I am an Assistant 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.
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. I address situations in which many commonly made yet unrealistic assumptions do not hold by leveraging the mathematical structure of the model at hand. My work spans causal inference, graphical models, tensor decomposition, non-parametric density estimation, hidden variable models, and super-resolution imaging. For example, I develop theory and algorithms for:
In addition to traditional tools from applied mathematics, I utilize algebra, geometry, and combinatorics, which often depict the structure of the models at hand. I have recently started collaborating with climate scientists to infer causal relationships among climate variables from time series data.
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.