Sandeep Madireddy’s research interests span the broader areas of probabilistic machine/deep learning, uncertainty quantification, and high-performance computing. Specifically, it is currently focused on developing machine/deep learning approaches to address key challenges in scientific data analysis across science and engineering application domains as well as performance modeling of leadership-class systems.
Before joining Argonne, he obtained his Ph.D. in mechanical and materials engineering from the University of Cincinnati, as part of the UC Simulation center (a UC Engineering and 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.
- Bayesian approaches to probabilistic machine learning and deep learning
- Performance modeling of complex distributed systems
- Application of machine and deep learning techniques to address challenges in physical sciences