
I am a Wu Tsai Interdisciplinary Fellow at Stanford Statistics and the Neurosciences Institute, where I work with Scott Linderman. My work lies at the intersection of probabilistic machine learning and statistical neuroscience—developing interpretable machine learning approaches to understand behavior and neural dynamics. I obtained my PhD at Princeton, advised by Jonathan Pillow. Long time ago, I was an undergrad at the Indian Institute of Technology in Delhi, where I worked with Sumeet Agarwal. I have also spent two wonderful summers working in industry—at Meta Reality labs working on wrist-based neural interfaces, and another at MosaicML working on large language models.
Aside from research: I like to overdose on literary/historical fiction and occasionally put on my creative writing hat. I also like running, painting, and listening to Bollywood music.
EDucation

Ph.D. in Electrical and Computer Engineering.
2019-2024. Princeton University [Thesis]

B.Tech in Electrical Engineering.
2015-2019. IIT Delhi
SELECTED PUBLICATIONS

BAYESIAN ACTIVE LEARNING FOR DISCRETE LATENT VARIABLE MODELS
Aditi Jha, Zoe C. Ashwood, Jonathan W. Pillow. Neural Computation. Volume 36, Issue 3. March 2023.
Paper / Talk at COSYNE workshops 2022

EXTRACTING LOW-DIMENSIONAL PSYCHOLOGICAL REPRESENTATIONS FROM CONVOLUTIONAL NEURAL NETWORKS
Aditi Jha, Joshua C. Peterson, Thomas L. Griffiths. Cognitive Science. Volume 47, Issue 1. January, 2023.

DYNAMIC INVERSE REINFORCEMENT LEARNING FOR CHARACTERIZING ANIMAL BEHAVIOR
Zoe C. Ashwood*, Aditi Jha*, Jonathan W. Pillow. Advances in Neural Information Processing Systems (NeurIPS) 35 (2022) [Oral Presentation].
Paper / Code / COSYNE Talk

FACTOR-ANALYTIC INVERSE REGRESSION FOR HIGH-DIMENSIONAL, SMALL-SAMPLE DIMENSIONALITY REDUCTION
Aditi Jha*, Michael J. Morais*, Jonathan W. Pillow. Proceedings of the 38th International Conference on Machine Learning (ICML), PMLR 139 (2021).
Paper/ Code/ Summary Video at Cosyne’21/ Invited Talk at MLSE 2020
Misc
Pillow Lab’s blog/ PNI’s CompNeuro Journal Club / My current read
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