I am an Associate Professor at the Department of Mathematics in the University of British Columbia in beautiful Vancouver, British Columbia. I am a member of the Institute of Applied Mathematics and CAIDA, an AMII Fellow and a Canada CIFAR AI Chair.

My research lies at the intersection of mathematical statistics, machine learning, combinatorics, multilinear algebra, and applied algebraic geometry. I particularly enjoy discovering mathematical structure which is inherently responsible for the successful solution to a statistical problem. I develop machine learning and optimization methods for inference in models that depict complex dependencies in data. My work spans causal inference, graphical models, tensor decomposition, non-parametric density estimation, and super-resolution imaging. For example, I develop theory and algorithms for:

• causal inference for observational data (both temporal and non-temporal) in the presence of hidden variables and causal feedback loops;
• tensor decomposition applied to statistics and machine learning problems;
• sparse inverse problems, such as super-resolution imaging;
• high-dimensional, non-parametric density estimation leveraging dependencies between the variables.

In addition to traditional tools from applied mathematics, I utilize algebra, geometry, and combinatorics, which often depict the structure of the models at hand.

Here is a link to my CV.

Prior to joining UBC I was priviliged to spend three years as a Statistics Instructor and an NSF Postdoctoral Fellow in the Department of Mathematics and the Institute for Data, Systems, and Society at MIT.

In the spring of 2016 I received my PhD in mathematics from UC Berkeley under the supervision of Bernd Sturmfels. Here is a link to my thesis, which won the Bernard Friedman Memorial prize in applied mathematics.

In 2011 I received my BS with Honors in mathematics and a minor in computer science from Stanford University.