Mentors

The 14 currently listed mentors are for the Spring 2025  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
Aadya Ranjan (Computer Science)	Aadya is a first-year Master’s student in Statistics-Analytics at UIUC with a Bachelor’s degree in Computer Science with Data Science. Passionate about technology and its noble utilities, her academic focus lies at the intersection of healthcare analytics, machine learning, and natural language processing, where she combines predictive modeling and data-driven approaches to address impactful challenges. Aadya has made significant contributions to healthcare analytics research and has spearheaded projects that showcase her proficiency in leveraging ML to solve real-world problems. Committed to continuous learning, she thrives on collaboration and mentorship within the field. Outside of academics, Aadya is a professional-level badminton player who embraces challenges both on and off the court, exemplifying determination and excellence. Description of Possible Projects: "Designing Explainable AI Systems for Enhancing Trust in Automated Decision-Making in Healthcare" This project focuses on creating machine learning models that not only predict outcomes but also provide human-interpretable explanations for their decisions, specifically tailored to healthcare applications. The goal is to explore and implement explainability techniques to enhance trust in automated decision-making systems in healthcare. The project will involve applying advanced explainability methods, such as SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations), to predictive models used for healthcare decision support, such as predicting patient outcomes or readmission risks. Target Skills and Majors: (preferred but not REQUIRED, an intent to learn and contribute to the project is what matters!) Majors: Computer Science, Data Science, Statistics, Engineering, Business Analytics, Information Management Skills: ML Basics (supervised learning, classification/regression, evaluation metrics), Python/R programming and related libraries and packages, Data cleaning and preparation, Optional: Familiarity with healthcare datasets, experience in model evaluation Timeline: Weeks 1–2 (Onboarding & Skill Development): Introduction to explainable AI (SHAP, LIME) and healthcare datasets. The student will learn necessary programming tools and frameworks for the project. Week 3(Literature Review & Research): Study relevant papers on explainability methods in healthcare, and review codebases for SHAP and LIME techniques. Weeks 4–8 (Ideation & Model Development): Select a healthcare prediction task, preprocess data, and develop the machine learning model. Implement SHAP or LIME to explain model decisions. Week 9 (Experimentation & Evaluation): Experiment with different models, evaluate performance, and assess the quality of the explanations generated. Weeks 10 (Refinement, Documentation & Presentation): Refine the model based on evaluation, prepare comprehensive documentation, and open-source the project on GitHub. Present the final results. Desired Skills and Time Commitment: Desired Skills: Some experience or familiarity with Python/Jupyter/R and their libraries and relevant packages is required. Time Commitment: Approximately 8–9 hours per week This project is designed to offer hands-on experience in machine learning and explainable AI in the context of healthcare, with opportunities to learn and develop research skills. The duration includes research, coding, experimentation, and meetings.
Amin Mirzaee (Electrical and Computer Engineering)	I'm a second-year ECE Ph.D. student working with Prof. Wenzhnen Yuan at RoboTouch Lab, CS department. I got my bachelor’s and master’s degrees in mechanical engineering which contributed to my current research interest in robotics, sensor design and simulation, tactile sensing, and robotic manipulation. The main objective of the projects is to optimize sensing and actuation systems for a variety of robotic tasks. Description of Possible Projects: There are several projects I work on that go into different categories. In the following, I provided some information about the two categories I will be mentoring. Students will start working on a specific project within a category. We select projects based on the student’s skills and interests. Category 1: Design of novel robotic systems. Tactile sensing enables robots to manipulate objects with more dexterity and perceive their physical environment more like a human. The majority of the research conducted in our lab involves working with high-resolution vision-based tactile sensors (VBTS) like GelSight (watch this https://www.youtube.com/watch?v=aKoKVA4Vcu0). The projects in this category mainly focus on the design and fabrication of new sensors for different robotic applications. Projects include but are not limited to: 1-Embedded tactile sensing design for soft grippers. GelSight-like sensors can be added to a variety of systems to enhance the robot's interaction with the environment. An interesting field is to integrate these sensors in soft robots, like soft fingers, suction cup grippers, etc. Refer to the following for more background information. https://ieeexplore.ieee.org/abstract/document/10160384/ https://ieeexplore.ieee.org/document/9635852/ 2- Sensor design for automated high-resolution surface inspection. GelSight-like sensors have great potential to be used for automated surface inspection systems, capturing high-resolution surface information for many industrial and healthcare applications. Refer to the following for more background information. https://ieeexplore.ieee.org/document/10833742 https://ieeexplore.ieee.org/abstract/document/10341590 3- Other than VBTSs we are also interested in designing new sensors based on other transduction mechanisms for tactile skins. These sensors are thinner than GelSights and can be mounted on different surfaces on the robot to achieve tactile signals. Category 2: Physics-based simulation and optimization for optical tactile sensors. A challenge in the design of new sensors is to predict the performance before the fabrication stage. We use a physics-based rendering technique for simulating vision-based tactile sensors (VBTS) to predict the performance and optimize the design. We optimize the design parameters such as sensor geometry, light, and materials. The goal is to design an interactive environment for users to simulate and optimize their design. Projects in this category mostly involve rendering sensor outputs and writing code to build on the toolbox that we currently have. We aim to have a interactive GUI for complete VBTS design, simulation, and optimization. As mentioned we conduct both geometry and material optimization. Desired Skills and Time Commitment: - Contribute at least 10 hours weekly to the research project, including ~2 meetings per week. -The final project topic will be decided during a meeting. - Share data regularly to GitHub/GDrive. - Read and take time to understand journal articles - Keep a Google Doc of progress and update it weekly. - Contribute slides weekly to group meetings. Note: Category 1: Experience working with CAD software is a plus. We will have training for the fabrication. Category 2: Experience working with the Python programming language.
Chandan Mahata (Agricultural and Biological Engineering)	Chandan Mahata is a second-year doctoral student in the Department of Agricultural and Biological Engineering at the University of Illinois Urbana-Champaign (UIUC). As a research student, Mr. Mahata published 18 peer-reviewed articles in the field of bioprocess engineering and presented research outcomes at several international conferences. He has experience in supervising industrial R&D work at Dhampur Sugar Mills, India (currently Dhampur Bio-Organics Ltd) for biohydrogen production from industrial wastewater under a technology license transfer agreement. His current research focuses on precision fermentation using genetically engineered microorganisms for the production of high-value platform chemicals and the recovery of bioproducts. He is currently a peer-reviewer of several international journals. Please visit https://www.chandanw2e.com/ for more detail Description of Possible Projects: Project 1: Production of high-value platform chemicals from renewable sources via precision fermentation using genetically engineered microorganisms. In this project, we will produce bioproducts in small scale experiments and in bioreactors. Mentees will learn fermentation process, sterile handling of microorganisms, and operation of bench-scale bioreactors. Project 2: Recovery of bioproducts from fermentation broth. Here, we will explore different downstream processes, including ion exchange chromatography, nano-filtration, and crystallization. Mentees will be trained to set-up experiments in bench-scale study. Having hands-on experience in the above projects would be advantageous to secure industrial internships. Desired Skills and Time Commitment: Students interested in wet lab experiments are welcome. Knowledge in basic statistics (design of experiments, ANOVA test), data analysis, data visualization is advantageous. For project 1, knowledge in industrial microbiology is beneficial. For project 2, chemistry and unit operations will be helpful. Meeting/working time is flexible. 5 – 10 hours per week is expected.
Dajin Cho (Neuroscience, Molecular and Integrative Physiology)	Dajin is a third-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.
Hyouin (Yueze) Liu (Electrical and Computer Engineering)	Yueze is a second-year Masters ECE student. He earned two bachelor's here, physics and computer engineering in 3 years, a person who LOVE science and everything related to it. His experience ranges from Reinforcement learning/Attention model application, operating systems, IOT microcontrollers/PCBs, electronic and photonic crystal fabrication. He has 3 papers under bio-ML, but these days he likes IOT and hardware more. Description of Possible Projects: Project 1: Self-conscious speech companions "Man I’m having my new exam tomorrow I don't understand anything" "(automatic response) I'm busy, respond to me in an hour" "No man one piece is really good at episode 700 you gotta watch it" The aforementioned are a few samples of our models Role-playing is the second most used function for LLMs in the US, yet its stories are often short, forced, and scenario related instead of long term. Indepth characterization often lack interesting topics or continuity. Here, you'll learn, change, and develop the most engaging agents in the world; whether it's more lively sounds, embedded hardware for interacting interface, or event-pipelined LLM prompt engineering and finetuning to dialog models, we've got circuits, pytorch, webscraping, all that you want in tech, we got it here. (this is really 3 papers packed into one) Timeline - 1 week for onboarding, 1 week for exploration, 10 weeks of refining pipeline, gathering data, or reading more papers, because not knowing is the romance of science 😉 Deliverable - We will have a chance to showcase our system at UI-CON, UIUC’s anime convention in April, and have a paper to showcase our system in user satisfaction for a large conference paper. Project 2: Optical computer This is a real beast. Humanities' future will move on to optical computing, and it's only a matter of time before we full send it. We make this, we can patent it and go face NVidia. The overarching goal is that to make an optical computer, one has to have a compact, low loss environment for optical signals. (Optical computers are 100000000% better than classical computers btw, if we can make it work). Recently most other universities have started research on this, and are receiving a lot of funding, but our university doesn't. The specifics will be designing and optimizing photonic metasurfaces using fourier optics and investigating fabrication techniques to increase the performances. Timeline - 1 week for onboarding, 1 week reading papers, 10 weeks design and fabrication interweaved whenever necessary Deliverable - A sample metasurface in fourier optics that rivals in power at least if not more than state of the art optical surfaces. Desired Skills and Time Commitment: Passion for life; skills can be learned so fast these days anyways Skills used for projects: reading papers python finding out what to use use them
Jesus Castro (Civil and Environmental Engineering)	Jesús Castro is a Ph.D. Candidate in Civil & Environmental Engineering, workin with Prof. Jeffery Roesler. His research focused on developing nondestructive testing methods (ultrasound) to evaluate the structural integrity of concrete elements. Description of Possible Projects: Air-coupled sensors prototype for monitoring fresh concrete properties. Development of a case prototype for an air-coupled transducer and sensors. This device monitors the initiation of Leaky-Rayleigh Waves in concrete surfaces, which is linked to a liquid-solid phase change. Currently, the experimental setup is the result of several iterations in the research scope. The objective of this project is to design and 3D print an encasing for all the electronic components, as well as to install a built-in screen capable of showing the wave signals in real time. In this project students with experience in 3D design, or printing, can apply their skills and print several iterations until achieving an optimal design with our lab’s 3D printer. If the student has enough time, he can also modify the current signal processing algorithm (python), which operates an NI data acquisition card, to display wave signals on a portable screen without needing an external PC. This is a flexible project, and student suggestions will be considered for additional improvements and modifications to the prototype. The expected time commitment is at least 3-5 hours weekly. I will be able to meet with you daily and discuss weekly progress. Desired skills: CAD (preferably Fusion), Arduino, prior experience with Python, and interest for instrumentation. Optional: Interest in cementitious materials (concrete). Desired Skills and Time Commitment: Any 3D modeling software (Fusion 360 or any equivalent). Interest for signal processing. Interest for prototyping Interest in construction materials is a plus. Interest in hands-on experience.
Jingyi Xiang (Computer Science)	I am a first-year Computer Science Ph.D. student working with Prof. Wenzhen Yuan in the field of robotics. My current research is focused on integrating tactile sensing into robotic perception and manipulation, giving robots a sense of touch. I obtained my B.S. in the ECE department at UIUC, during which I worked as an undergraduate researcher for over two years and published a few papers. I’ve also worked as a peer mentor for the Office of Undergraduate Research, helping undergraduate students land research opportunities. 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 focusing on the application of tactile sensing in robotic manipulation tasks. We will primarily work with vision-based tactile sensors (see a good example here: https://youtu.be/qtQ4rK66vlE?si=-qzjnIFCzvuTjd49, although the sensors we use on the robots are smaller, more similar to this one: https://youtu.be/HIFA83COlcc?si=N0W0H42JwgWxFAJM). There are many different types of vision-based tactile sensors, each with unique optical and mechanical designs. For this project, we are interested in investigating sensor-invariant tactile features required to complete various everyday tasks. There are two parts to this project: 1. Working with a UR5 industrial robot arm equipped with tactile sensors to complete a peg-in-hole task. When humans perform peg-in-hole tasks, we can determine in which direction the hole interior obstructs the peg based on fingertip forces, then adjust the peg alignment accordingly. The student’s task would be to achieve this on the robot arm using output from the mounted tactile sensors and some control framework such as a PID loop. 2. Working with different types of tactile sensors and categorizing their outputs for different contact events. The motivation for this part of the project is to generalize existing robotic manipulation frameworks (the peg-in-hole pipeline above, for example) developed with a specific type of tactile sensor to other types of tactile sensors. Depending on the student’s progress, their work could potentially be integrated into a paper for publication. Desired Skills and Time Commitment: - Students being considered for the position may be interviewed by me - The student should be willing to work around 10 hours per week and learn new skills along the way. 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 communicate promptly about their research progress and issues encountered. - The student should be willing to take initiative and explore different potential solutions. - Past experience with robotics and hardware projects will be a plus.
Kamil Czaplinski (Civil and Environmental Engineering)	I am a 1st-year graduate student in Civil and Environmental Engineering at the University of Illinois Urbana-Champaign under Dr. Tinoco. My research is focused on investigating the morphological effects on shorelines that oyster castles have when they are used as breakwaters. My work utilizes a wave flume to run experiments and collect large amounts of data using a variety of sensors, cameras and lasers. The purpose behind the work is to help restoration efforts in coastal regions where oysters were once abundant. Prior to joining the Ecohydarulics and Ecomorphodynamics Lab, I was a Civil Engineer doing a variety of roles primarily drainage engineering. Description of Possible Projects: Project 1: Investigation of coastal morphodynamics based on various tropical ecosystems. In this project, we seek to explore the morphological dynamics of a seabed within tropical ecosystems due to various structured habitats including oyster reefs, mangroves, and seagrass patches. The primary objective is to investigate this morphological sediment process in tropical habitats to complement ongoing research which is predominantly focused on their ecological benefits. By bridging this gap, the study will acknowledge the role of these habitats in shaping coastal resilience. Using a stream table, we will simulate these habitats with predetermined configurations to analyze the erosion and deposition caused by the different habitat structures. Data collection will involve using a stream gage and cameras to monitor and determine changes in the bed morphology. This data will be analyzed within programs like Excel, Matlab, and Python. Alternatively, data can be collected using a spot survey technique (measuring the bed height at various, consistent points throughout experiments). Desired Skills and Time Commitment: Being familiar with fluid dynamics and programming will be helpful, however not necessary for this project. The time commitment is flexible but specific duties must be completed with consistent effort to get the most out of this experience. At a minimum, 5 hours a week will be necessary to achieve meaningful results.
Mahdi Azizi (Agricultural and Biological Engineering)	Mahdi is a second-year PhD student in Agricultural and Biological Engineering, working on the particle tracking velocimetry (PTV) system to study fluid flows. Description of Possible Projects: The projects are related to various steps of doing experiments with the PTV system, including but not limited to preliminary setup, capturing and processing images from cameras, calibration, testing, and running an experiment with tracer bubbles. It’s a good opportunity to familiarize yourself with PTV systems. Desired Skills and Time Commitment: Familiarity with any of the followings is an advantage: computer vision, camera calibration, programming (cpp/Matlab/python), fluid dynamics, convolutional neural networks (CNN), and collecting and analyzing data. At this stage, we will work on labeling images for neural network training. Any prior experience in labeling and CNN training is encouraged. The timing is flexible, but once we agree on your specific duties, you should be responsible to devote the required time to do it.
Michael Harrigan (Aerospace Engineering)	Michael is a first-year PhD student in Aerospace Engineering working with the Laboratory for Advanced Space Systems at Illinois (LASSI) and Dr. Rovey on dual-mode monopropellant small satellite propulsion systems as well as other space systems and payloads. His primary interests are in space systems, program management, and systems engineering. His recent work involves developing a dual-mode monopropellant propulsion feed system for a suborbital test flight and requirement generation for a dual mode propulsion satellite. Description of Possible Projects: Project 1: Configurable FreeFlyer Model for Solar Sail Performance Analysis Objective: Develop a configurable simulation model in FreeFlyer to evaluate solar sail performance in various mission scenarios. The project will focus on understanding solar sail dynamics, implementing them in FreeFlyer, and analyzing key performance metrics such as light-pressure acceleration, attitude stability, and trajectory adjustments. Majors: Aerospace Engineering, Mechanical Engineering, Physics, or Computer Science Desired Skills: Basic understanding of orbital mechanics, programming (FreeFlyer scripting or any other high level language), and data analysis Timeline and Deliverables (14 Weeks): Weeks 1-4: Familiarize yourself with the principles of solar sails, including light-pressure physics and mission applications. Complete FreeFlyer certification training to build proficiency in the software. Deliverable: Short summary report on solar sail principles and FreeFlyer training completion. Weeks 5-6: Set up a basic FreeFlyer spacecraft model with placeholders for solar sail parameters (e.g., sail area, reflectivity, mass). Test basic orbital simulations to validate setup. Deliverable: Initial FreeFlyer model framework. Weeks 7-9: Incorporate solar sail dynamics into the model, including light-pressure forces and attitude control. Begin testing performance under predefined mission scenarios (e.g., station-keeping, orbit raising). Deliverable: Functional FreeFlyer model with solar sail dynamics. Weeks 10-12: Refine the model for accuracy and configurability. Enable adjustable parameters for sail area, orientation, orbital elements, and mission objectives. Deliverable: Finalized FreeFlyer model with user-configurable settings. Weeks 13-14: Conduct performance analyses for different mission scenarios and document findings. Prepare a final report and presentation, including model usage instructions and key results. Deliverables: Final analysis report summarizing performance metrics and insights. Well-documented FreeFlyer model ready for future use. --------- Project 2: Analysis-Driven Requirements and Conceptual Design for the Payload on a Dual-Mode Propulsion Satellite Objective: Perform analysis to generate system-level requirements and develop a conceptual design for a payload integrated with a dual-mode propulsion satellite. The project emphasizes using analytical methods to define functional and performance requirements that align with mission objectives, focusing on payload-propulsion interactions. Majors: Aerospace Engineering, Mechanical Engineering, Physics, Electrical and Computer Engineers, or Systems Engineering Skills: Basic understanding of satellite systems, proficiency in analytical tools (e.g., MATLAB, Python), and an interest in requirements development and conceptual design. Timeline and Deliverables (14 Weeks): Weeks 1-4: Learn about dual-mode propulsion systems, payload integration, and analysis-driven requirements generation. Familiarize yourself with tools and methodologies for conducting payload performance analyses. Deliverable: Summary report on payload and propulsion integration considerations. Weeks 5-7: Perform preliminary analyses to identify mission needs (e.g., power requirements, thermal constraints, operational modes). Derive initial payload requirements based on analysis results (e.g., mass, volume, interface constraints). Deliverable: Draft requirements document with supporting analysis. Weeks 8-10: Conduct detailed analyses to refine requirements and assess trade-offs (e.g., payload performance vs. mass/power constraints). Begin developing conceptual payload designs that meet the refined requirements. Deliverable: Analysis report and initial conceptual design options. Weeks 11-12: Finalize the conceptual design, ensuring compatibility with the propulsion system and overall spacecraft architecture. Generate visual aids (e.g., diagrams, simple CAD models) to illustrate the design. Deliverables: Finalized requirements document and conceptual design report. Weeks 13-14: Compile findings into a comprehensive final report and prepare a presentation. Include recommendations for further analysis and detailed design phases. Deliverables: Final report and presentation slides. Time Commitment: 5-10 hours per week over 14 weeks. This project provides hands-on experience in conducting analysis to drive requirements generation and applying systems engineering principles to conceptual payload design. Desired Skills and Time Commitment: Project 1: Desired Skills: Basic understanding of orbital mechanics (e.g., concepts from introductory physics or aerospace courses). Familiarity with coding or scripting (e.g., Python, MATLAB, or any programming basics). Willingness to learn FreeFlyer software and complete certification within the first month of the project. Strong problem-solving skills and attention to detail. Enthusiasm for space systems and propulsion concepts like solar sails (prior knowledge not required). Time Commitment: 5-10 hours per week over 14 weeks. Includes time for learning FreeFlyer, project meetings, completing tasks, and independent research or problem-solving. ---------- Project 2: Desired Skills: Basic understanding of physics and introductory concepts in aerospace or mechanical systems (e.g., learned from first-year physics or engineering courses). Familiarity with problem-solving and analytical thinking. Interest in learning analytical tools like MATLAB, Python, or similar (no prior experience required). Willingness to explore systems engineering concepts and satellite design principles. Ability to work collaboratively and ask questions when needed. Time Commitment: 5-10 hours per week over 14 weeks. Includes time for learning new concepts, conducting analyses, attending project meetings, and completing assigned tasks.
Ozgur Kara (Computer Science)	Ozgur is a PhD student at UIUC, CS program under the supervision of Founder Professor James Rehg. He is interested in computer vision, generative AI and its applications. Description of Possible Projects: Developing Methodologies to Defend Against Diffusion-Based Malicious Attacks Generative models have made remarkable strides in recent years, unlocking new possibilities across various domains, from art to scientific research. However, this rapid advancement has introduced critical safety concerns. One pressing issue is the potential for adversarial agents to manipulate original images without authorization, leading to threats like blackmail, misinformation, or identity theft. Such attacks often leverage diffusion models to subtly or drastically alter visual content, making it difficult to detect tampering. To address these risks, our project aims to analyze existing literature and methodologies that focus on enhancing the security of generative models. Specifically, we are interested in exploring techniques such as image immunization, which can preemptively safeguard images from being manipulated by diffusion-based attacks. The goal is to develop robust strategies that protect digital assets, ensuring the integrity of visual data in adversarial environments. Desired Skills and Time Commitment: Required: -A strong sense of motivation and dedication to the project’s goals -Willingness to learn, adapt, and invest effort in problem-solving Preferred Skills: -Experience in Python (familiarity with PyTorch is highly beneficial) -Basic knowledge in Machine Learning, Deep Learning Time Commitment: -Ideally, a commitment of around 10 hours per week. However, if you are passionate and engaged, rigid time tracking is secondary to meaningful contributions. -Regular weekly meetings (at least once a week) to discuss progress and ensure alignment on objectives.
Pranav Swaroop (Civil and Environmental Engineering)	Hello! I am Pranav Swaroop C N, a graduate student specializing in Construction Engineering and Management at the University of Illinois Urbana-Champaign. Currently, I work with the Facilities and Services team at UIUC, where I support construction management activities, including reviewing RFIs, shop drawings, and project estimates. My role involves enhancing project coordination and ensuring design alignment, further strengthening my practical expertise in construction management. With a strong foundation in civil engineering, I bring valuable teaching and research experience. I served as a Civil Engineering Tutor at Gnana Bindu Coaching Centre, improving students' academic performance and designing practical learning modules. During my undergraduate studies, I was a Teaching Assistant in CAD laboratory sessions, bridging theoretical knowledge with practical application. Beyond academics, I am also a National-level netball player. Winning six state-level championships taught me the importance of leadership, teamwork, and strategic planning—skills that I apply to both research and professional collaborations. My research on sustainable construction methods, specifically using bamboo as reinforcement and bamboo leaf ash as a cement replacement, was recognized at a national event hosted by the New Age Innovation Network (Government of India), earning ₹85,000 in funding. This project highlighted my commitment to innovative and eco-friendly construction solutions. I am excited to mentor undergraduates in the URSA program, helping them explore impactful projects, develop essential skills, and foster a passion for engineering research and innovation. Description of Possible Projects: Project 1: Exploring Sustainable Construction Materials Using Bamboo Description: This project focuses on investigating the use of bamboo as an alternative construction material. The mentee will examine its mechanical properties, including tensile and compressive strength, and explore potential applications in reinforced concrete or structural components. This research aligns with sustainable construction practices, providing insights into eco-friendly material utilization. Ideal Mentee Qualifications: Required Skills: Basic understanding of material science or civil engineering principles. Preferred Majors: Civil Engineering, Environmental Engineering, Materials Science. Optional Skills: Familiarity with laboratory work and AutoCAD for drafting designs. Timeline and Deliverables: Weeks 1–3: Literature review on bamboo in construction and identification of testing standards. Weeks 4–6: Sample preparation and mechanical property testing (e.g., tensile strength). Weeks 7–9: Data analysis and comparison with conventional materials. Weeks 10–12: Drafting a summary report and preparing a final presentation. Deliverables: Mechanical property analysis report. Presentation summarizing findings and proposing potential applications. Project 2: Developing a Digital Model for Sustainable Building Design Description: This project involves creating a digital model for a sustainable residential or commercial building using BIM (Building Information Modeling) tools. The mentee will explore design strategies to reduce energy consumption, optimize space utilization, and incorporate renewable materials like bamboo. Ideal Mentee Qualifications: Required Skills: Basic CAD or drafting knowledge. Preferred Majors: Civil Engineering, Architecture, Construction Management. Optional Skills: Experience with Revit or similar BIM software and interest in sustainable design. Timeline and Deliverables: Weeks 1–3: Literature review on sustainable building practices and BIM methodologies. Weeks 4–6: Initial conceptual model creation and material selection. Weeks 7–9: Refinement of design and simulation of energy consumption. Weeks 10–12: Finalize the digital model and prepare the presentation. Deliverables: A detailed BIM model showcasing sustainable design features. Presentation of the model with a focus on sustainability metrics and benefits. Desired Skills and Time Commitment: Basic Knowledge: A foundational understanding of civil or environmental engineering principles, such as material properties, basic construction techniques, or sustainability concepts. Familiarity with tools like AutoCAD or willingness to learn basic drafting software. Preferred (but not required): Interest in sustainable construction or digital modeling. Experience with hands-on projects, prototyping, or laboratory work (e.g., basic sample testing). Curiosity and a proactive attitude toward learning new tools and methods. Time Commitment: Weekly Hours: Mentees should expect to dedicate 3–5 hours per week to the project, which includes: Weekly meetings (30–60 minutes) with the mentor for guidance and progress checks. Independent work on research tasks, such as literature reviews, data analysis, or model creation. Total Time: The project is designed to span one semester (12 weeks), accommodating the learning curve for first- and second-year students with little or no prior research experience.
Shitao Shi (Computational Science and Engineering)	Shitao is a dedicated fourth-year Ph.D. student in Structural Engineering with dual concentrations in Computational Science & Engineering and Data Science and Engineering within the Civil and Environmental Engineering department. His research aims to mitigate building damage from earthquakes and windstorms through the application of natural hazard engineering principles, innovative computational methods, and data science techniques. Although his research is highly interdisciplinary and spans several areas, his mentees will focus on one or two key aspects that align with their interests. High-achieving mentees may have opportunities to advance the research further or apply for research awards in the following year. Description of Possible Projects: Project 1: Optimizing Computational Frameworks with Software Engineering Techniques This project focuses on learning and applying practical software engineering techniques to enhance performance and reduce computation time within a computational framework. Key techniques include vectorization of computations and parallelization. Applicants should be familiar with object-oriented programming (OOP), design patterns, and automated testing in Python. Experience in software development is preferred. Project 2: Investigating Machine Learning Methods to Enhance Surrogate Models The use of high-fidelity structural models to assess the impact of earthquakes on a region is computationally intensive, especially when accounting for uncertainties in design, construction, and other factors. As a result, surrogate models with fewer degrees of freedom are needed. These models offer significant reductions in computational cost while accurately simulating essential structural behaviors. By enabling faster computations, surrogate models allow for the analysis of many more buildings across a region. This project developed and implemented an integrated approach combining Finite Element (FE) modeling and machine learning (ML) techniques to identify the optimal types and parameters for low-degree-of-freedom models. Desired Skills and Time Commitment: General Requirement: - Proficient in Python, with knowledge of NumPy, Pandas, and other data processing libraries. - Time Commitment: Approximately 8 hours per week. Project 1 Optional Requirement: - Experience with Object-Oriented Programming (OOP) and Version control tools (e.g., Git) - Familiarity with parallel processing, task parallelization (e.g., Multiprocessing / Concurrent.futures) and pytest / Unittest. Project 2 Optional Requirement: - Experience with machine learning frameworks (e.g., TensorFlow, PyTorch) - A strong interest in exploring how ML techniques can enhance modeling, prediction, and optimization in structural engineering is highly encouraged.
Tommy Kimura (Computer Science)	Tomoyoshi (Tommy) Kimura is a second-year MSCS student at the University of Illinois at Urbana-Champaign (UIUC), working with Professor Tarek Abdelzaher. His research interests primarily lie in machine learning for multimodal time-series signals. More information can be found at https://www.tomoyoshikimura.com/. Description of Possible Projects: This project introduces undergraduate mentees to machine learning research focused on IoT time-series signals. The goal is to design and implement an end-to-end self-supervised learning pipeline. Students will work on tasks such as: 1. Building customized datasets for IoT applications. 2. Implementing state-of-the-art (SOTA) models and learning frameworks. 3. Developing and experimenting with customized deep learning modules. 4. Creating a general open-source codebase that can serve as a foundation for exploring more advanced concepts in machine learning. This project is ideal for students interested in learning hands-on skills in machine learning, working, and experimenting with multimodal time-series data. By the end of the project, the mentee will build: - Customized datasets for time-series signals. - SOTA Self-supervised learning frameworks (contrastive/masked reconstruction) - An end-to-end pipeline for self-supervised learning. - Performance evaluation and benchmarks on selected datasets (optional) - Short conference-style report paper (optional) Preferred Skills - Programming: Python (required). - Coursework: Data Structures, Basic Machine Learning. - Familiarity with deep learning frameworks (e.g., PyTorch, TensorFlow) (optional). - Interest in IoT and time-series data (optional). Timeline 1. Week 1-2: Onboarding and introduction to concepts and tools. 2. Week 3: Literature reviews of self-supervised learning techniques and related IoT applications. 3. Week 4-5: Initial implementation of the general pipeline, dataset preprocessing, and backbone models. 4. Week 6-10: Development and evaluation of advanced learning frameworks, conducting experiments on datasets. 5. Week 11-End: Finalizing the project, creating documentation, writing paper (optional), and releasing the open-source codebase. Desired Skills and Time Commitment: Python, CS 225, basic ML course (optional)