学术讲座

报告题目：A pan-disciplinary view of distributed & private computation: Statistics, Geometry, ML & Social Choice

讲座时间：2022年11月30日（周三）20:00-21:00

ZOOM会议：718-745-5496

报告摘要

Data in today's world is increasingly siloed across a wide variety of entities with varying resource constraints in collaboratively processing such data in order to draw actionable insights and wisdom. The quality of such wisdom is substantially better if such data is centralized at one spot prior to processing it, but this is prohibited due to privacy regulations, computational constraints, communication constraints, trade-secrets, trust issues and competition. This necessitates the development of distributed algorithms that are resource efficient for the entities involved while also preserving the privacy of this data and still ensure that the quality of wisdom obtained is on par with the case of data centralization. This talk covers some novel methods for the same in a pan-disciplinary manner tackling upstream problems with view-points in statistics, geometry, machine learning and social choice. Upstream problems are those root problems which when solved result in feeding into the solutions for several downstream problems leading to multi-pronged downstream impact.

主讲嘉宾

Praneeth Vepakomma is currently a PhD student at MIT.  His research focuses on developing algorithms for distributed scientific computation & ML under constraints of privacy, communication & efficiency.  He won the Meta (previously FB) 2022 Phd Research Fellowship in Applied Statistics. He has been selected as a SERC Scholar (Social and Ethical Responsibilities of Computing Scholar) by MIT’s Schwarzman College of Computing. His FedML work won a Best Paper Award at NeurIPS 2020-SpicyFL and his work on NoPeek-Infer won a Best Paper Runner Up Award at FG-2021 and the FL-IJCAI'22 Best Student Paper Award for "Visual Transformer Meets CutMix for Split Learning", at the International Workshop on Trustworthy Federated Learning in Conjunction with IJCAI 2022 (FL-IJCAI'22).  He was previously a scientist at Apple (intern), Amazon, Motorola Solutions, PublicEngines, Corning (intern) and various startups, all of which were eventually acquired. He holds an MS in Mathematical & Applied Statistics from Rutgers University, New Brunswick.