Aditi Chandrashekar

Courant Institute, NYU

I am a first-year Ph.D. student in CILVR at NYU Courant, advised by Professors Eero Simoncelli, Kyunghyun Cho, and Rob Fergus. In my time at Caltech I am grateful to have been mentored by Professors Bahareh Tolooshams, Anima Anandkumar, Soon-Jo Chung, and Katie Bouman.

My research seeks to understand and model the hidden dynamics of complex systems—particularly the brain—by combining representation learning, inverse-problem methodology, and generative AI.

For more info, please find my CV here.

News

June 2025

I graduated from Caltech!

May 2025

I will be starting my PhD at NYU Courant this Fall!

Jan 2025

I'm applying to PhD Programs for admission in Fall 2025!

Selected Publications

EquiReg: Symmetry-driven regularization for physically grounded diffusion-based inverse solvers

Bahareh Tolooshams*, Aditi Chandrashekar*, Rayhan Zirvi*, Abbas Mammadov, Jiachen Yao, Chuwei Wang, and Anima Anandkumar

Building Physically Plausible World Models at ICML 2025 [paper]

VARS-fUSI: Variable Sampling for Fast and Efficient Functional Ultrasound Imaging using Neural Operators

Bahareh Tolooshams, Lydia Lin, Thierri Callier, Jiayun Wang, Sanvi Pal, Aditi Chandrashekar, Claire Rabut, Zongyi Li, Chase Blagden, Sumner Norman, Kamyar Azizzadenesheli, Charles Liu, Mikhail G. Shapiro, Richard A. Andersen, and Anima Anandkumar

Submitted to Nature Communications [paper]

A Unified Model for Compressed Sensing MRI Across Undersampling Patterns

Armeet Singh Jatyani*, Jiayun Wang*, Aditi Chandrashekar, Zihui Wu, Miguel Liu-Schiaffini, Bahareh Tolooshams, Anima Anandkumar

CVPR 2025 [paper]

Posters/ Talks

Multi-Modal Diffusion Models to Reconstruct Dark Matter Fields

Aditi Chandrashekar, Saumya Chauhan, Eshani Patel, Maria Vazhaeparambil

In Preparation, 2025 [code]

Learning Biologically Meaningful Cellular Representations using Transformer Architectures

Aditi Chandrashekar, Rohan Gala, Andreas Tjärnberg, Saniya Khullar, Grace Huynh, Mariano Gabitto

SURF Seminar at Caltech/ ISCB-LATAM SoIBio CCBCOL, 2024 [poster, paper]

Building a Testbed for Motion Planning and Control Algorithms on a Modified RC car

Aditi Chandrashekar, John Lathrop, Ben Riviere, Soon-Jo Chung

SURF Seminar at Caltech, 2023 [paper, slides]

Feature Selection using Explainable AI to Refine Associations between Prominent Genes and Alzheimer’s Disease Neuropathology

Aditi Chandrashekar, Nicasia Beebe-Wang, Su-In Lee

SURF Seminar at Caltech, 2022 [paper]