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I am a PhD candidate in the Department of Computer Science at Emory University in Atlanta, Georgia. Prior to Emory, I studied Computer Science/Information Systems and Mathematics at The College of Saint Scholastica in Duluth, Minnesota. My research interests lie broadly in machine learning and mathematical optimization, specifically reinforcement learning and optimal control applications to critical domains, such as deep brain stimulation and glucose-insulin control.

Changelog

• 03/2024 Giving a lightning talk on "Learning Control Policies of Hodgkin-Huxley Neuronal Dynamics", at MCBIOS24
• 03/2024 Poster presentation on "A Neural ODE Approach to Glycemic Control" (based on my Lilly internship) at MCBIOS24
• 11/2023 Presented our work, "Learning Control Policies of Hodgkin-Huxley Neuronal Dynamics", at Machine Learning for Health (ML4H 2023)
• 08/2023 Completed my research internship with the Advanced Analytics and Data Sciences (AADS) team at Eli Lilly.
• 09/2022 Giving a talk on "Closed-loop Deep Brain Stimulation via Optimal Control" at the SIAM Mathematics of Data Science 2022 conference! [poster]
• 08/2022 Completed my research internship with the Responsible Tech team at IBM Research in Yorktown Heights, NY.
• 03/2022 In San Diego, CA, for the CRA Grad Cohort Workshop for Inclusion, Diversity, Equity, Accessibility, and Leadership Skills (IDEALS)
• 08/2021 Thrilled to be one of the recipients of the 2021 Google PhD Fellowship
• 05/2021 Started my research internship with the Tech for Justice team at IBM Research where I'm collaborating with researchers in the Center for Computational Health.

Research

At Emory, I have the amazing opportunity to work with Dr. Lars Ruthotto and Dr. Nicholas Au Yong on closed-loop neuromodulation via Reinforcement Learning and Optimal Control. Some representative projects include:

• Reinforcement Learning and Optimal Control
  [May 2020 - present] Developing closed-loop solutions to control problems in neuromodulatory interventions such as deep brain stimulation and biological systems such as the glucoregulatory system.


• Reinforcement Learning for Sustainable Agriculture
  [May - Aug 2022] Developed a reinforcement learning environment for crop management based on the Texas A&M Soil & Water Assessment Tool. Developed and benchmarked various RL algorithms to evaluate irrigation and fertilizer application strategies.


• Fair Reinforcement Learning
  [May 2021 - Dec 2021] Exploring bias in patient representation in healthcare and developing a fair treatment strategy by leveraging representation learning and reinforcement learning. Work done/in-progress as part of my internship with IBM Research in collaboration with the talented team at the Center for Computational Health: Mohamed Ghalwash, Zach Shahn, and Pablo Meyer Rojas.


• Reinforcement Learning and Graph Generation
  [Aug 2020 - Mar 2021] Applying model-based Reinforcement Learning strategies in the field of graph generation, in particular, to generative models that can learn to create novel graphs efficiently.
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