Tejasvi Kothapalli

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PhD Student

Vision Science

Yates Lab

Current Research

I’m broadly interested in biological vision and also building brain-inspired vision systems. My work focuses on understanding how the visual cortex processes information during natural vision. I work at the intersection of data-driven and normative theory. For our data-driven work, we are very fortunate to be able to collaborate with experimental labs that collect neural data. We primarily use machine learning techniques to predict neural data. For normative theory, I am interested in trying to predict neural data with Sparse Coding.

Background

I have always been interested in research. As a high schooler, I interned at NASA Ames and worked on Tensegrity Robots. I completed my undergraduate education at UC Berkeley in Electrical Engineering & Computer Science. My undergraduate coursework mainly revolved around machine learning and artificial intelligence. I also had the pleasure of conducting research with Professor Stella Yu, Professor Meng Lin, and Peter Wang (who was at the time a Vision Science PhD student). My research focused on applying computer vision and machine learning techniques to diagnose Dry Eye Disease. After earning my undergraduate degree in May 2022, I worked as a Machine Learning Engineer at Aizip, a startup focused on deploying tiny ML models onto IoT devices. At Aizip, my work was focused on training models for people detection in IR cameras and fall detection in wearable devices.

I joined Jacob Yates’ Lab in May 2023 and officially began my PhD studies in August 2023. Although I had exposure to Artificial Intelligence, I had never studied Computational Neuroscience until I joined the Yates’ Lab. My journey into Visual Neuroscience has been incredibly exciting, rewarding, and enjoyable.