Hey! My name is Andre. I'm a final year undergraduate studying Mathematics & Computer Science at the National University of Singapore (NUS).
Machine learning is an exciting field at the intersection of mathematical theory (brutal math courses finally paying off.. *_*) and software engineering. There's no shortage of groundbreaking research in the ML landscape, and I desire to be among those who bring these innovations into real-world applications.
I am now particularly interested in the intricacies of parallelism in training/inference optimization and distributed systems. I aspire to design the infrastructure of next-generation ML systems and pipelines.
Beyond academia, I am a casual climber (an occasional diver, and avid backpacker) and I am part of the university's Mountaineering club and Climbing club. Together with a couple of 𝘸𝘰𝘯𝘥𝘦𝘳𝘧𝘶𝘭𝘭𝘺 𝘧𝘶𝘯-𝘭𝘰𝘷𝘪𝘯𝘨 𝘤𝘶𝘤𝘬𝘰𝘰𝘴, we scaled the Himalayas and it was simply fantastic!
Delivered as my Final Year Thesis. What started off as a research contribution has since evolved into a full engineering system! Together with a small team, we are advancing toward a production-grade platform: a multi-agent system that integrates directly into Overleaf to provide LaTeX-aware debugging and targeted revision. I continue to develop our backend logic and custom XtraMCP orchestration layer.
Part of Prof He Bingsheng's research group, focusing on distributed ML systems and adaptations of the transformer architecture. Fortunate enough to make some publications along the way!
My stint as a CS2040S (Discrete Structures & Algorithms) has convinced several capable and passionate ex-students of mine to join me in developing an open-source teaching material for future cohorts. Lovely!
The bank was in its Agentic AI phase, so I learnt, built, and extended custom MCP integration and validation logic for backend. My primary work was ensuring equity market data was ready for downstream use.
Software Engineering meets Quantitative Trading - Learnt how to support the trading team. Taught me zero-tolerance engineering.
Gained practical knowledge on system design and was taught what simple, reliable, sustainable, and fault-tolerant systems look like.
Worked on finetuning LLMs using data and model parallelism techniques to contend with larger models at lower cost. Also learnt to design, build, and deploy ML pipelines in production. I trace my origins here.
Learnt ML production and deployment lifecycle, and worked on Quant Research projects affiliated with QRT.
Teaching Assistant for CS1010s (Programming Methodology in Python) and CS2040s (Data Structures and Algorithms); Won a teaching excellence award!
What's The Point of It All?
Bustling city. Classy heels. Dazzling lights. Furious pace. Utter shit.
No way ain't Norway.
Krabi '24 — When a Year of Effort Finally Paid Off
Python, Java, C++, Go, TypeScript
TensorFlow, PyTorch, Scikit-learn, OpenCV, pandas, NumPy
vLLM, llama.cpp, DeepSpeed, Lightning AI, LitGPT, LangChain
PostgreSQL, Spark, Flink, Kafka
Docker, Django, FastAPI, Spring Boot, Express, Nodejs