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Experience

Kipo AI

| San Francisco
Jun 2025 - Present

Software Engineering Intern

  • Built core infrastructure for a B2B parts intelligence platform, helping scale the product from stealth to ~1,000 active users and supporting enterprise-grade workflows.
  • Developed a scalable search and sourcing system indexing 2M+ components with live pricing and lead-time awareness, enabling real-time quoting and faster sourcing decisions.
  • Automated datasheet symbol extraction and BOM-to-sourcing pipelines, reducing manual engineering effort and accelerating readiness for paid enterprise adoption.
Languages:PythonTypescript
Frontend:Next.js
Backend:NestJSFastAPIOpenFGA
Databases:PostgreSQLMongoDBRedis
Services:AWS

DSBJ

| Singapore
Jan 2024 - Jul 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 - Jul 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