- Research Assistant
Praneeth Vepakomma is currently a researcher in MIT's Camera Culture group where his focus is on developing algorithms to support distributed and collaborative machine learning. He was previously a scientist at Amazon, Motorola Solutions, PublicEngines where he developed predictive and smart policing solutions for the law enforcement and at various startups all of which were successfully acquired. Some of his recent works include:
Recent talk on Split Learning at Datacouncil.ai SF 2019 (Slides)
Upcoming: CVPR 2019 Tutorial on “Distributed Private Machine Learning for Computer Vision: Federated Learning, Split Learning and Beyond”.
We are giving a half-day tutorial at CVPR 2019: On Distributed Private Machine Learning for Computer Vision: Federated Learning, Split Learning and Beyond by Brendan McMahan (Google, USA), Jakub Konečný (Google, USA), Otkrist Gupta (LendBuzz), Ramesh Raskar (MIT Media Lab, Cambridge, Massachusetts, USA), Hassan Takabi (University of North Texas, Texas, USA) and Praneeth Vepakomma (MIT Media Lab, Cambridge, Massachusetts, USA).&nb… View full description
Praneeth Vepakomma is currently a researcher in MIT's Camera Culture group where his focus is on developing algorithms to support distributed and collaborative machine learning. He was previously a scientist at Amazon, Motorola Solutions, PublicEngines where he developed predictive and smart policing solutions for the law enforcement and at various startups all of which were successfully acquired. Some of his recent works include:
Recent talk on Split Learning at Datacouncil.ai SF 2019 (Slides)
Upcoming: CVPR 2019 Tutorial on “Distributed Private Machine Learning for Computer Vision: Federated Learning, Split Learning and Beyond”.
We are giving a half-day tutorial at CVPR 2019: On Distributed Private Machine Learning for Computer Vision: Federated Learning, Split Learning and Beyond by Brendan McMahan (Google, USA), Jakub Konečný (Google, USA), Otkrist Gupta (LendBuzz), Ramesh Raskar (MIT Media Lab, Cambridge, Massachusetts, USA), Hassan Takabi (University of North Texas, Texas, USA) and Praneeth Vepakomma (MIT Media Lab, Cambridge, Massachusetts, USA).
Reducing leakage in distributed deep learning for sensitive health data, Accepted to ICLR 2019 Workshop on AI for social good.
(Project page)- Split learning for health: Distributed deep learning without sharing raw patient data, (PDF) (Project page)
- No Peek: A Survey of private distributed deep learning (PDF)
- Supervised Dimensionality Reduction via Distance Correlation Maximization, Electronic Journal of Statistics, volume 12 No.1, Pages 960--984, The Institute of Mathematical Statistics and the Bernoulli Society, 2018 (PDF)
- Combinatorics of Distance Covariance: Inclusion-Minimal Maximizers of Quasi-Concave Set Functions for Diverse Variable Selection (PDF)
- Optimal bandwidth estimation for a fast manifold learning algorithm to detect circular structure in high-dimensional data (PDF)
- A Fast Algorithm for Manifold Learning by Posing it as a Symmetric Diagonally Dominant Linear System , Applied and Computational Harmonic Analysis (PDF)
- A-Wristocracy: Deep Learning on Wrist-worn Sensing for Recognition of User Complex Activities (PDF)
- A Review of Homomorphic Encryption Libraries for Secure Computation (PDF)