Who/Where: PhD Student at Massachusetts Institute of Technology:  
I previously spent some time at Apple (intern), Amazon, Motorola Solutions and several startups after completing my MS in Mathematical & Applied Statistics at Dept. of Statistics, Rutgers, New Brunswick. I lived/worked in Visakhapatnam, New Brunswick, New York City, Atlanta, Salt Lake City and Seattle before moving to Boston. I won a Meta (previously FB) 2022 Phd Research Fellowship. If you are looking for an official Bio, scroll all the way to the end. Twitter: @ proneat. Am advised by professors Ramesh Raskar (primary advisor) & Alex "Sandy" Pentland (secondary advisor). Will be interning at Meta this summer (May 2023).
Current Research: My focus is on algorithms for distributed scientific computation in statistics & machine learning under constraints of privacy, communication & computational efficiency. My work is inspired by foundations of non-asymptotic statistics, randomized algorithms, learning augmented algorithms, combinatorics, and at times just by systems design. Am also part of a dynamic See Below the Skin expeditions.
Teaching: (Spring 2022) TA for 6.401 (6.481) Introduction to Statistical Data Analysis, EECSby Devavrat ShahUndergrad & Grad course with problem-sets, weekly recitations by TA's, mid-term, final & office hours. Past: (Spring 2020) TA: MAS.664 AI for Impact Towards Solving Societal-Scale Problems, by my advisor Ramesh Raskar.
  • (Award) Won the FL-IJCAI'22 Best Student Paper Award for "Visual Transformer Meets CutMix for Split Learning", at the International Workshop on Trustworthy Federated Learning  in Conjunction with IJCAI 2022 (FL-IJCAI'22).
  • (Fellowship) Won the Meta (previously FB) 2022 Phd Research Fellowship (37 fellows selected out of 2300 applicants)
  • Interning at Meta, NYC on privacy+stats+ml this summer.
  • Check out our exciting work on The Privacy-Welfare Trade-off that bridges differential privacy with social choice theory over here: (PDF) 
  • (Scholarship) Selected as a SERC Scholar (Social and Ethical Responsibilities of Computing Scholar) by MIT’s Schwarzman College of Computing.
  • (Award) NoPeek-Infer won a Mukh Best Paper Runner Up Award at IEEE International Conference on Automatic Face and Gesture Recognition (FG 2021) conference.
  • (Award) FedML work won a Baidu Best Paper Award at NeurIPS 2020-SpicyFL.
  • TA/Mentor for Spring 2020/MAS.664 AI for Impact Towards Solving Societal-Scale Problems. Course series recognized on MIT News
  • Interviewed in the book, 'Data Scientist: The Definitive Guide to Becoming a Data Scientist'.
  • Work on Split Learning featured in Technology Review.
  • (Award) Extra Mile award at PublicEngines (acquired by Motorola Solutions)

Selected works:

welcome requests for high-level insight, in-depth one/two-way feedback, collaboration and invited talks (check contact me). 

29.(New!) The privacy-welfare trade-off: Effects of differential privacy on influence & welfare in social choice (PDF), Ibrahim Suat Evren, Praneeth Vepakomma, Ramesh Raskar Topic: Fourier analysis of Boolean functions, Differential Privacy, Social choice theory (2022)


28. (New!) Private measurement of nonlinear correlations between data hosted across multiple partiesPraneeth Vepakomma, Subha Nawer Pushpita, Ramesh Raskar (PDF) -Topic: Statistics, Differential Privacy (2021)



27. (New!) PrivateMail: Differentially private supervised manifold learning of deep features with privacy, @AAAI 2022, 36th AAAI Conference on  Artificial Intelligence, (AAAI 2022) (Oral), Praneeth Vepakomma, Julia Balla, Ramesh Raskar (PDF) -Topic: Differential Privacy, Privacy Preserving ML, On-device ML (2022)


26. (New!) LocFedMix-SL: Localize, Federate, and Mix for Improved Scalability, Convergence, and Latency in Split Learning, @ WWW 2022 : International World Wide Web Conference/The Web Conf(WWW 2022)Seungeun Oh, Jihong Park, Praneeth Vepakomma, Sihun Baek, Ramesh Raskar, Mehdi Bennis and Seong-Lyun Kim (PDF) Distributed ML, Split Learning  



25.(New!) An Automated Framework for Distributed Deep Learning--A Tool Demo, Gharib Gharibi, Ravi Patel, Anissa Khan, Babak Poorebrahim Gilkalaye, Praneeth Vepakomma, Ramesh Raskar, Steve Penrod, Greg Storm, Riddhiman Das Topic: Demo, @ICDCS 2022, 42nd IEEE International Conference on Distributed Computing Systems (2022)



24. DISCO: Dynamic and invariant sensitive channel obfuscation for deep neural networks@ CVPR 2021, IEEE Computer Vison and Pattern Recogniton Conference, A.Singh, A.Chopra,V.Sharma, E.Garza, E.Zhang, P.Vepakomma, R.Raskar-Main Paper: (PDF) on  -Topic: Preventing reconstruction attacks, Distributed Inference (2021)


23. (New!) NoPeek-Infer: Preventing face reconstruction attacks in distributed inference after on-premise training, @FG 2021, IEEE International Conference on Automatic Face and Gesture Recognition (IEEE FG 2021) (Oral) , Praneeth Vepakomma, Abhishek Singh, Emily Zhang, Otkrist Gupta, Ramesh Raskar, (PDF)  -Topic: Reconstruction attacks, ML (2021) (Mukh Best Paper Runner Up Award at IEEE FG 2021)



22. Supervised Dimensionality Reduction via Maximization of Distance Correlation, @Electronic Journal of Statistics(Journal),  P.Vepakomma, C. Tonde and A.Elgammal, (PDF) -Topic: Statistics, Optimization, ML 



21. FedML: A research library and benchmark for federated machine learning C He, S Li, J So, M Zhang, H Wang, X Wang, P Vepakomma, A Singh, R Raskar (PDF)  (Best Paper Award at NeurIPS 2020-SpicyFL), -Topic: Federated ML/Distributed ML/Systems (2021)



20. AirMixML: Over-the-Air Data Mixup for Inherently Privacy-Preserving Edge Machine Learning, @ GLOBECOM 2021, IEEE Global Communications Conference, Yusuke Koda, Jihong Park, Mehdi Bennis, Praneeth Vepakomma, Ramesh Raskar  (PDF)– Topic: Differential Privacy, ML, Wireless, Analog communication, Over-the-air, Early proof of concept (Early PoC) 



19. AdaSplit: Adaptive trade-offs for resource-constrained distributed deep learning, Ayush Chopra, Surya Kant Sahu, Abhishek Singh, Abhinav Java, Praneeth Vepakomma, Vivek Sharma, Ramesh Raskar (PDF) -Topic: Distributed ML/Split Learning (2022)


18. Diverse data selection via combinatorial quasi-concavity of distance covariance: A polynomial time global minimax algorithm, (PDF)Praneeth Vepakomma, Yulia Kempner, @Discrete Applied Mathematics (Journal)-Topic: Statistics, optimization (2019)


17. Advances and open problems in federated learning, (PDF)  @Foundationa & Trends in Machine Learning, Vol 14, Issue 1–2 with 58 authors from 25 institutions, (Journal), -Topic: Federated ML/Distributed ML (2021)


16. Parallel Quasi-concave set optimization: A new frontier that scales without needing submodularity, Praneeth Vepakomma, Yulia Kempner, Ramesh Raskar, SubSetML: Subset Selection in Machine Learning: From Theory to Practice @ ICML 2021 Workshop (PDF) -Topic: Distributed ML, Combinatorial statistics, Combinatorial optimization (2021)


15. D.A.M.S: Meta-estimators of private sketch data structures for differentially private COVID-19 contact tracing(PDF), P.Vepakomma, S.N. Pushpita, R. Raskar, PRIML AND PPML JOINT EDITION @ NeurIPS-2020 Workshop, -Topic: Statistics, Differential Privacy, Privacy Preserving ML  (2020)



14. Split learning for health: Distributed deep learning without sharing raw patient data(PDF) Praneeth Vepakomma, Otkrist

Gupta, Tristan Swedish, Ramesh Raskar https://arxiv.org/pdf/1812.00564.pdf, Project page: https://splitlearning.github.io/,

@ICLR 2019 Workshop on AI for social good. -Topic: Federated ML/Distributed ML, Split Learning (2018)



13. A Fast Algorithm for Manifold Learning by Posing it as a Symmetric Diagonally Dominant Linear System, (PDF)Praneeth

Vepakomma & Ahmed Elgammal, Applied and Computational Harmonic Analysis, (Journal) Topic: ML


12. Server-Side Local Gradient Averaging and Learning Rate Acceleration for Scalable Split Learning, @ FL-AAAI 2022 Workshop, Shraman Pal, Mansi Uniyal, Jihong Park, Praneeth Vepakomma, Ramesh Raskar, Mehdi Bennis, Moongu Jeon, Jinho Choi (PDF) -Topic: Distributed ML, Split Learning (2022)


11. Splintering with distributions: A stochastic decoy scheme for private computation, P Vepakomma, J Balla, R Raskar

arXiv preprint arXiv:2007.02719 -Topic: Privacy Preserving ML


10. SplitNN-driven Vertical Partitioning(PDF) I Ceballos, V Sharma, E Mugica, A Singh, A Roman, P Vepakomma, arXiv preprint

arXiv:2008.0413, -Topic: Federated ML/Distributed ML


9. Privacy in Deep Learning: A Survey(PDF) F Mirshghallah, M Taram, P Vepakomma, A Singh, R Raskar-Topic: Privacy Preserving ML


8. PPContactTracing: A Privacy-Preserving Contact Tracing Protocol for COVID-19 Pandemic, (PDF) P Singh, A Singh, G

Cojocaru, P Vepakomma, R Raskar arXiv preprint arXiv:2008.06648-Topic: Cryptographic Tools


7. Assessing Disease Exposure Risk with Location Histories and Protecting Privacy: A Cryptographic Approach in

Response to A Global Pandemic, (PDF) A Berke, M Bakker, P Vepakomma, R Raskar, K Larson, AS Pentland-Topic: Cryptographic Tools


6. ExpertMatcher: Automating ML Model Selection for Clients using Hidden Representations, (PDF) Vivek Sharma, Praneeth

Vepakomma, Tristan Swedish, Ken Chang, Jayashree Kalpathy-Cramer, and Ramesh Raskar, NeurIPS Workshop on

Robust AI in Financial Services: Data, Fairness, Explainability, Trustworthiness, and Privacy, -Topic: Federated ML/Distributed ML


5. A Review of Homomorphic Encryption Libraries for Secure Computation, (PDF) Sai Sri Sathya, Praneeth Vepakomma,

Ramesh Raskar, Ranjan Ramachandra, and Santanu Bhattacharya,  -Topic: Cryptographic Tools


4. Optimal bandwidth estimation for a fast manifold learning algorithm to detect circular structure in highdimensional

data, (PDF) Praneeth Vepakomma and Susovan Pal, Topic: ML


3. Scoring practices for remote sensing of land mines (PDF), Praneeth Vepakomma, Adel Al Weshah, Aaron Bardall, Valeria

Barra, Dean Duffy, Hamza Ghadgali, Sarafa Iyaniwura , Hangjie Ji, Qingxia Li, Richard Moore, Kenneth Morton, Ryan

Pellico, Christina Selby, Razvan Stefanescu, Melissa Strait, Zhe Wang, Andres Vargas, Mathematical Problems in

Industry, Duke University-Department of Mathematics, -Topic: ML


2. A-Wristocracy: Deep Learning on Wrist-worn Sensing for Recognition of User Complex Activities, (PDF)Praneeth

Vepakomma, Debraj De, Sajal K. Das & Shekhar Bhansali at IEEE Body Sensor Networks Conference, at MIT Media Lab,

Cambridge, IEEE-BSN 2015, -Topic: ML, Wearables, Hardware/Software Integration


1. Split Learning on FPGA’s and FPIA’s: The first Field Programmable Imaging Arrays, Hannah Whisnant, Praneeth

Vepakomma, Ramesh Raskar, -Topic: Federated ML, On-Device ML, Hardware/Software Integration

...more on Google Scholar.


Community Service: 

  • Volunteered and conducted interactive demos for students at Shiloh Point Elementary School, Cumming, GA (Atlanta) for the Family Science Night.
  • Service at homeless shelter in Salt Lake City, UT with PublicEngines. Helped with sorting of donation material (non-monetary). 

Bio: Praneeth Vepakomma is currently a PhD student. His main research page is @ https://praneeth.mit.edu. His research focuses on developing algorithms for distributed computation in statistics & machine learning under constraints of privacy, communication & efficiency. His technical work is inspired by foundations of non-asymptotic statistics, randomized algorithms, learning augmented algorithms, combinatorics, and at times just by systems design. He won the Meta PhD research fellowship. He has been selected as a SERC Scholar (Social and Ethical Responsibilities of Computing Scholar) by MIT’s Schwarzman College of Computing.  He won the Best Student Paper Award for "Visual Transformer Meets CutMix for Improved Accuracy, Communication Efficiency, and Data Privacy in Split Learning", at FL-IJCAI'22, a Baidu Best Paper Award at NeurIPS 2020-SpicyFL for his work on FedML and his work on NoPeek-Infer won the Mukh Best Paper Award at IEEE FG-2021. He was Interviewed in the book, 'Data Scientist: The Definitive Guide to Becoming a Data Scientist'. His work on Split Learning featured in Technology Review. He has organized workshops on Distributed & Private ML at ICLR, Federated Learning at ICML,  and Split Learning at SLDML/MIT. He has given a tutorial on the same at CVPR. He was previously a scientist at Apple (intern), Amazon, Motorola Solutions, PublicEngines, Corning (intern) and various startups, all of which were eventually acquired.