Hi!
I'm a Research Engineer at Google DeepMind, where I work on making Gemini the best coding model in the world, especially in realistic SWE environments. Most recently, I worked on training Gemini for Jules, an end-to-end software engineering agent.
I was a graduate student at the Language Technologies Department, Carnegie Mellon University, for the MIIS program. I was part of the MultiComp Lab, where I worked with Chaitanya Ahuja and Prof. Louis-Philippe Morency on multimodal gesture generation.
I interned at DeepMind for the summer (2022), working on code generation. I was previously a Research Engineer (on contract) at Google Research India, working with Dr. Partha Talukdar in the NLU Group. Here, I worked on entity-aware translation for educational content, and neural temporal parsing of queries.
Before this, I spent an amazing year at Microsoft Research India, and worked with Prof. Monojit Choudhury and Dr. Kalika Bali on various problems in low-resource language systems and probing NLI models.
I did my undergraduate thesis at Microsoft Research under Dr. Navin Goyal and Prof. Monojit Choudhury, on semantic parsing applied to the conversion of natural language to regular expressions and SQL. I graduated with a B.E. (Hons.) in Computer Science from BITS Pilani, Goa, India, in 2019.
Experience
-- Post-Training for Code.
-- Core Contributor to Gemini, Gemini 1.5 Pro, and Gemini 2.5 Pro.
-- Contributor to CodeGemma.
-- Core model training for products like Jules, Gemini Code Assist, and Duet AI.
-- Devised continual learning techniques for generative models. Focused on preventing catastrophic forgetting in multimodal gesture generation.
-- Work published at ICCV'23.
-- Trained and implemented large language models for code generation. (AlphaCode style models).
-- Explored multi-task training and reinforcement learning objectives for sampling and re-ranking.
-- Created a scalable entity-aware translation+transliteration pipeline to generate subtitles for English-medium college lecture videos (from NPTEL) for various Indian languages.
-- Developed neural temporal parsing models for multilingual queries using knowledge graph infused techniques.
-- Contributed to creating a challenge for Indian multilingual QA.
-- Probed large pretrained lanuage models for NLI reasoning, by designing a taxonomy of reasoning capabilities, annotating an existing NLI dataset based on the capabilities, then evaluating the model performance on this new dataset.
-- Worked on a range of multilingual problems, such as quantitative analysis of language diversity in ACL, efficacy of code-mixing chatbots, measuring quality of crowdsourced speech data, among others.
-- Works published at ACL, CoNLL, CSCW, LREC, EMNLP Workshop, and ICON (see publications below).
-- Worked on semantic parsing and its applications to natural language to code. Particularly focused on natural language to regular expressions (NL2Regex), and to SQL queries.
Implemented and experimented with different neural architectures for NL2Regex, and conducted an error analysis. Subsequently, I also analyzed quality of various semantic parsing datasets
using a range of metrics and indicators.
-- Research carried out also contributed to my undergraduate thesis.
-- Created voice interface application for truck drivers. Used pocketsphinx-android from CMUSphinx to power voice recognition.
Customized to recognize key commands in English, Hindi, and Indian-English.
-- Created capabilities like an in-built support system, location and route support, and verbal data entry.
-- Presented prototype to I-Loads administration (CEO,CFO,CTO) and tech team.
Publications
Talks and Panels
The State and Fate of Linguistic Diversity and Inclusion in the NLP World
Lee Language Lab at OntarioTech University, Remote
video
The Nuts and Bolts (and Nuances) of Foundation Models
Centre for Democracy and Technology Panel, Remote
video |
description
NLP for the Long Tail
Google Research, Remote
The State and Fate of Linguistic Diversity in the NLP World
NLP with Friends, Remote
video |
description
Reviewing