Speaker Profile

Ph.D., Scientist, A*STAR - I²R

A scientist in the Cybersecurity Department of the Institute for Infocomm Research (I²R), Benjamin’s research focuses on privacy-preserving technologies such as federated learning, differential privacy and homomorphic encryption, as well as post-quantum cryptography. Leveraging his expertise in these technologies, he has been working with public agencies and companies in Singapore to develop solutions that enable secure collaborative ML and data analytics without compromising personal data privacy. He is also part of the A*STAR’s Fides team, endeavouring to proliferate privacy-preserving technologies to industry through R&D partnerships with organizations facing RWD privacy challenges. Benjamin continues to challenge his team and himself through participation in renowned competition and conferences. One of it was the IDASH Privacy and Security Workshop 2021 where his team won the 1st place (Track II) with their solution applying Homomorphic Encryption-based techniques for secure strain classification of 8,000 genomes. He authored a total of 18 publications with 1 patent awarded.


AI and Data Sciences Showcase:

Institute for Infocomm Research create digital world innovations for a thriving and resilient Singapore harnessing AI, Connectivity and Cyber Security.

First Global federated learning network secured with FHE
A novel environment for leveraging federated healthcare data for purposes of complex population analytics and machine learning.

 Session Abstract – PMWC 2023 Silicon Valley

Showcase Track S1 - January 25 9.00 A.M.-1.15 P.M.,Showcase Track S1 - January 26 11.30 A.M.-1.15 P.M.,Showcase Track S1 - January 27 11.00 A.M.-1.15 P.M.

The PMWC 2023 AI Company Showcase will provide a 15-minute time slot for selected AI companies to present their latest technologies to an audience of leading investors, potential clients, and partners. We will hear from companies building technologies that expedite the pre-clinical and clinical drug discovery and development process, accelerate patient diagnosis and treatment, or develop scalable systems framework to make AI and deep/machine learning a reality.