Sandeep Madireddy — Computer Scientist

About

📋 Biography

Sandeep Madireddy is a Computer Scientist in the Mathematics and Computer Science Division at Argonne National Laboratory. His research spans across the areas of probabilistic machine learning, bio-inspired and energy-efficient learning, high-performance computing and generative AI with an emphasis on safety and robustness. His research integrates algorithmic research in these areas with applied research aimed to advance scientific discovery in critical areas such as fusion energy sciences, cosmology and high-energy physics, weather and climate, and material science.

He previously was a postdoc and assistant computer scientist in Mathematics and Computer Science Division advised by Prasanna Balaprakash and Stefan Wild. Before joining Argonne, he obtained his Ph.D. in mechanical and materials engineering (focusing on Probabilistic machine Learning) from the University of Cincinnati, as part of the UC Simulation center (a Procter & Gamble Collaboration). Before that, he obtained his masters from Utah State University and bachelors from Birla Institute of Technology and Science (BITS-Pilani) in India.

🔬 Projects & Grants

Sandeep serves as a Co-investigator (and AI lead) for DOE and NSF-funded projects:

ModCon/Genesis Mission, Lead for Multimodal Scientific AI Reasoning Models BASE Capability Thrust
AuroraGPT, Co-lead for Evaluation and AI-Safety Thrust
RAPIDS3: A SciDAC Institute for Computer Science, Data, and Artificial Intelligence, AI Thrust co-lead
CETOP: A Center for Edge of Tokamak OPtimization, Co-investigator and AI Lead

📝 Professional Service

Sandeep also provides professional services to various machine learning, high performance computing and domain science conferences and journals.

PC member (HPC Conferences) (Super Computing 2023, 2026, CCGRID 2024, INFOCOMP 2019-23, HPCC 2017); Reviewer: IPDPS, IEEE Cluster
Reviewer (AI Conferences): ICML (2021-24); ICLR (2021-24); NeurIPS (2021-25), AISTATS (23,25)
Journals: ML/AI (Nature,Neural Networks, NeuroComputing, JMLMC, SIAM SISC); HPC (JPDC, TPDS, Parallel Computing, TCC, JoS); Domain Science: (MNRAS, CISE, IEEE TPSC, JoLT)

Research Areas

Probabilistic Machine Learning

Research at the intersection of Bayesian Inference and Information-Theoretic learning.

Scientific Machine Learning and AI for Science

Theoretical and Applied Machine learning for Large-scale Science including Fusion Energy, Astrophysics, HPC, and Climate Science

Multi-modal Foundation Models

Theoretical and Applied research on developing and evaluating (for skill and safety) Multimodal Language Models as scientific research assistants and Spatio-Temporal Foundation models for science.

Energy Efficient AI

Energy-efficient AI with Bio-inspired architectures such as Neuromorphics for large scale AI models and life-long learning in this paradigm.

News & Announcements

Oct 1, 2025

(New Funding) The Transformational AI Models Consortium (ModCon)

Senior Personnel (ANL), Lab 25-3560, (Lead PI: Rick Stevens, Argonne National Laboratory), FY26-FY28

Oct 1, 2025

(New Funding) Multimodal AI Foundation Modeling of Turbulent, Multiphase, and Reacting Flows for Propulsion and Power Applications

Co-PI (ANL), Foundational Models for Energy Applications, LDRD Prime Focus Area, (Lead-PI: Pinaki Pal, Argonne National Laboratory), FY26-FY28

Oct 1, 2025

(New Funding) The RAPIDS3 Institute for Artificial Intelligence, Computer Science, and Data

Co-PI (ANL), LAB 25-3510.000002, (Lead PI: Robert Ross, Argonne National Laboratory), FY26-FY31

Oct 1, 2025

(New Funding) Supply Chain Optimizer for Understanding Trends in Critical Minerals (SCOUT-CM)

Co-PI (ANL), Critical Materials and Supply Chains, LDRD Prime Focus Area, (Lead-PI: Barron YoungSmith, Argonne National Laboratory), FY26-FY28

Dec 1, 2024

(New Funding) BIA A CoDesign Methodology to Transform Materials and Computer Architecture Research for Energy Efficiency

Microelectronics Science Research Center Projects for Energy Efficiency and Extreme Environments (LAB 24-3320), (Lead PI: Valerie Taylor, Argonne National Laboratory), FY25-FY27 https://www.energy.gov/sites/default/files/2024-12/122324-msrc-awards-list.pdf