teaching

Rutgers Courses

300 and 400-level classes are undergraduate, 500-level are graduate.

• Spring 2024 ECE 549 (Statistical Inference and Learning for Engineers)
• Fall 2022 ECE 345 (Linear Systems and Signals)
• Spring 2023 ECE 539 (High Dimensional Probability and Applications)
• Fall 2022 ECE 345 (Linear Systems and Signals)
• Spring 2022 ECE 549 (Statistical Inference and Learning for Engineers)
• Spring 2022 ECE 539 (High Dimensional Probability and Applications) co-taught with Yuqian Zhang
• Fall 2021 ECE 345 (Linear Systems and Signals) co-taught with Salim El Rouayheb
• Spring 2021: ECE 549 (Statistical Inference and Learning for Engineers)
• Fall 2020: ECE 345 (Linear Systems and Signals) co-taught with Salim El Rouayheb
• Spring 2020: ECE 549 (Detection and Estimation Theory)
• Fall 2019: ECE 345 (Linear Systems and Signals) co-taught with Sophocles Orfanidis
• Spring 2019: Sabbatical!
• Fall 2018: ECE 345 (Linear Systems and Signals) co-taught with Sophocles Orfanidis
• Spring 2018: ECE 322 (Principles of Communication Systems)
• Spring 2018: Byrne Seminar (Data Science: The Good, The Bad, and the Ugly) co-taught with Waheed U. Bajwa
• Fall 2017: ECE 345 (Linear Systems and Signals) co-taught with Vishal Patel
• Spring 2017: ECE 322 (Principles of Communication Systems)
• Fall 2016: ECE 436: Signal and Data Analysis
• Spring 2016: ECE 549 (Detection and Estimation Theory)
• Fall 2015: ECE 542 (Information Theory and Coding)
• Fall 2015: Byrne Seminar (Data: What is it Good For? (Absolutely Something!)) co-taught with Waheed U. Bajwa
• Spring 2015: ECE 549 (Detection and Estimation Theory)
• Fall 2014: ECE 539 (Statistical Learning and Optimization)
• Fall 2014: Byrne Seminar (Data: What is it Good For? (Absolutely Something!)) co-taught with Waheed U. Bajwa
• Spring 2014: ECE 542 (Information Theory and Coding)

Short courses and tutorials

I have given a few short courses and tutorials.

• A ``brief’’ introduction to differential privacy, short course taught through the JPSM program at UMD, Fall 2021, Fall 2022
• An introduction to statistical inference and machine learning, Pacific Northwest National Labs, 2020
• Security and Privacy in Data Science, short course at Munich Re, 2018
• Differentially Private Machine Learning: Theory, Algorithms, and Applications (with Kamalika Chaudhuri), a tutorial at NIPS 2017
• Differential privacy and machine learning (with Kamalika Chaudhuri), a tutorial at WIFS 2014

Course Descriptions

Undergraduate

Linear Systems and Signals (ECE 345)

I have taught this junior-level introduction to signals and systems for several years. In Fall 2022 I’ll start the process of flipping the class using the video lectures developed during the last two years of remote instruction.

Semesters: Fall 2023, Fall 2022, Fall 2021 (online), Fall 2020 (online), Fall 2019, Fall 2018, Fall 2017

Principles of Communication Systems (ECE 322)

This is a first course in communication systems, covering both analog and digital communications. The course used the textbook by Proakis and Salehi.

Signal and Data Analysis (ECE 436)

This was the “topics in ECE” elective course aimed at senioros which covered the basics of (non-deep) machine learning including PCA, classification/regression, and optimization methods. This material is now covered in a regular course, Machine Learning for Engineers (ECE 443)

Semesters: Fall 2016

Byrne Seminar on Data Science

Prof. Waheed U. Bajwa and I taught a 1-credit seminar for first-year undergraduates through the Byrne Seminar program. The latest incarnation was called “Data Science: The Good, The Bad, and the Ugly” and earlier versions were called “Data: What is it Good For? (Absolutely Something!)”. We gave an introduction to data science and machine learning (at a more descriptive level) and discussed statistical, engineering, and ethical challenges when trying to use “data” to “solve” complex issues.

Semesters: Spring 2018, Fall 2015, Fall 2014

Graduate

High Dimensional Models in DSP and ML (ECE 549)

This is an “advanced topics” class covering mathematical tools for analyzing high-dimensional statistics and machine learning problems. The material mostly followed the book High Dimensional Probability by Roman Vershynin.

Semesters: Spring 2023, Spring 2022

Statistical Inference and Learning for Engineers (ECE 549)

This is a graduate course in statistical inference and machine learning which used to be called “Detection and Estimation Theory.”

Semesters: Spring 2024, Spring 2022, Spring 2021 (online), Spring 2020 (partly online), Spring 2016, Spring 2015

Information Theory and Coding (ECE 542)

This is a standard first course in information theory, largely following the book of Cover and Thomas.

Semesters: Fall 2015, Spring 2014

Statistical Learning and Optimization (ECE 539)

I taught this “special topics” course once (and may never teach it again, alas). It covers the basics of statistical learning theory, including empirical risk minimization, VC dimension, and so on.

Semesters: Fall 2014