Shreya Saxena

About

Shreya Saxena is an Assistant Professor in the Biomedical Engineering Department at Yale University and a core member of the Center for Neurocomputation and Machine Intelligence at the Wu Tsai Institute. Her research broadly focuses on the neural control of complex, coordinated behavior, with an emphasis on understanding the relationship between neural activity and behavior through constraints-based modeling approaches that incorporate anatomy and physiology. Saxena's work aims to improve the inference of quantitative dynamical models for cognition and motor control, addressing challenges in large-scale neural and behavioral data analysis. Her academic background includes a Ph.D. from the Massachusetts Institute of Technology in Electrical Engineering and Computer Science, where she studied the closed-loop control of fast movements from a control theory perspective. She also holds an M.S. in Biomedical Engineering from Johns Hopkins University and a B.S. in Mechanical Engineering from the Swiss Federal Institute of Technology (EPFL). Prior to her current role, she was an Assistant Professor at the University of Florida's Department of Electrical and Computer Engineering and a Swiss National Science Foundation Postdoctoral Fellow at Columbia University’s Zuckerman Mind Brain Behavior Institute. Saxena has been recognized as a Rising Star in both Electrical Engineering and Biomedical Engineering and was awarded a Sloan Research Fellowship in 2025.

Research topics

• Artificial Intelligence
• Computer Science
• Physics
• Neuroscience
• Mathematics
• Medicine
• Engineering
• Chemistry
• Biology
• Psychology

Selected publications

• Minimizing Factual Inconsistency and Hallucination in Large Language Models

  arXiv (Cornell University) · 2023-11-23 · 4 citations

  preprintOpen access

  Large Language Models (LLMs) are widely used in critical fields such as healthcare, education, and finance due to their remarkable proficiency in various language-related tasks. However, LLMs are prone to generating factually incorrect responses or "hallucinations," which can lead to a loss of credibility and trust among users. To address this issue, we propose a multi-stage framework that generates the rationale first, verifies and refines incorrect ones, and uses them as supporting references…

  PublisherOA PDFDOI

• muSim: A goal-driven framework for elucidating the neural control of movement through musculoskeletal modeling

  bioRxiv (Cold Spring Harbor Laboratory) · 2024-02-04 · 3 citations

  preprintOpen accessSenior authorCorresponding

  Abstract How does the motor cortex (MC) produce purposeful and generalizable movements with the complex musculoskeletal system in a dynamic environment? To elucidate the underlying neural dynamics, we use a goal-driven approach to model MC by considering its goal as a controller driving the musculoskeletal system through desired states to achieve movement. Specifically, we formulate a model of MC as a recurrent neural network (RNN) controller producing muscle commands while receiving sensory fee…

  PublisherOA PDFDOI

• Multitasking Recurrent Networks Utilize Compositional Strategies for Control of Movement

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-09-16 · 1 citations

  preprintOpen accessSenior authorCorresponding

  The brain and body comprise a complex control system that can flexibly perform a diverse range of movements. Despite the high-dimensionality of the musculoskeletal system, both humans and other species are able to quickly adapt their existing repertoire of actions to novel settings. A strategy likely employed by the brain to accomplish such a feat is known as compositionality, or the ability to combine learned computational primitives to perform novel tasks. Previous works have demon-strated tha…

  PublisherOA PDFDOI

• Inference of Neural Dynamics Using Switching Recurrent Neural Networks

  2024-01-01 · 1 citations

  article1st authorCorresponding
  PublisherDOI

• Analysis of Mesoscope Imaging Data

  Neuromethods · 2024-10-07 · 1 citations

  book-chapterSenior author
  PublisherDOI

Frequent coauthors

• John P. Cunningham

  Columbia University

  30 shared

• Liam Paninski

  Columbia University

  22 shared

• Taiga Abe

  18 shared

• Ian Kinsella

  Columbia University

  17 shared

• E. Kelly Buchanan

  Columbia University

  14 shared

• Anne K. Churchland

  University of California, Los Angeles

  10 shared

• Simon Musall

  Forschungszentrum Jülich

  10 shared

• Mark M. Churchland

  Columbia University

  10 shared

Labs

• Saxena LabPI

Education

• PhD, Department of Electrical Engineering and Computer Sciences

  Massachusetts Institute of Technology

  2017

Awards & honors

• Sloan Research Fellowship (2025)
• Rising Stars in Electrical Engineering, UIUC (2019)
• Rising Stars in Biomedical Engineering, Johns Hopkins Univer…
• Honoree of the Graduate Women of Excellence Award, MIT (2017…

Similar researchers at Yale University

• Douglas L. Rothmanh-125
• Nicholas Christakish-110
• D.S. Fahmeed Hyderh-85