Computer Science

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Department Head: Mohammed Zaki

Associate Department Head: Stacy Patterson

Home Page: https://compsci.rpi.edu/

The Computer Science Department conducts cutting-edge pedagogy and research that spans foundational theory, systems innovation, and interdisciplinary applications. Faculty expertise integrates core areas, such as artificial intelligence and machine learning, network science and complex systems, semantic web and knowledge graphs, distributed and cyber-physical systems, software engineering and verification, quantum computing, and security, privacy, and trust.

The department emphasizes both the development of fundamental algorithms and the deployment of scalable trustworthy systems that address real-world challenges across domains that include healthcare, finance, bioinformatics, autonomous systems, and scientific computing. With strong collaboration across disciplines, the department fosters innovations in data-centric AI, trustworthy machine learning, resilient and adaptive systems, and decentralized intelligent platforms.

Grounded in excellence, the department is committed to advancing computing for the benefit of society, driving both fundamental discovery and impactful translational research.

Research Areas

Artificial Intelligence, Machine Learning, and Trustworthy AI

Research in AI and machine learning spans core algorithmic foundations, large-scale models, data-centric approaches, and interdisciplinary applications. Emphasis is placed on building trustworthy, explainable, and robust AI systems that operate reliably in safety-critical domains, such as healthcare, finance, and autonomous systems. The department also advances AI hardware and novel training paradigms, including federated and decentralized learning. Representative topics include:

• Large language models
• Trustworthy and explainable AI
• Federated learning
• AI hardware
• Data-centric and safe AI
• AI for health, finance, and science

Network Science, Complex Systems, and Graph Learning

This research area focuses on understanding and modeling large, interconnected systems, such as social, biological, and information networks. Faculty develop algorithms for graph mining, resilience analysis, complex system modeling, and graph-based learning, with applications in epidemiology, bioinformatics, cybersecurity, and financial systems. Representative topics include:

• Social networks
• Graph learning and network mining
• Resilience and percolation
• Complex systems modeling
• Bioinformatics and health networks

Semantic Web, Knowledge Graphs, and Web Science

Faculty in this area advance the development of semantic web technologies and knowledge graphs to enable intelligent information integration, reasoning, and explainability across diverse domains. Research includes ontologies, hybrid symbolic-neural reasoning, decentralized intelligent systems, and applications to web science, health informatics, and financial technology. Representative topics include:

• Knowledge graphs and ontologies
• Semantic web and web science
• Hybrid AI reasoning
• Explanation systems
• Decentralized intelligent systems
• FinTech and health informatics applications

Distributed Systems, Cloud/Edge Computing, and Cyber-Physical Systems

This research area addresses the design of reliable, scalable, and adaptive distributed systems for both cloud-scale and embedded environments. Research includes real-time systems, middleware for edge/cloud computing, cyber-physical system security, and verification for safety-critical applications, such as autonomous systems and flight control. Representative topics include:

• Parallel and distributed computing
• Edge and cloud computing
• Middleware and adaptive systems
• Cyber-physical systems (CPS)
• Real-time and exascale systems
• Safety, verification, and sensor fusion

Software Engineering, Programming Languages, and Verification

This area develops advanced methodologies for building reliable and secure software systems. Research includes program analysis, formal verification, compilers, concurrent programming models, and correctness of both classical and quantum software stacks. Representative topics include:

• Program analysis and verification
• Compilers and programming languages
• Concurrent and adaptive systems
• Reliable and secure software engineering
• Quantum software stacks

Quantum Computing and Algorithms

Leveraging RPI’s on-campus IBM quantum computer, faculty advance quantum computing across multiple layers of the stack, including architecture, compilers, and algorithms, with applications to optimization, machine learning, and scientific computing. Research also explores how quantum techniques intersect with classical AI, networks, and security. Representative topics include:

• Quantum architecture and compilers
• Quantum machine learning
• Quantum optimization algorithms
• Hardware-software co-design
• Quantum applications to AI and networks

Security, Privacy, and Trust in Computing

This research area focuses on ensuring the security, privacy, and trustworthiness of computing systems at all levels, from hardware to distributed infrastructures. Research includes privacy-preserving machine learning, federated data sharing, secure cyber-physical systems, and blockchain-based decentralized security models. Representative topics include:

• Data privacy and secure AI
• Secure cyber-physical systems
• Privacy-preserving distributed learning
• Blockchain and decentralized systems
• Trust in autonomous and financial systems

Computational Economics, Algorithmic Game Theory, and Strategic Decision-Making

This research area explores the interface between computer science and economics, focusing on how computational systems interact with agents in large-scale networks and marketplaces. Research includes algorithmic game theory, mechanism design, decentralized decision-making, approximation algorithms, and economic aspects of networked and distributed systems. These methods have broad applications to online markets, financial networks, resource allocation, and multi-agent systems. Representative topics include:

• Algorithmic game theory
• Mechanism design
• Strategic agents in networks
• Economics of distributed systems
• Approximation and optimization algorithms
• Computational finance and financial networks

Cross-Cutting Application Areas

In addition to the above research areas, there are many cross-cutting domains and applications of interest. Representative topics include:

• Health informatics
• Financial analytics and FinTech
• Scientific and high performance computing
• Bioinformatics and medical informatics
• Autonomous systems
• Education and open source software

Undergraduate Programs

The undergraduate degree program in computer science provides an excellent background for students entering the work force directly upon graduation and for those pursuing graduate studies.

Outcomes of the undergraduate curriculum

Upon completing the undergraduate program in computer science at Rensselaer, students will be able to demonstrate:

• An ability to develop a computational formulation of a problem, one or more data structures and algorithms to solve the problem, a program to implement a solution, and strategies for testing and verifying that the program is correct.
• Knowledge of the mathematical and theoretical underpinnings of computer science.
• An ability to adapt a solution to different computing platforms, paradigms, and/or programming languages.
• An advanced depth of knowledge in one or more of the following areas (tracks): systems and software; vision, graphics, robotics, and visualization; theory and algorithms; artificial intelligence, machine learning, and data science.

Dual Major Programs

Computer science students can obtain a dual major with any other major offered at Rensselaer. In many cases, students can obtain a dual major within the 128 credits of a single degree, since many courses can be counted toward both majors. Popular majors often combined with computer science include: mathematics; physics; business and management; business analytics; games and simulation arts and sciences; various fields of engineering (which require additional credits hours); information technology and web science; and electronic media, arts, and communication.

Special Undergraduate Opportunities

The Computer Science Department strongly encourages students to take part in the following special programs.

Co-operative Education and Internships

Numerous employment opportunities exist for computer science majors during their course of study. Students are encouraged to pursue at least one internship and/or co-op experience during their academic career, in particular as part of their Arch away semester. More detailed information on the co-op program is available in the School of Science introduction section and the Educational Programs and Resources section of this catalog. Internships are typically available over the summer, even after a student’s freshman year.

Undergraduate Research Program (URP)

This program allows students to participate in faculty research activities. The department encourages students to take advantage of these opportunities, through which students can earn either pay or course credit. Each student is required to take at least 2 credits of URP or RCOS (see below). Additional benefits may include being named co-authors on journal papers or the opportunity to make presentations at professional conferences. Additional information is available in the School of Science introduction section and the Educational Programs and Resources section of this catalog.

Rensselaer Center for Open Source Software (RCOS)

This program provides a creative, intellectual, and entrepreneurial outlet for students to use open-source software platforms to develop open-source applications that address societal problems. RCOS team members participate in a collaborative environment for sharing and practicing programming and project management skills, including code reviews and project presentations. Students participate in RCOS for course credit. Each student is required to take at least 2 credits of RCOS or URP (see above). At the beginning of each semester, including the full summer session, faculty and student leaders review project proposals and determine project teams, goals, and milestones. Students may participate in RCOS across multiples semesters to maximize their experience.

Graduate Programs

The Department of Computer Science offers M.S. and Ph.D. degrees in computer science. Applications for the M.S. or Ph.D. in computer science should be sent to the Graduate Admissions Office to be received no later than January 1 for Ph.D. and March 1 for M.S. for fall admission; August 15 is the deadline for spring admission. Applicants must provide transcripts, two letters of recommendation, a current résumé, and a statement of goals; GRE scores are strongly recommended. Each student’s background is expected to include courses in discrete mathematics, calculus, data structures, computer organization, and computing languages, none of which can be counted toward the graduate degree. Admission is extremely competitive, and meeting the minimum requirements does not assure admission.

Course Descriptions

Courses directly related to all Computer Science curricula are described in the Course Description section of this catalog under the department code CSCI.

Faculty*

Professors

Adali, S.—Ph.D. (University of Maryland); natural language processing; trust; social network analysis; information integration; information retrieval; database systems.

Anshelevich, E.—Ph.D. (Cornell University); economics and computation; theory and algorithms, especially for large decentralized networks; strategic agents in networks and algorithmic game theory; approximation algorithms.

Carothers, C.—Ph.D. (Georgia Institute of Technology); artificial intelligence hardware; exascale systems; parallel and distributed systems; simulation; networking and real-time systems.

Hendler, J.—Ph.D. (Brown University); artificial intelligence; semantic web; agent-based computing; high-performance processing.

Magdon-Ismail, M.—Ph.D. (California Institute of Technology); theory, algorithms, and applications of machine learning; computational finance; learning from networked data (social networks, hyperlinked networks); quantum computing.

McGuinness, D.L.—Ph.D. (Rutgers University); knowledge graphs; ontologies; semantic web; explanation; hybrid large language models; artificial intelligence applications.

Milanova, A.—Ph.D. (Rutgers University); software engineering; programming languages; compilers; program analysis; software testing; verification; reliable software systems.

Stewart, C.—Ph.D. (University of Wisconsin); computer vision with applications in ecology, medicine, and zoology.

Szymanski, B.K.—Ph.D. (National Academy of Sciences, Warsaw, Poland); network science; social networks; applications of generative artificial intelligence; algorithm design.

Varela, C.A.—Ph.D. (University of Illinois at Urbana-Champaign); safer flight systems; software verification; cloud and edge computing; middleware for adaptive distributed systems; concurrent programming models and languages.

Yener, B.—Ph.D. (Columbia University); complex networks; bioinformatics; medical informatics; security and privacy; computer networks; combinatorial optimization.

Zaki, M.—Ph.D. (University of Rochester); machine learning and data mining; graph learning; knowledge graphs; financial text analytics; personal health informatics.

Associate Professors

Cutler, B.—Ph.D. (Massachusetts Institute of Technology); computer graphics; interactive visualization; computational geometry; open-source software for education.

Gao, J.—Ph.D. (Shanghai Jiao Tong University); network science; complex systems; percolation; resilience; artificial intelligence.

Gittens, A.—Ph.D. (California Institute of Technology); trustworthy machine learning; large-scale machine learning; applications of randomized numerical linear algebra.

Patterson, S.—Ph.D. (University of California, Santa Barbara); distributed  systms and algorithms; machine learning; data privacy; quantum computing.

Slota, G.—Ph.D. (Pennsylvania State University); graph and network mining; big data analytics; machine learning, bioinformatics, and their relation to parallel, scientific, and high-performance computing.

Assistant Professors

Ivanov, R.—Ph.D. (University of Pennsylvania); safe and secure autonomy; neural network verification; cyber-physical systems (CPS); CPS security; control theory; sensor fusion.

Liang, Z.—Ph.D. (University of Notre Dame); hardware-software co-design for quantum computing; quantum architecture and compilers; quantum machine learning and optimization.

Ma, Y.—Ph.D. (Michigan State University); machine learning with graphs; trustworthy artificial intelligence; data-centric artificial intelligence; large language models.

Mohammadi Amiri, M.—Ph.D. (Imperial College, London, UK); large language models; data-centric machine learning; federated learning.

Seneviratne, O.—Ph.D. (Massachusetts Institute of Technology); decentralized intelligent systems; web science; knowledge graphs; semantic web; blockchain; FinTech; health informatics.

Yu, L.—Ph.D. (Georgia Institute of Technology); data privacy and security; trustworthy artificial intelligence; machine learning systems; mobile/cloud computing.

Professors of Practice

Goldschmidt, D.E.—Ph.D. (Rensselaer Polytechnic Institute); operating systems; systems programming; network programming; software engineering; large-scale software engineering; computer ethics; computer science education.

Turner, W.—Ph.D. (Rensselaer Polytechnic Institute); software engineering; scientific research; open-source software.

Senior Lecturers

DiTursi, D.—Ph.D. (University at Albany); algorithms; computer science theory; discrete mathematics; graph theory.

Gilder, M.—Ph.D. (Rensselaer Polytechnic Institute); cloud computing; high-performance computing; cybersecurity; computer networks.

Kuzmin, K.—Ph.D. (Rensselaer Polytechnic Institute); network science; social networks; community detection; high-performance computing.

Mushtaque, U.—Ph.D. (Rensselaer Polytechnic Institute); data science; recommender systems; supply chain modeling; operations research.

Kuzmin, K.—Ph.D. (Rensselaer Polytechnic Institute); network science; social networks; community detection; high-performance computing.

Xiao, J.—Ph.D. (College of William and Mary); network security and defense; operating systems.

Lecturers

Keshan, N.—Ph.D. (Rensselaer Polytechnic Institute); artificial intelligence; ontology- and knowledge graph-based modeling, semantic representation, and knowledge evaluation; semantic web; educational research; marginizalized communities; stress detection and mitigation; unconventional research.

Sturman, J.—M.S. (University at Buffalo); software engineering; software design; systems analysis; systems documentation; documentation management.

Zarifneshat, M.—Ph.D. (Michigan State University); wireless networks; operating systems; computer architecture.

Professors Emeritus

Glinert, E.—Ph.D. (University of Washington); assistive technology; universal access; human-computer interaction; multimedia information visualization.

Hardwick, M.—Ph.D. (Bristol University, UK); database systems for engineering and manufacturing applications.

Krishnamoorthy, M.S.—Ph.D. (Indian Institute of Technology); programming environments; design and analysis of combinatorial algorithms; performance issues in Internet; analysis of Web documents; network visualization.

Musser, D.—Ph.D. (University of Wisconsin); programming methodology; generic software libraries; formal methods of specification and verification; mechanized logic and proof methodology, applied to correctness and optimization of abstract algorithms.

Spooner, D.—Ph.D. (Pennsylvania State University); database systems; database security; computer science and information technology education.

*Departmental faculty listings are accurate as of the date generated for inclusion in this catalog. For the most up-to-date listing of faculty positions, including end-of-year promotions, please refer to the Faculty Roster section of this catalog.

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