I am also affiliated with Stanford's Center for Open and Reproducible Science, Stanford's Center for Research on Foundation Models, and the Max Planck Institute for Human Development.
I am interested in:
- Brain decoding
- Transfer learning
- Model interpretability & robustness
- Open science & reproducibility
- [March, 2022] Had a great time at this year’s future leader’s summit at the Michigan Institute for Data Science
- [October, 2021] I had the opportunity to give a practical tutorial on reproducible modelling for the 2021 fall lecture series of Stanford's Center for Open and REproducible Science; A recording of the talk can be found here and the accompanying GitHub repository here
- [September, 2021] The Massachusetts Society for Medical Research wrote a brief summary of our work on many-alternative choices.
- [August, 2021] We uploaded a new preprint in which we discuss challenges (and solutions) for deep learning methods in brain decoding; together with with Russ Poldrack and Chris Ré
- [August, 2021] Happy to have contributed to the report on foundation models (i.e., broadly pretrained models with wide adaptation; e.g., GPT-3, BERT, CLIP) by Stanford HAI with a section on the interpretability of foundation models
- [June, 2021] I will be working along side an amazing team as head technical mentor for Stanford's Data Science For Social Good (DSSG) summer program this year
- [May, 2021] Honored to be awarded a Google Cloud Computing Grant by Stanford HAI
- [April, 2021] Our paper on the computational mechanisms underlying many-alternative choices appeared in eLife!
- [January, 2021] Thrilled to begin my work as a Ram and Vijay Shriram postdoctoral fellow with Stanford Data Science
Key research projects
On the computational mechanisms of simple choice:
On the analysis of fMRI data with deep learning models:
📩 reach out: firstname.lastname@example.org