Suvir Mirchandani

I'm a first-year Ph.D. student in Computer Science at Stanford University. My research interests are in machine learning and human-AI interaction. This past year, I was an AI Resident at Meta AI, where I worked on multimodal representation learning and self-supervised learning with Ning Zhang, Licheng Yu, and Tao Xiang.

I received my M.S. and B.S. in Computer Science from Stanford. I was a member of the Stanford Intelligent and Interactive Autonomous Systems Group where I was fortunate to be advised by Dorsa Sadigh.

Previously, I have done research at MIT Lincoln Laboratory on machine learning for threat prediction and at The University of Edinburgh on on accessible voice interfaces. I have also interned at Facebook Messenger and

Feel free to get in touch at


Conference Papers

Assistive Teaching of Motor Control Tasks to Humans

Megha Srivastava, Erdem Bıyık, Suvir Mirchandani, Noah Goodman, Dorsa Sadigh.

Conference on Neural Information Processing Systems (NeurIPS), 2022.


@inproceedings{srivastava2022assistive, title={Assistive Teaching of Motor Control Tasks to Humans}, author={Srivastava, Megha and Biyik, Erdem and Mirchandani, Suvir and Goodman, Noah D. and Sadigh, Dorsa}, booktitle={Conference on Neural Information Processing Systems (NeurIPS)}, year={2022} }

FaD-VLP: Fashion Vision-and-Language Pre-training towards Unified Retrieval and Captioning

Suvir Mirchandani, Licheng Yu, Mengjiao Wang, Animesh Sinha, Wenwen Jiang, Tao Xiang, Ning Zhang.

Conference on Empirical Methods in Natural Language Processing (EMNLP), 2022.


@inproceedings{mirchandani2022fadvlp, title={FaD-VLP: Fashion Vision-and-Language Pre-training towards Unified Retrieval and Captioning}, author={Mirchandani, Suvir and Yu, Licheng and Wang, Mengjiao and Sinha, Animesh and Jiang, Wenwen and Xiang, Tao and Zhang, Ning}, booktitle={Conference on Empirical Methods in Natural Language Processing (EMNLP)}, year={2022} }

How do People Incorporate Advice from Artificial Agents when Making Physical Judgments?

Erik Brockbank*, Haoliang Wang*, Justin Yang, Suvir Mirchandani, ‪Erdem Bıyık, Dorsa Sadigh, Judith Fan.

Annual Conference of the Cognitive Science Society (CogSci), 2022.

BibTeX Abstract PDF Code

@inproceedings{brockbank2022people, title={How do People Incorporate Advice from Artificial Agents when Making Physical Judgments?}, author={Brockbank, Erik and Wang, Haoliang and Yang, Justin and Mirchandani, Suvir and Biyik, Erdem and Sadigh, Dorsa and Fan, Judith}, booktitle={Cognitive Science Society Conference (CogSci)}, year={2022} }

How do people build up trust with artificial agents? Here, we study a key component of interpersonal trust: people’s ability to evaluate the competence of another agent across repeated interactions. Prior work has largely focused on appraisal of simple, static skills; in contrast, we probe competence evaluations in a rich setting with agents that learn over time. Participants played a video game involving physical reasoning paired with one of four artificial agents that suggested moves each round. We measure participants’ decisions to accept or revise their partner’s suggestions to understand how people evaluated their partner’s ability. Overall, participants collaborated successfully with their agent partners; however, when revising their partner’s suggestions, people made sophisticated inferences about the competence of their partner from prior behavior. Results provide a quantitative measure of how people integrate a partner’s competence into their own decisions and may help facilitate better coordination between humans and artificial agents.

ELLA: Exploration through Learned Language Abstraction

Suvir Mirchandani, Siddharth Karamcheti, Dorsa Sadigh.

Conference on Neural Information Processing Systems (NeurIPS), 2021.

BibTeX Abstract PDF Code Talk

@inproceedings{mirchandani2021ella, title={ELLA: Exploration through Learned Language Abstraction}, author={Mirchandani, Suvir and Karamcheti, Siddharth and Sadigh, Dorsa}, booktitle={Conference on Neural Information Processing Systems (NeurIPS)}, year={2021} }

Building agents capable of understanding language instructions is critical to effective and robust human-AI collaboration. Recent work focuses on training these agents via reinforcement learning in environments with synthetic language; however, instructions often define long-horizon, sparse-reward tasks, and learning policies requires many episodes of experience. We introduce ELLA: Exploration through Learned Language Abstraction, a reward shaping approach geared towards boosting sample efficiency in sparse reward environments by correlating high-level instructions with simpler low-level constituents. ELLA has two key elements: 1) A termination classifier that identifies when agents complete low-level instructions, and 2) A relevance classifier that correlates low-level instructions with success on high-level tasks. We learn the termination classifier offline from pairs of instructions and terminal states. Notably, in departure from prior work in language and abstraction, we learn the relevance classifier online, without relying on an explicit decomposition of high-level instructions to low-level instructions. On a suite of complex BabyAI environments with varying instruction complexities and reward sparsity, ELLA shows gains in sample efficiency relative to language-based shaping and traditional RL methods.

Preprints & Reports

On the Opportunities and Risks of Foundation Models

Rishi Bommasani et al.

Center for Research on Foundation Models (CRFM), 2021.

Robotics (§2.3): Siddharth Karamcheti, Annie Chen, Suvir Mirchandani, Suraj Nair, Krishnan Srinivasan, Kyle Hsu, Jeannette Bohg, Dorsa Sadigh, Chelsea Finn.

BibTex Abstract PDF

AI is undergoing a paradigm shift with the rise of models (e.g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks. We call these models foundation models to underscore their critically central yet incomplete character. This report provides a thorough account of the opportunities and risks of foundation models, ranging from their capabilities (e.g., language, vision, robotics, reasoning, human interaction) and technical principles(e.g., model architectures, training procedures, data, systems, security, evaluation, theory) to their applications (e.g., law, healthcare, education) and societal impact (e.g., inequity, misuse, economic and environmental impact, legal and ethical considerations). Though foundation models are based on standard deep learning and transfer learning, their scale results in new emergent capabilities,and their effectiveness across so many tasks incentivizes homogenization. Homogenization provides powerful leverage but demands caution, as the defects of the foundation model are inherited by all the adapted models downstream. Despite the impending widespread deployment of foundation models, we currently lack a clear understanding of how they work, when they fail, and what they are even capable of due to their emergent properties. To tackle these questions, we believe much of the critical research on foundation models will require deep interdisciplinary collaboration commensurate with their fundamentally sociotechnical nature.

@article{bommasani2021foundation, title={On the Opportunities and Risks of Foundation Models}, author={Rishi Bommasani and Drew A. Hudson and Ehsan Adeli and Russ Altman and Simran Arora and Sydney von Arx and Michael S. Bernstein and Jeannette Bohg and Antoine Bosselut and Emma Brunskill and Erik Brynjolfsson and S. Buch and Dallas Card and Rodrigo Castellon and Niladri S. Chatterji and Annie S. Chen and Kathleen A. Creel and Jared Davis and Dora Demszky and Chris Donahue and Moussa Doumbouya and Esin Durmus and Stefano Ermon and John Etchemendy and Kawin Ethayarajh and Li Fei-Fei and Chelsea Finn and Trevor Gale and Lauren E. Gillespie and Karan Goel and Noah D. Goodman and Shelby Grossman and Neel Guha and Tatsunori Hashimoto and Peter Henderson and John Hewitt and Daniel E. Ho and Jenny Hong and Kyle Hsu and Jing Huang and Thomas F. Icard and Saahil Jain and Dan Jurafsky and Pratyusha Kalluri and Siddharth Karamcheti and Geoff Keeling and Fereshte Khani and O. Khattab and Pang Wei Koh and Mark S. Krass and Ranjay Krishna and Rohith Kuditipudi and Ananya Kumar and Faisal Ladhak and Mina Lee and Tony Lee and Jure Leskovec and Isabelle Levent and Xiang Lisa Li and Xuechen Li and Tengyu Ma and Ali Malik and Christopher D. Manning and Suvir P. Mirchandani and Eric Mitchell and Zanele Munyikwa and Suraj Nair and Avanika Narayan and Deepak Narayanan and Benjamin Newman and Allen Nie and Juan Carlos Niebles and Hamed Nilforoshan and J. F. Nyarko and Giray Ogut and Laurel Orr and Isabel Papadimitriou and Joon Sung Park and Chris Piech and Eva Portelance and Christopher Potts and Aditi Raghunathan and Robert Reich and Hongyu Ren and Frieda Rong and Yusuf H. Roohani and Camilo Ruiz and Jack Ryan and Christopher R'e and Dorsa Sadigh and Shiori Sagawa and Keshav Santhanam and Andy Shih and Krishna Parasuram Srinivasan and Alex Tamkin and Rohan Taori and Armin W. Thomas and Florian Tram{\`e}r and Rose E. Wang and William Wang and Bohan Wu and Jiajun Wu and Yuhuai Wu and Sang Michael Xie and Michihiro Yasunaga and Jiaxuan You and Matei A. Zaharia and Michael Zhang and Tianyi Zhang and Xikun Zhang and Yuhui Zhang and Lucia Zheng and Kaitlyn Zhou and Percy Liang}, journal={ArXiv}, year={2021}, url={} }


Prior to college, I studied piano for a number of years under the tutelage of Prof. Luz Manríquez. Some old recordings are featured below.

Senior Recital

July 2017

Piano Concerto No. 2, I. Moderato

S. Rachmaninoff

Ballade No. 1 in G minor

F. Chopin

Toccata in E-flat minor

A. Khachaturian

Piano Concerto No. 3, Op. 50, I. Allegro Molto

D. Kabalevsky

Blue Johannes

R. Vali (b. 1952)

Piano Concerto No. 3, I. Andante - Allegro

S. Prokofiev

Toccata in E minor

J. S. Bach

Suggestion Diabolique

S. Prokofiev


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