Projects

An interactive and real-time perfect clear solver for Tetris.

The solver algorithm is written in Rust and uses multiple heuristics to achieve acceptable performance. It is then compiled to WebAssembly for native performance in the browser.

Wave is a simple pomodoro timer to help you stay focused while working. It alternates between a 25 minute focus mode and a 5 minute break mode while displaying beautiful animations.

Wave also keeps your timer in sync with multiple devices and lets you stay on top of your timers with alert notifications!

All your activity is anonymised and no longer stored after two days. Instead, Wave uses a data-minimal algorithm to provide recommendations for the best hours of the day when you are the most productive without compromising your privacy.

meetwhen.io helps you schedule meetings and hangouts around the world. Quickly create and join meets with no sign up required, or sign in with an account to keep track of your previous and upcoming meets!

You can also add relevant links and details for easy access later and see when your friends are the most available.

CODE_EXP 2021 · 1st Place

HelloWorld is a quick hackathon project that provides a taste of tourism when travel was restricted during the pandemic.

You can decorate your personal spaces with virtual landmarks and attractions to immerse yourself in the cultural icons of your favorite destinations. Once you’ve built your world, hop onto the Discover page to learn more about each landmark.

Techtris was created by a small group of friends who wanted to work together on hobby projects, hackathons, and group assignments.

The showcase site was primarily built to develop a simple understanding of Three.js.

For our Final Year Project, we developed pipelines for efficiently ingesting data logs, building machine learning models, and deploying those models on AWS, Azure, and GCP.

This involved an analysis and exploration of the many services provided by the different cloud providers to meet the functional requirements for the pipelines. In the process, we designed and trained basic machine learning models using the pipelines developed.

Kopi Time is a digital marketplace for coffee-related art and collectables.

We implemented a reliable and robust microservice architecture to handle market trading functionality, listing expirations, and user notifications.

Most notably, we developed a framework-agnostic implementation of the saga pattern we named the Saga Engine to orchestrate transactions and compensating actions across multiple microservices.

We designed a highly available and maintainable backend architecure for our SMU-X sponsor – COMO Group.

Most notably, our architecture fails gracefully when third-party external services are unavailable.

To do so, we continuously check the health of external services. When an external service fails a healthcheck, we queue all client requests and notify users of the temporary disruption. Once the external service is healthy, we automatically trigger a recovery phase and continue with regular execution.

We conducted market research and discovered an unfulfilled niche for a comprehensive database and search engine for hawker food in Singapore.

Using this database, we developed a product, risk mitigation strategies, and a feature roadmap for future growth.

We designed a simple trading platform for our SMU-X sponsor – Credit Suisse.

Given multiple core functionalities in the requirements, we used microservices to reduce the complexity of each component and enable developers to work semi-independently.

We also developed a simple machine-learning-powered stock recommendation engine.

For our efforts, we were awarded the Best Technical Team achievement.

We modelled various gerrymandering and Group Representation Constituency allocation strategies using an agent-based simulation to explore the impacts of voter flexibility and distribution on the fairness of representation.

We emulated a simplified version of the MAC and IP protocol on top of network sockets for our physical layer.

With the emulated network, the project requires a study and implementation of IP spoofing, IP filtering, and packet sniffing. Our group went beyond the requirements to implement private and public network separation, a simple public routing protocol, the ARP protocol, and IP traceback.

We analyzed data on hotel booking demand and explored a variety of machine learning techniques to predict the cancellation of a given hotel booking based on pre-existing features.