Hi there! I am a postdoctoral researcher at the Chang lab in the Department of Neurosurgery, UC San Francisco.

I just finished my MS/PhD in Computer Science at the Huth Lab, UT Austin where I collaborated with researchers at Google AI Language and Intel Brain-Inspired Computing Lab. My thesis focused on jointly interpreting how systems like the human brain and neural NLP models process language. During my PhD, I was generously supported by fellowships from the Dingwall Foundations of Language and UT Austin Graduate School. I am very grateful to have won the best dissertation award from the Society for the Neurobiology of Language and the Glushko Dissertation Prize from the Cognitive Science Society.

Before that, I did my undergrad at NITK, Surathkal and spent some time with the ML group at Leuphana University, Germany.

I can most effectively be contacted through email {shailee.jain at ucsf.edu or shaileesjain at gmail.com}. I also tweet sometimes (working on it!) (well, I guess it’s mastodon now; or is it BlueSky?). Here are links to my CV and Google Scholar profile.

Recent News

• October, 2024:
  • Giving a talk at SfN’24 & presenting a poster at APAN’24 on time- & context-sensitive neurons in human speech cortex.
• September, 2024:
  • We just launched the JoCNForum! As one of the handling editors, I hope this serves as a unique platform to discuss, debate and disseminate neuroscientific research.
  • Gave a talk at NAISys’24 on time- & context-sensitive neurons in human speech cortex.
  • New preprint on an LLM-based generative explanation framework for voxel function.
• August, 2024:
  • Gave a talk at CCN’24 on context-sensitive speech encoding in human cortex. [Video] (starts at ~18min)
• April, 2024:
  • Very honored to receive the Glushko Dissertation Prize from the Cognitive Science Society! See you in the Netherlands for a talk on my PhD work.
• January, 2024:
  • My new postdoc work on *modeling single human neurons with ANNs* was selected as a talk at COSYNE’24! See you in Portugal!
  • Grateful for a generous speaker travel grant from COSYNE.
• August, 2023:
  • Check out our new preprint on investigating temporal processing in the brain w/ interpretable LMs.
  • Moved to SF and started as a postdoc at the Chang lab!
• July, 2023:
  • Honored to receive the Dissertation Award from the Society for the Neurobiology of Language.
  • I just defended my PhD, hoorah! A big thank you to my wonderful advisor, committee, collaborators, friends and family!
• May, 2023:
  • Our large-scale fMRI dataset paper was accepted at Nature Scientific Data! [OpenNeuro] & [Code]
  • New preprint with Microsoft Research on model interpretability in language models and the brain!
  • Stoked that my SNL’23 symposium proposal was accepted— “Can we investigate linguistic modularity in the brain with non-modular NLP systems?” See you in Marseille!
• April, 2023: Our new decoding study in Nature Neuroscience was featured in NYT, NPR, Scientific American, CNBC, NBC & more! Here are my inputs in Stat News & CBS Austin.
• January, 2023:
  • Our op piece on in silico experimentation with LM-based encoding models is now out in NoL!
  • Our paper on decoding language from the brain was accepted at Nature Neuroscience!
• December, 2022:
  • Very excited to be selected for the Miller Research Grant from UC Berkeley!
  • I finally set up my mastodon.
• October, 2022: TWO new preprints! Reconstruting speech from fMRI & a large-scale naturalistic language fMRI dataset

Publications

(*equal contribution)

1. A natural language fMRI dataset with voxelwise encoding models
   A. LeBel, L. Wagner, S. Jain, A. Adhikari-Desai, B. Gupta, A. Morgenthal, J. Tang, L. Xu and A. G. Huth Nature Scientific Data (to appear) [PDF]
2. Semantic reconstruction of continuous language from non-invasive brain recordings
   J. Tang, A. LeBel, S. Jain and A. G. Huth
   Nature Neuroscience (to appear) [PDF]
3. Computational language modeling and the promise of in silico experimentation
   S. Jain, V. A. Vo, L. Wehbe and A. G. Huth
   Neurobiology of Language 2022 (to appear) [PDF]
4. Self-supervised models of audio effectively explain human cortical responses to speech
   A. Vaidya, S. Jain and A. G. Huth
   International Conference on Machine Learning (ICML) 2022 [PDF]
5. Voxelwise encoding models show that cerebellar language representations are highly conceptual
   A. LeBel, S. Jain and A. G. Huth
   Journal of Neuroscience 2021 [PDF]
6. Interpretable multi-timescale models for predicting fMRI responses to continuous natural speech
   S. Jain, V. A. Vo, S. Mahto, A. LeBel, J. S. Turek and A. G. Huth
   Advances in Neural Information Processing Systems (NeurIPS) 2020 [PDF]
7. Approximating stacked and bidirectional recurrent architectures with the delayed recurrent neural network
   J. S. Turek, S. Jain, V. A. Vo, M. Capota, A. G. Huth and T. L. Willke
   Proceedings of the 37th International Conference on Machine Learning (ICML) 2020 [PDF]
8. Incorporating context into language encoding models for fMRI
   S. Jain and A. G. Huth
   Advances in Neural Information Processing Systems (NeurIPS) 2018 [PDF] [Video] [Poster]
9. Spatial language representation with multi-Level geocoding
   S. Kulkarni*, S. Jain*, M. J. Hosseini, J. Baldridge, E. Ie and L. Zhang
   Second International Combined Workshop on Spatial Language Understanding and Grounded Communication for Robotics (SpLU-RoboNLP) @ACL-IJCNLP 2021 [PDF]
10. Mining user trajectories in electronic text books
    A. Boubekki, S. Jain and U. Brefeld
    Proceedings of the Eleventh Annual International Conference on Educational Data Mining (EDM) 2018 [PDF]

Manuscripts in submission and preparation

(*equal contribution)

1. A unifying computational account of temporal processing in natural speech across cortex
   V. A. Vo*, S. Jain*, N. M. Beckage, H. S. Chien, C. Obinwa and A. G. Huth (in submission) [PDF]
2. A generative framework to bridge data-driven models and scientific theories in language neuroscience
   C. Singh, R. Antonello, S. Jain, A. Hsu, J. Gao, B. Yu and A. G. Huth (in submission) [PDF]
3. Explaining black box text modules in natural language with language models
   C. Singh, A. R. Hsu, R. Antonello, S. Jain, A. G. Huth, B. Yu, J. Gao (in submission) [PDF]
4. Adding noise to model inputs can improve the prediction performance of speech encoding models for fMRI
   C. Obinwa, S. Jain, A. Vaidya and A. G. Huth (in preparation)
5. Discovering distinct patterns of semantic integration across cortex using natural language encoding models for fMRI
   S. Jain and A. G. Huth (in preparation)

Teaching

1. CS 342 Neural Networks with Dr. Alex Huth - TA + Guest lecturer
   
   • Recurrent Neural Networks
     
   • Language Models [Slides]
2. NSC 110H Mapping the human cortex with Dr. Alex Huth - Guest lecturer
   
   • Temporal organization of language in the brain [Slides]

Service

1. Can we investigate linguistic modularity in the brain with non-modular NLP systems? - Symposium Organizer (SNL 2023 Paris, France)
2. Memory in Artificial and Real Intelligence (MemARI) - Workshop Organizer (NeurIPS 2022 New Orleans, USA)
3. How Can Findings About The Brain Improve AI Systems? - Workshop Organizer (ICLR 2021 virtual)
4. Context & Compositionality in Biological and Artificial Neural Systems](https://context-composition.github.io) - Workshop Organizer (NeurIPS 2019 Montreal, Canada)
5. Women In Computer Science (WiCS) @UT Austin - Graduate Mentor (2019, 2020, 2023)
6. UT CS Masters Admissions Committee (2021, 2022)
7. NeurIPS, ICLR, ICML, Nature Human Behavior, Nature Communications, Neuron (assisted), Nature Neuroscience (assisted) - Reviewer
8. JoCN Forum - Handling Editor

In the Press

• Language decoding (Tang et al.): NYT, NPR, Scientific American, CNBC, NBC, Stat News & CBS Austin.
• Inspired AI Inspires Insights About The Brain (And Vice Versa) Texas Advanced Computing Center Brain 2019
• How AI Is Deepening Our Understanding of the Brain SingularityHub