Experience

Kipo AI

| San Francisco
Jun 2025 - Present

Software Engineering Intern

Built and hardened core platform systems as the product scaled from stealth to ~1,000 active users with 2M+ indexed components.

  • Rewrote the authentication and session system end-to-end to unblock OAuth and enterprise rollout, with cross-browser end-to-end tests guarding the flow.
  • Made the AI agent reliably usable for ~1,000 users by introducing a durable workflow engine and moving chat state onto Redis, eliminating the race conditions that were dropping live responses.
  • Led an architectural split that decoupled the chat and search subsystems, and hardened the deploy pipeline with reproducible builds, faster caching, and end-to-end test coverage.
Languages:PythonTypescript
Frontend:Next.js
Backend:NestJSFastAPIOpenFGA
Databases:PostgreSQLMongoDBRedis
Services:AWS

DSBJ

| Singapore
Jan 2024 - Jun 2024

Application & Data Developer Intern

  • Built a model visualization platform with LLM-based diagnostics, enabling non-technical stakeholders to interpret complex mathematical models via natural language.
  • Centralized and deployed a Single Sign-On (SSO) framework, unifying authentication across 6 internal applications to enhance organizational security.
Languages:PythonTypescript
Frontend:Next.js
Backend:NestJSFastAPIRBAC + JWT Authentication
Databases:PostgreSQL

Gigworks

| Singapore
Feb 2021 - Jun 2021

Mobile Application and Web Application Developer Intern

  • Owned development across mobile and web on a revenue-generating e-commerce platform, contributing ~80% of the production codebase.
  • Reduced user-reported defects by ~90%, improving checkout reliability and customer trust.
  • Accelerated time-to-market by helping close a ~6-month delivery gap with the design team.
Languages:DartTypescript
Frontend:FlutterAngular
Databases:Firebase

Civil Defence Academy

| Singapore
Jan 2020 - Feb 2021

Mobile Application Developer

  • Pioneered a mission-critical attendance system during National Service using Flutter and Firebase, that digitised status tracking for 1,000+ personnel across the military base.
Languages:Dart
Frontend:Flutter
Databases:Firebase

Research

AI4X 2025

| First Author

Quantifying Uncertainty in Physics-Informed Neural Networks

We used deep evidential regression to quantify uncertainties in physics-informed neural networks, demonstrating it on the Burgers and Laplace experiments.

ABSTRACTWe integrate a state-of-the-art method to quantify aleatoric and epistemic uncertainties in physics-informed neural networks and observe that they can be captured effectively while maintaining predictive accuracy.

Available at: Paper
Physics-Informed Neural NetworksUncertainty Quantification

Education

Nanyang Technological University

Bachelor of Engineering (Honours), Computer Engineering

  • Minor in Business
  • Elective Focus in Security & Artificial Intelligence