About

I am a research scientist specializing in applied machine learning for large scientific datasets (such as satellite observations for Earth and Space Sciences) in NASA Science and Implementation teams.

My projects are primarily centered around unsupervised and self-supervised learning, change and anomaly detection, time-series analysis, geospatial foundation models, and generating analysis ready datasets using AI/ML. These translate to a variety of research applications related to time-series analysis of high-dimensional Earth and Space Science datasets for detecting, understanding and forecasting changes, natural hazards, extracting rare class signals for scientific discovery.

I am a NASA Black Marble Science Team member and a large part of my work is currently focused on tailoring machine learning algorithms as the PI for studying the Earth at Night. The derived inferences have a broad variety of applications ranging from monitoring nighttime clouds, fires, gas flaring, volcanoes to studying urban areas for tracking urbanization, power outages and disaster impact, conflicts, and electricity access and reliability to accelerate downstream analysis. I am also a NASA FireSense Implementation team PI member for AI/ML applications for wildfire management. I am a Co-I in multi-institution collaborations for developing foundation models for satellite data in geospatial imagery for land and weather applications and red-teaming the downstream use cases for operationalization.

I am also a research associate in AI Governance at the Future Impact Group, working on Open Source AI Governance. I also serve as the Secretary and Working Group Member with IEEE Standards Association on Environmental Impact of AI. I was in the Science Policy cohort (2024-2025) for AGU’s Voices for Science program. I served as a volunteer with the NASA SMD AI/ML working group, conducting surveys, hosting workshops, co-convened yearly meetings at the American Geophysical Union Fall Meeting centered around cross-divisional applications of machine learning from 2020-2024 and contributed to working group reports. I also led the Earth Science use case for generating training datasets to improve machine learning algorithms. I also served as a technical co-lead in the IEEE Geoscience and Remote Sensing Image Analysis and Data Fusion Working Group from 2021-2023.

Prior to this I was a NASA Postdoctoral Fellow at Goddard Space Flight Center and USRA, working with the Black Marble Science Team. I received a PhD in Computer Engineering with Profs. Antonia Papandreou-Suppappola and Philip Christensen at Arizona State University focusing on modeling satellite image time-series for change and novelty detection in Earth and Planetary observations.

Broadly, I am interested in ongoing advances in AI/ML to derive insights from large datasets and accelerate analyses, and at the interface between AI and Policy.

Contact: srija[dot]c[dot]chakraborty[at]gmail[dot]com