10.022 Modelling Uncertainty - Engineering Systems and Design (ESD)
Fall 2021
Probability and statistics are two powerful and complementary ways to explain, forecast, and visualise uncertainty. Probability uses knowledge of a system’s behaviour to predict its future outcomes, while statistics analyses data from past outcomes to model a system’s behaviour. In this course, we will introduce the fundamentals of probability and statistics and apply them in real life problems.
40.319 Statistical Machine Learning - Engineering Systems and Design (ESD)
Spring 2021
An agent is intelligent if it perceives its environment and takes actions that maximize its chances of successfully achieving its goals. In this course, we will study how to imbue machines with intelligence, focusing on foundational principles and mathematical theories of real-world modelling, problem solving and statistical learning.
Capstone Project (SUTD)
Spring/Summer 2017 – 2020, Summer 2021
The Capstone project brings together students from different Majors to work in design teams, contributing their respective expertise and skills to solve real-world challenges. It also provides them with a realistic design situation where projects usually span multiple disciplines and require team-based efforts to create a solution.
40.651 Algorithmic Game Theory - Engineering Systems and Design (ESD) - Graduate
Fall 2016, Spring 2019
This is a graduate level course in Algorithmic Game Theory which aims at providing the fundamental concepts of non-cooperative game theory, at exploring its connections to computational issues, and at showing a broad spectrum of applications in different fields. The topics to be covered in this course include strategic-form games, Nash equilibria (and variants), price of anarchy, auctions, and learning.
40.316 Game Theory - Engineering Systems and Design (ESD)
Summer 2016, Summer 2020
The course will provide a consistent framework for game-theoretic concepts, some of which have already been informally presented to the students in other courses. The course will cover the basic concepts in game theory, allowing the students to use strategic models in their capstone research.
10-001-Advanced-Mathematics-I
Summer 2015, Summer 2016
The main objective of the subject is to provide firm foundations of calculus.
CS 8803: Advanced Topics in Algorithmic Game
Theory
Spring 2013
Co-taught with Jugal Garg and Ruta Mehta
A research oriented course focusing on the intersection of computer science and game theory. During the first half of the course, the participants will be exposed to key ideas and results from algorithmic game theory. In the second part we will be exploring research tangents, looking into open questions and working on formulating novel problems.
Discrete Fourier Analysis and Applications
Spring 2012
Co-taught with Elena Grigorescu, Will Perkins and Lev Reyzin
Discrete fourier analysis provides a set of techniques that have found wide applicability in mathematics and computer science. The underlying insight is that the projection of functions onto the Fourier basis can often provide the right angle in analyzing properties of various mathematical objects, such as boolean functions, graphs, PRGs, and sets of integers.