- Research Assistant
Praneeth Vepakomma is currently a PhD student and 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. Some of his recent works include:
Publications:
- DAMS: Meta-estimation of private sketch data structures for differentially private COVID-19 contact tracing (PDF)
- Splintering with distributions: A stochastic decoy scheme for private computation
- Assessing Disease Exposure Risk With Location Histories And Protecting Privacy: A Cryptographic Approach In Response To A Global Pandemic
- Advances and open problems in federated learning (with, 58 authors from 25 institutions!) (PDF) (2019)
- 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 Mathemat… View full description
Praneeth Vepakomma is currently a PhD student and 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. Some of his recent works include:
Publications:
- DAMS: Meta-estimation of private sketch data structures for differentially private COVID-19 contact tracing (PDF)
- Splintering with distributions: A stochastic decoy scheme for private computation
- Assessing Disease Exposure Risk With Location Histories And Protecting Privacy: A Cryptographic Approach In Response To A Global Pandemic
- Advances and open problems in federated learning (with, 58 authors from 25 institutions!) (PDF) (2019)
- 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 (Journal) (PDF)
- Combinatorics of Distance Covariance: Inclusion-Minimal Maximizers of Quasi-Concave Set Functions for Diverse Variable Selection, Discrete Applied Mathematics (Journal) (PDF)
- Reducing leakage in distributed deep learning for sensitive health data, Accepted to ICLR 2019 Workshop on AI for social good.
- 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)
- Apps gone rogue: Maintaining personal privacy in an epidemic
- 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