Mentors

The 10 currently listed mentors are for the Spring 2026 semester.

If you have any questions about the information below, please reach out to the URSA Committee at URSA-group@office365.illinois.edu.

Why become an URSA Mentor?

Mentor	Description
Aagam Jain (Aerospace Engineering)	I am Aagam Jain, a Master’s student in Aerospace Engineering at the UofI, an Analog Astronaut at HI-SEAS, Hawaii (also known as the home of NASA’s 5 successful long duration mars simulation missions), and a recent Aerospace Engineering graduate from VIT University, India. As a researcher at the Space Applications Centre (SAC) and the Vikram Sarabhai Space Centre (VSSC), both part of the prestigious Indian Space Research Organisation (ISRO), I gained valuable experience in developing innovative aerospace solutions, specifically in the materials domain and human factors in aerospace. My research focuses on aerospace innovation and human factors to enhance astronauts’ performance and resilience in extreme environments, driven by perseverance and a vision for interplanetary civilization. In addition to my background with ISRO and analog missions, I serve as a Research Mentor for AE 298 here at UofI. I am also a core member of ARKASA, where we work on advanced habitat designs for the Moon and Mars by bridging the gap between ancient technologies and modern space-based solutions. My current thesis work involves creating a unique neuroadaptive biofeedback system using AR/VR to support astronaut mental health, a field I am excited to pioneer at this university. I am looking for motivated undergraduates to join me in these projects, whether your interests lie in smart materials and SolidWorks or in psychology and human-centered design. I am eager to help you build your technical skills and confidence as we work together to reinvent the future of space exploration. Description of Possible Projects: Project 1: Bio-Inspired Morphing Structures for Lunar Habitats This project explores the intersection of ancient technologies and modern aerospace materials. We will investigate how Shape Memory Alloys like Nitinol and origami-inspired folding patterns can be used to create self-deploying habitat modules. This work is inspired by historical architectural geometry and is a core focus of my work with ARKASA. Our goal is to find ways to reduce payload volume while maximizing structural integrity for missions to the Moon or Mars. I am looking for students in Aerospace, Mechanical, or Materials Science majors. It would be great if you have an interest in CAD software like SolidWorks or OpenVSP, or if you enjoy hands-on prototyping. Knowledge of Arduino for controlling actuators is a plus but not required. The timeline starts with an introduction to smart material properties and a study of geometric folding patterns (Weeks 1 to 4). In the second month, we will move into conceptual CAD modeling of a morphing joint (Weeks 5 to 8). We will spend the final month building a physical low-fidelity prototype using Nitinol wire and 3D-printed parts (Weeks 9 to 12). The final deliverable will be a functional desktop deployment model and a design report that highlights how we evolved ancient concepts into modern solutions. Project 2: Neuroadaptive Interfaces: Enhancing Astronaut Resilience through AR/VR This project focuses on the human factors side of deep-space missions, which is my current research thesis topic at UIUC. We are developing a neuroadaptive biofeedback system that uses immersive AR/VR environments to monitor and mitigate stress. This is a unique research gap here at the university. We want to see how neurophysiological data can trigger real-time changes in a virtual environment to help astronauts maintain peak performance. I am also looking for student researchers to assist with sample collection across various participants. You will be involved in the non-invasive testing of our system using AR/VR to detect stress levels in human subjects. Please note that our IRB (Institutional Review Board) process for this testing is currently ongoing. I highly encourage students from Psychology, Bioengineering, Computer Science, or Aerospace to apply. A curiosity about human behavior in extreme environments is essential. I strongly recommend that you have taken or are currently taking introductory courses in Psychology or Human Factors to help with the conceptual side of this work. During the first month, we will conduct a literature review on isolation psychology and neuroadaptive systems (Weeks 1 to 4). The middle of the semester will focus on setting up the participant testing framework and coordinating the logistics for sample collection (Weeks 5 to 8). In the final weeks, we will collect data using the prototype biofeedback interface and prepare a feasibility analysis (Weeks 9 to 12). The final deliverables will include a VR testing protocol and a research poster detailing how the system impacts simulated stress levels. Desired Skills and Time Commitment: Because my current research is centered on human factors and neuroadaptive biofeedback, students with an interest in or basic knowledge of Psychology are highly encouraged to apply. To get the most out of this research area, I recommend (though do not require) taking introductory courses in Human Factors or Introductory Psychology, as these provide a vital foundation for understanding how we can enhance astronaut resilience in extreme environments. The main requirement is a consistent commitment of at least 3-4 hours per week for our team meetings and independent project work.
Dajin Cho (Neuroscience)	Dajin is a fourth-year Ph.D. Candidate in Neuroscience working with Dr. Patrick Sweeney, and she is currently studying gut-brain satiety signaling in lactating mice. She is from South Korea and is primarily interested in behavioral neuroscience. Before joining the Sweeney lab at UIUC, she earned her B.Sc in Biological Sciences and Biomedical Engineering at City University of Hong Kong, and she worked as an undergraduate research assistant at an auditory neuroscience lab. Description of Possible Projects: The project will investigate how lactation affects feeding behavior by examining changes in gut-brain signaling that regulate hunger and satiety. Mentees will assist in experiments using pharmacological agents, transgenic mice, and advanced imaging techniques to explore the brain’s role in lactational hyperphagia. Through these experiments, students will learn about brain circuits and how lactation influences metabolism, providing valuable hands-on experience for future research opportunities in neuroscience and metabolism. You will learn: Mice handling, basic molecular biology techniques such as PCR and gel electrophoresis for genotyping, mouse colony organization, behavioral experiments Desired Skills and Time Commitment: High commitment and passion. Everything is wet-lab. We do not require any specific skill, but your time commitment and passion to learn! Since the experiments will be time-specific, communication with me will be very critical to let me know when you are available to join and conduct experiments.
Gyuha Lim (Aerospace Engineering)	Gyuha Lim is a Ph.D. candidate in Aerospace Engineering at the University of Illinois Urbana–Champaign, advised by Professor Deborah A. Levin. He received his B.S. and M.S. in Mechanical Engineering from Hong Kong University of Science and Technology. His research focuses on computational plasma physics and electric propulsion, with an emphasis on high-fidelity kinetic simulations of gridded ion thrusters and ground test facility effects. His work centers on developing and applying hybrid PIC–DSMC / PIC–MCC frameworks to study plume dynamics, charge-exchange and momentum-exchange collisions, electron–neutral interactions, and plasma–surface interactions. He is particularly interested in understanding how facility pressure, neutral backflow, and chamber geometry influence near-field plume behavior and diagnostic measurements. More broadly, his research interests include kinetic plasma modeling, GPU-accelerated particle simulations, plasma–material interactions, and the integration of physics-based reduced models for predictive, resource-efficient electric propulsion simulations. Description of Possible Projects: This project focuses on analyzing ground testing effects on gridded ion thruster performance using data from large-scale, multi-GPU plasma simulations. The undergraduate student will work with existing simulation datasets generated using Particle-in-Cell (PIC)–based kinetic plasma models and will primarily focus on post-processing, visualization, and physical interpretation of the results. The student will use Python for data analysis and Tecplot for scientific visualization to examine quantities such as plasma density, potential, ion flux, and charge-exchange collision signatures. Through this project, the student will gain hands-on exposure to plasma physics, kinetic theory, and computational modeling techniques used in electric propulsion research. Proposed timeline: Weeks 1–4: Introduction to electric propulsion, plasma physics fundamentals, and simulation data structure Weeks 5–10: Python-based post-processing and Tecplot visualization of simulation results Weeks 10–14: Physical interpretation of trends (e.g., pressure effects, plume spreading, charge-exchange signatures), Refinement of analysis and preparation of final deliverables Deliverables: Single Particle Simulation: Developed Python-based simulations to track particle trajectories in electric fields. Data Processing (Python): Developed custom scripts for automated data extraction and statistical analysis of simulation outputs. Visualization (Tecplot & Python): Generated 2D/3D contour plots and vector fields to visualize potential gradients and beam profiles. Desired Skills and Time Commitment: Preferred background / skills (not all required): Majors: Aerospace Engineering, Nuclear, Plasma, and Radiological Engineering, Mechanical Engineering, Physics, or related fields Interest in: Plasma physics or fluid dynamics Electric propulsion (optional) Python programming Data analysis and visualization
Heng-Sheng Chang(Mechanical Engineering)	Heng-Sheng Chang is a postdoctoral researcher with a passion for advanced science, technology, and innovation. He earned his B.S. in Mechanical Engineering from National Taiwan University in 2017 and completed his M.S. and Ph.D. in 2025 at the University of Illinois Urbana-Champaign (UIUC) as a member of the Mehta research group. His doctoral work focused on modeling, control, and estimation of soft actuators, advancing the field of soft robotics through design, simulation, and experimental evaluation. His current research involves time series signal estimation, prediction, and generation. Description of Possible Projects: Project 1: Trustworthy Arithmetic LLMs: Training, Alignment, & Interpretability** **Project Overview** This project investigates the reliability of Large Language Models (LLMs) by focusing on a controlled, domain-specific task: integer arithmetic. You will oversee the full lifecycle of a model to determine if we can eliminate “hallucinations” and visually verify the model’s reasoning process. **Specific Research Tasks** – [Model Pre-Training] Train a model from scratch on a dataset of integer arithmetic problems (e.g., addition, multiplication) until it achieves high accuracy. – [Post-Training via On-Policy Distillation] You will generate arithmetic chains using the model’s current policy, identify self-generated errors (hallucinations), and use distillation to correct the model’s behavior based on its own generated mistakes rather than just passive teacher forcing. – [Interpretability] Integrate visualization tools to render the model’s attention matrix. You must map how attention heads focus on specific tokens (digits/operands) during calculation to correlate attention patterns with accurate results versus hallucinations. **What We Are Looking For** We are looking for students who are interested in the “why” behind model failure. Your application should demonstrate familiarity with PyTorch, an understanding of Transformer architecture, and an interest in mechanistic interpretability and knowledge distillation. — Project 2: Voice-Activated Agentic AI **Project Overview** This project focuses on building a “Voice-First” agentic application that acts as an intelligent interface between a user and a complex dataset. You will build an autonomous system that listens to user queries, reasons about them using the Model Context Protocol (MCP), and retrieves accurate feedback from a knowledge base. **Specific Research Tasks** – [Speech Interface Implementation] Build a robust Speech-to-Text (STT) pipeline (utilizing models like OpenAI’s Whisper) to serve as the primary input interface for the application. – [Agentic Architecture] Design an agent using the Model Context Protocol (MCP) framework. The agent must function as a “Controller” that accepts the transcribed text, plans a multi-step execution, and queries a specific dataset (e.g., a file system or database). – [Feedback Loop] Integrate an existing LLM to analyze the retrieved data and generate audible or text-based feedback for the user, ensuring the system can handle complex, multi-turn interactions. **What We Are Looking For** We are looking for students capable of full-stack AI development. Your application should highlight experience with API integration, audio processing pipelines (STT/TTS), and building autonomous agents or RAG (Retrieval-Augmented Generation) systems. Desired Skills and Time Commitment: 1. Proficiency in Python 2. Interest in machine learning and time series data Optional: 1. Foundational understanding of linear algebra 2. Experience with machine learning libraries such as TensorFlow or PyTorch
Javier Balta (Mechanical Engineering)	Javier Balta is a graduate researcher in robotics and materials at the University of Illinois Urbana-Champaign, with a background in microengineering from EPFL. His work focuses on additive manufacturing technologies for space applications, combining hands-on experimentation with system-level design. He is currently developing an automated materials testing system, drawing on years of personal experience building microcontroller-based projects and custom hardware. He is excited to mentor motivated students and share what research can look like across disciplines—from concept to lab reality. Description of Possible Projects: The robotic platform at the center of this work is designed to accelerate the discovery and testing of frontally polymerizable polymers. It features a robotic arm that autonomously mixes monomers, powders, and other liquid components, then runs a series of experiments to evaluate material performance. These include front speed measurements using custom-designed molds, differential scanning calorimetry (DSC), and proto-rheology tests such as vial tilting to assess viscosity. The two proposed student projects aim to refine and stabilize the existing setup while helping implement subsystems for the DSC and proto-rheology experiments that are still under development. Project Title: Automation and Prototyping for High-Throughput Polymer Testing This project involves contributing to the development of a robotic platform for high-throughput polymer formulation and characterization. The student will focus on refining the design of one or more subsystems, which may include designing and 3D-printing mechanical components, wiring sensors and actuators, programming microcontrollers (Arduino), and verifying the system both as a standalone module and within the larger integrated setup. Attention to detail and a tidy work ethic in the lab are essential, as this work often involves wet lab procedures alongside electronics. Recommended background: Experience (or strong interest) in Arduino or embedded systems programming, CAD (e.g. Fusion 360 or SolidWorks), and hands-on prototyping. Familiarity with lab work and safe handling of materials is a plus, but not required. Majors or minors in Mechanical Engineering, Electrical Engineering, Robotics, Materials Science, or similar fields would benefit most, but all motivated students are welcome. General Structure & Timeline Both projects will begin with a ~4-week onboarding and training period, during which the student will become familiar with the lab environment, tools, and ongoing projects. During this time, they’ll also be encouraged to address any skill gaps through self-paced learning (with guidance). Once familiar, students will identify their specific contribution focus, and we will collaboratively set semester goals. The remaining semester will be divided into three work phases, each ending in an informal review to assess progress and adjust goals if needed. Final deliverables will include a short presentation or report summarizing their work, organized notes, and ideally, a working prototype or demonstrable improvement to the system. Desired Skills and Time Commitment: No specific prior skills are required for these projects, though any experience with programming (especially Arduino or Python), 3D modeling, electronics, or hands-on lab work—whether from classes or personal hobbies—will definitely be useful. That said, what’s most important is a strong willingness to learn quickly, both during lab hours and independently when needed. These projects are designed to give students their first exposure to real, applied research, so while I don’t expect mastery, I do expect curiosity, initiative, and a readiness to build new skills as we go. Students who enjoy tinkering, building, or solving practical problems will feel right at home, especially since the work is closely aligned with the kind of creative, hands-on experimentation often found in hobbyist projects. The lab is well-equipped, and I’m actively working on the same systems they’ll be contributing to, so students will get guidance and mentorship throughout—but with room to propose ideas and take ownership of parts of the work. I’m very flexible with scheduling, but I prefer students to come in for 2–3 hour blocks at a time, since that allows us to take full advantage of the equipment and workflow setup. There’s no hard prerequisite in terms of coursework; any class that brings relevant technical or analytical skills (intro to programming, engineering fundamentals, basic chemistry or physics, etc.) can be helpful, but isn’t required.
Jingyi Xiang(Computer Science)	I am a second-year Computer Science PhD student working with Prof. Wenzhen Yuan on tactile sensing and robotic manipulation. My research interests include developing and integrating robotic tactile sensing hardware and software for robots to better complete manipulation tasks (cooking, chores, the tasks one would want a general-purpose household assistive robot to do). I completed my undergraduate degree in the ECE department at UIUC, during which I worked as an undergraduate researcher for 2.5 years and served as a peer mentor at the UIUC Office of Undergraduate Research. I’m always excited to work with motivated undergraduate students. More about me can be found on my homepage: https://jingyi-xiang.github.io/. Description of Possible Projects: The student will work on a project integrating one or more types of tactile sensors into imitation learning (learning from human demonstrations) frameworks. There are two key components in this project: 1. Tactile sensing. There are many types of tactile sensors out there, such as vision-based sensors (https://youtu.be/qtQ4rK66vlE?si=-qzjnIFCzvuTjd49), pressure arrays (https://binghao-huang.github.io/3D-ViTac/), and magnetic-based sensors (https://e-flesh.com/). Different sensors output different signals to capture and represent “the sense of touch”. 2. Imitation learning. Imitation learning is a machine learning approach in which a robot learns a task-specific policy that maps observations to actions by mimicking expert (human, in our case) demonstrations. It removes the need to explicitly program the robot on how it should act in each specific scenario. As imitation learning has become increasingly popular for robotic manipulation in the past few years, various recent research works have attempted to integrate tactile sensors into popular imitation learning frameworks. For this project, the student will implement one or more of such works, thoroughly test their performance on an extensive set of tasks, and gain insight on the current research gaps in tactile-driven robot learning. The ultimate goal is to use such insight to develop new algorithms and/or sensors to address these gaps. The project timeline will be highly dependent on the student’s background. If the student has limited knowledge in machine learning, the first two or three weeks might be spent helping the student get familiar with the core concepts. For the majority of the semester, the student will be working on implementing the selected tactile-driven imitation learning research works and adapting them to the robot system in our lab. In the final two or three weeks, the student will (ideally) begin testing the implemented methods on various manipulation tasks and compile their findings. Desired Skills and Time Commitment: – The student should be willing to work around 10 hours per week and learn new skills along the way. Please note that, since the project involves working with physical sensors and industrial robot arms, the student will need to come into our research lab to work from time to time. There will be two required meetings per week: a one-on-one meeting with me and a junior researcher group meeting with other undergraduate students and Prof. Yuan. – The student should promptly communicate their research progress and any issues encountered. – The student should be willing to take initiative and explore different potential solutions. – The student will work with Robot Operating Systems (ROS), PyTorch, machine learning, various sensors/electronics, and robot teleoperation. The student does not need to have prior experience with these things, but a good applicant should be able to convince me they are willing to commit the time and effort to learn. – The student should have some familiarity with Python. Experience with robotics, hardware, and/or machine learning projects will be a plus. – I might reach out to some applicants for a quick interview. Unfortunately, since the application review timeline is very tight, all interviews will have to be scheduled between 1/24 noon and 1/25 evening.
Phuong Cao (National Center for Supercomputing Applications)	Phuong Cao is a research scientist at the National Center for Supercomputing Applications (NCSA) at the University of Illinois at Urbana-Champaign. His research interests include securing high-performance scientific computing through large-scale measurements of network protocols, designing probabilistic models to detect cyber-attacks, and synthesizing machine-assisted proofs of federated authentication protocols. His research has won a Best Paper Award, a Best Hackathon Award, and an Outstanding Mentor recognition for advising students in cybersecurity research. Phuong is a Trusted CI Fellow of the NSF Cybersecurity Center of Excellence. Description of Possible Projects: The AICyberLake project curates a security data lake by sourcing cyberattacks from the DeltaAI system at NCSA and its peer supercomputing centers. The data includes Zeek network cryptographic metadata, graphics processing unit (GPU) interconnect vulnerabilities, and ground truth incident reports. The resulting data lake provides a real-time, anonymized stream of attack attempts to vetted research teams for evaluating their agentic AI-based detection models against unseen adversaries. Desired Skills and Time Commitment: C++ Python Networking
Stephen Zou (Mathematics)	Stephen Zhou is a fifth-year Ph.D. student in mathematics at UIUC. He is interested in using mathematical methods to study quantum information theory and quantum algorithms. His recent works involve quantum state discrimination, quantum entropy of coherence, and sample complexity of quantum learning problems. Description of Possible Projects: Project: A new bound on the success probability of the pretty good measurement This project will investigate a (new) mathematical bound in a quantum information theory problem. The students will write codes to find numerical evidence or counter examples and try to formulate a rigorous proof. Recommended background: Familiarity with calculus (MATH 447 level) and linear algebra (MATH 416 level) is recommended. Python coding skills are helpful. Quantum information theory is not required since we will introduce everything we need in the first month. Timeline: In the first 3 weeks we will review key mathematical backgrounds and introduce relevant knowledge in quantum information theory and probability theory. Then we will spend 1 – 2 weeks to review the existing literature and discuss our research methods. The rest of the semester will be devoted to coding and formulating rigorous proofs at the same time, as numerical evidence will help us better understand the conditions for the bound to hold. Finally, we will leave some time for making the report and poster, as well as discussing future research plan for interested students. Deliverables: Theoretical report (potentially publishable), codes for numerical experiments, and poster for symposium. Desired Skills and Time Commitment: Desired skills: We recommend familiarity with linear algebra and rigorous mathematical reasoning is necessary, as well as the ability to learn new math and read research articles. Coding experience is helpful, but the coding will not be complicated. Overall, apart from basic mathematical background, the most important thing is the excitement about learning relevant math knowledge and the ability to apply it to practical problems! Time commitment: We will have weekly meetings for lectures and progress report. The students are expected to devote some time to coding, reading research articles and formulating proofs outside the weekly meetings.
Theresa Sandborn (Computer Science)	Theresa Sandborn is a first-year PhD student in Aerospace Engineering working in the Electric Propulsion Laboratory on dual-mode monopropellant small satellite propulsion systems. Her primary research focus is on electrospray propulsion, and her current work focuses on the development of a multimode propulsion system for a 12U CubeSat. Description of Possible Projects: Video Editing Mobile/Desktop Application: Monarch Cubesat Propulsion System Design, Manufacture, and Test: This project involves the development of a novel multimode propulsion system for the Monarch CubeSat with the LASSI Lab. The preliminary design is mostly complete, so the next steps involve polishing the designs, building prototypes, and testing. This project is split into 3 main subsystems that can be considered their own separate projects, with strong interdependencies. All majors are encouraged, but aerospace engineering, electrical engineering, physics, plasma engineering, and mechanical engineering will be the most applicable. The intended timeline has students beginning manufacture and assembly within the first few weeks, running their first tests by week 6, and working to collect clean data by week 10. Subsystem 1 – Chemical Thruster This work will start with assembly of a catalytic decomposition chemical microthruster system based on an existing design. Students will design and build a simple pressure system for propellant feed, run thruster tests with a variety of propellants, and collect data to characterize thruster performance. The propellant used in testing will change as the system is proven at different steps, starting with air, then water, then more volatile materials. Final steps involve integration with the other subsystems. Subsystem 2 – Electrospray Thruster This work will start with assembly of an electrospray thruster system. Students will design and build a small pressure system for propellant feed, and work with high voltage instruments to run the thruster. Once the thruster behavior has been characterized, it will be integrated with the other subsystems. Subsystem 3 – Feed System This work involves the design of a multimode propellant feed system to supply the two types of thruster. It will operate independently of the thrusters until functionality has been proven, then it will be integrated. Students will finalize the design of the system, manufacture a prototype, test, and then repeat until the system works as intended. They will coordinate solenoid valve operation, read in temperature and pressure sensor data, and measure flow rates. Tests will be run with air first, then water, then more volatile materials as performance is proven. Desired Skills and Time Commitment: No specific experience is required, but experience with high voltage, pressure systems, safe chemical handling, and/or instrumentation is helpful. The most important qualities are enthusiasm and attention to detail. Time commitment of at least 8 hours per week is suggested.
Vishesh Prasad(Electrical and Computer Engineering)	Vishesh Prasad is a first-year M.S./Ph.D. student in ECE at the University of Illinois Urbana-Champaign. He earned his B.S. in Computer Engineering from UIUC with minors in Mathematics, Statistics, and Econometrics. His research interests focus on game-theoretic reinforcement learning, mathematical language processing, and statistical learning. Vishesh specializes in solving research challenges algorithmically and translating theory to practical applications. He values both breadth and depth of knowledge, demonstrated by additional research in software engineering and compilers. As an undergraduate, Vishesh received the Indira Gunda Saladi Research Scholarship and the Edward C. Jordan Award from the ECE department. Additionally, he participated in the ISUR and IBM-IL (IDAII) research programs. Outside of research, Vishesh enjoys teaching and mentoring, with extensive experience as a course assistant and, more recently, as a teaching assistant. He looks forward to guiding enthusiastic undergraduates interested in research and potential collaboration on future projects. Description of Possible Projects: Project Title: Mathematical Derivation Graphs Project Description: The student will contribute to a project at the intersection of natural language processing, large language models, and mathematical reasoning. Students have a rare opportunity to define a novel research problem through a relation-extraction task: extracting derivation graphs from stem manuscripts. Derivation graphs illustrate explicit and implicit information flow or computational dependencies between equations in research manuscripts. We define the problem and dataset and present approaches to address it using analytical algorithms, machine learning models, and large language models. Students will expand the dataset and design novel algorithms to advance the research. This role offers meaningful engagement at all stages of introducing a new research problem. Major and Skill Requirements: We welcome students from all majors. You should be comfortable with Python, basic data structures and algorithms, Git, and code documentation. A willingness to learn is essential. Timeline: Weeks 1-2: Literature review and skills development. Weeks 3-6: Dataset collection. Weeks 6-10: Algorithm design and further dataset expansion. Weeks 11-12: Final documentation, report writing, poster creation, and presentations. Deliverables: Dataset samples and algorithm development/analysis. A concise report or small poster describing the research problem students address. Desired Skills and Time Commitment: This project welcomes all backgrounds. We seek motivated students interested in algorithms, machine learning, and large language models. Students must have experience with Python, basic data structures and algorithms, version control (git), good code documentation, and a readiness to learn. Students will engage with concepts from courses such as ECE 364, ECE 365, ECE 449/CS 440, and ECE 449/CS 446; these courses are not prerequisites, and learning will occur through project participation. Time Commitment: Applicants will be asked to commit approximately 10-12 hours per week. This includes time for weekly meetings. We understand how stressful students can get around exam time, so there is some flexibility in the time commitment as the semester progresses, but this is dependent on the project’s progress.