Subhashini Venugopalan

Subhashini Venugopalan

I work on machine learning applications motivated in healthcare and sciences. Some of my work pertains to improving speech recognition systems for users with impaired speech, others to transfer learning for bio/medical data (e.g. detecting diabetic retinopathy, breast cancer), and I have also developed methods to interpret such vision/audio models (model explanation) for medical applications. During my graduate studies, I applied natural language processing and computer vision techniques to generate descriptions of events depicted in videos and images. I am a key contributor to a number of works featuring in the Healed through A.I. documentary. Please refer to my website (https://vsubhashini.github.io/) for more information and my Google Scholar page for an up-to-date list of my publications.
Authored Publications
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SpeakFaster Observer: Long-Term Instrumentation of Eye-Gaze Typing for Measuring AAC Communication
Katrin Tomanek
Richard Jonathan Noel Cave
Bob MacDonald
Jon Campbell
Blair Casey
Emily Kornman
Daniel Vance
Jay Beavers
CHI23 Case Studies of HCI in Practice (2023) (to appear)
Context-Aware Abbreviation Expansion Using Large Language Models
Katrin Tomanek
Ajit Narayanan
Annual Conference of the North American Chapter of the Association for Computational Linguistics, 2022 (2022) (to appear)
Assessing ASR Model Quality on Disordered Speech using BERTScore
Qisheng Li
Katie Seaver
Richard Jonathan Noel Cave
Katrin Tomanek
Proc. 1st Workshop on Speech for Social Good (S4SG) (2022), pp. 26-30 (to appear)
Scaling Symbolic Methods using Gradients for Neural Model Explanation
Subham Sekhar Sahoo
Li Li
Rishabh Singh
Patrick Francis Riley
International Conference on Learning Representations (ICLR) (2021)