The 18 currently listed mentors are for the Fall 2023 semester.

If you have any questions about the information below, please reach out to the PURE Committee at

AJ Taylor
(Food Science & Human Nutrition)
AJ is a 5th year food science graduate student working on cacao and chocolate. There are currently three different projects that AJ is working on and needs help with. His focus for mentees is that mentees have to be the ones to put in the work and get the reward. Those that succeed are the ones who try.

Description of Possible Projects:
Project 1: One project that I have now is looking at a cacao tea product and creating a sensory panel for it. This would, potentially, filling out paperwork for IRB, looking into resources for conducting a sensory panel, running said sensory panel, working with panelists, managing sample creations/descriptions/data collection, and statistics later on.

Project 2: The second project is using NIR to build a prediction model for quality of cacao beans, along with GIS data too. This one is very data centric and focuses on the collection and cleaning of data for model building using MATLAB or R. To be ABSOLUTELY clear, we are NOT yet at the prediction modeling yet. We have two more variables to collect, which undergraduates interested must recognize, and THEN we will work on the modeling (likely Winter/Spring-time).

Project 3: The last project is working on defining properties of cold brew and hot brew coffee that is reused. This would be under a senior undergraduate of mine and myself to look at the acidic, polyphenolic, chemical, and other parameters of coffee. You will get hands-on experience for data collection, management, and then reporting in this project.

Desired Skills and Time Commitment:
Desired Skills: Skills is more on good chemical background, a quick and appreciative learner, social and able to communicate well, and to be flexible in the project.

Time Commitment: 10 hours a week, minimum. You have to know what's going on and can't just do 1 or 2 hours a week.
Austin Ellis-Mohr
(Electrical and Computer Engineering)
Hello, I am a fourth year ECE PhD student. I study the world of neural networks, both biological (like in the brain) and artificial (like in ChatGPT and self-driving cars). I love teaching and have a planned experience to help you learn the fundamentals about artificial neural networks.

Description of Possible Projects:
These projects are completely computational and will require a good bit of programming, but please don't be discouraged by any words you don't know (that's what I am here for). I don't expect prior knowledge of neural networks.

Project 1: In artificial neural networks, there are specific knobs designers need to choose called hyper-parameters. These play a crucial role in determining the performance and behavior of the network. A fundamental part of all neural networks is that they include some nonlinear component called activation functions. This research project aims to investigate a specific set of hyper-parameters surrounding activation functions.

Project 2: The second project I have available is to study how neural networks interpret noise (random signals forced on the network). In this project, I want you to explore with you how we can provide unique noise patterns to understand how the network tries to understand this (non-sensical) data.

Desired Skills and Time Commitment:
Desired Skills: Programming experience in Python is mandatory. Everything else, I have no doubt you can get up to speed on.

Time Commitment: The time commitment will be about 6-10 hours / week, but this of course can be modulated based on productivity as well as within reason to your exam schedule (I was an undergrad too and know 10 hours/week is sometimes just not attainable so we will make the schedule accordingly). The most important requirement is that you have a strong interest to learn and are curious.
Brandon Kamiyama
(Nuclear, Plasma, & Radiological Engineering)
Brandon is a 3rd year PhD student and NSF Graduate Research Fellow in Prof. Sankaran's research group in NPRE. Brandon's current work involves synthesizing nitrogen products (e.g., ammonia, nitrate) using an atmospheric pressure plasma-liquid reactor. He has previous experience in high-temperature fusion plasma-material interactions and low-pressure plasma experiments. He received his B.S. in nuclear engineering from Oregon State University in 2021. Throughout his undergraduate study, he worked on numerous projects with national laboratories and industry involving thermal-fluid sciences, advanced fission reactor design, material degradation in extreme environments, nuclear core design, and the US’ future energy portfolio.

Description of Possible Projects:
There are two major projects that I have right now.

Project 1: Characterization of nitrogen products in a plasma-liquid reactor using pulsed DC operation.

Project 2: Studying the effects of gas and liquid phase temperature on product distribution.

Both of these studies will help complement the diagnostic and modeling work in my group to determine the plasma-liquid chemistry (reaction mechanisms, pathways) that is happening in my system.

All majors are welcome, but undergraduates in NPRE are desired for both projects. Electrical engineering students would also be great for the first project. In terms of tasks, I envision the undergraduate students running experiments, doing characterization work (UV/VIS spectroscopy or ionic chromatography), doing circuit design, or working with CAD. Statistical analysis and coding might also be needed (such as MATLAB, Python, or R).

In terms of a timeline, I am planning on running experiments, doing characterization work, and reading papers all semester long.

Desired Skills and Time Commitment:
Desired Skills: As long as you are willing to learn, you will learn all of the hard and soft skills required. No class requirements.

Time Commitment: I want no less than 10 hrs. per week. Being in the lab more will only accelerate you in this program and as a researcher.
Chirayu Kothari
(Civil and Environmental Engineering)
I am a 3rd year Ph.D. student at the Department of Civil and Environmental Engineering. My work mostly involves experiments related to the chemistry and characterization of construction materials.

Description of Possible Projects:
I will be working on a project which involves understanding the chemistry and rheological properties of low CO2 cements. The student shall be motivated to work in the laboratory. The tasks will involve sample preparation for some advanced characterization and experimental data collection during the initial months. The later part will involve basic data analysis.

Desired Skills and Time Commitment:
Desired Skills: An undergrad who is motivated to learn the fundamentals and willing to work in the laboratory. Preliminary background in automation/prototyping and basic programming is a plus.

Time Commitment: 5-10 hrs/week.
Haoran Qiu
(Computer Science)
Haoran Qiu is a 5th-year PhD student in Computer Science. His research focus is mainly on machine learning and systems. His recent research project is about designing and integrating reinforcement learning-based solutions into the management of production cloud systems such as workload autoscaling, SLO violation mitigation, and congestion control.

Description of Possible Projects:
Project 1: Serverless Workload Classification
In this project, we would like to explore and understand serverless workloads characteristics by clustering and classification. We will start with a collected dataset of the serverless workloads running in public cloud. The dataset consists of performance metrics as well as systems metrics such as utilizations, cache hits, etc. After clustering, we'd like to see if new applications fall into the major clusters that we find in the dataset. The cluster/classification results can facilitate model adaptation in ML-for-systems solutions as it enables us to adapt a trained model across applications within the same cluster. This project involves data processing, clustering techniques, ML (unsupervised learning), and serverless function development/deployment.

Project 2: Energy-aware Workload Autoscaling
In this project, we would like to integrate workload autoscaling with power management as nowadays in datacenters, it is critical to reduce carbon footprint while maintaining application service-level agreement at a stable level. The project will be on Kubernetes (an open-sourced container orchestration platform). We will start with multi-dimensional pod autoscaler (MPA) with reinforcement learning (RL). Then we will look at how core frequency and power affect the decisions made by the RL agent (e.g., study the policy performance degradation when frequency drops). Finally, we will design an energy-aware autoscaling solution (possibly with RL as well). This project involves systems development (in Go), data processing, and ML/RL (in Python).

Desired Skills and Time Commitment:
Project 1 would involve skills in data processing and ML algorithms such as clustering/classification algorithms. The programming language will be mostly in Python. There could also be some experience related to working on an open-source serverless platform, OpenWhisk. The project might involve concepts/techniques from databases, data mining, distributed systems, and applied machine learning (but students are not required to take those courses as a prerequisite, e.g., CS 412, 425, 441). The time commitment for this project would be 4-6 hours per week.

Project 2 would involve skills/knowledge in computer systems, distributed systems, Kubernetes (an open-source container orchestration platform), and RL. The programming language will be mostly in Go and Python. The project might be related to courses CS 425, CS 443, CS 446, CS 498 (Cloud Computing) but students are not required to have taken those courses. In this project, we are directly contributing to the Kubernetes open-source community. The time commitment for this project would be 5-8 hours per week.
Houxiang Ji
(Computer Science)
Houxiang is a computer science Ph.D. candidate major in computer architecture, memory systems, and the fascinating intersection of machine learning and systems. His research focuses on enhancing system performance through innovative hardware/software co-design, advanced memory techniques, and the utilization of machine learning methods. Additionally, Houxiang explores hardware acceleration for various applications, with a particular emphasis on machine learning. He has successfully collaborated with several undergraduates on various projects, making good progress so far. Hope to work with you to build better computer systems!

Description of Possible Projects:
Project 1: I am currently leading an exciting big project that involves exploring a new memory technology. This undertaking demands a comprehensive understanding of computer architecture and memory systems. Initially, I will provide selective guidance through relatively simpler tasks, such as system profiling, experimental setup, and evaluation. As you progress, you will have the opportunity to tackle more challenging aspects, such as hardware programming or implementing some small ideas. By the end of the semester, I expect you to have gained a deeper understanding of the system, and together we can generate a concise paper or report.

Project 2: In addition, there is a smaller yet equally compelling project about building a machine learning accelerator. Initially, we will identify hardware bottlenecks to support state-of-the-art AI models and propose optimizations from both the software and hardware perspectives. Proficiency in Python is desirable, and proficiency in ML frameworks can be acquired during the project. Similar to the previous project, we aim to generate a short report or paper summarizing our work by the end of the semester.

Both projects offer the opportunity to continue working in our lab beyond the PURE program.

Desired Skills and Time Commitment:
Desired Skills: Familiar with one of following programming languages: C, C++, Python. Hardware description languages such as Verilog, System Verilog, or VHDL would be a significant advantage, but are not mandatory. Completion of CS233 or an equivalent course is highly preferred. You may find advanced courses such as ECE391, ECE411, CS433 useful in the research but they are not necessary. We can discuss and consider adjusting the skill requirements or project if you are really passionate and motivated to the research in a meaningful way.

Time Commitment: I know undergraduates have a heavier course workload. I would recommend 4-5 hours per week to ensure good progress. I am flexible in adjusting our schedule to accommodate the demands of the semester e.g. short pauses during mid-terms/finals are totally fine with me.
Kevin Daniel
(Materials Science and Engineering)
Kevin is a 2nd year PhD student in the Materials Science and Engineering department working with Prof. Shaloo Rakheja. His research focuses on physics-based modeling and simulation of 2D materials. Currently his research revolves around investigating the effect of spin, charge and energy for 2D materials under the application of strain.

Description of Possible Projects:

Project 1: Graphene-based electrical and spin interconnects
Develop and simulate a circuit model for graphene-based electrical interconnects and compare the performance with traditional metal interconnects. Solve the spin diffusion and drift equation in graphene and model the transport efficiency of graphene spin-based interconnects.

Project 2: Theoretical analysis of strained graphene
Model the graphene nanostructure under strain and predict the dispersion relation of strained graphene.

Desired Skills and Time Commitment:
Desired skills:
  • (1) Proficiency in programming languages such as Python or MATLAB
  • (2) Understanding of fundamental concepts in quantum mechanics and solid-state physics
  • (3) Strong analytical and problem-solving skills
  • (4) Ability to work independently

    Time Commitment: This project requires about 10 hours per week of commitment.
  • Kun Wu
    (Electrical and Computer Engineering)
    Kun is currently a rising fifth-year Ph.D. student at UIUC, advised by Prof. Wen-mei Hwu. His research interest lies in compilers and libraries for graphics processing units and parallel computer architecture. He is currently working on a CUDA sparse compiler for Relational Graph Neural Networks (RGNNs) in collaboration with the Amazon DGL team, and has previously contributed to several impactful projects, involving PyTorch-Direct and Pylog. He has also done several software engineering and research internships in different companies, including Google, Amazon, HPE, etc.

    Kun received his bachelor’s degree in Electronic Engineering from Tsinghua University. Before that, he published one first-authored paper in Design Automation Conference under the supervision of Prof. Yuan Xie and Prof. Yu Wang.

    Description of Possible Projects:
    Project 1: Memory-intensive CUDA kernel trace analysis and automation
    The applicants will use profiling tools to profile existing CUDA kernels to understand the performance bottlenecks. After such familiarization with the profiling and tuning workflow, we are to develop Python code to automatize the process that we identify as important and/or tedious in the optimization flow.

    Project 2: Preliminary comparative study on generic programming using Python vs. C++
    Type annotation has been increasingly adopted in Python programming in order to serve larger-scale project scaled up from simple Python prototypes. Many projects, including PyTorch, a deep learning library, and MLIR, a compiler infrastructure, allow developers to devise logic in either Python or C++. While Python is very productive in prototyping, the success of its type system would largely influence its adoption in larger projects. On the other hand, C++ has been painful to use in large project development due to its static nature. Some of the issues involve the difficult template meta programming as a feature to generate code, and hard-to-use generic typing. A comparative study to understand the C++ type system and Python type system in detecting errors and developers’ mental burden provides insight into the profitability of migrating current software development practice into using Python from C++.

    Desired Skills and Time Commitment:
    Desired Skills: Familiarity with common Linux commands and Python. It is preferable to have gained first-course knowledge to computer systems.

    Time Commitment: I don't seek in this mentorship to get more publications/other deliverables for myself. Surely, if the applicants wish to achieve more I would be very glad to work together with them! However, to substantiate this program, I do feel the applicants need to expect themselves to do enough work so that we can on average discuss for at least 10 minutes per week. I would say at least around 4 hours every week.
    Maggie Zhang
    (Computer Science)
    I am a PhD candidate in communication and a master of computer science in Grainger. I study (anti-)social computing and social media. My scholarship is at the intersection of computational social science, social computing and political communication. My research combines natural language processing and computer vision methods as well as experiments to look into people’s opinion and behavior. My recent research projects aims to make advances on the problem of coordinated inauthentic behavior online. I have been studying trolls, social bots and information operations for the past few years.

    Description of Possible Projects:
    Project 1: One of my research projects involves investigating the contagion effect of ideas/opinions/behaviors on social networks. Specifically, I am interested in whether and how coordinated inauthentic behavior spreads. This project involves building a mini-Twitter interface and backend to set up the experiment platform, inviting participants to interact on the platform and analyzing the experiment results. Students with a computer science background are welcome, especially those with frontend and backend experiences.

    The general timeline: before Oct 2023: building the platform; Oct - Dec 2023: experiment; Jan - Feb 2024: analysis and writeup for conference submission

    Project 2: Another research project aims at investigating the effect of content moderation on the community members: whether exposure to censorship of others will lead to a chilling effect in the audience. The main task for this project is to build a topic classifier to test the topic shift after the moderation. This project welcomes students with a machine learning background, especially in text mining and NLP.

    The general timeline: before Oct 2023: text processing & building the classifier; Oct - Dec 2023: model fine-tuning to improve the performance; Jan - Feb 2024: analysis and writeup for conference submission

    Desired Skills and Time Commitment:
    Desired Skills: Front-end interface and backend database machine learning and NLP.

    Time Commitment: 5-10 hours per week.
    Ram Padmanabhan
    (Electrical and Computer Engineering)
    I'm Ram Padmanabhan, and I'm a first-year PhD student in Electrical and Computer Engineering, advised by Prof. Melkior Ornik. My work will focus mainly on the learning and control of systems in hostile/adversarial environments, affecting their performance and how easily/quickly their objectives can be achieved. I have previously worked on the analysis of gradient descent based on methods from control, designing robust discrete-time systems, and iterative learning control. I'm quite happy to chat about any of these!

    Description of Possible Projects:
    I'd like to keep this as flexible as possible, depending on students' background. My primary objective is to introduce students to tools and methods we use in control, as well as general practices and techniques in academic research. My plan is to construct a project based on a student's background and experience, to benefit them.

    That said, any project will be based on simulation in Matlab/Simulink or Python. A simple example would be to simulate a recursive algorithm such as the Kalman filter for a real-world system (possibly an aircraft or spacecraft). Students with more experience may be able to work on simple, real-world sensor data analysis which is a key factor in data-driven control. They could possibly assist me with simulations relating to my research. Depending on progress made and your experience, the opportunity to join my research group could also arise.

    Desired Skills and Time Commitment:
    Desired Skills: A lot of my work is based on simulation in Matlab or Python, so some experience in coding is necessary. However, prior experience in Matlab is NOT necessary; that's partly the purpose of this project. A strong mathematical background is definitely preferred. You are not expected to be familiar with fundamental concepts in control — you will quickly find out that it's extremely intuitive and simulation work is fairly straightforward. Familiarity with the concepts in ECE 210 is an advantage.

    Time Commitment: Quite flexible, but 3-5 hours per week is a reasonable number to ensure sufficient progress.

    The general stuff applies: being punctual and working hard goes a long way.
    Samyak Rawlekar
    (Electrical and Computer Engineering)
    I am currently a first-year PhD student, focusing on Computer Vision under the guidance of Prof. Narendra Ahujha. My research primarily revolves around the fascinating field of 3D reconstruction from images or videos. In the past, I have delved into representation learning specifically tailored for medical imaging and have also explored learned compression techniques. Apart from my academic, I have a keen interest in sports. I enjoy both playing and watching soccer. Additionally, I am an avid fan of NBA and Tennis.

    Description of Possible Projects:
    The projects will primarily focus on the intersection of machine learning and computer vision. No prerequisites are required, as I am willing to provide instruction and guidance on the necessary concepts.

    The project will be in line with my research on 3D reconstruction during the fall semester. However, I am also open to other machine learning projects that students find intriguing and need assistance with. The main goal is to offer students an insight into the research process and help them develop essential research techniques.

    Desired Skills and Time Commitment:
    Desired Skills: Python programming skills are preferred.
    Bonus (but not required): Exposure to machine learning and computer vision. However, all necessary concepts will be taught.

    Time Commitment: There is no specific time limit. We will have regular meetings to discuss progress, and if necessary, additional meetings can be scheduled.
    Shensheng Zhao
    (Electrical and Computer Engineering)
    I am a 4th year PhD student and I am a Beckman graduate fellow and Mavis Future Faculty fellow. My current research interests focus on biomedical photoacoustic and ultrasound imaging for diagnosis and therapy. I hope to work with great undergrads to develop novel biomedical techniques that benefit people’s lives.

    Description of Possible Projects:
    We offer two exciting projects for students:

    Project 1: Ultrasound Neuromodulation System Development
    This project provides an excellent opportunity to delve into the world of biomedical engineering. Students will work with cutting-edge equipment, mastering its operation, and gaining hands-on experience in equipment integration using LABVIEW. Preferably, students should be familiar with electronics devices, like oscilloscopes, RF amplifiers and functional generators.

    Project 2: Deep learning based Vascular Segmentation Algorithm Development
    This project is programming oriented. Students will receive access to comprehensive vascular datasets, and they will learn how to develop deep learning models and analyze vascular images.

    Desired Skills and Time Commitment:
    Desired Skills: Students are required to have high motivation and a willingness to learn diverse knowledge are essential attributes for project participation. Knowledge of LABVIEW and MATLAB is preferred but not mandatory.

    Time Commitment: Students should commit to a minimum of 8 hours of weekly lab work.
    Shubham Ugare
    (Computer Science)
    I am a PhD student in the Computer Science Department at University of Illinois, Urbana-Champaign. My research interest lies in the intersection of Programming Languages and Machine Learning. I am being advised by Prof. Sasa Misailovic and Prof. Gagandeep Singh.

    More information on the website:

    Description of Possible Projects:
    Project 1: Improving Large Language Models (LLM) for code with the CFG constraint
    Enhance large language models (LLMs) like GPT/Llama2 to generate code aligned with Context Free Grammar (CFGs). Students will apply programming skills and machine learning fundamentals to develop a tool that produces reliable code adhering to CFG constraints.

    Project 2: Robust methods for detecting LLM-generated code
    This project aims to develop robust methods for identifying code generated by Large Language Models (LLMs) such as GPT/Llama2. Detecting LLM-generated code is important for maintaining code quality, ensuring security, and differentiating between human-written and machine-generated code. The students will explore various features and characteristics of LLM-generated code that can be used to distinguish it from human-written code and develop a detection system.

    Desired Skills and Time Commitment:
    Desired Skills:
    Required: Programming proficiency in languages like Python, machine learning fundamentals.

    Optional: Understanding of basic compiler concepts, knowledge of natural language processing (NLP) techniques, and experience with large language models.

    Time Commitment: 10 hours/week
    Simon Zhang
    (Agricultural and Biological Engineering)
    I am a third year PhD student in Agricultural and Biological Engineering, my research involves study airflow around human body and airborne particle transmission (such as COVID) using cameras and tracer bubbles. I did my undergrad and masters here as well, so I know the place very well. I also TA several classes, most noticeably SE101 Engineering Graphics and Design (CAD). I have been teaching this class since undergrad. I love 3D CAD modeling and 3D printing as well.

    Description of Possible Projects:
    Mostly setup and running simple experiments involves studying airflow using cameras and tracer bubbles. No particular skills are required, but the project does involve programming, CAD, and CFD simulations. So if you are interested in learning these skills, that is also welcome. One or two students are able to run the experiments and collect data. Timeline-wise, a few experiments during the semester, and each experiment generally will take 2-3 hours, but it is flexible.

    Desired Skills and Time Commitment:
    The experiments are mostly procedural, no advanced knowledge required. But patience and carefulness is definitely appreciated. If interested, I can teach more about the programming, CAD, and CFD simulations.
    Sudharsan Rathna Kumar
    (Civil and Environmental Engineering)
    I am a second year MS student working with Prof. Nishant Garg in the civil engineering department. My research involves using sustainable concrete systems including low clinker limestone calcined clay cement and testing its performance. I mainly work with improving its carbonation resistance while also studying opportunities for CO2 uptake, providing an avenue for permanent mineralization of CO2 in concrete. I have been both a mentor and a mentee before and understand the unique bond this relationship creates. I am looking forward to finding an enthusiastic and curious individual with independent ideas, who can learn from the project and further grow in the field in the future.

    Description of Possible Projects:
    I am currently working on cement paste specimens with supplementary cementitious materials (SCMs) and understanding its carbonation performance. This is a huge topic of interest currently in the cement chemistry world, to reduce cement in concrete- thereby preparing sustainable blends. Since, inherently low-clinker concrete is susceptible to a greater carbonation depth, the perception is that these systems are not durable. The goal about the project is to study the microstructure changes upon carbonation, and to design a blend that is both low-clinker and has higher carbonation resistance. The work is mostly experimental, where different sample preparation techniques can be learnt for further microstructure characterization like Scanning Electron Microscopy, Raman imaging.

    Desired Skills and Time Commitment:
    Desired Skills: Although some basic knowledge on cement chemistry is preferred, no prerequisite knowledge is expected, and I will teach the student about all concepts as we go along. Since most of the work is experimental, it will involve preparation of samples in the laboratory. Also, the student will be expected to process some data, for which I can guide as well. Some coding knowledge will be beneficial for this but is not mandatory.

    Time Commitment: As an undergrad, balancing work, research, academics and other commitments can be pretty daunting. I am happy to assist with any scheduling, or any help from my end. A minimum of 10 hours/week is expected throughout the semester. Above all, a willingness to learn and question is most preferred.
    Ti-Chung Cheng
    (Computer Science)
    Ti-Chung is a fourth-year PhD candidate in Computer Science at the University of Illinois at Urbana-Champaign (UIUC). He is currently working on collective decision-making mechanisms and decision-making through data visualizations under the guidance of Prof. Karrie Karahalios and Prof. Hari Sundaram, leaders of the Social Spaces group and the Crowd Dynamics Lab, respectively. Prior to his PhD, Ti-Chung pursued a Masters at UIUC, where he contributed to the Dataspread Project, working with Prof. Aditya Parameswaran and Prof. Karrie Karahalios. His research interests encompass Human-Computer Interaction (HCI) and Computer Supported Cooperative Work (CSCW), focusing on topics like voting and survey design, data visualization, and human-AI interaction, particularly smart home applications. Before coming to UIUC, he earned his B.Sc degree in Computer Science and Engineering from The Chinese University of Hong Kong, conducting research in distributed machine learning algorithms in nearest-neighbor search as part of The Husky Team supervised by Prof. James Cheng. Apart from his academic pursuits, Ti-Chung is also a skilled full-stack web developer and technology educator. He has gained industry experience as a software engineering intern at Salesforce, a machine learning intern at KKBOX, and a research intern at Microsoft.

    Description of Possible Projects:
    There are two major research projects that I am currently doing research on.

    Project 1: The first project focuses on a voting mechanism called quadratic voting, where we try to understand and support individuals to make better decisions collectively. In this project, we investigate using a human-computer interaction lens to design to support and theorize how people vote and what the outcome should look like. In this project, you would likely be tasked to prototype and design several visualizations for decision-making. However, there are also several different aspects, such as qualitative and quantitative data analysis, that you can be involved in.

    Project 2: The other project focuses on understanding human-AI interaction with a focus on supporting large-scale learning during peer learning. This project is in an earlier stage where you would likely be involved in literature research and early-phase research exploration.

    While I am not actively recruiting for other projects, I have unplanned projects on smart homes (privacy) and human-AI interaction (persona) for survey design that is open to discussion.

    Desired Skills and Time Commitment:
    Desired Skills: All the above research has components that only require extensive reading and writing, which assumes no prerequisites as long as you are interested in the subject matter. For more technical parts of the research, a general Python programming skillset, some experience in JavaScript or Python data visualization, and some experience in web-based programming (i.e., React.js) are preferred. The most important skill set applications should have strong motivation and interest in the subject matter. The rest can be picked up along the way.

    Time Commitment: Approx. time commitment would highly depend on the applicant but expect to work 3-4 hours per week, including the time you need to learn and explore.
    Tony Pham
    (Chemical and Biomolecular Engineering)
    I'm a 2nd year PhD student, working on plastic degradation by bacteria. Besides lab and class, I do Taekwondo, play soccer, and go swimming. I'm looking forward to inspiring the youngsters and creating a new generation of scientist. As a mentor, not only my undergrads do lab works, I can also discuss/help them determine their possible career paths (if they need). My work place is diverse, friendly, and collaborative. Anyone can do anything regardless of their background.

    Description of Possible Projects:
    Since it's a bio lab, the work hours are flexible (some weeks need more hours, some weeks require none), so mentees need to be ready to be time flexible. Undegrad students will learn fundamental bio lab techniques. Some weeks are busier due to the exact timings of the experiment and more patience-required works.

    Desired Skills and Time Commitment:
    Desired Skills: No prior lab skills are required, but I do require my mentees to be professional: be on time, responsible, and honest. I prefer ones who are willing to learn, committed (not a one-semester person), and aiming-to-go-beyond-expectations.

    Time Commitment: No required work hours per week.
    Yuan Shen
    (Computer Science)
    Hey, this is Yuan. I am currently a CS PhD candidate at UIUC, working on 3d generative modeling. My research not only touches on object-level generation but large scale 3D scene generation as well. I am interested to work with students who show strong research interest in 3D generative AI or have interesting real-world problems that need 3D generative models as potential solutions.

    Description of Possible Projects:
    Project 1: AI Dance Cinemographer
    Prior work collected large amount of human dance video and explored using a generative model to do choreography. However, there is still one gap missing from generated dance to those stunning, dynamic, and eye-catching dance video online, that is, cinemography. In other words, we are investigating how to sample novel camera trajectory to match with music and dance movements. In this project, assuming we have perfect novel view synthesis techniques, we aim to use generative models to generate camera trajectory that align well with the music, dance movements and some style specified by text prompt. Our plan is to collect large amounts of stage dance video online, e.g., K-pop dance from 1million studio; fit a human mesh to the dancer in the video; and get a camera pose trajectory using structure-from-motion. Then, we will design architecture to realize conditional camera trajectory generation.
    Check out these interesting links:

    Expectation: we aim to push for publication at top conference, e.g., CVPR or ICCV or ECCV, which can have a deadline around early March next year. The pace can be fast. Hopefully it will be fun to work on this exciting project!

    Project 2: SphericalNeRF: Creating a Spherical 3D World with NeRF
    Since Magellan made the first-ever sea voyage around the globe, it is now a common fact that the earth is spherical rather than flat. However, prior work on large-scale 3D world generation ( holds on to the flat world assumption and thus can only generate a large-scale but Euclidean world. In terms of 3D representation, existing NeRF representations assume that target objects or scenes reside on planar surfaces, and hence do not have the capability to model 3D volume in non-Euclidean geometry, e.g., spherical geometry. How to optimize a NeRF scene in curved geometry is still a rather open area.

    In this project, we plan to develop a novel 3D scene framework that can represent, render and generate scene residing on spherical surfaces represented with NeRF. Under our framework, we can create a powerful first-person rendering engine in spherical world, and simulate mirage effects caused by light refraction if curved rays are considered explicitly. From practical standpoint, various kinds of applications can benefit from a spherical world model, including gaming and film production. In fact, many open world game engines adopt the spherical world assumption but using traditional ray tracing for rendering, such as RimWorld, No Man’s Sky, Outer Wilds, and Astroneer.

    Check out these interesting links:

    Expectation: we aim to push for publication at top conference, e.g., CVPR or ICCV or ECCV, which can have a deadline around early March next year. The pace can be fast. Hopefully it will be fun to work on this exciting project!

    Desired Skills and Time Commitment:
    Desired Skills: The projects are most relevant to advanced courses, such as CS 445 or CS 543, and you should at least know how to write Python and PyTorch. Knowledge of basic computer vision principles, such as those in Stanford CS231n, is preferred. For the first project about AI Dance Cinemographer, it is good for candidates to have any Cinemography experience. As for the second project, ideal candidates should be familiar with the basic of NeRF.

    Time Commitment: The project is expected to take 10 hours per week.

    The Fall 2023 PURE Mentor Application period has ended. If you are still interested in becoming a PURE mentor, fill out our Interest Form for info on next semester’s application.

    Why become a PURE Mentor?