Andrew Gan

Andrew Gan

Introduction

Hi! I am a PhD candidate in the Purdue University School of Electrical and Computer Engineering.

Research

Research illustration (click me) drawing

My work involves Security and Privacy, Hardware Accelerators, and Machine Learning to develop efficient and trustworthy ML systems. I bridge the performance goals of hardware accelerators with the security goals of the ML community, where tasks are increasingly distributed across parties and new attack vectors against ML are always discovered.

I have designed GPU accelerated frameworks for privacy-preserving computation, end-to-end frameworks for ML artifacts authentication, and ML training pipelines that leverage hardware for differential privacy. I am also involved in an AI/ML security project with the Open Source Security Foundation (OpenSSF), part of the Linux Foundation.

Bio

I completed half of my undergraduate studies at Taylor’s University, Malaysia :malaysia: and the rest at Purdue University, USA :us:, graduating with a BSc. in Computer Engineering in 2020. After a brief experience in the industry, I enrolled into graduate school at Purdue and received a MSc. in ECE in 2024 before continuing on to PhD candidacy.

Publications

One RNG to Rule Them All - How Randomness Becomes an Attack Vector in Machine Learning

  • Kotekar Annapoorna Prabhu, Andrew Gan, Zahra Ghodsi
  • 2026 IEEE Conference on Secure and Trustworthy Machine Learning (SaTML)
  • Paper, Proceedings

Sentry: Authenticating Machine Learning Artifacts on the Fly

  • Andrew Gan, Zahra Ghodsi
  • 2025 ACM SIGSAC Conference on Computer and Communications Security (CCS)
  • Paper, Slides, Proceedings

cuOT: Accelerating Oblivious Transfer on GPUs for Privacy-preserving Computation

  • Andrew Gan, Setsuna Yuki, Timothy Rogers, Zahra Ghodsi
  • 2025 IEEE International Symposium on Hardware Oriented Security and Trust (HOST)
  • Paper, Slides, Proceedings

Industrial Experience

drawing

  • Nokia Bell Labs
  • PhD intern
  • Sept 2026 - Dec 2026
  • Stuttgart, Germany :de:

drawing

  • Amount, Inc.
  • Software Test Engineer
  • May 2021 - June 2022
  • Chicago, USA :us:

Projects

GPU-Based Model Integrity SIG

  • A project by the OpenSSF AI/ML Security workgroup, part of the Linux Foundation
  • Establish hardware-agnostic workflow for GPU-based model authentication
  • Model trainers produce cryptographically signed artifacts with accelerated integrity operations

Teaching

ECE 47920: Privacy-preserving Machine Learning [Spring 2025]
ECE 36200: Microprocessor Systems and Interfacing [Spring 2023]
ECE 39595: Object-oriented Programming in C++ [Fall 2022]

Awards

Design Automation Conference (DAC) 2025 Young Fellow