CMS welcomes new faculty

2021

 

James Bremer

James Bremer is a numerical analyst. His research interests include the solution of the Helmholtz equation in the high-frequency regime, integral equation methods for elliptic partial differential equations, special functions and quadrature.

James graduated from the University of Maryland in 2001 with B.S. degrees in Mathematics and Computer Science. He received his Ph.D. in mathematics from Yale University in 2007, under the direction of R.R. Coifman. From 2007 to 2021, he was a faculty member in the Department of Mathematics at the University of California, Davis. In 2012, James was awarded an Alfred P. Sloan Research Fellowship.

Michael Cavers

Michael Cavers has been a member of the Department of Computer and Mathematical Sciences at the University of Toronto Scarborough since 2021.  Prior to that, he was an instructor at the University of Calgary and at the University of Toronto Mississauga.  He is active in outreach activities and is a board member for various mathematical contests. 

Purva Gawde

After completing master's in computer science, I completed my PhD in computer science from Kent State University, Ohio, USA. During PhD, I taught and developed curriculums for various subjects. After that, I worked as a post-doctoral researcher and curriculum developer at Ryerson University before joining UTSC as a lecturer in Computer Science since Fall 2020.

Teaching and developing undergraduate and graduate level courses made me aware of the challenges faced by students and teachers.  This experience inspired me to start exploring the world of pedagogy in Computer Science.

Parker Glynn-Adey

Parker Glynn-Adey was born near Thunder Bay, and grew up in London, ON. He received his undergraduate degree in mathematics and philosophy from Trent University, where he got to know his professors and share in the fun of discovering mathematics together. His PhD at the University of Toronto focussed on the geometry of high dimensional manifolds. He believes professors should be approachable and friendly, and that mathematics is open for everyone and anyone to explore. When he is not hiking with his family, playing with his daughter, or writing, he can be found teaching mathematics, organizing mathematics education events, or chatting with students.

Marcelo Ponce

Marcelo obtained his PhD in Astrophysics from the Rochester Institute of Technology, as a member of the Center for Computational Relativity and Gravitation. His research expertise focuses in the area of Computational Astrophysics, more specifically Numerical Relativity, i.e. solving Einstein’s equations of General Relativity numerically, using supercomputers to simulate extreme scenarios, such as, multiple black holes and binary neutron star mergers. Before joining CMS, Marcelo worked as a computational scientist at SciNet the supercomputer center at the University of Toronto, and as graduate lecturer at the Department of Physics and the Institute of Medical Sciences at the University of Toronto.

Shahriar Shams

Shahriar Shams is a PhD in Biostatistics candidate at the Dalla Lana School of Public Health at the university of Toronto. His research focuses on applying Bayesian paradigm in reducing uncertainty in the measurement of health utilities. Before joining UTSC, he was teaching at the Statistics department at the St. George campus as a teaching stream limited term faculty. And before starting his PhD program he was working as a Biostatistician at university health network (UHN).

2020

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.

2019

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.

2018

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.