Tejasvi Kothapalli

I am a second-year Vision Science PhD student at UC Berkeley. I am in the Active Vision and Neural Computation Lab and advised by Professor Jacob Yates. I am also a part of the Redwood Center for Theoretical Neuroscience directed by Professor Bruno Olshausen. I am incredibly grateful to be able to collaborate with Professor Alex Huk and Postdoctoral Scholar Declan Rowley, working alongside them to study the neural recordings they collect from primates. I work at the intersection of neuroscience and artificial intelligence, an area known as NeuroAI. I am generously funded by the CIVO Fellowship.

I graduated from UC Berkeley in 2022 with a Bachelor of Science degree in Electrical Engineering & Computer Science. As an undergrad, I was a student research assistant jointly at the Clinical Research Center (CRC), International Computer Science Institute (ICSI), and Berkeley Artificial Intelligence Research (BAIR). There, I had the pleasure of working with Prof. Meng Lin, Prof. Stella Yu, and Dr. Peter Wang. My research focused on applying computer vision and machine learning techniques to diagnose Dry Eye Disease.

email: tejasvi.kothapalli [at sign] berkeley.edu

CV  /  Google Scholar

Harsh, Avinash, Me, Andi

• [08/2023] Started PhD in Vision Science at UC Berkeley


• [08/2023] Named CIVO Fellow by the UC Berkeley Center for Innovation in Vision and Optics


• [10/2022] Paper accepted at NeurIPS 2022, Medical Imaging Workshop


• [05/2022] Received UC Berkeley EECS B.S. degree

• Meng C. Lin, Peter Wang, Tejasvi Kothapalli, Andrew D. Graham, Stella X. Yu
  American Academy of Optometry (AAOPT) Annual Meeting, 2022 (Oral Presentation)
  abstract / slides / press (Healio)


• Meng C. Lin, Andrew D. Graham, Tejasvi Kothapalli, Peter Wang, Jennifer Ding, Vivien Tse, Stella X. Yu
  Association for Research in Vision and Ophthalmology (ARVO) Annual Meeting, 2023
  abstract


• Andrew D. Graham, Tejasvi Kothapalli, Peter Wang, Jennifer Ding, Vivien Tse, Penny A. Asbell, Stella X. Yu, Meng C. Lin
  Heliyon, 2024
  paper


• Andrew D. Graham, Peter Wang, Tejasvi Kothapalli, Helen Tasho, Alisa Molina, Vivien Tse, Sarah M. Chang, Stella X. Yu, Meng C. Lin
  Nature Scientific Reports, 2024
  paper

We propose a machine learning approach that predicts dry eye related signs, symptoms and diagnoses from subject data.

We use computer vision to track the tear film lipid layer spread.

Studied Dry Eye Disease diagnosis with machine learning in two regimes: (1) patient tabular data and (2) ocular surface recordings. Advised by Prof. Stella Yu and Prof. Meng Lin. Earned an A+ :). Also, huge thanks to Peter Wang for all the mentorship and guidance.

In order to reduce energy wastage in homes, this work detects the presence of residents through Wi-Fi device usage and tries to automatically turn off unused electronic appliances.

This study investigates a control pattern for successful locomotion in tensegrity robots through an evolutionary algorithm.

This is a very preliminary version of Tracking the Dynamics of the Tear Film Lipid Layer.

We approached Open AI's Retro contest with two new approaches. We first explore meta-learning with recurrent policies. Second, we explored the use of enhanced exploration with Random Network Distillation.

We created a model that converts images of LaTeX into its original markup expression.

The intention of our technique was to understand how different segments in a review affect the overall prediction through sliding window methods. We hope that our contribution can help people better understand how these models make decisions.

I help organize and teach about general Vision Science to 4th graders around the East Bay. We work with the organization Bay Area Scientists Inspiring Students (BASIS).

After graduating from Berkeley, I have taught the Inpsirit AI Scholars curriculum to high students in-person at Khan Lab School, Bellarmine College Preparatory, Evergreen Valley High School, and Bentley School. Teaching at the Khan Lab School in July 2022 with my good friend Sohum Hulyalkar was a truly rewarding experience.

Huge thanks to Jon Barron for website template!