I'm a computational scientist working on machine learning (a branch of Artificial Intelligence) and its application to complex biology problems. I focus on a problem called transfer learning, which in layman's terms could be described as the capacity to automatically transfer knowledge between different tasks. It comes naturally to us, e.g. we use what we learned for walking when learning to run, but almost all machine learning algorithms build models from nothing.
I'm mostly working on:
- The design of algorithms models capable of transferring knowledge.
- Statistical relational learning (in a nutshell: the union of logic and probability theory).
- Probabilistic graphical models.
- High performance scientific computing, increasingly with GPUs (CUDA/OpenCL).
- Applications to biology, most notably ecology.
I'm currently a Ph.D. candidate at the Canada Research Chair on Integrative ecology ( Université de Sherbrooke), Tim Poisot's lab (Université of Montréal), and the Quebec Center for Biodiversity Science (McGill U.). My work is supported by an Alexander Graham Bell Graduate Scholarship from the NSERC and a generous Azure for Research Award from Microsoft Research. I'm a member of the Institute of Electrical and Electronics Engineers and their Computational Intelligence society. You can get my short CV here.
My work depends on many high-quality open-source software, especially the following projects: LLVM/Clang, Git, Linux, Debian/Ubuntu, Rust, Glasgow Haskell Compiler, NodeJs, Vim, Geany, ZeroMQ, GNOME, PostgreSQL, Cassandra.