I'm a Pre-Doctoral Researcher at Google Research India, working with Prateek Jain and Gaurav Aggarwal towards privacy-preserving graph neural networks. I'm broadly interested in developing machine learning models for scientific applications.

I was a Caltech SURF Award recipient in 2019 and won the ACM SIGBED Scholars Award in 2020.

Previously, 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.

Much of my code is open-source: I've especially contributed to TensorFlow Datasets and Flax.

I've also spent time as a volunteer at Youth4Jobs Shine where I mentored differently-abled youth on career planning and employment tips, and at English On Call where I taught English to non-native speakers.

Feel free to look at my CV!


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

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

Understanding Convolutions on Graphs
Ameya Daigavane, Balaraman Ravindran and Gaurav Aggarwal
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, 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, KDD'20 | 6th Workshop on Mining and Learning from Time Series
article | video | slides

Talks and Presentations

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

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, Villeurbanne, France
article | abstract | slides

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


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

Google Research
2020 - Present
Indian Institute of Technology, Guwahati
2016 - 2020
NASA Jet Propulsion Laboratory
Summers of 2019 and 2020
Indian Institute of Science
Summer of 2018
Indian Institute of Technology, Gandhinagar
Summer of 2017