid, Fully Remote
With space becoming increasingly accessible, the need for sophisticated space trajectory design has surged, as we challenging existing computational capabilities. This project tackles the intricate and computationally demanding task of spacecraft trajectory optimization and real-time orbit determination by developing and benchmarking advanced numerical algorithms integrated into Cal Poly’s mission design platform, PolySpace. Leveraging cutting-edge Python routines and interfaces with NASA’s General Mission Analysis Tool (GMAT), the research addresses mission-critical scenarios, including multi-gravity-assist interplanetary missions and efficient solar-electric orbit transfers. Benchmarking the solution approach against past missions, the project ensures rigorous accuracy and reliability. This experience will help bring research into a classroom environment and empower students with practical, industry-relevant experience. The project will also advance Cal Poly’s Astronautics capabilities, complementing and expanding upon the CubeSat program
Faculty: Long Wang, CEEN
Email: lwang38@calpoly.edu
Accepted Projects Modes: In-person
This interdisciplinary research, in collaboration with Sony, aims to improve the fit and comfort of socket prosthetics for amputees by utilizing sensing technology and data analytical techniques. Many amputees face issues with prosthetic fit, which can lead to discomfort, pain, and even tissue damage. Our goal is to address these problems by developing a low-cost, universal, and wearable sensing system that can continuously monitor the pressures at the residual limb and prosthetic socket interface. This product will provide feedback to the user, allowing for real-time adjustments, ensuring a comfortable fit, and avoiding injury. Our research group has extensive experience in wearable sensors and machine learning/artificial intelligence. We plan to integrate Sony’s electronic boards with wearable sensing systems and investigate the performance of different data analytical algorithms. The students will have the opportunity to closely work with Dr. Wang’s interdisciplinary research group.
Faculty: Long Wang, CEEN
Email: lwang38@calpoly.edu
Accepted Projects Modes: In-person
Sensing technologies play significant roles in structural health monitoring (SHM) systems for monitoring and assessing structural conditions in real-time, which can enhance the safety and reliability of various structures. While engineered nanomaterial (ENM)-based sensors have remarkable potential to transform conventional sensing devices, large volume of ENMs released into the environment can significantly jeopardize the environment and public health. Thus, there is a pressing need to develop next-generation sensing materials in a more eco-friendly and sustainable manner. The goal of this interdisciplinary proposal is to sustainably develop sensing nanocomposites for monitoring structural damage by re-using waste materials as nano-/micro-scale functional material components. This project will lay the foundation for developing innovative, mechanically robust, and sensing structural materials in an eco-friendly, scalable, and low-cost manner.
Faculty: Xuan Wang, IME
Email: xwang12@calpoly.edu
Co-Advisor: Jenny Wang, IME
Accepted Projects Modes: Hybrid
As 3D printing technology continues to transform the healthcare industry, the need for FDA-compliant additive manufacturing facilities in public universities has become increasingly important. While private companies and medical research institutions have successfully integrated 3D printing for medical device prototyping and surgical planning, public universities—including Cal Poly—lack the dedicated infrastructure needed to support FDA-regulated medical manufacturing and research. This project aims to bridge that gap by researching, designing, and implementing a dedicated FDA-compliant section within Cal Poly’s multi-purpose 3D Printing Facility. Over the course of eight weeks, student researchers will study FDA regulations, design an optimized facility layout, and implement a structured additive manufacturing workflow to ensure compliance with FDA, ISO 13485, and Good Manufacturing Practices (GMP) standards. The designated section will feature controlled workspaces for medical 3D printing, post-processing, quality control, and regulatory documentation, ensuring adherence to safety and validation requirements. Once operational, this facility will provide students, faculty, healthcare professionals, medical researchers, and industry partners with a regulatory-compliant workflow for designing and producing medical-grade 3D prototypes, while also serving as a foundation for future FDA-compliant proof-of-concept developments.
Faculty: Xuan Wang, IME
Email: xwang12@calpoly.edu
Co-Advisor: Jenny Wang, IME
Accepted Projects Modes: In-person, Hybrid
Surgeons require pre-surgical models to practice procedures and improve success rates. However, the current process of creating physical models for practice using 3D printing is limited to producing homogeneous mimics of the human body, while the human body is a heterogeneous structure composed of multiple materials with varying properties, including bones, muscles, and skin. Additive manufacturing methods cannot easily print multiple materials simultaneously, as current AM machines only print homogeneous material, unlike the human body. In this proposal, we aim to develop a multi-material additive manufacturing system, from software to hardware, to manufacture pre-surgical models. We will investigate multi-material AM methods by modifying existing filament extrusion printers.
Faculty: Zhiyuan Wei, IME
Email: zwei03@calpoly.edu
Accepted Projects Modes: Hybrid
Facility location optimization is a foundational area in operations research and data analytics that is concerned with determining the optimal placement of facilities to serve a spatially distributed demand. However, traditional facility location decision is often approached from a centralized perspective, where the facility planner dictates the allocation of customers to facilities. In this setting, it is typically assumed that customers patronize the nearest facility, and their preferences are not explicitly modeled. This traditional decision-making framework may not accurately reflect real-world dynamics where customers may choose facilities based on a variety of factors beyond proximity. Thus, this project aims to develop a novel decentralized decision-making framework for facility location modeling that explicitly considers people’s actual mobility patterns and behavioral preferences. By accounting for people’s nuanced mobility patterns in facility location optimization, the decentralized facility location model can provide more realistic and precise decision support for stakeholders, such as city planners and policy makers, with the goal of improving access to essential services. Ultimately, this project offers unique opportunities to leverage real human mobility data in the development of a data-driven, optimization-based framework for facility location problems.
Faculty: Michael Whitt, BMED
Email: mdwhitt@calpoly.edu
Accepted Projects Modes: In-person
A recent article entitled ‘Cow Fertility, Pregnancy Rate Important Economic Numbers’ in NDSU Extension and Ag Research News made the statement: “Reproduction is the most important economic trait in a beef cow herd.” One bovine fertility organization, Trans Ova Genetics, currently documents on their website a 70% pregnancy success rate for Embryo Transfer (donor) and 55% pregnancy success rate for In Vitro Fertilization (self). Tran Ova Genetics also documents that only 30% of oocytes develop into viable embryos. Activities on this SURP experience will include an engineer working alongside an animal sciences student. The project sponsor, ReproHealth Technologies, Inc (RHT) has developed two medical devices that will be used during this SURP project; an intravaginal culture (IVC) device and novel sperm separation (SS) device that improves bovine sperm motility thus improving the probability of fertilization success. RHT has already received an approved Institutional Animal Care and Use Committee (IACUC) protocol to perform the procedure where embryos will be aspirated form a heifer and result in a full term pregnancy using the IVC and SS devices. In addition to Whitt (in biomedical engineering), the SURP researchers will also be mentored by a fertility doctor/embryologist (MD) and large animal veterinarian (DVM).
Faculty: Ava Wright, Noyce
Email: avwright@calpoly.edu
Accepted Projects Modes: Hybrid, Fully Remote
LLMs must be finetuned so that their responses align with human moral values. But the dominant economic approach to doing so construes human rational agency instrumentally and moral values in terms of preference-utility maximization. In this project, we will finetune and assess an open-source AI large language model for its alignment with Kantian deontological moral principles, instead. The project will, therefore, have two parts: 1) the finetuning of the model, and 2) the creation of a suitable benchmark for assessing it. Kantian deontological approaches that construe human rational agency substantively and moral value in terms of autonomous willing have been neglected in both parts, despite their importance to moral theory. Existing efforts to finetune LLMs align with Kantian principles are ad hoc at best, and no good benchmarks currently exist to assess how well a model aligns with Kantian deontology. This project aims to begin to explore how to fill this gap in research
Faculty: Xi Wu, ME
Email: xwu@calpoly.edu
Accepted Projects Modes: In-person
This proposal aims to design and manufacture effective dampening landing gear systems using CAD and dynamic software ADAMS, followed by the fabrication and testing of prototype designs. Unstable landings pose a significant challenge in Unmanned Aerial Vehicle (UAV) operations, affecting structural integrity, payload protection, and overall system longevity. To address this, twelve different landing gear configurations will be designed using CAD software and analyzed with ADAMS simulation software. These simulations will evaluate impact forces and other parameters to identify the most effective designs. The two or three highest-performing landing gear systems will be fabricated using rapid prototyping techniques and subjected to experimental drop tests with accelerometer measurements to validate simulation predictions and determine the most successful drone damping system. The goal is to identify the best damping system that maintains structural integrity, payload protection, and system longevity.
Faculty: Siyuan Xing, ME
Email: sixing@calpoly.edu
Accepted Projects Modes: In-person
This research project aims to extend the Combinatorial Operation Neural Network (CombOpNet) framework to discover Lagrangian of high-dimensional nonlinear dynamical systems from data. While recent advances in data-driven methods have shown promising results in recovering governing differential equations, most approaches focus on identifying the vectorized state-space representation rather than the underlying Lagrangian structure. The Lagrangian formulation provides deeper physical insights, such as conservation laws and symmetries, which are crucial for understanding complex phenomena in physics and engineering. By leveraging our recently developed CombOpNet architecture and modifying it to identify variational principles, this project will develop a novel data-driven framework capable of extracting interpretable Lagrangian formulations. This approach will be validated on several benchmark systems and then applied to more complex nonlinear lattice models that are prevalent in condensed matter physics and nonlinear optics.
Faculty: Masoud Yekanifard, ME
Email: myekanif@calpoly.edu
Accepted Projects Modes: Hybrid, Fully Remote, In-person
Auxetic metamaterials are a category of material that exhibit a negative Poisson’s ratio (NPR). NPR materials contract when compressed and expand when stretched. NPR properties are typically produced through a complex unit cell consisting of folds, internal angles, or other specially designed shapes with internal voids capable of collapsing in on themselves. Individual unit cells that display these properties are developed and stacked or layered in a repeating pattern such that a larger body can be manufactured, maintaining the same properties. NPR auxetic structures show either a stretch-dominated mechanism (SDM) or a bending-dominated mechanism (BDM). SDM-based NPR structures are favored for high stiffness-to-weight ratio but lack high energy absorption capability. BDM-based structures have the advantage of high energy absorption and the disadvantage of low stiffness. This SURP project will focus on developing a novel SDM-BDM auxetic metamaterial with high stiffness and enhanced energy absorption. Students will learn the numerical analysis technique ANSYS and study the behavior of the newly invented material using ANSYS. The new metamaterial has vast implications in support structures and energy dissipation systems in defense, aerospace, and biomedical fields.
Faculty: Alan Zhang, ME
Email: zhangas@calpoly.edu
Accepted Projects Modes: In-person
Tensegrity structures are composed of stiff rods and elastic cables suspended in a flexible tension network. In particular, the biotensegrity model proposes that all biological systems exhibit tensegrity-like characteristics across multiple scales, ranging from the cellular level to the musculoskeletal system of tendons, ligaments, and fascia, to the human body as a whole. Compared to the traditional biomechanical models used in exoskeleton design, it can be a more accurate representation of how motion emerges from natural forms, but further work is needed to fully understand the heterarchical nature of human anatomy. This project will focus on developing a powered electrical subsystem to integrate with existing wearable tensegrity prototypes. The power requirements of the system will be tuned to match the human user’s inputs, and the performance will be measured through motion capture experiments.
Faculty: Stefan Talke, CEEN
Email: stalke@calpoly.edu
Accepted Projects Modes: In-person
How fast is sea-level rising? How fast is coastal land sinking? Inexpensively measuring water levels and vertical land motion (VLM) remains a surprising—but pervasive—barrier in assessing sea-level rise and the evolution of coastal flood risk, due in large part to cost. Typical high-quality tide-gauge installations cost $25k, and a global navigation satellite system (GNSS) installation for assessing vertical land motion may cost even more. In this SURP project, our goals are to (a) further develop and stress-test an inexpensive radar gauge sensor at <10% the market price of $5-6k; (b) compare the gauge to our inexpensive ultrasonic gauges and existing commercial radar and pressure gauges (c) incorporate GNSS/GPS technology into the radar gauge (d) work on improving ease of use, both for deployment and post-processing. If successfully built, this combination of instruments will be field-tested in Morro Bay and at the Cal-Poly pier to measure tides, waves, vertical elevation, and longer-term sea-level variability on the Central Coast. Multiple engineering issues must first be solved. These include improving hardware and software design to minimize power use, improve solar recharge, improve robustness in the harsh marine environment, and improving the ease of construction and post-processing. We will also explore whether E-Sims or similar technology can be used for remote transmission of data. The use of software post-processing to reduce uncertainty and bias will be explored. Experience with electrical engineering, mechatronics, computer hardware, and/or programming is preferred. Experience with designing and implementing designs in the field is a plus. Because the proposed work includes both hardware development and software post-processing, two SURP students would be preferred.
Faculty: Long Wang, CEEN
Email: lwang38@calpoly.edu
Accepted Projects Modes: In-person
Our group has been developing a novel manufacturing technique to fabricate flexible sensing devices using
electrostatic force. Previously, we focused on utilizing nanomaterials as the sensing materials to measure
strains. Building on our experience with the manufacturing technique, we will move forward to incorporate
other smart materials in the flexible device to achieve self-powered sensors. Besides, to better understand
the fabricated material properties, we are interested in characterizing the microstructural behavior of the
material networks via microscopic imaging and finite element simulations. The students will have the
opportunity to closely work with Dr. Wang’s interdisciplinary research group.
Faculty: Jonathan Ventura, CSSE
Email: jventu09@calpoly.edu
Accepted Projects Modes: Hybrid
Microscopic imaging is essential to characterize multi-scale material behavior and understanding structure-property relationships. Recently our group developed a deep learning approach based on a Generative Adversarial Network (GAN) to reconstruct and artificially generate microstructures of strain-sensing nanomaterial networks based on microscope imagery. In this SURP project we would like to evaluate an alternative approach called diffusion to see if we can improve the quality of our results. Furthermore, we aim to test our approaches on a wider variety of materials, which will have different microstructures, to evaluate how versatile our models are.