- Check out our exciting work on The Privacy-Welfare Trade-off that bridges differential privacy with social choice theory over here: (PDF)
- Selected as a (Social and Ethical Responsibilities of Computing Scholar) by MIT’s Schwarzman College of Computing.
- NoPeek-Infer won a Mukh Best Paper Runner Up Award at IEEE International Conference on Automatic Face and Gesture Recognition (FG 2021) conference.
- 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'.
- Recognized in FB fellowship program under the "Security and privacy" category in 2021 as a finalist.
- Work on Split Learning featured in Technology Review.
- Extra Mile award at PublicEngines (acquired by Motorola Solutions)
I welcome requests for high-level insight, in-depth one/two-way feedback, collaboration and invited talks (check contact me).
28. (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)
27. (New!) PrivateMail: Differentially private supervised manifold learning of deep features with privacy, @AAAI 2022, 36th AAAI Conference on Artificial Intelligence, (AAAI 2022), 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, Distributed ML, Split Learning
25. (New!) Private measurement of nonlinear correlations between data hosted across multiple parties, Praneeth Vepakomma, Subha Nawer Pushpita, Ramesh Raskar (PDF) -Topic: Statistics, Differential Privacy (2021)
24. 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)
23. 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)
22. (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), 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)
21. 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
20. 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)
19. 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)
18. 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)
17. 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)
16. 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)
15. 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)
14. 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)
13. Split learning for health: Distributed deep learning without sharing raw patient data, (PDF) Praneeth Vepakomma, Otkrist
@ICLR 2019 Workshop on AI for social good. -Topic: Federated ML/Distributed ML, Split Learning (2018)
12. 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
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
- Organized: ICML International Workshop on Federated Learning for User Privacy and Data Confidentiality, 2021
- Keynote Speakers: Sebastian U. Stich, Nic Lane, Ramesh Raskar, Ameet Talwalkar, Filip Hanzely, Dimitris Papailiopoulos and Salman Avestimehr
- Organized: ICLR Workshop on Distributed and Private Machine Learning (DPML, 2021).
- Keynote Speakers: David Evans, Lalitha Sankar, Gauri Joshi & Graham Cormode.
- CVPR Tutorial On Distributed Private Machine Learning for Computer Vision: Federated Learning, Split Learning and Beyond, 2019
- Tutorial Speakers: Brendan McMahan, Jakub Konečný, Ramesh Raskar, Praneeth Vepakomma, Otkrist Gupta, Hassan Takabi
- Organized: Workshop on Split Learning for Distributed Machine Learning (SLDML’21).
- Keynote Speakers: Peter Kairouz, OpenMined, Geeta Chauhan and Supriyo Chakraborty.
- 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 has been selected as a SERC Scholar (Social and Ethical Responsibilities of Computing Scholar) by MIT’s Schwarzman College of Computing. He won a Baidu Best Paper Award at NeurIPS 2020-SpicyFL for his work on FedML. 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.