For the past one decade I have been developing expertise in energy modeling, nowcasting, remote sensing, and machine learning with the goal to make energy systems more sustainable and carbon efficient. My current research uses self-supervised learning to develop AI foundation models to better leverage the spatio-temporal information in meteorological data to address problems related to sustainable energy, climate change, and social good.
Since Jan'22, I am a Machine Learning Scientist (Postdoctoral Fellow) at CIRA CSU and NSF AI2ES hosted by Dr. Imme Ebert-Uphoff . I earned my Ph.D. in Computer Engineering from the University of Massachusetts, Amherst, where I was advised by Prof. David Irwin. My doctoral work focuses on using ML and satellite imagery for control, modeling and, forecasting solar power. Prior to that, I have an MS in Information Technology from IIIT-Bangalore, and B.Tech in Computer Science. I have spent one year at NTU Singapore researching real-time operating systems before starting Ph.D. I have also interned at the GeoAI group at Oak Ridge National Lab during my Ph.D.
Research Intern - GeoAI group @Oak Ridge National Labs, Tennessee, June-August 2021
Research Assistant (Pre-Doc) - School of Computer Science & Engineering - NTU, Singapore, 2014-2015
Ph.D. in Computer Engineering, University of Massachusetts Amherst, Sep. 2015 - Oct. 2021
M.S. by Research in Information Technology, IIIT- Bangalore (India), 2011-2014
B.Tech in Computer Science, MDU Rohtak (India), 2007-2011
Released our latest work "Tools for Extracting Spatio-Temporal Patterns in Meteorological Image Sequences: From Feature Engineering to Attention-Based Neural Networks" on arXiv
Presenting two talks from the recent work at the 103rd AMS Annual Meeting in January'23
Artificial Intelligence for Low-Level Moisture from GOES-R Series
A Primer on Neural Network Architectures to Extract Information from Meteorological Image Sequences
Speaking at the Satellite Data for Energy Analysis and Policy at University of Wisconsin - Madison in October'22
Speaking at the Transformers for Environmental Science (ESST) Workshop in September'22
Join us at the NOAA 4th Workshop on Leveraging AI in Environmental Sciences in September'22
Our paper "A Moment in the Sun: Solar Nowcasting from Multispectral Satellite Data using Self-Supervised Learning" got accepted in ACM e-Energy'22 conference. Catch the virtual talk on 29th June'22 at e-Energy'22
Just released the final chapter of my Ph.D. thesis in our new paper
I successfully passed my Ph.D. thesis defense on 18th October'21 !! Yayyyyyy
Our paper "Self-Supervised Learning on Multispectral Satellite Data for Near-Term Solar Forecasting" got accepted in ICML Workshop on Tackling Climate Change with Machine Learning'21.
Super excited to be spending the Summer'21 researching at the GeoAI group, Oak Ridge National Labs!
I won third prize in the ECE 3-Minute Thesis (3MT)'21 Competition at UMass Amherst.
Serving as TPC member for FATEsys'21.
I passed my Ph.D. Dissertation Proposal titled, "Data-Driven Control, Modeling, and Forecasting for Residential Solar Power", at UMass Amherst.
Our Paper "See the Light: Modeling Solar Performance using Multispectral Satellite Data", received honorable mention under best presentation award in ACM Buildsys'20
Our Paper "See the Light: Modeling Solar Performance using Multispectral Satellite Data", Akansha Singh Bansal and David Irwin got accepted in ACM Buildsys'20.
Our Paper "Exploiting Satellite Data for Solar Performance Modeling", Akansha Singh Bansal and David Irwin got accepted in IEEE Smartgridcomm'20.
I am teaching an undergraduate first-year seminar course at UMass Amherst this Fall'2020 titled - "Staring at the Sun: An Introduction to Solar Energy".
I am attending Grace Hopper Celebration (GHC'19) at Orlando, Florida - (October 1 - October 4' 2019).
I will be presenting our paper (On the Feasibility, Cost, and Carbon Emissions of Grid Defection) at IEEE Smartgridcomm'19 in Beijing, China - (October 20 - October 24' 2019).