Senior Scientist, Cryptography
Ahmad Al Badawi, Ph.D.
I build the cryptography that lets AI run on data it never gets to see. Co-author of PALISADE/OpenFHE, technical lead on the DARPA DPRIVE 12 nm FHE accelerator, and principal investigator on private LLM inference under fully homomorphic encryption.
Now Senior Cryptography Scientist at Duality Technologies.
- 50+ Peer-reviewed papers
- 2,300+ Citations
- 20 h-index
- 27 i10-index
- $15M DARPA 12 nm FHE ASIC, Tech Lead
- $22M+ Funded research
- EB-1A · O-1A Extraordinary Ability
Past & Present Affiliations
Recent publications
Cryptology ePrintPreprint SoK: Private LLM Inference using Approximate Homomorphic Encryption
IACR CiCJournal Application-Aware Approximate Homomorphic Encryption: Configuring FHE for Practical Use
IJRESJournal Hardware Design for Fast Gate Bootstrapping in Fully Homomorphic Encryption over the Torus
Recent writing
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The Math of Slot Rotation in BGV and BFV
Ahmad Al Badawi
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SIMD Packing in BGV/BFV FHE Schemes
Ahmad Al Badawi
Recent talks
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OpenFHE: A Community-Driven Open-Source Project for FHE
I had the pleasure of participating in the NUMFOCUS Project Summit 2024, which was held at the Microsoft New England Research & Development (NERD) Center, Cambridge, MA, United States, to share...
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FHEllo, world! FHE Community Workshop
I had the pleasure of participating in the FHEllo World! workshop, a community-oriented online event focused on Fully Homomorphic Encryption (FHE). During the workshop, I presented a talk on the Op...
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enCRYPTON: Summer School on Cryptographic Solutions for Privacy Enhancing Technologies
I participated in this workshop as an instructor and gave five lectures on the following topics:
About
I run research on fully homomorphic encryption and privacy-preserving AI, with a focus on what it takes to deploy them at production scale. My contributions include PALISADE (now OpenFHE), a co-authored open-source FHE library now used across industry and academia; the first GPU homomorphic CNN (HCNN); the RNS variants of BFV that became standard building blocks in modern FHE libraries; and the technical leadership of DPRIVE, a $15M DARPA program that delivered a 1 GHz, 12 nm FHE accelerator from concept to tape-out-ready RTL.
I hold a Ph.D. in Electrical and Computer Engineering from the National University of Singapore. I have built and led research teams in industry, government, and academic settings, and I currently work on making large language model inference run under FHE with practical latency.
I am open to research collaboration, technical advising for founders building privacy-first infrastructure, and program committee service in cryptography and trustworthy ML. Feel free to reach out via my contact page.
For the agenda behind the work, see my research statement. For shipped open-source projects, see code.