Dr Bodin’s current research interests include performance analysis and optimisation; embedded systems and robotic applications. His work on dataflow analysis and compilation has been adopted by Kalray, a semiconductor company making a many-cores system-on-chip for embedded systems. He also leads the design and development of SLAMBench, a performance evaluation framework for Simultaneous Localisation and Mapping systems (SLAM) that is used by industry partners. Overall, Dr Bodin has published over 20 papers in premier international journals and conferences in his areas: ASPLOS, DAC, EMSOFT, ICRA, PACT.
Professor Danvy is interested in all aspects of programming languages, from their logic and semantics to their implementation, including programming, transforming programs, program transformations, and reasoning about programs and about program transformations (for one man’s program is another program’s data). As a Scheme programmer, he is familiar with parentheses and he is not afraid to use them. Also, for several years now, he has become convinced that the Coq Proof Assistant is the greatest thing since sliced bread and that it has the potential to transcend Computer Science college education, so watch this space. He is also interested in scientific communication.
YSC3208 Programming Language Design and Implementation
YSC3203 Advanced Algorithms and Data Structures
Dr Gastner is interested in the mathematical modelling and analysis of complex systems. His interdisciplinary research includes work on social, biological and technological networks, as well as economic geography and cartography. In parallel, he maintains an active research agenda in statistical physics, especially percolation theory.
Dr Heinecke’s research focuses on applied harmonic analysis, in particular frame theory, time-frequency analysis and wavelets along with their applications to signal and image processing. His interests also include functional analysis and the theory of Banach spaces.
Dr Hobor’s research focuses on improving the reliability and security of software with an emphasis on formal verification. His three most important publications are Oracle Semantics for Concurrent Separation Logic (ESOP 2008), A Theory of Indirection via Approximation (POPL 2010), and The Ramifications of Sharing in Data Structures (POPL 2013). His research was recognised by the awarding of a Lee Kuan Yew Postdoctoral Fellowship.
Assoc Prof Ilya Sergey does research in programming language theory, including, but not limited to types, semantics, software verification, and program synthesis. His particular areas of interest include:
- Applied logic for program verification (especially for concurrency and distributed systems);
- Design and implementation of programming languages;
- Static program analysis (in particular, in application to higher-order and concurrent programs).
Assistant Professor David Smith is an applied mathematician, working on partial differential equations and the spectral theory of linear differential operators. He looks into solving problems motivated by Physics, and studying the solution methods used. I am particularly interested in boundary value problems, with nonstandard boundary conditions that complicate the problem. Once a solution has been obtained, he is interested in looking for efficient ways to describe that solution.
Assistant Professor Matthew Stamps' research investigates algebraic, geometric, and topological methods in combinatorics and applied mathematics. Dr Stamps is particularly fascinated by questions that explore how well discrete objects (such as networks and polyhedra) and continuous objects (such as curves and surfaces) model one another, and how answers to such questions can be used for applications in computer science and biology. Graphs, posets, matroids, circle packings, polytopes, manifolds, cell complexes, groups, and modules are just a few of the objects that show up frequently in his work.
Associate Professor Robby Tan's main research is in computer vision and deep learning (machine learning). Computer vision is to make computers "see" the world through images and video, and machine learning is a probabilistic artificial intelligence that learns itself through data. Computer vision and machine learning (or recent deep learning) are useful for many applications like self-driving cars, automatic surveillance, robot's eyes, crowd monitoring, etc. Both are part of Artificial Intelligence.
On the more mathematical side, one of Dr Timothy Wertz's areas of concentration is studying the mathematical properties of models of physical systems. For example, the motion of an electron through a semi-conductor with impurities is given by the Anderson model, which gives rise to a lot of interesting mathematics. Such models belong to a larger class of mathematical objects, and we can learn about specific models by studying properties of the abstract collection.
Pedagogically, Dr Wertz is interested in team-based and flipped classroom approaches to mathematics education and quantitative literacy for non-mathematicians.