Bio: Saibal De is a 5th year PhD candidate in Applied and Interdisciplinary Mathematics. His research involves using high-performance computing and novel algorithms for accelerating physics-based simulation frameworks, and developing faithful reduced-order models of black-box high-fidelity simulations.

TENSOR METHODS FOR DATA COMPRESSION: With the advancement of computing software and hardware, physics-based simulations have gained notoriety in many scientific and industrial applications due to their highly accurate prediction capabilities. However, in addition to being computationally expensive, even a single of these high-fidelity simulations produce massive amounts of data. Storing and processing all these data thus requires novel approaches. In this talk, I will present how we can use tensor factorization methods for compressing scientific data, leading to dramatic savings in disk-space usage. Towards the end of the talk, I’ll also touch upon how we can potentially construct reduced-order models out of these compressed datasets.

This event is part of MICDE’s Winter 2021 seminar series featuring Ph.D. students in the Scientific Computing program. This series is open to all. University of Michigan faculty and students interested in computational and data sciences are encouraged to attend.

Questions? Email [email protected]