Coursework
Computer Science
- CS 1340: Consequences in Computing
- CS 1710: Cognitive Science
- CS 2110: Data Structures and Object-Oriented Design
- CS 2800: Discrete Mathematics
- CS 2850: Networks
- CS 3110: Data Structures and Functional Programming
- CS 3410: Computer Systems
- CS 4414: Systems Programming
- CS 4701: AI Practicum
- CS 4780: Machine Learning
- CS 4787: Principles of Large-Scale Machine Learning
- CS 4820: Algorithms
- CS 5643: Physically Based Animation
- CS 6662: Computational Imaging
- CS 6740: Advanced Language Technologies
- CS 6756: Learning for Robot Decision Making
- CS 6850: Information Networks
Electrical and Computer Engineering
- ECE 4960: Dynamic Networks and Games
- ECE 6210: Theory of Linear Systems
- ECE 6960: Special Topics: Micro and Nano Robotics
- ECE 6960: Special Topics: Robust and Stochastic Optimization
Miscellaneous
- MAE 6760: Model-Based State Estimation
- PHYS 1110: Introduction to Experimental Physics
- PHYS 1112: Mechanics and Heat
- PHYS 2213: Elecromagnetism
- MATH 2930: Differential Equations
- ENGRD 2700: Probability and Statistics
- MATH 25C: Multivariate Calculus
- MATH 31: Linear Algebra
- PHYS 20A: Mechanics of Solids and Fluids
Teaching
CS 4782: Deep Learning
Spring 2025
This class is an introductory course to deep learning. It covers the fundamental principles behind training and inference of deep networks, the specific architecture design choices applicable for different data modalities, discriminative and generative settings, and the ethical and societal implications of such models.
CS 4780: Machine Learning
Fall 2023, Spring 2024, Fall 2024
The course provides an introduction to machine learning, focusing on supervised learning and its theoretical foundations. Topics include regularized linear models, boosting, kernels, deep networks, generative models, online learning, and ethical questions arising in ML applications.
ECE 4960: Dynamic Networks and Games
Spring 2024
This course will provide the necessary mathematical and modeling tools needed to describe and understand these network systems. Questions of interest will be how the network structure impacts the dynamics of network systems, how network properties can be exploited to maximize system performance or resilience and how one can address these questions while also accounting for strategic human behavior. The course will introduce tools that can be used to address these questions and successfully overcome challenges related to the coupled, distributed, and large-scale nature of network systems in environments with limited sensing, communication, and control capabilities.
ECE 6210: Theory of Linear Systems
Fall 2024
State-space and multi-input-multi-output linear systems in discrete and continuous time. The state transition matrix, the matrix exponential, and the Cayley-Hamilton theorem. Controllability, observability, stability, realization theory. At the level of Linear Systems by Kailath.