Ameya Daigavane

I am a 4th year PhD student at MIT EECS in Professor Tess Smidt's group, the Atomic Architects. I am currently funded by the NSF Graduate Research Fellowship.

I am broadly interested in machine learning techniques for understanding biological systems. Most recently, I have been working on generative models for conformational ensembles of proteins and small molecules.

In 2024 and 2025, I was an intern at Prescient Design team at Genentech, where I worked on JAMUN, a new sampler for conformational ensembles of small molecules, with Joseph Kleinhenz and Bodhi P. Vani, providing an order-of-magnitude speedup over diffusion-based methods and molecular dynamics.

Previously, I was a Pre-Doctoral Researcher at Google Research India, where I worked with Prateek Jain and Gaurav Aggarwal on privacy-preserving graph neural networks.

I was a Caltech SURF Award recipient in 2019 and won the ACM SIGBED Scholars Award in 2020. At the time, I was an intern with the MLIA group at NASA Jet Propulsion Laboratory, where I worked with Kiri Wagstaff and Gary Doran on time-series algorithms to improve the responsiveness of the Plasma Instrument for Magnetic Sounding on the upcoming Europa Clipper mission.

I graduated with a B.Tech in Computer Science and Engineering (with a minor in Mathematics) from the Indian Institute of Technology, Guwahati in 2020.

I've also spent time as a volunteer at Youth4Jobs Shine where I mentored differently-abled youth on career planning and seeking employment, at English On Call where I taught English to non-native speakers, and at the MIT EECS GAAP program for helping underrepresented applicants with graduate school admissions.

JAMUN: Bridging Smoothed Molecular Dynamics and Score-Based Learning for Conformational Ensembles
Ameya Daigavane^*, Bodhi P. Vani^*, Darcy Davidson, Saeed Saremi, Joshua Rackers and Joseph Kleinhenz
NeurIPS'25
article | openreview | code | huggingface

EquiJump: Protein Dynamics Simulation via SO(3)-Equivariant Stochastic Interpolants
Allan dos Santos Costa, Ilan Mitnikov, Franco Pellegrini, Ameya Daigavane, Mario Geiger, Zhonglin Cao, Karsten Kreis, Tess Smidt, Emine Kucukbenli and Joseph Jacobson
GEM at ICLR'25
article | openreview

The Price of Freedom: Exploring Expressivity and Runtime Tradeoffs in Equivariant Tensor Products
YuQing Xie, Ameya Daigavane, Mit Kotak and Tess Smidt
ICML'25
article | openreview | code

Symphony: Symmetry-Equivariant Point-Centered Spherical Harmonics for Molecule Generation
Ameya Daigavane, Song Kim, Mario Geiger and Tess Smidt
ICLR'24
article | openreview | code

Differentially Private Graph Neural Networks for Medical Population Graphs and the Impact of the Graph Structure
Tamara T. Mueller, Maulik Chevli, Ameya Daigavane, Daniel Rueckert, and Georgios Kaissis
IEEE ISBI'24
article

Learning Integrable Dynamics with Action-Angle Networks
Ameya Daigavane, Arthur Kosmala, Miles Cranmer, Tess Smidt, and Shirley Ho
ML4PS at NeurIPS'22
article | poster | code

Unsupervised Detection of Saturn Magnetic Field Boundary Crossings from Plasma Spectrometer Data
Ameya Daigavane, Kiri Wagstaff, Gary Doran, Corey Cochrane, Caitriona Jackman, and Abigail Rymer
Computers and Geosciences
article | code

Resource Consumption and Radiation Tolerance Assessment for Data Analysis Algorithms Onboard Spacecraft
Gary Doran, Ameya Daigavane, and Kiri Wagstaff
IEEE Transactions on Aerospace and Electronic Systems
article | code

Integrating Deep Learning and Unbiased Automated High-Content Screening to Identify Complex Disease Signatures in Human Fibroblasts
Lauren Schiff, Bianca Migliori, et al
Nature Communications
article

Node-Level Differentially Private Graph Neural Networks
Ameya Daigavane, Gagan Madan, Aditya Sinha, Abhradeep Thakurta, Gaurav Aggarwal, and Prateek Jain
PAIR2Struct at ICLR'22
article | video | openreview

Understanding Convolutions on Graphs
Ameya Daigavane, Balaraman Ravindran, and Gaurav Aggarwal
Distill
article | code | reviews

Interactive Media for Understanding ML Methods: A Case-Study on Graph Neural Networks
Ameya Daigavane, Balaraman Ravindran and Gaurav Aggarwal
Rethinking ML Papers at ICLR'21
article | video | slides | openreview

Detection of Environment Transitions in Time Series Data for Responsive Science
Ameya Daigavane, Kiri Wagstaff, Gary Doran, Corey Cochrane, Caitriona Jackman and Abigail Rymer
MiLeTS at KDD'20
article | video | slides

Time-Series Analysis Methods for Onboard Detection of Magnetic Field Boundaries by Europa Clipper
Ameya Daigavane, Kiri Wagstaff, Gary Doran, Corey Cochrane, Caitriona Jackman and Abigail Rymer
Second AI and Data Science Workshop for Earth and Space Sciences
poster

2-uniform words: cycle graphs, and an algorithm to verify specific word-representations of graphs
Ameya Daigavane, Benny George and Mrityunjay Singh
Workshop on Words and Complexity
article | slides

An Introduction to (Modern) TensorFlow
Simran Khanuja and Ameya Daigavane
CVIT Summer School, IIIT Hyderabad
slides

Magnetic Field Boundaries in Cassini Plasma Spectrometer Data
Caitriona Jackman, Michelle Thomson, Michele Dougherty and Ameya Daigavane
An open-access dataset of CAPS ELS instrument observations with labelled crossing events.
zenodo | cite

Predicting Biological Activities of Molecules with Graph Neural Networks
Ameya Daigavane and Thomas Kipf
An official Flax example.
demo | source

Visualizing Graph Algorithms
Interactive visualizations of the incremental Delaunay triangulation and other graph algorithms.
demo | source