Tanwi Mallick, PhD.

I am an assistant computer scientist in the Mathematics and Computer Science Division (MCS) at Argonne National Laboratory since October 2018. My work includes the development of scalable data-efficient machine learning methods for network congestion modeling, high-performance computing, traffic modeling, and natural language processing for Community and Infrastructure Assessment for COVID-19. Prior to Argonne, I worked as a Senior Data Scientist at General Electric. I received my Ph.D. in Computer Science from the Indian Institute of Technology, Kharagpur, India.  

My current research interest

• Spatiotemporal Graph Neural Network (GNN)
• Foundational Models
• Natural Language Processing (NLP) driven ML analytics
• Uncertainty quantification and trustworthy SciML
• Scalable data-efficient deep learning approaches
• Large-scale machine learning on high-performance computing systems

• (Active) Climate Action Through Large Language Models (CALLM): A Tool for Analyzing Climate Risk and Building Resilience
• (Active) Tachyon: Intelligent Multi-Scale Modeling of Distributed Resilient Infrastructure and Workflows for Data Intensive HEP Analyses
• (Active) Composable Digital Twins for Science Network Infrastructures using Parallel Discrete Event Simulation
• (Active) RAPIDS2:A SciDAC Institute for Computer Science, Data, and Artificial Intelligence
• (Finished) Developing Machine Learning Surrogate Models For Regional Scale Transportation Network Models
• (Finished) High-Performance Computing and Big Data Solutions for Mobility Design and Planning
• (Finished) Community and Infrastructure Adaptation to Climate Change: An AI-Driven Research Tool
• (Finished) A Demonstration Project on the Application of Artificial Intelligence for Identifying Hybrid Threats Affecting National Critical Functions in the United States and Allied Nations
• (Finished) Accelerating graph-convolution-based deep learning framework for large-scale highway traffic forecasting with SambaNova

• Received the Impact Argonne Award for enhancement of Argonne's reputation, March 2024.
• Received second place in the NeurIPS Forecast TracK: CityLearn Challenge 2023 as Team Vanguards in collaboration with Yangxinyu Xie and Ngoc Tran. The recognition includes a cash prize and co-authorship for a summary manuscript, December 2023.
• Received Impact Argonne Award in recognition of the outstanding contributions to the AI for Science training series and enhancement of Argonne's reputation by reaching out to its next generation of scientific users, January 2022.
• Travel award: 14th Women in Machine Learning (WiML) Workshop co-located with NeurIPS, Vancouver, Canada December, 2019
• Selected to participate in the 2nd Heidelberg Laureate Forum as one of the 100 most qualified Young Researchers in Computer Science to meet a number of Turing Awardees, September, 2014
• The paper "Characterization of Noise in Kinetic Depth Images: A Review" as one of the 25 most downloaded papers for April and May 2014

• One of the 7 finalist of Samsung Innovation Award 2014. In total 50 groups had participated for the award. -- News Article

• Received TCS research fellowship during Ph.D.

• Travel award from DST, Govt. of India for VISAPP 2015, and from Rajaghari Fund of IIT Kharagpur for 2nd Heidelberg Laureate Forum

• Received GATE (Graduate Aptitude Test in Engineering) scholarship duting masters

• Press release: Bumper to bumper, Can AI and supercomputers solve Californias traffic? Science Node, 25 January, 2021
• Press release: Can a national lab forecast traffic jams to prevent them in future? Federal News Network, 4 December, 2020
• Press release: On the road to efficiency, Newswise, DOE science news source, 3 June, 2019
• Feature Stories - Building a better traffic forecasting model, Argonne website, 12 November, 2020.