I am a Research Scientist at Google Research where I focus on developing LLM agents with specialized capabilities for scientific discovery and automated coding. My work centers on enabling models to reason over scientific data and complex codebases to significantly accelerate research workflows. Parallel to this, I lead research on evaluating video understanding to advance how multimodal models can make digital content more accessible to blind and low-vision users.
I am motivated by AI's potential to accelerate scientific breakthroughs and create assistive technologies that empower users. My contributions to healthcare and accessibility were featured in the documentary Healed through A.I. and recognized with a 2025 Bronze Cannes Lions award for Transformative Design.
I earned my Ph.D. from UT Austin, advised by Ray Mooney and co-advised by Kate Saenko and Trevor Darrell. This collaborative environment, enriched by research stays at UC Berkeley and Google Brain internships, enabled my doctoral work to contribute foundational models for video captioning and sequence-to-sequence multimodal reasoning.
Research Focus
Agents & Science
Designing autonomous agents capable of scientific coding, parsing literature, and aiding in complex research reasoning.
Multimodal Learning
Developing models that integrate visual perception, speech, and language reasoning for video understanding.
AI for Accessibility
Creating assistive technologies to empower users with diverse vision, speech and motor capabilities through ML.
Selected Talks
Publications
CBGB The Cloud-Based Geospatial Benchmark: Challenges and LLM Evaluation
Jeffrey A. Cardille, Renee Johnston, Simon Ilyushchenko, Johan Kartiwa, Zahra Shamsi, Matthew Abraham, Khashayar Azad, Kainath Ahmed, Emma Bergeron Quick, Nuala Caughie, Noah Jencz, Karen Dyson, Andrea Puzzi Nicolau, Maria Fernanda Lopez-Ornelas, David Saah, Michael Brenner, Subhashini Venugopalan, Sameera S Ponda
Terrabytes @ ICML 2025, PMLR 2025
Using large language models to accelerate communication for eye gaze typing users with ALS
Shanqing Cai, Subhashini Venugopalan, Katie Seaver, Xiang Xiao, Katrin Tomanek, Sri Jalasutram, Meredith Ringel Morris, Shaun Kane, Ajit Narayanan, Robert L. MacDonald, Emily Kornman, Daniel Vance, Blair Casey, Steve M. Gleason, Philip Q. Nelson, Michael P. Brenner
Nature Comms 2024
SpeakFaster Observer: Long-Term Instrumentation of Eye-Gaze Typing for Measuring AAC Communication
Shanqing Cai, Subhashini Venugopalan , Katrin Tomanek, Shaun Kane, Meredith Ringel Morris, Richard Cave, Bob MacDonald, Jon Campbell, Blair Casey, Emily Kornman, Daniel Vance, Jay Beavers
CHI 2023
A machine-learning based objective measure for ALS disease severity
Fernando G. Vieira*, Subhashini Venugopalan *, Alan S. Premasiri, Maeve McNally, Aren Jansen, Kevin McCloskey, Michael P. Brenner, Steven Perrin
npj Digital Medicine 2022
Integrating deep learning and unbiased automated high-content screening to identify complex disease signatures in human fibroblasts.
Lauren Schiff, Bianca Migliori, Ye Chen, Deidre Carter, Caitlyn Bonilla, Jenna Hall, Minjie Fan, Edmund Tam, Sara Ahadi, Brodie Fischbacher, Anton Geraschenko, Christopher J. Hunter, Subhashini Venugopalan , and 30 others.
Nature Comms 2022
Guided Integrated Gradients: An Adaptive Path Method for Removing Noise
Andrei Kapishnikov, Subhashini Venugopalan , Besim Avci, Ben Wedin, Michael Terry, Tolga Bolukbasi
CVPR 2021
Predicting Risk of Developing Diabetic Retinopathy using Deep Learning
Ashish Bora, Siva Balasubramanian, Boris Babenko, Sunny Virmani, Subhashini Venugopalan , Akinori Mitani, Guilherme de Oliveira Marinho, Jorge Cuadros, Paisan Ruamviboonsuk, Greg S Corrado, Lily Peng, Dale R Webster, Avinash V Varadarajan, Naama Hammel, Yun Liu, Pinal Bavishi
THE LANCET, Digital Health 2021
Scientific Discovery by Generating Counterfactuals using Image Translation
Arunachalam Narayanaswamy*, Subhashini Venugopalan *, Dale R. Webster, Lily Peng, Greg Corrado, Paisan Ruamviboonsuk, Pinal Bavishi, Rory Sayres, Abigail Huang, Siva Balasubramanian, Michael Brenner, Philip Nelson, Avinash V. Varadarajan
MICCAI 2020
Predicting optical coherence tomography-derived diabetic macular edema grades from fundus photographs using deep learning
Avinash V Varadarajan, Pinal Bavishi, Paisan Ruamviboonsuk, Peranut Chotcomwongse, Subhashini Venugopalan , Arunachalam Narayanaswamy, Jorge Cuadros, Kuniyoshi Kanai, George Bresnick, Mongkol Tadarati, Sukhum Silpa-Archa, Jirawut Limwattanayingyong, Variya Nganthavee, Joseph R Ledsam, Pearse A Keane, Greg S Corrado, Lily Peng, Dale R Webster
Nature Comms 2020
Batch Equalization with a Generative Adversarial Network
Wesley Wei Qian, Cassandra Xia, Subhashini Venugopalan , Arunachalam Narayanaswamy, Jian Peng, D Michael Ando
ECCB 2020
It's easy to fool yourself: Case studies on identifying bias and confounding in bio-medical datasets
Subhashini Venugopalan*, Arunachalam Narayanaswamy*, Samuel Yang*, Anton Gerashcenko, Scott Lipnick, Nina Makhortova, James Hawrot, Christine Marques, Joao Pereira, Michael Brenner, Lee Rubin, Brian Wainger, Marc Berndl
NeurIPS LMRL 2019
Applying Deep Neural Network Analysis to High-Content Image-Based Assays
Samuel J Yang*, Scott L Lipnick*, Nina R Makhortova*, Subhashini Venugopalan*, Minjie Fan*, Zan Armstrong, Thorsten M Schlaeger, Liyong Deng, Wendy K Chung, Liadan O' Callaghan, Anton Geraschenko, Dosh Whye, Marc Berndl, Jon Hazard, Brian Williams, Arunachalam Narayanaswamy, D Michael Ando, Philip Nelson, Lee L Rubin
SLAS DISCOVERY: Advancing Life Sciences R&D 2019
Semantic Text Summarization of Long Videos
Shagan Sah, Sourabh Kulhare, Allison Gray, Subhashini Venugopalan , Emily Prud'hommeaux, Raymond Ptucha
WACV 2017
Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs
Varun Gulshan, Lily Peng, Marc Coram, Martin C Stumpe, Derek Wu, Arunachalam Narayanaswamy, Subhashini Venugopalan , Kasumi Widner, Tom Madams, Jorge Cuadros, Ramasamy Kim, Rajiv Raman, Philip C Nelson, Jessica L Mega, Dale R Webster
JAMA 2016
