Split Learning Project Page: https://splitlearning.github.io/

Recent papers

(2019) Reducing leakage in distributed deep learning for sensitive health data, Praneeth Vepakomma, Otkrist Gupta, Abhimanyu Dubey, Ramesh Raskar, Accepted to ICLR 2019 Workshop on AI for social good. (Project Page for Split Learning)

(2019) Split learning for health: Distributed deep learning without sharing raw patient data, Praneeth Vepakomma, Otkrist Gupta (LendBuzz/MIT), Tristan Swedish (MIT), Ramesh Raskar (MIT),  Accepted to ICLR 2019 Workshop on AI for social good. (Project Page for Split Learning

Press Coverage: MIT Technology Review https://www.technologyreview.com/the-download/612567/a-new-ai-method-can-train-on-medical-records-without-revealing-patient-data/ 

Project Page: https://splitlearning.github.io/

Recent talk on Split Learning at Datacouncil.ai SF 2019 (Slides)

(2019) 2.) Praneeth Vepakomma and Yulia Kempner (HIT, Israel), “Diverse data selection via combinatorial quasi-concavity of distance covariance: A polynomial time global minimax algorithm”, Discrete Applied Mathematics


(2018) 3.) Praneeth Vepakomma, Chetan Tonde (Rutgers, Amazon) and Ahmed Elgammal (Rutgers) "Supervised Dimensionality Reduction via Distance Correlation Maximization”, Electronic Journal of Statistics

(2018) 4.) Sai Sri Sathya1⋆ , Praneeth Vepakomma2,3⋆⋆, Ramesh Raskar2,3 , Ranjan Ramachandra1 , and Santanu Bhattacharya (Rutgers) “A review of homomorphic encryption libraries for secure computation”, Electronic Journal of Statistics

(2016) 5.) Susovan Pal (UCLA/Ecole Polytechnique, France) and Praneeth Vepakomma, "Optimal bandwidth estimation for a fast manifold learning algorithm to detect circular structure in high-dimensional data".

(2016) 6.) Praneeth Vepakomma and Ahmed Elgammal (Rutgers) "A Fast Algorithm for Manifold Learning by Posing it as a Symmetric Diagonally Dominant Linear System", Applied and Computational Harmonic Analysis.

My Math talk at Rutgers University Math Dept: Part 1

Part 2 (Contd)                                               



Other Smaller Conference/Workshop Papers:

Iterative Embedding with Robust Correction using Feedback of Error Observed, Praneeth Vepakomma & Ahmed Elgammal at  International Conference on Machine Learning, Machine Learning for Interactive Systems Workshop, at Lille, France (ICML Workshop)

Distance Correlation Maximization using Graph Laplacians, Praneeth Vepakomma, Chetan Tonde & Ahmed Elgammal , New England Machine Learning 2014 at Microsoft Research, New England.

A-Wristocracy: Deep Learning on Wrist-worn Sensing for Recognition of User Complex Activities, Praneeth Vepakomma, Debraj De, Sajal K Das, Shekhar Bhansali 2015, IEEE Body Sensor Networks Conference, MIT Media Lab

Embedding Super-Symmetric Tensors of Higher-Order Similarities of High-Dimensional Data, Praneeth Vepakomma, Ahmed Elgammal, Tensor Methods for Machine Learning at European Conference on Machine Learning (ECML Workshop)

Tech report: 

 "Scoring Practices for Remote Sensing of Land Mines", Mathematical Problems in Industry, Duke University, Slides.

Other:

Mentored 65+ students on Springboard in data science

(Interviewed in book:) Data scientist: the definitive guide to becoming a data scientist

Consulting provided in data science to various corporate organizations

Member: Data Driven Justice Tech Consortium, Chicago 2016. 

Training/Participation in Summer Schools, training and workshops:

i) NSF-CBMS 2016 Regional Conference (Summer School) on Topological Data Analysis at University of Texas, Austin, May 31 to June 4 2016. 

ii) 2016 Mathematical Problems in Industry (MPI) interactive workshop, to be held at Duke University on June 13-17, 2016 with travel/accommodation grant. (WPI/Duke Travel Grant) 

iii) Machine learning and physical models at IPAM, UCLA, Los Angeles, 2016.  

iv) Data Science for Social Good, University of Chicago, 2016 (Motorola Solutions, Travel Sponsor) 

v) Joint Statistical Meetings, JSM 2016, Chicago. (Motorola Solutions, Travel Sponsor) 

vi) Workshop on Interface of Statistics and Optimization, SAMSI, Duke University, Feb 8-10 2017 (SAMSI Travel Grant).

vii) Functional Near-Infrared Spectroscopy Symposium, Connectivity Course: Structural and Functional Brain Connectivity via MRI and fMRI (Numerdox Travel Fund), Boston Univ. & Harvard Univ.

© Vepakomma, Praneeth 2017