Sandeep Madireddy is an Assistant Computer Scientist in the Mathematics and Computer Science Division at Argonne National Laboratory. His research interests include machine learning, probabilistic modeling and high performance computing, with applications across science and engineering. His current research aims at developing deep learning algorithms and architectures tailored for scientific machine learning, with a particular focus on improving training efficiency, model robustness, uncertainty quantification and feature representation learning. He has experience applying these approaches to address diverse problems in various domains, ranging from physical sciences (material science, high energy physics, climate science) to computer systems modeling and neuromorphic computing.
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
- Deep latent-variable models for representation learning and surrogate modeling
- Neuromorphic computing and bio-inspired learning
- Machine learning-based performance modeling of complex distributed systems
- Dynamical system view of deep learning