Graduate Coursework

Electrical Engineering Courses

1. Random Processes (ECE 534)

Fall 2016. A very enjoyable course; I learned a lot. I mainly studied from the course textbook and the instructor's notes. The book 'Stochastic Processes: Theory for Applications' by Robert Gallager was also useful.  Some concepts, e.g., almost sure convergence, were better understood after measure theory.

2. Information Theory (ECE 563)

Fall 2016. I got interested in the connections between information theory, statistics and machine learning through this course. I started studying these notes on this topic, but did not pursue this interest in research. Other than that, the course covered standard material from the textbook by Cover and Thomas.

3. Statistical Learning Theory (ECE 543)

Spring 2017. My first (and to date, only) course in machine learning. It had lots of fun applications of probability and optimization. The course notes  by Prof. Hajek and Prof. Raginsky were great. The book 'Understanding Machine Learning' by Shai Shalev-Shwartz and Shai Ben-David was also useful. I studied the paper 'Gradient Estimation with Simultaneous Perturbation and Compressed Sensing' for my course project. 

4. Introduction to Optimization (ECE 490)

Spring 2017.  Very good selection of topics in this introductory course on optimization. The textbook 'Nonlinear Programming' by Dmitri Bertsekas and 'Convex Optimization' by Stephen Boyd are standard sources. In a later iteration taught by Prof. Bin Hu of UIUC, there are some very interesting course notes.

5. Linear Control Systems (ECE 515)

Fall 2017. This course gave a comprehensive introduction to the various mathematical concepts in linear systems. Prof. Daniel Liberzon taught with amazing clarity. I found his course notes to be the best source for this topic.

6. Big Data Optimization (IE 598)

Spring 2018. This special-topics course focused on the different optimization algorithms used in machine learning, especially the different variants of gradient descent. I studied two papers as course projects: 'The O.D.E. Method for Convergence of Stochastic Approximation and Reinforcement Learning' and 'Accelerating Stochastic Gradient Descent for Least Squares Regression'.

7. Reinforcement Learning (ECE 597)

Spring 2019. This was a one-off independent study course offered by Prof. Srikant. It primarily revolved around this paper. It covered a wide range of concepts, ranging from control theory to Markov chain mixing times, and showed their application in the analysis of reinforcement learning algorithms.

Computer Science Courses

1. Algorithms (CS 473)

2. Distributes Systems (CS 425)

3. Principles of Blockchains (ECE 598)

4. Topics in Algorithms (CS 498)

5. Consensus Algorithms (CS 598)

6. Topics in Cryptography (CS 598)

Mathematics Courses

1. Real Variables (MATH 447)

2. Graph Theory (MATH 412)

3. Real Analysis (MATH 540)

4. Theory of Probability 1 (MATH 561)

5. Theory of Probability 2 (MATH 562)