Engineering
My first job was at Applied Intuition, a startup building software for autonomous vehicles and automotive. I joined the company at ~90 employees and saw it grow to 350+ over 2 years. I did work in a bunch of things: full stack development for a new product, compilers, OS apis, optimization, and cloud infra. I also talked to customers daily, held external and internal workshops, conducted PoCs, and helped with recruiting.
I spent summer 2020 on the simulation team at Nuro, a startup building autonomous delivery vehicles. I built a caching mechanism and a timeout feature for a distributed job scheduling framework.
I spent the summer of 2019 interning at Zilliqa, a startup focused on applying sharding to build scalable blockchains. I worked on more efficient communication between the core Blockchain process and the Smart Contract interpreter, and my work led to a reduction in communication overheads from linear to constant. I also built a dApp as part of a marketing campaign.
I spent summer 2018 at Apple in Cupertino working on Machine Learning Infrastructure for Siri. I built a search indexing system to efficiently query Siri's ontology, and was selected to present my project to the head of Siri (Apple VP).
I took a semester off from school to do a second internship at Twitter. I worked on Applied Machine Learning for the Twitter Home Timeline. I used Deep Neural Networks to build predictive models for user engagement on videos. I launched many iterations of experiments to evaluate the performance of the models on production traffic.
I spent the summer of 2017 at Twitter in San Francisco working on Twitter Lite as part of the Responsive Web Team. I worked in React, Redux, and ES6. I built Twitter Lite's Live Video experience, and worked on integrating the Chrome credentials management API. All my code was shipped, and used by over 6 million users daily.
I spent the summer of 2016 working as a Full Stack Web Developer at Xfers, a startup from the YCombinator Summer batch of 2015 looking to simplify the Payments process in Southeast Asia. I helped build an onboarding system used daily by customers and also built an internal analytics dashboard.
Research
I worked in the Stanford Compiler Group with Professor Fredrik Kjolstad, on TACO. I specifically worked on better support for blocking and workspaces.
I worked with 2 other students and Professor John Mitchell to analyse Proof of Work protocols using Statistical Model Checking Tools. We published our work at the IEEE Blockchain-2020 Conference. You can read the paper here.
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2018 - 2019
I worked on mitigating the effect of adversarial client nodes in Federated Learning as part of my Final Year Dissertation during my undergraduate studies. I was supervised by Professor Bryan Low.
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2017
I explored the use of Bayesian Optimisation and Automatic Differentiation for tuning the hyperparameters of Machine Learning algorithms, as a part of NUS' Undergraduate Research Opportunities Program (UROP). I played around with PyTorch and learnt a lot about Statistics and Machine Learning. I was supervised by Professor Bryan Low.
Teaching
Created Stanford's and (to our knowledge) the world's first class on Transformers. Taught the first iteration of the class in Fall 2021.
I was a teaching assistant for Operating Systems, under Professor David Mazières. Before that I was a TA for Deep Learning for 4 quarters, under Kian Katanforoosh and Professor Andrew Ng. My responsibilities include holding office hours, grading assignments, and conducting sections
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