Aerospace Engineering
Faculty: Lubos Brieda, AERO
Email: lbrieda@calpoly.edu
Accepted Projects Modes: In-person
Densities, temperatures, potentials, and energies in plasma thruster plumes are characterized using Langmuir, Faraday, and Retarding Potential Analyzer probes. The objective of this summer research is to design, build, and test a small sensor suite containing these three probes that will be used in Cal Poly’s new 6×14 foot STELLA vacuum chamber. The participant will also construct an electrical harness, readout circuitry, and a simple LabView screen to aid in data collection. Activities will be summarized in a conference paper. The sensor pack developed as part of this SURP will be utilized by future graduate students and external clients performing plasma propulsion testing in this facility.
Faculty: Nandeesh Hiremath, AERO
Email: nhiremat@calpoly.edu
Accepted Projects Modes: In-person
Autonomous flight systems require precise tuning of control inputs to account for changing flight conditions. The project aims to achieve an autopiloted flight system for a high-aspect-ratio wing designed for high-altitude, low-density flight conditions. The project scope is to integrate and assemble the flight controller and autopilot system into a flight demonstrator within the permissible weight category for consumer UAVs. This would include pre-programmed flight path planning, implementation of flight mechanics, and trim based on the onboard inertial measurement units. The desired control inputs will be developed using (a) a tabletop setup of a scaled wing and control surface with integrated autopilot modules, (b) followed by integration to the flight platform. This entails limited wind tunnel and flight testing.
Faculty: Stephen Kwok Choon, AERO
Email: skwokcho@calpoly.edu
Accepted Projects Modes: In-person
This Summer Undergraduate Research Project focuses on the proposed design, development, manufacturing, and preliminary testing of a Reaction Wheel for use on a Floating Spacecraft Simulator test bed. This project aims to design, build, and test a reaction wheel module that shall be installed on a Floating Spacecraft Simulator vehicle to be used for a teaching laboratory experiment and research applications in the Space Robotics Laboratory and classroom (AERO-471 Introduction to Space Robotics – Lab).
Faculty: Madhusudan Vijayakumar, AERO
Email: msudan@calpoly.edu
Accepted Projects Modes: Hybrid, Fully Remote
With the cost of launch becoming more affordable, Low Earth orbit (LEO) is becoming increasingly congested, elevating the importance of accurate orbit prediction for space traffic management. However, current operational orbit prediction methods depend on uncertain or unavailable spacecraft metadata, time-varying environmental effects, and limited knowledge of maneuver intent. Further, publicly available orbit catalogs provide broad coverage but are noisy and irregularly sampled. This project aims to develop a scalable, maneuver-aware orbit prediction pipeline that learns from historical TLE to predict nominal orbital evolution over 48-hour window and flags potential maneuvers providing an associated reliability measure. Using publicly available TLE for congested LEO regimes, physics-based simulation for baseline comparison and controlled maneuver injection, we will create a curated dataset. By developing a time series neural network, we will benchmark prediction performance and evaluate maneuver detection on both synthetically labeled cases and known maneuvering objects such as the ISS. The outcomes of this project will be integrated into PolySpace, Cal Poly’s mission design and operations platform enabling reproducible research in next-generation space traffic management.
Biomedical Engineering
Faculty: Christopher Heylman, BMED
Email: cheylman@calpoly.edu
Accepted Projects Modes: In-person
Our lab is developing a microfluidic “tumor-on-a-chip” device that will allow for the growth and maintenance of 3D vascularized human colorectal cancer tumors. These tumor tissues will be used for screening the effects of novel drugs on human colorectal cancer before resorting to costly pre-clinic animal models and human clinical trials. These devices are created by injecting a mixture of human fibroblasts, endothelial cells, colorectal cancer cells and extracellular matrix proteins into a central incubation chamber in a microfluidic device. Cell culture medium is then perfused through the tissue using the fluidic channels of the device. Given the appropriate ratio of cell types, nutrients in the medium, and flow rates, a 3D tumor with an integrated network of blood vessels can be grown. This summer research project aims to identify and develop new assays for assessing the metabolic state of these tumors on a chip. The skill sets that students will develop in this project include human cell culture, molecular assays, fluorescent microscopy, and image analysis using ImageJ software. Successful completion of the aims of this project will open the door for further research and use of these devices to screen drugs for efficacy in treating colorectal cancer. Establishing assays for quantitative analysis of these tumor tissues will increase the functionality of these devices as a tool for the pharmaceutical industry.
Faculty: Thomas Katona, BMED
Email: tkatona@calpoly.edu
Accepted Projects Modes: In-person
This summer research experience for undergraduates offers an opportunity to investigate the effects of light on human physiology and health through both literature-based and experimental approaches. The student will conduct an in-depth review of current scientific research on how light exposure— including intensity, duration, timing, and spectral composition— influences human health outcomes such as circadian rhythms, sleep cycles, alertness, and overall well-being. In parallel, the student will systematically tear down and analyze commercially available lighting products that claim health benefits, as well as products suspected of having detrimental health effects, to characterize their optical properties and performance. Experimental measurements of light intensity and spectrum will be compared against established findings in the scientific literature to evaluate the validity of marketing claims and potential health implications. This project provides hands-on experience in scientific research, critical analysis of health-related claims, optical measurement techniques, and the integration of experimental data with peer-reviewed literature.
Faculty: Luke Perreault, BMED
Email: lperreau@calpoly.edu
Accepted Projects Modes: In-person
The demand for transplantable organs and tissues vastly exceeds availability, and an average of 22 people die each day while waiting for a donor. Tissue engineering offers a promising method to increase the availability of viable tissues for patients by growing cells within biomaterial scaffolds designed to mimic native tissue structure. However, current scaffold fabrication methods are costly and struggle to replicate the vascular networks required for cellular oxygenation. Nature offers an elegant solution: through decellularization with common detergents, our lab can repurpose plants into cost-effective, sustainable scaffolds with intricate structures and vasculature networks. Despite these advantages, plant-derived cellulose lacks mammalian cell-binding motifs, requiring surface modification to support cell adhesion and survival. In collaboration with Dr. Morgan Hawker of CSU Fresno, a specialist in biomaterial plasma modification, we will evaluate nitrogen plasma surface activation on decellularized spinach leaf scaffolds as a cost-effective strategy to improve scaffold biocompatibility. The student researcher will prepare plant scaffolds, quantify plasma-induced changes in surface wettability and protein adsorption, and evaluate cell adhesion and viability using fluorescence microscopy. By developing a scalable method to enhance cell integration on plant-based scaffolds, this project advances sustainable biomaterials toward therapeutic relevance within a cross-CSU, Learn-by-Doing, tissue engineering research experience that bridges kingdoms of life to improve human health.
Faculty: Soph Ziemian, BMED
Email: sziemian@calpoly.edu
Accepted Projects Modes: In-person
Female athletes have a 2–8 times higher risk of anterior cruciate ligament (ACL) injury compared to male athletes. Sex differences in ACL injury incidence are largely attributed to differences in movement mechanics, anatomical structure, and hormonal influences. This project will be a pilot data collection and validation study examining the kinematics and kinetics of dynamic movements specifically linked to ACL injury risk. The data will be collected in the Mobile Biomechanics Lab using both traditional marker-based motion capture (Cortex) and a mobile markerless method (OpenCap). A SURP student will collect synchronized trials across multiple dynamic movements (i.e. jumping, landing, squatting, and change-of-direction tasks), perform kinematic and inverse dynamic analysis of data, and quantify agreement and reliability across methods. The main scientific objective is to assess if OpenCap-derived measures will be sufficiently accurate and sensitive to detect sex differences in college-aged participants in mechanics across these movements. Results will directly inform the feasibility, protocols, and analysis strategies for a planned larger-scale study to assess sex differences in dynamic movements associated with ACL injury risk in youth athletes. This project will also work towards longer-term goals of improving scalable mobile biomechanics assessment methods for research and building community outreach efforts that connect youth athletes with biomechanics to promote engagement and sustained involvement in STEM.
Civil and Environmental Engineering
Faculty: Giovanni De Francesco, CEEN
Email: gidefran@calpoly.edu
Accepted Projects Modes: In-person, Hybrid
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 to be 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: Ann Goodell, CEEN
Email: anngood@calpoly.edu
Accepted Projects Modes: Hybrid
Approximately 11% of the U.S. construction industry is made up of women (Center for Construction Research and Training, 2025). Among women in the industry, 39% hold leadership roles, while 68% work in sales or office positions, underscoring the concentration of women in non-field roles. Participation has increased steadily year over year since 2010, per the U.S. Bureau of Labor Statistics (Gallagher, 2022). At the same time, construction is projected to grow faster than the national average, suggesting substantial opportunity for workforce entry and advancement. Despite these favorable conditions, women remain significantly underrepresented in the industry. Many women report experiencing sexual or racial harassment, discrimination in hiring, and gender bias on the job, which may contribute to retention and advancement barriers (Mefferd, 2024). The goal of this research project is to analyze the current available literature on women’s underrepresentation in construction and civil engineering and identify the primary factors most strongly correlated with this gender gap.
Faculty: Ann Goodell, CEEN
Email: anngood@calpoly.edu
Accepted Projects Modes: Hybrid
Reinforced concrete bridges experience progressive deterioration driven by chloride ingress and rebar corrosion, which reduces steel cross-section, weakens bond, induces cracking/spalling, and ultimately degrades stiffness and load-carrying capacity. This project develops and applies a finite element (FE) model that utilizes a simplified chemical-reaction/transport representation of corrosion initiation and propagation with structural response of reinforced concrete bridge components (e.g., decks and girders). Using a finite element program capable of incorporating corrosion-related material degradation, this study will simulate time-dependent deterioration under realistic exposure scenarios and quantify its effect on key performance metrics such as strength, stiffness, serviceability cracking, and dynamic effects. This finite element model will be compared with a real bridge under dynamic loads. The project will involve a close partnership with local jurisdictions to select and model a bridge which has corrosive damage. The work advances knowledge in bridge engineering by considering the effectiveness of the finite element mechanisms to capture true structural performance outcomes—supporting improved prediction of remaining capacity and better-informed maintenance and retrofit decisions.
Faculty: Kris Isaacson, CEEN
Email: krisaacs@calpoly.edu
Accepted Projects Modes: In-person
Hydrocarbon contaminants are frequently detected in drinking water supplies following urban wildfire and chemical spills. Yet despite common public guidance to use pitcher-style activated carbon filters as an interim mitigation measure, there are limited data quantifying their efficacy for these compounds under realistic use conditions. Preliminary results indicate that a representative pitcher filter exhibits incomplete benzene, toluene, ethylbenzene, and xylene (BTEX) removal, consistent with mass-transfer and contact-time limitations at typical operating flow rates. The overall goal of this work is to redesign the typical pitcher filter to maximize hydraulic retention time to better meet contaminant removal requirements in post-disaster situations. The SURP student will help lay the foundation for this project by quantifying the sorbent capacity for several commonly used pitcher-filters. Sorbent capacity will be determined for a selection of wildfire relevant organic compounds that span a range of chemical characteristics that impact sorption.
Faculty: Derek Manheim, CEEN
Email: dmanheim@calpoly.edu
Co-Advisor: Fatemeh Nayebloie (fnayeblo@calpoly.edu)
Accepted Projects Modes: In-person
Finished organic composts derived from dry thermophilic anaerobic digestion can concentrate microplastics (MPs), raising concerns about their mobilization into agricultural soils and subsequent risks to soil health, groundwater quality, and human exposure through food systems. MP release and retention are governed by complex, nonlinear interactions among abiotic factors such as ultraviolet irradiation, temperature, pH, and moisture, as well as soil properties that influence transport and chemisorption processes. However, mobilization pathways from compost to receiving soil microenvironments remain poorly characterized. This study proposes an integrated experimental and machine learning (ML) framework to elucidate and predict MP dynamics in compost-amended soils. Laboratory bench-scale 1-D flow-through column experiments will quantify MP leaching under controlled variations in polymer type, particle size (1 μm–5 mm), morphology, concentration, and environmental conditions. Experimental data will be used to train and evaluate ML models—including random forest, support vector regression, gradient boosting, and extreme gradient boosting—to identify dominant mobilization pathways and determine the relative importance of interacting abiotic drivers through an explainable AI approach. By coupling controlled experimentation with advanced data-driven modeling, this research aims to inform mitigation strategies, support sustainable compost management, and provide foundational data for future environmental and public health risk assessments.
Faculty: Stefan Talke, CEEN
Email: stalke@calpoly.edu
Co-Advisor: John Ridgely (jridgely@calpoly.edu), ME
Accepted Projects Modes: In-person
In this SURP project, we propose to continue developing and implementing a prototype, radar-based water level sensor (~$100-200). Radar gauges have many advantages over other technologies used to measure coastal tides and sea-level rise. They measure the air-gap between the transducer and the water, and are therefore are less prone to marine degradation. Further, radar-measurements are (nearly) impervious to air temperature, humidity, wind, and other environmental fluctuations; for this reason, the National Ocean Service (NOAA-NOS) has recently been shifting from ultrasonic to radar technology at its tide gauges. However, radar technology requires significant power, which limits autonomous applicability, and commercial versions are prohibitively expensive ($5k-$25k). Recently, however, the price of radar transducers has reduced significantly, and we have successfully tested Cal-Poly developed radar gauges in Morro Bay for small time scale deployments (1-2 weeks). In this SURP, we will continue developing and testing this radar gauge. Significant effort is needed to optimize the software of a low-power microcontroller and integrate our design with solar panels capable of recharging on-board batteries. Additionally, we need to stress test our design under harsh marine conditions, and validate/quality assure measurements using time series from other instruments. Tests will be done to compare the radar sensor with other instrumentation, both in the lab and in the field. As needed, our pressure-gauge and DIY ultrasonic-based water level instrumentation will be refurbished and co-deployed along with the radar-based technology. Experience with programming, microcontrollers, electronic components, and instrumentation is preferred.
Faculty: Long Wang, CEEN
Email: lwang38@calpoly.edu
Co-Advisor: Jonathan Ventura (jventu09@calpoly.edu), CSSE
Accepted Projects Modes: Hybrid
Microscopic imaging is essential to characterize multi-scale material behavior and understanding structure-property relationships. Recently our group has been developing a deep learning approach based on Generative Adversarial Networks (GANs) and Diffusion Models to reconstruct and artificially generate microstructures of strain-sensing nanomaterial networks based on microscope imagery. In this SURP project we would like to compare the performance of different models and evaluate alternative approaches 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.
Faculty: Long Wang, CEEN
Email: lwang38@calpoly.edu
Accepted Projects Modes: Hybrid, In-person
Structural Health Monitoring (SHM) is a data-intensive discipline for real-time assessment of structural integrity and safety. Modern SHM depends on applied computing (signal processing, ML/AI, and computer vision) to interpret sensing data and diagnose damage. This SURP project aims to develop and pilot a new course, CE 5558: Structural Health Monitoring, an interdisciplinary course that puts applied computing at the center of hands-on SHM investigations. The SURP student will build and test a low-cost SHM experimental kit, develop reusable MATLAB/Python notebooks and datasets for four course modules (sensing/data acquisition, damage feature extraction, ML-based damage detection, and computer vision), and help draft assessment artifacts to evaluate learning, teamwork, and ethical reasoning about data/algorithmic bias.
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.
Computer Science and Software Engineering
Faculty: Hisham Assal, CSSE
Email: hhassal@calpoly.edu
Accepted Projects Modes: Hybrid, In-person
In this research we will investigate the current issues of deepfakes being submitted as evidence in the courtroom. As Artificial Intelligence becomes increasingly popular and people have more access to these technologies we need to be aware of deepfakes being submitted as evidence in a legal setting. We plan to train a model that will detect these images with a certain degree of accuracy. Furthermore, we plan to create a platform for law professionals to use in order to check photo evidence being submitted to a case. This will be a resource for judges, judicial support staff, attorneys, and litigants. We hope that this technology would eventually become a part of the legal process for verification and validation of images submitted as evidence in a court case.
Faculty: Alexander Bisberg, CSSE
Email: abisberg@calpoly.edu
Accepted Projects Modes: In-person, Hybrid
Large-scale online games can offer spaces for identity exploration and social connection, but those benefits are not always distributed equitably across marginalized identities [5]. Sky: Children of the Light (Sky) is a social massive multiplayer online (MMO) game with unique design affordances to promote prosocial interactions. For example, unknown player avatars appear as gender-neutral silhouettes, which may shape how safe players feel about expressing their identity. Previous work on Sky have linked design elements like mentorship and generosity to downstream gains in social capital and reciprocity [1, 3, 4, 6, 7]. Existing analyses of gender in Sky have largely relied on binary categories (male/female), leaving gender-diverse players comparatively underexamined. This SURP project uses data already collected from an in-game survey, focusing on respondents who identify their gender as “Other.” The student will clean the survey data related to gender identity (e.g., missing values, fuzzy matching), query player survey responses, and link those responses to behavioral measures. The central research questions target whether Sky’s gender-neutral affordance functions as an identity-safety “buffer”: (1) Do players who identify as “other” gender feel less, equally, or more autonomous than those with identified with a specific gender? (2) Do players who identify as “other” gender receive less, the same, or more mentorship when first joining Sky?
Faculty: Daniel Frishberg, CSSE
Email: dfrishbe@calpoly.edu
Accepted Projects Modes: In-person, Hybrid, Fully Remote
We explore a class of algorithms known as Markov chain Monte Carlo (MCMC) algorithms, whose applications range from probabilistic modeling and document classification to statistical physics. The study of MCMC algorithms is a rich theoretical field. However, we take an empirical approach: we run the algorithms and attempt to evaluate their running time, more technically known as their mixing time. In part because the algorithms are randomized, evaluating the running time is not as easy as it sounds—but we have techniques for estimating it. The algorithm we will evaluate is a well-studied algorithm for randomly generating k-ary trees, a variation on the binary search trees many computer science majors encounter in a first-year data structures course. We use the same algorithm to randomly generate geometric tilings of a polygon with smaller polygons, known as k-angulations. The mentor advised a Cal Poly MS thesis in 2024 studying the special case of binary search trees (equivalently, triangulations). We will extend the code base from this project to the case of ternary search trees (equivalently, quadrangulations).
Faculty: Javier Gonzalez Sanchez, CSSE
Email: javiergs@calpoly.edu
Accepted Projects Modes: In-person
Human collaboration is shaped not only by explicit commands, but also by subtle perceptual cues such as gaze, gesture, posture, hesitation, and shared attention. These embodied signals enable people to coordinate fluidly, adapt to one another’s cognitive state, and build trust. In contrast, most human–robot interaction (HRI) systems remain primarily command-driven, relying on manual input devices and deterministic control models that lack awareness of the human partner’s perceptual and cognitive context. This project investigates how immersive presence and multimodal perceptual awareness influence operator performance and situational awareness in remote collaborative robotics. We aim to develop a platform that supports embodied collaboration between humans and robots. Building on prior work, the project will implement a remote interaction environment in which users control and monitor collaborative robotic arms and autonomous guided vehicles through an extended reality (XR) interface. The system will integrate brain–computer interface (BCI) signals, eye gaze tracking, gesture recognition, and body movement to improve inference of operator intent, cognitive workload, and shared attention. By fusing neural, visual, and motor signals, the platform will explore adaptive robot behaviors—such as pausing, confirming actions, adjusting pace, or signaling acknowledgment—in response to human cognitive and environmental cues. A real-time digital twin and cloud-synchronized communication layer will ensure low-latency interaction and safe operation. Expected outcomes include a modular research platform and empirical evaluation results that inform the design of next-generation perceptive and collaborative robotic systems.
Faculty: April Grow, CSSE
Email: amgrow@calpoly.edu
Accepted Projects Modes: In-person, Hybrid, Fully Remote
In collaboration with the department of Psychology and Child Development, we want to design and develop first-person 3D VR scenarios that confront the (teenage) player with positive but tough choices. It’s easy to say, in theory, that one might ask someone out, or take a club leadership role, but the increased immersion of a VR experience will more accurately test a player’s resolve! We currently have 6 hypothetical scenarios to model, including: a foreign language tour guide, taking a leadership role, trying a new sport, entering a sport competition, raising an objection, telling a joke, and asking someone new out. However, the depth, scope, number, and topics of scenarios are open to being redesigned with student input! We plan to develop the scenarios in Unity (C#) over SURP 2026, for a pilot study in Fall 2026 and a complete polished experience by Spring 2027. Experience with Unity, 3D modeling, and/or game design is a plus!
Faculty: Borislav Hristov, CSSE
Email: bhristov@calpoly.edu
Accepted Projects Modes: In-person, Hybrid
Rapid advancements in next generation sequencing techniques has led to the development of a myriad of experimental techniques that allow researchers to measure different properties of single cells. However, these techniques are typically destructive, preventing scientists from measuring multiple properties at the same time, i.e paired measurements. Having disjoint datasets that reside in different spaces and that need to be integrated (paired) in order to build a fuller picture of the molecular processes in the cell is a key problem in computational multi-omics. Integrating these measurements to find a pairing between the cells is a particularly challenging task from the standpoint of computer science because the modalities may differ significantly in structure, scale, or noise characteristics, posing unique challenges for correspondence learning and classification. In this project, we aim to develop a novel deep learning framework for finding a shared latent embedding of the cell measurements. The key idea is that two measurements that come from the same cell should be close to one another when projected in the latent space. We will use a small subset of the data as training cells to learn separate autoencoder models per modality and then employ cross training (or GAN) to align their embedding spaces. Once trained, this dual autoencoder architecture can be used to project a cell from one measurement space into another by using the encoder1 and then decoder2. The ultimate goal is to have a deep learning model that is capable of efficiently integrating large scale multi-omics datasets.
Faculty: Paris Kalathas, CSSE
Email: pkalatha@calpoly.edu
Co-Advisors: Franz Kurfess (fkurfess@calpoly.edu), CSSE and Jenny Zheng Wang (jwang96@calpoly.edu), IME
Accepted Projects Modes: Fully Remote
This proposal describes a research activity aimed at designing and evaluating prompt scaffolding strategies for agentic tutoring environments powered by large language models (LLMs). As LLMs and LLM-driven tutoring systems become parts of the educational settings, it is critical to understand how carefully structured prompts can guide learners through complex problem-solving tasks promoting the ethical use of AI. The goal of this research is to design and test prompt scaffolding strategies that can be integrated into agentic learning environments for CS laboratories. Historically, scaffolding theory has informed instructional design by providing temporary support structures (Wood, Bruner & Ross, 1976). The emergence of LLM-based systems creates a new infrastructure to operationalize such scaffolding dynamically, adapting in real time based on learner’s responses and errors (Kasneci et al., 2023). Research in prompt engineering has demonstrated that the structure, specificity, and sequencing of prompts significantly influence the quality and pedagogical value of AI-generated responses (White et al., 2023). This project extends that work into an educational context, asking how prompt design can be harnessed intentionally to support learning rather than merely to elicit correct answers.
Faculty: Fahim Khan, CSSE
Email: fkhan19@calpoly.edu
Accepted Projects Modes: Hybrid
This project is a continuation of my prior SURP 2025 project that developed a mobile app and created an annotated dataset to train computer vision models for detecting infrastructure relevant to accessibility in public spaces (e.g., curb ramps, crosswalk features, sidewalk conditions, and obstacles). Building on that foundation, the follow-up project will extend the system from smartphone-based capture to wearable and edge-AI deployment using AI camera glasses and NVIDIA Jetson Orin Nano boards. The goal is to enable real-time, hands-free sensing and feedback, while also expanding data diversity and improving model robustness in varied environments (lighting, weather, surface types, and urban layouts). Students will be onboarded with structured training so they can effectively build on top of the existing codebase, dataset, and model pipeline from the previous project. The team will integrate the camera-glasses streaming pipeline with the Jetson Orin Nano for on-device inference, optimize models for low-latency performance, and connect detections to the existing mapping/reporting workflow. The final deliverables will include a wearable prototype system, updated datasets and models, evaluation results, and a deployment-ready demonstration in real public settings.
Faculty: Franz Kurfess, CSSE
Email: fkurfess@calpoly.edu
Co-Advisors: Jenny Wang (jwang96@calpoly.edu), IME and Paris Kalathas (pkalatha@calpoly.edu), CSSE
Accepted Projects Modes: In-person, Hybrid, Fully Remote
This project investigates how structured ethical rules influence agent behavior within collaborative AI environments. As part of foundational research in the context of Agentic Design frameworks, this study focuses on designing and evaluating an ethics-monitoring layer to mitigate undesirable behaviors such as hallucinations or unsafe decisions in multi-agent systems. Using frameworks like AutoGen, the student will implement a specialized “constraint-monitor agent” capable of performing real-time privacy and safety checks. The research aims to determine if explicit constraint layers can strengthen system reliability and provide a robust foundation for responsible AI in complex domains. The work is directly relevant to several other ongoing activities like AI for Search and Rescue, Agentic Design for Engineering Labs, and Sustainability in AI.
Faculty: Joydeep Mukherjee, CSSE
Email: jmukherj@calpoly.edu
Accepted Projects Modes: Hybrid, Fully Remote
This project proposes the design and implementation of a small-scale Internet of Things (IoT) system that allows users to remotely display text messages on an LED display using a mobile phone connected to any cellular network. The system will integrate an Arduino microcontroller, a Raspberry Pi, and an LED matrix or LED panel, coordinated through a mobile-friendly web application. Messages sent from a smartphone will be transmitted over a cellular data network to the Raspberry Pi, which will process the request and relay display commands to the Arduino for real-time output on the LED. From a technical perspective, students will work with embedded systems programming, device-to-device communication, and networked application design. The Raspberry Pi will function as a lightweight server, handling incoming HTTP or MQTT requests from the mobile application, while the Arduino will manage low-level control of the LED hardware. Students will explore serial communication, basic networking protocols, and message parsing, as well as system reliability and latency considerations in remote device control. The mobile interface will be implemented as a simple web or hybrid application, enabling cross-platform access without reliance on a specific carrier or operating system. This design emphasizes interoperability and real-world IoT constraints, such as connectivity, scalability, and security. The project will be delivered as a hands-on learning module in which students build, program, test, and refine a complete IoT system. By integrating hardware, software, and networking components, the project provides a comprehensive experiential learning opportunity that prepares students for advanced work in IoT development, mobile applications, and networked systems engineering.
Faculty: Sumona Mukhopadhyay, CSSE
Email: mukhopad@calpoly.edu
Accepted Projects Modes: In-person, Hybrid
This project aims to develop a Python-based interactive visualization platform for analyzing EEG brain activity in relation to cognitively meaningful events in learning tasks. Inspired by professional neurophysiology tools such as MNE, the proposed system extends traditional EEG viewers by integrating time-locked experimental events (e.g., question onset/offset, auditory markers), survey text, and participant responses into a unified interface. The platform will enable researchers to visually inspect raw and preprocessed EEG signals alongside protocol-defined events and associated textual stimuli, supporting exploratory analysis, data quality assurance, and interpretability of multimodal learning data. Designed entirely in Python, CANDY emphasizes reproducibility, modularity, and extensibility, and serves as a foundation for future machine learning–based cognitive event modeling and human-in-the-loop annotation workflows.
Faculty: Dev Sisodia, CSSE
Email: dsisodia@calpoly.edu
Accepted Projects Modes: Hybrid, In-person
Wildfires increasingly threaten California communities, yet most existing risk maps operate at coarse regional scales and rely on opaque models that fail to link risk scores to clear, verifiable mitigation actions. Building on two years of prior work by the Perch Sensing team, including structured interviews with homeowners, fire officials, insurers, and researchers, this project addresses a key gap: mitigation efforts are often performed but remain undocumented or unverifiable, contributing to mistrust and insurance instability. The team previously developed a conceptual wildfire framework organized around three components – Predict, Prevent, and Protect – capturing ignition potential, structural vulnerability, vegetation characteristics, topography, and community factors. This SURP project will operationalize that framework by developing an interpretable, property-scale wildfire risk model using publicly available geospatial data. The student researcher will implement transparent statistical methods to decompose risk into defensible-space and exposure components, incorporate uncertainty quantification, and simulate how mitigation actions change predicted risk. Outcomes will include a reproducible modeling pipeline, technical report, and SURP poster, advancing research at the intersection of computing, fire science, and community resilience.
Faculty: Jonathan Ventura, CSSE
Email: jventu09@calpoly.edu
Accepted Projects Modes: In-person, Hybrid, Fully Remote
3D reconstruction methods such as Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) achieve photorealistic view synthesis by reconstructing a detailed model of a scene from a collection of input images. However, such models are unable to model missing areas of the scene, such as occluded regions. We propose to incorporate multi-view image generation models into the scene optimization process, to combine data-driven scene reconstruction with generative scene completion. Ideal candidates will have experience in computer vision, 3D graphics, and deep learning.
Industrial and Manufacturing Engineering
Faculty: Puneet Agarwal, IME
Email: pagarw05@calpoly.edu
Accepted Projects Modes: Hybrid, Fully Remote
In an age of constant exposure to digital information, distinguishing credible content from factual inaccuracies has become a challenge. A growing accessibility to generative AI tools has amplified the already prevalent spread of misinformation on social media, and yet potential use of these same systems to mitigate misleading narratives remains largely unexplored. This research project seeks to evaluate the ability of advanced large language models (LLMs) to detect and assess the validity of information across diverse forms of text media. Building on an established proof of concept from our 2025 summer pilot, we now aim to deepen our understanding by implementing nuanced scoring systems and condition-specific datasets that will produce findings on when and how LLMs reliably detect various textual forms of misinformation. We will construct several datasets containing both factual and misleading content, including human-authored news articles from both reliable and unreliable sources, professionally fact-checked short-form claims, AI-generated long-form articles with defined agendas, and AI-rewritten versions of human-authored texts. Each item will be thoroughly reviewed and labeled with ground scores to ensure reproducibility. We will prompt three state-of-the-art LLMs (GPT-5, Claude, and Grok) to evaluate each sample across four metrics: factual accuracy, intent to deceive, emotional manipulation, and confidence in their evaluation, to produce a multi-dimensional analysis of both class and severity of misleading content. Each model will return structured scores using a standardized prompt framework to ensure consistency. Ground-truth labels are derived from verified fact-checking sources and structured human evaluations, while cross-model consensus will be measured as an independent reliability metric. Our analysis seeks to clarify the limitations of LLMs in real-world misinformation detection and uncover the conditions under which the LLMs perform best. This project is set to produce three deliverables; first, transparently labeled datasets with associated ground-scores, which will be made publicly available to advance misinformation research; second, a peer-reviewed publication documenting our methodology and findings; and third, a web application that allows real-time evaluation across our four metrics, enabling users to input text and receive immediate feedback. We aim to build upon the established benchmarks for AI-assisted information evaluation, providing researchers and consumers with functional tools to navigate the modern information landscape. By prioritizing reproducibility and transparency, we will ensure findings are both verifiable and ethically communicated.
Faculty: Mohamed Awwad, IME
Email: mawwad@calpoly.edu
Accepted Projects Modes: Hybrid, In-person, Fully Remote
Large Language Models (LLMs) are increasingly proposed as decision agents in supply chain planning, procurement, and logistics systems. However, existing research primarily demonstrates single model implementations without systematic cross-model benchmarking, standardized evaluation protocols, or quantitative validation across operational scenarios. This project develops a structured and reproducible benchmarking framework to evaluate multiple large language models on defined supply chain decision tasks such as inventory control, disruption response, and supplier selection. Using consistent input conditions and scenario variations, models will be assessed against operational baselines using measurable performance metrics, including cost impact, service level effects, and output consistency. By providing empirical comparison across models within a controlled decision environment, the project advances methodological rigor in evaluating generative AI for industrial engineering applications and contributes new evidence on the practical performance and limitations of LLM-based decision support systems in supply chain contexts.
Faculty: Byeongmok Kim, IME
Email: bkim142@calpoly.edu
Accepted Projects Modes: In-person, Hybrid
This project proposes a collaborative intelligence framework to support human decision-making in multi-robot systems under multi-objective tradeoffs. In complex human–robot collaboration scenarios, Pareto optimization can generate multiple optimal coordination strategies balancing objectives such as efficiency, safety, energy consumption, and workload distribution. However, selecting among these alternatives can be cognitively demanding for human operators. The proposed framework introduces a generative decision-guidance module that translates Pareto-optimal solutions into structured, human-readable tradeoff summaries, interactively incorporates operator preferences, and provides explainable, data-driven justifications derived from multi-robot system states. The system will be implemented and evaluated within a computer simulation environment modeling multi-robot collaborative tasks. Performance will be assessed in terms of decision quality, interpretability, and decision efficiency. The expected outcome is a validated human-centered decision-support framework for collaborative robotics.
Faculty: Jill Speece, IME
Email: jespeece@calpoly.edu
Accepted Projects Modes: Hybrid
TurboRad’s radiology reporting software is integrating artificial intelligence to support radiologists during report creation. The AI system is designed to offer real-time diagnostic suggestions that help ensure all clinically relevant conditions are considered, based on the reason for exam, the radiologist’s preliminary findings, and specific patient demographics. This summer research project will recruit two undergraduate students to help expand this AI-driven capability and to verify and validate its effectiveness in real-world reporting scenarios. Students will contribute to refining the logic behind diagnostic suggestions, evaluating system performance across diverse clinical cases, and assessing how well the AI supports thorough and accurate radiology reporting without disrupting clinical workflow.
Faculty: Jenny Wang, IME
Email: jwang96@calpoly.edu
Co-Advisors: Prof. Franz Kurfess (fkurfess@calpoly.edu), CSSE and Paris Kalathas (pkalatha@calpoly.edu), CSSE
Accepted Projects Modes: Hybrid
Collaborative multi-agent AI systems increasingly distribute tasks across specialized agents to solve complex problems. However, when agents produce contradictory recommendations, pursue competing objectives, or operate with incomplete information, system-level conflicts can emerge. This project investigates structured methods for detecting and resolving conflicts in multi-agent systems through experimentally evaluated arbitration mechanisms. The student will design controlled conflict scenarios and implement alternative resolution strategies—including hierarchical override, voting-based arbitration, and mediator agents—to evaluate their impact on system stability, decision quality, and computational efficiency. The project advances knowledge in agentic AI architecture by developing empirically grounded design guidelines for robust multi-agent coordination.
Faculty: Xuan Wang, IME
Email: xwang12@calpoly.edu
Co-Advisor: Jenny Wang (jwang96@calpoly.edu), IME
Accepted Projects Modes: In-person, Hybrid, Fully Remote
Adolescent Idiopathic Scoliosis (AIS) is a three-dimensional spinal deformity for which brace design remains largely empirical, with limited mechanistic understanding of brace–torso–spine interaction. This project aims to develop and biomechanically validate a patient-specific digital twin framework that integrates radiation-free 3D torso surface scanning with radiographic spinal alignment to simulate brace-induced mechanical loading using finite element methods. By constructing subject-specific computational models, the research will quantitatively characterize corrective force transmission, pressure distribution, and spinal deformation response. The work advances knowledge in computational biomechanics by establishing a validated, patient-specific modeling approach for evidence-based brace optimization.
Faculty: Zhiyuan Wei, IME
Email: zwei03@calpoly.edu
Accepted Projects Modes: Hybrid
Emergency services, such as fire and medical response, are essential for safeguarding lives and ensuring public safety during crises. However, current practices of first responders in fire departments rely heavily on experience, which may limit the effectiveness and adaptability of decision-making in dynamic and uncertain environments. This project aims to develop a data-driven contextual stochastic optimization (CSO) framework to enhance fire department service planning and real-time operational decision-making under uncertainty. First, multi-source data will be leveraged to extract and quantify contextual information characterizing operational uncertainty. Next, we develop an end-to-end learning and optimization framework that integrates predictive models and operational decisions. This framework captures the stochastic nature of fire engine availability using contextual information to dynamically adapt deployment decisions to evolving incident conditions. Lastly, computational experiments will be conducted to demonstrate that the proposed end-to-end CSO framework enhances overall system resilience and robustness against uncertainty. This project provides students with hands-on experience in applying data analytics and stochastic optimization modeling techniques to real-world challenges in emergency response management. The insights generated through this work have the potential to support a shift from reactive, experience-based practices to proactive, data-informed operations, contributing to more resilient and responsive emergency service systems.
Materials Engineering
Faculty: Nicole Johnson-Glauch, MATE
Email: njohns66@calpoly.edu
Accepted Projects Modes: In-person, Fully Remote, Hybrid
Have you ever wondered whether the classes you take and the order in which you take them are setting you up to be a successful engineer? As a state institution, Cal Poly’s primary goal is to serve all students of California with high quality, Learn by Doing educational experiences. Could our engineering curriculum be serving some students but not others? This project idea arose from noticing that the six-year graduation rate differs across different engineering programs, especially in the General Engineering (GENE) program. The six-year graduation rate for GENE ranges from 14-62 percentage points lower than the six-year College of Engineering graduation rate for cohorts who started in Fall 2013 – Fall 2019. This is surprising because GENE has the most flexible program with 40 units dedicated to technical electives to customize the degree. The College of Engineering is interested in understanding how systemic structures such as program complexity impact the lived experiences of engineering students from different backgrounds across the college of engineering, especially in GENE. A student on this project could explore the relationship between program complexity, students’ socio, economic, or demographic backgrounds, and student success metrics (time to graduation, job placement, etc…). Alternatively, a student could explore whether the intended progression through engineering curricula is the one most commonly experienced by students from different backgrounds. Results from this study will be used to inform how we could revise engineering curricula, develop tools to more effectively advise engineering students, and identify areas where college-level reform could better support students.
Faculty: Nahmoon Kim, MATE
Email: nkim113@calpoly.edu
Accepted Projects Modes: In-person, Hybrid, Fully Remote
Cyclic oxidation resistance plays a critical role in the durability of high-temperature turbine alloys, as insufficient resistance can lead to oxide spallation, material loss, and reduced component lifetime in energy and aerospace systems. In the 1990s, NASA conducted extensive cyclic oxidation experiments across a wide range of alloy compositions and temperatures, generating a valuable dataset that can benefit from modern quantitative analysis. This project will use this legacy NASA dataset to investigate how alloy composition influences cyclic oxidation resistance through a structured, data-driven approach. The student researcher will organize compositional and oxidation data into a reproducible format, extract key oxidation metrics, and analyze compositional trends across alloys and temperatures. By combining classical oxidation metallurgy with modern data-analysis tools, this work advances understanding of composition–property relationships in turbine alloys and enables more systematic comparison of oxidation performance across complex alloy systems.
Faculty: Kareesa Kron, MATE
Email: kkron@calpoly.edu
Co-Advisor: Jennifer Mott (jpeuker@calpoly.edu), ME
Accepted Projects Modes: In-person, Hybrid, Fully Remote
Cal Poly, San Luis Obispo is interested in improving the accessibility of course materials and improving the perceptions of accessibility tools amongst faculty members. Dr. Mott and Dr. Kron have been working towards this goal, including outreach to faculty and work on accessibility tools in the context of designing course materials. A survey is currently being collected at department meetings that aims to assess current perceptions of and implementation of accessibility in course material design by Cal Poly faculty. This SURP project will start by processing and analyzing the data of the survey(s) collected. The data analysis will enable us to describe current faculty perceptions and predict potential areas where faculty perceptions are poor and should be targeted with further training. The second portion of the project will prepare surveys and assessments that will be deployed in MATE 210 to explore the impact of alternative grading on students with accommodations. Alternative grading is frequently described as more accessible, but it is under-researched as to how it affects students with varying accommodation needs.
Faculty: Kareesa Kron, MATE
Email: kkron@calpoly.edu
Accepted Projects Modes: In-person
This project aims to develop high-quality polyurethane (PU) foam for surfboard cores using recycled polyethylene terephthalate (PET) as a primary feedstock. Post-consumer PET will be chemically depolymerized through glycolytic transesterification to produce hydroxyl-terminated polyester oligomers. These recycled polyols will then be reacted with castor oil and methylene diphenyl diisocyanate (MDI) to form a polyurethane prepolymer, which will be foamed through controlled CO₂ generation. The resulting foam will be evaluated for density, buoyancy, water absorption, and UV stability to determine its suitability for marine environments. The goal is to create a structurally sound, lightweight, and environmentally sustainable alternative to petroleum-based polyurethane foam.
Mechanical Engineering
Faculty: Eltahry Elghandour, ME
Email: eelghand@calpoly.edu
Accepted Projects Modes: In-person, Hybrid, Fully Remote
This study investigates the bulletproof capabilities of Kevlar composites fabricated using advanced manufacturing and additive manufacturing techniques. In this research, conventional ceramic armor plates will be replaced with lightweight plates made from multifunctional composite materials, with the goal of increasing impact resistance while reducing overall system weight. Continuous Fiber Reinforced 3D Printing (CFRP) is employed to manufacture Kevlar-reinforced polymer matrix composites (PMCs) with optimized structural configurations specifically designed for ballistic protection. The primary objective is to evaluate the impact resistance of these multifunctional composites against 9mm projectiles through systematic experimental testing. A series of ballistics tests will be conducted using a high-velocity gas-gun system to launch 9mm projectiles at varying velocities and angles. Testing will take place at the San Luis Obispo Range, where projectiles will be fired under controlled conditions at multiple impact angles. In addition to manufacturing parameters, the influence of different sawing processes applied to the raw Kevlar composite materials will also be investigated. Variations in cutting methods can affect fiber integrity, edge quality, microstructural damage, and residual stress, all of which may significantly influence ballistic resistance and energy absorption performance. Key parameters, including fiber volume fraction, reinforcement strategy, infill patterns, and post-processing methods, will be analyzed to determine their effects on energy absorption, penetration resistance, and failure mechanisms. The experimental results will be evaluated to assess energy dissipation, damage propagation, and overall structural integrity across different composite configurations. By replacing traditional ceramic plates with advanced multifunctional composite plates and integrating additive manufacturing techniques with rigorous ballistic testing, this research aims to contribute to the development of lightweight, high-strength armor systems. The findings offer potential advancements in protective gear and defense applications through improved performance, optimized processing methods, and enhanced impact resistance.
Faculty: Eric Espinoza-Wade, ME
Email: erwade@calpoly.edu
Accepted Projects Modes: Hybrid, In-person
Infants who sustain asymmetrical perinatal brain injury (APBI) have a 30-68% risk of developing cerebral palsy (CP). Hemiparesis (motor deficits on one side of the body) are typical of CP, and are not clinically detectable prior to six months of age. However, earlier detection could lead to early physical therapy, and improved quality of life as these infants age. The current study is designed to investigate our ability to detect asymmetry in children with APBI under three months old. We hypothesize that quantitative analyses of infant movements using computer vision and sensors placed on wrists and ankles will result in sensitive measures of asymmetry. If successful, these outcomes may shift clinical science, justifying early rehabilitation for infants in the neonatal intensive care unit (NICU). We are seeking students to refine existing code used to analyze videos of infants, to analyze wearable sensor (accelerometer) data, and to develop new measures of motor asymmetry. This project will involve collaboration and interaction with the physical and occupational therapists from Chapman University (Irvine, CA) and NICU staff from the Children’s Hospital of Orange County.
Faculty: Michael Holden, ME
Email: meholden@calpoly.edu
Accepted Projects Modes: In-person
Small autonomous boats are frequently used to map depth and bottom contours in bodies of water such as lakes, estuaries, and oceans. This project will use an autonomous boat (n3m0) to investigate the usefulness of commercially available depth sensors. The goals are to advance knowledge regarding optimal sensor specifications and to give the student practical experience developing and operating an autonomous vessel. The project will focus on quantifying error due to lag in a low-update rate (1 Hz) commercially available depth sensor. After measuring the bottom contour at various speeds, downsampling techniques will be used to match the depth soundings with the GPS position updates, and depth errors will be quantified. The results will be used to suggest solutions (both software and hardware) to minimize errors in the depth measurements. Finally, the solutions will be trialed and the project results will be presented. Note: this project’s field work will be at the Vallejo campus of Cal Poly Maritime and other nearby locations and is best suited to a student living in the San Francisco bay area in the summer. Both Solano and San Luis Obispo students are encouraged to apply.
Faculty: Benjamin Lutz, ME
Email: blutz@calpoly.edu
Accepted Projects Modes: Fully Remote, Hybrid
Generative artificial intelligence (AI) tools such as ChatGPT are now commonly used by engineering students to support problem solving, concept explanation, and idea generation. These activities are central to engineering work, and so how students engage with AI during engineering tasks is critical to how they develop a sense of self as an engineer. We therefore argue that student use of AI is not simply a technical or ethical question, but an identity relevant practice that shapes how students understand learning and professional development. This research project examines how engineering students decide when and how to use AI, and how those decisions relate to their developing engineering identities. In particular, we focus on engineering identity—students’ beliefs about their competence, interest, and recognition by others—to understand how AI use shapes learning experiences. The Summer Undergraduate Research Program (SURP) project will support an undergraduate researcher in helping to establish the theoretical and methodological foundation needed to launch this study. During the summer, the student researcher will engage in guided literature review, qualitative research training, development of interview protocols, and pilot data analysis. The outcomes of this project will include a refined conceptual framework, a finalized interview protocol, and a research poster presented at the SURP symposium. This work will contribute to ongoing research on AI in engineering education and provide the student with hands-on experience in education research methods and scholarly dissemination. This project provides an opportunity for students interested in engineering, education, or human-centered research to develop research skills while contributing to timely questions about learning and professional development in engineering.
Faculty: Eric Ocegueda, ME
Email: ocegueda@calpoly.edu
Accepted Projects Modes: In-person, Hybrid
Densely packed granular materials are ubiquitous across geo-mechanical (e.g., gravel), agricultural (e.g., crops), and many more fields. In each of these fields, granular materials support external loads through complex, heterogeneous networks of inter-particle forces known as force chains. The structure of these chains depends on particle scale physics and directly influences macroscopic behaviors such as stability and elasticity. The standard computational tool for measuring force chains is the discrete element method (DEM), which provides detailed particle-level data but at a high computational cost. Recently, researchers have explored data-driven alternatives to DEM, with Graph Neural Networks (GNNs) showing particular promise due to their ability to naturally represent granular assemblies as graphs, where particles are nodes and contacts are edges. The proposed SURP project aims to build off a previous SURP project, where an in-house GNN model for force chain prediction was developed, to test the GNN model and prepare for experimental comparison. Specific goals are (1) testing the GNN model’s sensitivity to various inputs, (2) exploring the GNN model’s accuracy with different error choices for training, and (3) researching the Granular Element Method (GEM), an experimental approach to predict force chains. One SURP student (from ME, AERO, CE, or other related disciplines) will work on familiarizing themselves with the developed GNN code in Python, explore machine learning literature for different loss function choices, implement sub-routines to test chosen errors and create an in-depth plan for future experimental testing of the GNN model with GEM. 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: Xi Wu, ME
Email: xwu@calpoly.edu
Accepted Projects Modes: In-person
This project focuses on the design, analysis, and experimental validation of novel foldable drone frame mechanisms for the Poly UAS Club to enhance portability while maintaining structural integrity during flight. Existing foldable arm designs exhibit limitations in rigidity, locking reliability, and structural efficiency. To address these issues, several new distinct folding arms will be designed, developed, and analyzed using CAD modeling, finite element analysis (FEA), and kinematic simulations to evaluate stiffness, compactness, locking reliability, weight efficiency, and fatigue resistance. The highest-performing designs will be fabricated using rapid prototyping methods and subjected to load, bending, and cyclic durability testing to validate structural performance and fold-cycle endurance. The expected outcome is the identification of an optimized foldable frame design that preserves flight performance while maximizing portability and long-term durability across repeated deployment cycles.
Faculty: Masoud Yekanifard, ME
Email: myekanif@calpoly.edu
Accepted Projects Modes: Fully Remote, Hybrid
Atomic force microscopy (AFM) and nanoindentation are powerful tools for characterizing the properties of heterogeneous materials that contain small particles. In the context of the 2026 Summer Undergraduate Research Program (SURP) project, the researchers will use ANSYS finite element analysis (FEA) to model AFM interactions. The heterogeneous material being studied consists of nodules of varying sizes and embedment depths placed within a membrane. The aim of the proposed study is to develop a model of the nodule-membrane structure in ANSYS and to simulate AFM indentation to extract mechanical properties. The ultimate goal is to create a simplified model that can assist in interpreting AFM data from heterogeneous and soft samples, such as biomaterials and polymers. Utilizing AFM and FEA to characterize modified polymers with semiconductor inclusions presents significant opportunities for the semiconductor industry, which has remained largely unexplored. Fillers have great potential to enhance the performance of rubber nanocomposites, especially within the security industry. Furthermore, studying biological samples at the nanoscale is crucial for the scientific community, as developments in AFM technology have enabled the characterization of live cells and tissues, particularly in cancer research.
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 implementing feedback control on an upper-limb tensegrity exoskeleton to evaluate a user’s muscle efforts in rehabilitation. The undergraduate researcher will integrate sensors (e.g., IMUs) to track a user’s motions, adjust the exoskeleton motion in response, and compare the effects on muscle effort (i.e., EMGs) to optimize the design and control of the system.