Scholar
Praneeth Vepakomma
Google Scholar ID: T_mPgZIAAAAJ
Massachusetts Institute of Technology, MBZUAI
Citations & Impact
Citations
12,860
H-index
20
i10-index
31
Publications
20
Co-authors
16
list available
Contact
Publications
Resume (English only)
Academic Achievements
- Papers: FedML won the Baidu Best Paper Award at NeurIPS 2020-SpicyFL; NoPeek-Infer won the Mukh Best Paper Runner Up Award at IEEE FG 2021; “Visual Transformer Meets CutMix for Split Learning” won the FL-IJCAI’22 Best Student Paper Award at IJCAI 2022. Awards: Meta 2022 PhD Research Fellowship; OpenDP Academic Fellow; ADIA Lab (Abu Dhabi Investment Authority Lab) Fellow; SERC Scholar; Fatima Fellowship Mentor; Financial Times Digital Innovation Award; Extra Mile Award at PublicEngines. Other Achievements: Featured on MIT News; Mentioned on MIT Technology Review.
Research Experience
- Currently a Visiting Assistant Professor at MIT IDSS and an Assistant Professor at MBZUAI. Previously worked for 9 years in industry at Amazon (AWS), Motorola Solutions, several startups, Apple (intern), Meta (intern), and Corning (Statistical Engineering Co-Op).
Education
- PhD: Massachusetts Institute of Technology (MIT), with a major focus on trustworthy/responsible and collaborative ML; Advisor: Ramesh Raskar. MS: Department of Statistics, Rutgers, New Brunswick, Mathematical & Applied Statistics.
Background
- Research Interests: trustworthy/responsible and collaborative ML. Professional Focus: extremely-efficient LLM fine-tuning, collaborative LLM fine-tuning, collaborative machine learning, decentralized learning, federated learning and its variants, differential privacy, and data/model markets. Overview: Aiming to harness collaborative and trustworthy intelligence from networks of organizations and people in data-driven economies while achieving scale and maintaining ethics.
Miscellany
- Interests: Involved in teaching activities such as courses on Privacy and Fairness, Trustworthy Machine Learning, and more. Social Media: Twitter: @proneat.
