Prior to joining the University of Oxford, I was a Lecturer in the Computer Science department at Yale University and a Postdoctoral Associate at the Yale Institute for Network Science, hosted by Sekhar Tatikonda.
I have a Ph.D. in Operations Research and Financial Engineering from Princeton University, where I worked in Applied Probability under the supervision of Ramon van Handel.
Here is my Curriculum Vitae.
My research interests lie at the intersection of Probability, Statistics, and Computer Science. I am interested in the investigation of fundamental principles in high-dimensional probability, statistics and optimization to design computationally efficient and statistically optimal algorithms for machine learning.
I am a Turing Fellow at the Alan Turing Institute London. On June 11 2018 I co-organized the one-day workshop The Interplay between Statistics and Optimization in Learning. On January 13-14 2020 I co-organized the two-day workshop Statistics and Computation.
I am a member of the European Laboratory for Learning and Intelligent Systems (ELLIS). I am part of the management team for the Imperial-Oxford StatML Centre for Doctoral Training. I am an alumnus of the Yale Institute for Network Science, and an alumnus of the Princeton Statistical Laboratory.
During the 2019/2020 academic year I am co-organizing a reading group on Theoretical Machine Learning. Notes are here.
Since 2018, I have been teaching Algorithmic Foundations of Learning, for which I received the 2019 Oxford MPLS Teaching Award.
In Spring 2018 I taught Advanced Simulation Methods.
During the 2017/2018 academic year I organized a reading group on optimization for Machine Learning. Notes are here.
While at Yale University, in Fall 2016 I served as the Head Instructor for CS50 — Introduction to Computing and Programming — taught jointly with Harvard University. This was a coverage on the Yale Daily News. Here is the intro class in Machine Learning and Python, or its VR version.
From 2015 to 2017 I supervised a group of senior students on research projects in Machine Learning, investigating the development of algorithms for natural language processing, sparse regression, and distributed optimization.
I was a member of the Yale Postdoctoral Association, with the goal to facilitate and promote teaching experiences for postdocs in the sciences. For three years in a row, from 2015 to 2017, I organized the Julia Robinson Mathematics Festival at Yale, a celebration of ideas and problems in mathematics that enable junior high and high school students to explore fun math in a non-competitive setting.
In 2013 I received the Excellence in Teaching Award from the Princeton Engineering Council while serving as head teaching assistant for ORF 309 (Probability and Stochastic Systems) at Princeton University.
I was also a fellow of the McGraw Center for Teaching and Learning at Princeton University.