Course Catalog

Machine learning discovers and reproduces patterns in existing data. Thus, unthinking application of ML risks perpetuating societal biases including racial and gender bias. This course is about the emerging science of fairness in ML. Topics include: sources of bias in ML; methods for detecting, measuring, and mitigating bias; comparison of fairness criteria; data modeling versus algorithmic modeling; causal inference and fairness; other topics in AI ethics including privacy, accountability, transparency, power, and justice. Students take on hands-on empirical projects of their choosing.

Introduction to basic computational methods used for problems arising in molecular biology. Topics include computational approaches to: sequence similarity and alignment, phylogenic inference, gene recognition, gene expression analysis, structure prediction, and whole- and cross-genome analysis.

Cross-listed as MOL551 Department of Molecular Biology.

Introduces students to computational issues involved in analysis and display of large-scale biological data sets. Algorithms covered will include clustering and machine learning techniques for gene expression and proteomics data analysis, biological networks, joint learning from multiple data sources, and visualization issues for large-scale biological data sets. No prior knowledge of biology or bioinformatics is required; and introduction to bioinformatics and the nature of biological data will be provided. In depth knowledge of computer science is not required, but students should have some understanding of programming and computation.

Cross-listed as MOL557 Department of Molecular Biology.

Survey of computer networks covering end-to-end principle, multiplexing, virtualization, packet switching vs. circuit switching, router design, network protocols, congestion control, internet routing architecture, network measurement, network management, and overlay networks. Survey of research papers from classic literature through contemporary research.

Course covers pivotal developments in computing, including hardware, software, and theory. Material will be covered by reading seminal papers, patents, and descriptions of highly-influential architectures. Course emphasizes a deep understanding of the discoveries and inventions that brought computer systems to where they are today, and class is discussion-oriented. Final project or paper required. Graduate students and advanced undergraduates from ELE, COS, and related fields welcome.

Cross-listed as ELE583.

The notions of entropy and information, originated in statistical physics and electrical engineering, have become central in almost all branches of modern science. The course gives an introduction to information theory and its applications, from the perspective of math, computer science and theoretical computer science. The course is intended for students from all areas of computer science, as well as math, physics, bio-informatics and electrical engineering. Background in probability theory and linear algebra is required.

The notions of entropy and information, originated in statistical physics and electrical engineering, have become central in almost all branches of modern science. The course gives an introduction to information theory and its applications, from the perspective of math, computer science and theoretical computer science. The course is intended for students from all areas of computer science, as well as math, physics, bio-informatics and electrical engineering.

Summer research project designed in conjunction with the student's advisor and an industrial, NGO, or government sponsor that will provide practical experience relevant to the student's research area. Start date no earlier than June 1. A final paper is required.

One-term full time research internship at a host institution to perform scholarly research directly relevant to a student's dissertation work. Research objectives will be determined by the student's advisor in consultation with the outside host. Monthly progress reports and a final paper are required. Enrollment limited to post-generals students. Students will be permitted to enroll in this one-semester course at most twice. Participation will be considered exceptional.

Discussion and study of problems and research results of current interest in computer systems.

Topics in computational complexity, the analysis of algorithms, and other areas of theoretical computer science.

Research-oriented topics in the design and implementation of software systems. Specific topics are determined by the current literature and by student and faculty interest.

Topics involving current research in computer science and applications in other fields. Click here to see recently taught topics.