Computer science has played a pivotal role in developing innovative technologies to monitor patients' behaviors and behavioral phenotypes beyond the traditional laboratory or clinical environments. A profound understanding of patient behaviors has the potential to unlock a myriad of applications, including the implementation of targeted behavioral interventions to drive substantial improvements in health-related outcomes, monitoring the progress of individuals undergoing rehabilitation, and assessing the efficacy of emerging therapeutic interventions.

This course provides an in-depth examination of the issues in system security, and assumes prior knowledge of operating system concepts. We will start with learning the classic approaches to security attacks and defenses from the perspective of a single host system. Topics include access control, information flow control, system auditing, privilege separation, security policies, host-based intrusion detection etc.

This course will cover Bayesian and Markov networks and their dynamic and relational extensions; exact and approximate inference methods; estimation of both the parameters and structure of graphical models.

This course covers a broad range of advanced level topics in natural language processing. It is intended for graduate students in computer science who have familiarity with machine learning fundamentals. It may also be appropriate for computationally sophisticated students in linguistics and related areas. Topics include probabilistic models of language, computationally tractable linguistic representations for syntax and semantics, and selected topics in discourse and text mining. After completing the course, students should be able to read and evaluate current NLP research.

In-depth introduction to Artificial Intelligence concentrating on aspects of intelligent agent construction. Topics include: situated agents,advanced search and problem-solving techniques, principles of knowledge representation and reasoning, reasoning under uncertainty, perception and action, automated planning, and learning.

An in-depth examination of principles of distributed systems and advanced operating systems. Topics include client-server programming, distributed scheduling, virtualization and cloud computing, distributed storage, IoT. Familiarity with an undergraduate course on operating systems (COMPSCI 377 or equivalent) is helpful.

The course will teach students algorithms that intelligently process, analyze and generate visual data. The course will start by covering 2D image and 3D shape representations, classification and regression techniques, and the fundamentals of deep learning. The course will then provide an in-depth background on analysis and synthesis of images and shapes with deep learning, in particular convolutional neural networks, recurrent neural networks, memory networks, auto-encoders, adversarial networks, reinforcement learning methods, and probabilistic graphical models.

Optimization techniques are frequently used in many areas of computer science, in particular in machine learning, to handle a variety of large-scale, data intensive problems. Moreover, algorithmic tools of ever-increasing sophistication are introduced at a fast pace, offering unparalleled opportunities to solve problems efficiently. This class will cover a wide range of optimization methods, including convex, nonconvex and discrete optimization.

Covers topics such as the relational model, query languages, database design; storage and indexing, query processing and optimization, transaction management; Big Data systems; information integration; data stream systems; database security.

This course examines sociotechnical problems with existing modes of social media and works towards building new, affirmative visions for social media through technical and policy means. Students will examine interventions to address problems with contemporary social media and design and develop possible interventions.