AI for healthcare
This REU project is focused on harnessing the power of Artificial Intelligence (AI) and Machine Learning (ML) to unravel the complexities of Lyme disease, a chronic illness whose research communities have recently joined collaboration with other "long" illnesses such as long covid, multiple sclerosis and fibromyalgia. In collaboration with a nonprofit partner, this cutting-edge initiative seeks to analyze our extensive dataset to gain deeper insights into the epidemiology, diagnosis, and treatment of Lyme disease. Under the mentorship of leading experts in AI and medical research, participants will explore innovative techniques to analyze data from over 10,000 patients. By leveraging advanced algorithms, as well as novel adaptations of those methods, our team aims to identify patterns, correlations, and predictive markers that can enhance our understanding of Lyme disease dynamics and aid in the development of more effective prevention and intervention strategies. This REU project provides a unique opportunity for aspiring researchers to contribute to the intersection of technology and healthcare, making meaningful strides towards combating one of the most prevalent vector-borne illnesses. Participants will not only n hands-on experience in AI and ML applications but also play a vital role in advancing Lyme disease research with real-world implications. Join us in this exciting journey of discovery and make a lasting impact on the fight against Lyme disease. Experience in linear algebra, machine learning and programming are a plus but not a requirement.
AI for Justice
This REU project is at the forefront of justice, where the fusion of Artificial Intelligence (AI) and large language models (LLM) is harnessed to empower innocence centers in their mission to rectify wrongful convictions. What takes a human attorney 9 months to process and analyze now takes an LLM under 30 minutes. This will undoubtedly revolutionize the criminal justice system, but there are many vital questions we need to understand.Collaborating with several innocence projects across the country, this transformative initiative aims to develop predictive and analytical tools that transform the processing of case files, ultimately contributing to the liberation of the wrongfully incarcerated. As a participant, you will delve into the use of AI in case processing and flagging, working alongside leading experts in AI and legal advocacy. Your role will involve crafting sophisticated algorithms capable of parsing through vast amounts of legal data, identifying patterns, anomalies, and potential avenues for exoneration, while simultaneously searching for inconsistencies, missing information, and tradeoffs. This REU opportunity offers a unique chance to blend technological innovation with social justice, as you contribute to the development of tools that have the potential to make a tangible impact on the lives of those unjustly imprisoned. Join us in this groundbreaking endeavor to revolutionize the way innocence centers approach case analysis, making strides towards a more equitable and fair legal system. Experience in linear algebra, machine learning and programming are a plus but not a requirement.
Particle Laden Flow
This is a laboratory based project involving measurement and modeling of slurries. Students will learn laboratory skills and how to take and record data. Students will also work with conservation law models from nonlinear PDE so some background in partial differential equations is desired.
Here are some past REU-involved papers on this topic:
Dominic Diaz, Jessica Bojorquez, Joshua Crasto, Margaret Koulikova, Tameez Latib, Aviva Prins, Andrew Shapiro, Clover Ye, David Arnold, Claudia Falcon, Michael R. Lindstrom, Andrea L. Bertozzi, Investigation of Constant Volume and Constant Flux Initial Conditions on Bidensity Particle-Laden Slurries on an Incline, American J. Undergraduate Research, 16(3), pp. 43-54, 2019.
N. Murisic, J. Ho, V. Hu, P. Latterman, T. Koch, K. Lin, M. Mata, and A. Bertozzi, Particle-laden viscous thin-film flows on an incline: experiments compared with an equilibrium theory based on shear-induced migration and particle settling, Physica D, 240(20), pp. 1661-1673, 2011,
Wing Pok Lee, Jonathan D. Woo, Luke F. Triplett, Yifan Gu, Sarah C. Burnett, Lingyun Ding, Andrea L. Bertozzi, A comparative study of dynamics models for gravity-driven particle-laden flows, 2024
Machine Learning and Nonlinear Dynamics
This project combines neural networks and scientific modeling. Students will work on constructing and applying neural networks toproblems in computational physics. Background: The main requirement is knowledge and/or experience in Python(knowledge in PyTorch or TensorFlow would be helpful). The mathematical requirement is successful completion of a course in ordinary differential equations, e.g. Math upper division differential equations.
Quantum Sensing
This project addresses a pressing challenge for society - how to track and to respond to a rapidly changing climate. Scientists at UCLA are developing quantum sensing platform that features an in situ distributed sensor network to measure and understand atmospheric chemistries and climate variables. At its most fundamental level, an entangled quantum network is capable of sensing multiple geographically and locally distinct systems with higher precision than the summation of each system probed individually. Entangled networks can thus elucidate non-classical correlations between probes over distributed nodes. Students will work on mathematical algorithms for this new technology in collaboration with the device development science.
MORE TOPICS MAY BE FORTHCOMING. Please keep an eye on the list.