Welcome to new CMS faculty



Jakob Foerster

Jakob Foerster received a CIFAR AI chair in 2019 and is starting as an Assistant Professor at the University of Toronto and the Vector Institute in fall 2020. During his PhD at the University of Oxford, he helped bring deep multi-agent reinforcement learning to the forefront of AI research and interned at Google Brain, OpenAI, and DeepMind. His work received awards at some of the top machine learning conferences. He has since been working as a research scientist at Facebook AI Research in California, where he will continue advancing the field up to his move to Toronto. He was the lead organizer of the first Emergent Communication (EmeCom) workshop at NeurIPS in 2017, which he has helped organize ever since.

Alexander Kupers

Alexander did his undergraduate at Utrecht University in the Netherlands. He received a PhD from Stanford University for work on the topological groups of diffeomorphisms of high-dimensional manifolds, under the supervision of Søren Galatius. He then did a postdoc at Copenhagen University and held a Benjamin Pierce fellowship at Harvard University, before coming to UTSC. In general, his work is on automorphism groups of various objects from geometry and algebra, in particular diffeomorphism groups and general linear groups, which he studies using the methods of algebraic topology.

Nandita Vijaykumar

Before joining the University of Toronto, Nandita was a research scientist in the Memory Architecture and Accelerator Lab at Intel Labs. She received her Ph.D. and M.S. in 2019 from the Electrical and Computer Engineering Department at Carnegie Mellon University. She was advised by Prof. Onur Mutlu and Prof. Phil Gibbons. Nandita was fortunate to also work with the Systems Group in the Computer Science Department at ETH Zurich as a visiting student. In the past, she has also worked for AMD, Intel, Microsoft, and Nvidia.

Nandita's research interests lie in the general area of computer architecture, compilers, and systems with a focus on the interaction between programming models, systems, and architectures. Her recent interests are also in the system-level and programming challenges of large-scale machine learning and robotics.


Yun William Yu

Yun William Yu received his PhD in applied mathematics from MIT in 2017, where he was a Hertz Fellow and won the Johnson Prize for a math graduate student research paper in a major journal. He was a Wells Scholar and Goldwater Scholar at Indiana University during his undergraduate studies, and also received an MPhil and MRes from Imperial College London as a Marshall Scholar. Prior to joining the University of Toronto, he spent two years as a Research Fellow at Harvard Medical School. His research program focuses on developing novel algorithms for bioinformatics applications and translating existing tools from the CS literature to biology. Most recently, his primary research themes have been on probabilistic sketches and compressive algorithms for data science, with a predominant focus on computational biology and bioinformatics.


Linbo Wang

Linbo Wang received his PhD in Biostatistics from University of Washington in 2016.  Prior to joining the University of Toronto, he spent two years at Harvard Causal Inference Program. His research interest includes causal inference, graphical models, and modern statistical inference in infinite-dimensional models. He is the recipient of several research awards, including a NSERC Discovery Accelerator Supplement in 2019. To read more, see Linbo's website

Ting-Kam Leonard Wong

Ting-Kam Leonard Wong received his PhD in Mathematics from the University of Washington in 2016. Before joining the University of Toronto in 2018, he was a non-tenure track assistant professor in financial mathematics at the University of Southern California. His research interests include probability, mathematical finance, optimal transport, information geometry, as well as applications in statistics and data science. Read more here.