Teaching

Undergraduate

  • EK381: Probability, Statistics, and Data Science for Engineers [Website] [ASEE Prism Article]
    This is the required undergraduate probability class for BU engineering students across all departments. I created this class along with my colleague David Castañón to give engineering students the necessary background in probability to succeed in subsequent machine learning courses, regardless of their majors. It includes
    1. Flipped, fully synchronized lecture sections that cover the same material, with the same homework, exams, grading as well as shared discussion sections and online forum.
    2. High-quality concept videos. Before each lecture, students watch one or two pre-lecture videos that introduce a new concept along with worked examples. To hold visual attention, the videos use animated, accelerated handwriting combined with voiceover narration. [YouTube Playlist]
    3. Working with real, high-dimensional data. Programming problems throughout the semester connect abstract probability concepts to simple machine learning algorithms, applied to interesting datasets. The main focus is on a “cats and dogs” dataset curated specifically for this class, consisting of 64 x 64 registered grayscale images of 1000 cats and 1000 dogs.

Graduate

  • EC508: Wireless Communications
    This is an introduction to wireless communications, focused on signal and information processing at the physical layer. It closely follows the book Fundamentals of Wireless Communications by David Tse (Stanford) and Pramod Viswanath (Princeton).

  • EC517: Introduction to Information Theory
    This is an introduction to information theory. Recent offerings have included topics on learning and inference, such as minimax lowers bounds.

  • EC700: High-Dimensional Probability
    This is an advanced course on non-asymptotic bounds in high-dimensional probability, with applications to machine learning, closely following the book High-Dimensional Probability by Roman Vershynin (UC - Irvine).

Service

Workshop Organization

  • North American Information Theory Summer School
    Boston University, 2019. General Chair.
    [Website] [YouTube Playlist] [ITSoc Newsletter Article]

  • Workshop on Interactions between Number Theory and Wireless Communication
    One-week workshop hosted by the Department of Mathematics at York University in July 2016. The goal was to bring together number theorists working on Diophantine approximation and information and coding theorists working on applications of number theory and Diophantine approximation in wireless communications. Co-organized with Victor Beresnevich, Alister Burr, and Sanju Velani, all at York University. Proceedings from the workshop are now available as an edited volume. (If you are working actively in this area, I have a few extra physical copies of the book.)
    [Edited Volume]

  • Nexus of Information and Computation Theories
    Three-month program hosted by the Institut Henri Poincare, Spring 2016. The goal was to bring together researchers working at the interface of information theory and theoretical computer science. Co-organized with Mark Braverman (Princeton), Anup Rao (UW), Aslan Tchamkerten (Télécom Paristech).
    [Website] [YouTube Playlists] [ITSoc Newsletter Article]

  • Workshop on Interference in Networks One-day workshop at Boston University (immediately preceding ISIT 2012 at MIT) on information-theoretic approaches to interference in wireless networks. Salman Avestimehr (USC).