The Department of Computer Science, with over 80 faculty members, has a long track record in grooming leaders for the digital economy and IT workforce. The department's internationally recognized faculty members perform research in the areas of Artificial Intelligence, Database Management, Media, Systems and Networking, Computational Biology, Programming Languages and Software Engineering. Significant results and project highlights include:

  • For those who’ve taken the plunge into the world of wearable devices — 61 million of us by the year’s end, as estimates predict — the leap can be liberating. Want to go for a run and clock your distance, time, and calories burnt without having to lug your phone with you? Just slap on a fitness tracker and head out the door. Cooking and want to control the music playing on your phone that’s atop a kitchen counter safely away from the food? Just give the Music app on your smartwatch a tap. Want to know who’s texting you while in a dark cinema without getting death stares because of the bright betraying glow of your screPreview  en? Take a subtle glance at your wrist instead.
  • The chosen few. To address the problem, Scarlett and his colleagues adopted an approach that builds on a widespread technique called subsampling. The idea behind subsampling is ...
  • Confirming the flooding. When we talk about the diameter of a network, Yu says, what we’re really asking is this: When you propagate a piece of information through the network, how long does it take to reach everyone?......
  • The power of prediction. Parity games provide a useful theoretical framework for model checking and verification as they help distil a complex problem into fairly simple one. Solve a parity game and you can solve a model-checking problem. In essence, a parity game is played by two players who take turns moving a token along a game board, or graph, comprising of nodes. 
  • Tracer: a verification tool based on symbolic execution with "Lazy Annotations". The general technique is to formulate program analysis as a combinatorial optimization problem, and uses dynamic programming in symbolic search. TRACER system has been used for many applications, such as timing analyses, slicing, testing, and concurrency optimization. This general approach is a foundation that holds big promise for solving the difficult problem of providing a practical and uniform basis for analyzing, verifying, and testing computer programs both statically and dynamically.
  • RetinaCloud: Platform for the detection, screening and management of major eye diseases, and study of systemic diseases. It allows for the documentation of retinal structural alterations, and monitor these changes over time. The platform currently support two flagship systems: SIVA and SELENA. SIVA extracts retinal blood vessel structure and derives a spectrum of quantitative measurements including caliber, tortuosity, bifurcation and fractals. It is able to attain the desired clinical sensitivity for large-scale population studies to be conducted, and greatly reduces the manpower requirements for analyzing retinal images. SELENA focuses on the detection of lesions, particular the lesions of diabetic retinopathy, in retinal images. This system optimizes the trade-off between high sensitivity and specificity to automatically screen patients for diabetic retinopathy at the polyclinics and other primary care providers, leading to a considerable reduction in workload in grading by family physicians, increased efficiency and potentially, substantial savings to the Singapore health system and patients.
  • SINGA: A General Distributed Deep Learning Platform SINGA is an Apache Incubator Project. It is a general distributed deep learning platform for training big deep learning models over large datasets. The key goals of SINGA is usability, scalability and extensibility. SINGA offers a simple and intuitive programming model, making it accessible to even non-experts. SINGA provides a general architecture to exploit the scalability of different training frameworks. Synchronous training frameworks improve the efficiency of one training iteration, and asynchronous training frameworks improve the convergence rate. Given a fixed budget (e.g., cluster size), users can run a hybrid framework that maximizes the scalability by trading off between efficiency and convergence rate. SINGA is extensible and able to support a wide range of applications requiring different deep learning models.
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