Hi! I am a fifth-year Ph.D. student at Princeton where I am advised by Jonathan Pillow. I work at the intersection of probabilistic machine learning and statistical neuroscience. I develop interpretable machine learning approaches to understand behavior and neural activity in humans and animals. Before this, I was an undergrad at the Indian Institute of Technology, Delhi, where I worked with Sumeet Agarwal. My research is generously supported by a Google Ph.D. Fellowship. I spent two wonderful summers working in industry—at Meta Reality labs working on wrist-based neural interfaces, and with MosaicML on large language models.
Aside from research: I like to overdose on literary/historical fiction and occasionally put on my creative writing hat. I like going on photography tours, running, painting, and listening to Bollywood music.
EDucation
Ph.D. in Electrical and Computer Engineering.
2019-Present. Princeton University
B.Tech in Electrical Engineering.
2015-2019. IIT Delhi
Research
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
EXTRACTING LOW-DIMENSIONAL PSYCHOLOGICAL REPRESENTATIONS FROM CONVOLUTIONAL NEURAL NETWORKS
Aditi Jha, Joshua C. Peterson, Thomas L. Griffiths. Proceedings of the 42nd Annual Conference of the Cognitive Science Society (CogSci), 2020.
DO DEEP NEURAL NETWORKS MODEL NONLINEAR COMPOSITIONALITY IN THE NEURAL REPRESENTATION OF HUMAN-OBJECT INTERACTIONS?
Aditi Jha, Sumeet Agarwal. Proceedings of the 3rd Computational Cognitive Neuroscience Conference (CCN) Berlin, Germany. 2019
Misc
Pillow Lab’s blog/ PNI’s CompNeuro Journal Club/ For Fitzgerald enthusiasts/ My current read
Lastly, if you love this show too, we can be best friends