I am a researcher and engineer interested in understanding and optimizing complex systems: computational, social, and cognitive alike.

With a background in computer science and psychology, I am inspired by insights from cognitive science on learning and memory, as well as the parallels between cognitive and computational architectures.

At present, I am engaged in research and engineering efforts focused on scalable, high-performance machine learning systems. I have had the privilege of collaborating with and learning from world-class talents such as Shen Li and Yongxiong Ren, amongst many others.

Experiences

• 2024 – Present: Software Engineer, Machine Learning, Meta.
  • Accelerating and scaling the training of traditional and LLM-based recommendation models via efficient distributed training, GPU kernel optimizations (e.g., Triton/CUDA kernels), and hardware-software co-design.
  • I will be sharing more about our ML efficiency work surrounding the recommendation models powering Reels at NVIDIA GTC 2026. Join us if you’re around the area!
• 2023 – 2024: Data Engineer, HoYoverse (subsidiary of miHoYo).
• 2022 – 2023: Software Engineer, TikTok.
• 2022 – 2022: Software Engineer, Autodesk.

My brief stint in academia:

• 2023 – 2024: Cognitive Science @ Language and Computation Lab.
  • Psycholinguistic research on English semantic variation, presented at AJL’23.
• 2022 – 2023: Distributed Systems @ Singapore Blockchain Innovation Programme.
  • Built Blockbench v3, a benchmark suite for L1 blockchains, presented at VDBS’23.
• 2021 – 2022: Cognitive Science @ Computational Affective and Social Cognition Lab.
  • Computational modeling of emotional causal inference, presented at CogSci’22.

Education

• National University of Singapore
  Master of Computing (MComp), Artificial Intelligence.
• National University of Singapore
  Bachelor of Computing (BComp), Computer Science.
  Bachelor of Arts (BA), Psychology.