Praneeth Vepakomma

Camera Culture
  • 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 Apple, Amazon, Motorola Solutions, PublicEngines and at various startups all of which were eventually acquired. He will be interning at a startup, this summer.  Some of his recent works include: 

 Project pagehttps://splitlearning.github.io/

Publications:

  1. Splintering with distributions: A stochastic decoy scheme for private computation
  2. Assessing Disease Exposure Risk With Location Histories And Protecting Privacy: A Cryptographic Approach In Response To A Global Pandemic
  3. Advances and open problems in federated learning (with, 58 authors from 25 institutions!) (PDF) (2019)
  4. Split learning for health: Distributed deep learning without sharing raw patient data, (PDF) (Project page)
  5. No Peek: A Survey of private distributed deep learning (PDF)
  6. Supervised Dimensionality Reduction via Distance Correlation Maximization, Electronic Journal of Statistics, volume 12 No.1, Pages 960--984, The Ins… 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 Apple, Amazon, Motorola Solutions, PublicEngines and at various startups all of which were eventually acquired. He will be interning at a startup, this summer.  Some of his recent works include: 

 Project pagehttps://splitlearning.github.io/

Publications:

  1. Splintering with distributions: A stochastic decoy scheme for private computation
  2. Assessing Disease Exposure Risk With Location Histories And Protecting Privacy: A Cryptographic Approach In Response To A Global Pandemic
  3. Advances and open problems in federated learning (with, 58 authors from 25 institutions!) (PDF) (2019)
  4. Split learning for health: Distributed deep learning without sharing raw patient data, (PDF) (Project page)
  5. No Peek: A Survey of private distributed deep learning (PDF)
  6. 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 (Journal) (PDF)
  7. Combinatorics of Distance Covariance: Inclusion-Minimal Maximizers of Quasi-Concave Set Functions for Diverse Variable Selection,  Discrete Applied Mathematics (Journal)  (PDF)
  8. Reducing leakage in distributed deep learning for sensitive health data, Accepted to ICLR 2019 Workshop on AI for social good. 
  9. Optimal bandwidth estimation for a fast manifold learning algorithm to detect circular structure in high-dimensional data (PDF)
  10. A Fast Algorithm for Manifold Learning by Posing it as a Symmetric Diagonally Dominant Linear System , Applied and Computational Harmonic Analysis (PDF)
  11. A-Wristocracy: Deep Learning on Wrist-worn Sensing for Recognition of User Complex Activities (PDF)
  12. Apps gone rogue: Maintaining personal privacy in an epidemic
  13. A Review of Homomorphic Encryption Libraries for Secure Computation (PDF)

Invited Talk at  Workshop on Federated Learning and Analytics at IBM Thomas J Watson Research Center, Feb 2020 https://federated-learning.bitbucket.io/ibm2020/ 

CVPR 2019 Tutorial on “Distributed Private Machine Learning for Computer Vision: Federated Learning, Split Learning and Beyond”.

We gave a half-day tutorial at CVPR 2019: On Distributed Private Machine Learning for Computer Vision: Federated Learning, Split Learning and Beyond