Rishabh Joshi

Google DeepmindGoogle Research - Brain Team
Google, Mountain View, CA

I am a software engineer working on language research at Google Deepmind (formerly Google Research - Brain Team). My current research focus is on improving the generation and alignment capabilities of large language models. Before Google, I did my research masters in Language Technologies (MLT) from Language Technologies Institute, School of Computer Science at Carnegie Mellon University, where I was co-advised by Alan W Black, Yulia Tsvetkov and Alex Rudnicky.

Before I joined CMU, I worked in Samsung Research Institute, Bangalore, India where I was working in the Voice Intelligence Team on dialogue systems and chat-bots. Before that, I spent a wonderful semester working with Partha Talukdar at Machine and Language Learning (MALL) Lab in the Indian Institute of Science, Bangalore. I graduated with a B.Eng. in Computer Science from Birla Institute of Technology and Science, Pilani, India. In my Bachelor's thesis, I worked on incorporating external knowledge in distantly supervised neural relation extraction methods as part of iNELL (which is based on NELL).


Jan 20, 2023

Our work on Calibrating Sequence likelihood Improves Conditional Language Generation was accepted in The Eleventh International Conference on Learning Representations (ICLR) 2023.

Jan 1, 2023

Our work on Unsupervised Keyphrase Extraction via Interpretable Neural Networks was accepted at the 2023 Conference on European Association for Computational Linguistics.

Oct 11, 2021

I joined Google Research to continue my reserach on language generation with the Brain Team.

Jul 4, 2021

Our work on Improving Broad-Coverage Medical Entity Linking with Semantic Type Prediction and Large-Scale Datasets was accepted at the Journal of Biomedical Informatics.

Jan 13, 2021

Our work on DialoGraph: Incorporating Interpretable Strategy-Graph Networks into Negotiation Dialogues. was accepted at the International Conference on Learning Representations (ICLR) 2021.

Jan 3, 2021

Our work on ResPer: Computationally Modelling Resisting Strategies in Persuasive Conversations was accepted at the European Association for Computational Linguistics (EACL) 2021.

Dec 3, 2020

Our work on MedType: Improving Medical Entity Linking with Semantic Type Prediction was accepted at the AMIA 2021 Virtual Informatics Summit.

Sep 21, 2020

Our work on Keeping Up Appearances: Computational Modeling of Face Acts in Persuasion Oriented Discussions was accepted at the 2020 Conference on Empirical Methods in Natural Language Processing.

Jul 1, 2020

Our work on Incorporating Multi-Level Features for Multi-Granular Propaganda Span Identification was accepted at the 14th International Workshop on Semantic Evaluation, 2020.

Jun 1, 2020

Our team, Tartan, reached the semi-finals of the 3rd Alexa Dialogue Challenge, 2019.