David Froelicher

David Froelicher

Postdoctoral researcher

Massachusetts Institute of Technology & The Broad Institute of MIT and Harvard

Hi, I am a postdoctoral researcher working with Prof. B. Berger in the Computer Science and Artificial Intelligence Laboratory (CSAIL) at the Massachusetts Institute of Technology (MIT) and with Dr. H. Cho at the Broad Institute of MIT and Harvard. I am currently working on privacy-preserving federated analytics and genomic privacy. I am designing new secure and distributed solutions by building on top of applied cryptography, using homomorphic encryption and secure multiparty computation.

I received my PhD from EPFL for my work with Prof. J-P. Hubaux at the Laboratory for Data Security (LDS) and B. Ford at the Decentralized and Distributed Systems Laboratory (DeDiS). I earned my MSc and BSc in Computer Science with a specialisation in IT Security from EPFL in 2016. In 2015, I did a master thesis internship in the NEC research laboratory in Heidelberg, Germany, where I have been involved in the design and implementation of a system enabling proofs of retrievability on deduplicated data.

  • dfroelic@mit.edu

  • G-574 CSAIL MIT, 77 Massachusetts Avenue, Cambridge 02139, MA, United States

  • Ph.D. in Computer Science, 2016-2021

    École Polytechnique Fédérale de Lausanne (EPFL)

  • Master of Science in Communication Systems, 2016

    École Polytechnique Fédérale de Lausanne (EPFL)

  • Bachelor of Science in Communication Systems, 2014

    École Polytechnique Fédérale de Lausanne (EPFL)









January 2021 – Present California

Responsibilities include:

  • Analysing
  • Modelling
  • Deploying
University X
Professor of Semiconductor Physics
University X
January 2016 – December 2020 California
Taught electronic engineering and researched semiconductor physics.


Neural Networks and Deep Learning
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Formulated informed blockchain models, hypotheses, and use cases.
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Object-Oriented Programming in R
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Recent Publications

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(2023). Secure and Federated Genome-Wide Association Studies for Biobank-Scale Datasets. Under review.

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(2023). Scalable and Privacy-Preserving Federated Principal Component Analysis. IEEE Symposium on Security & Privacy 2023.

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(2023). sfkit: A Web-Based Toolkit for Secure and Federated Genomic Analysis. Web Server issue of Nucleic Acids Research.


(2021). Truly Privacy-Preserving Federated Analytics for Precision Medicine with Multiparty Homomorphic Encryption. Nature Communications.

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(2021). POSEIDON: Privacy-Preserving Federated Neural Network Learning”. NDSS 2021.

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