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Detailed Information

Programs of Study

The Department of Computer Science at Rensselaer Polytechnic Institute offers Master of Science and Ph.D. degree programs in computer science. Major research areas include bioinformatics, computational geometry, computational science and engineering, computer graphics, computer vision, data mining, database systems, machine and computational learning, networking, parallel computing, pervasive computing, robotics, semantic Web, software design, and theoretical computer science.

The Master of Science degree in computer science at Rensselaer is a technical degree from which students may advance to positions of responsibility in the computing field with a solid foundation of knowledge to serve them. A number of students continue into Ph.D. study similarly well prepared.

A significant requirement of the program is the 6-credit master’s thesis based on original research.

The Ph.D. in computer science is the highest professional degree awarded by the Department. With it students may advance to university teaching and research and to careers in industrial research, with a solid foundation of knowledge and an ability to carry through original investigations in computer science.

Research Facilities

Research is supported by state-of-the-art facilities and equipment including the Rensselaer Libraries, whose electronic information system provides access to collections, databases, and the Internet from campus and remote terminals; the Rensselaer Computing System, a network of Unix and Windows workstations that permeates the campus with a coherent array of more than 7,000 nodes of distributed laptops, desktops, advanced workstations, and servers; a shared toolkit of applications for interactive learning and research and high-speed Internet connectivity; one of the country’s largest academically based, class 100 clean room facilities; high-performance campuswide computing facilities that allow for serial or parallel computation; and five core laboratories for molecular biology, proteomics, bio-imaging, and tissue engineering.

Rensselaer’s research capabilities have been enhanced with the addition of the Computational Center for Nanotechnology Innovations (CCNI). The result of a $100-million collaboration with IBM and New York State, the CCNI is the world’s most powerful university-based supercomputing center and a top ten supercomputing center of any kind in the world. The CCNI is made up of massively parallel Blue Gene supercomputers, POWER-based Linux clusters, and Opteron-based clusters, providing more than 100 teraflops of computational muscle and approximately a petabyte of shared online storage. The CCNI is available to faculty members and students of the Department of Computer Science who are engaged in research that requires a high-performance computing system. Application areas include bioinformatics, social network modeling, data mining, and robot design and motion planning.

Other facilities and research centers include the Center for Pervasive Computing and Networking, the Scientific Computation Research Center, the Center for Biotechnology and Interdisciplinary Studies; the George M. Low Center for Industrial Innovation; research centers for integrated electronics, terahertz science, nanotechnology, fuel cell and hydrogen research, lighting research, and infrastructure and transportation studies; and the Darrin Fresh Water Institute. With the help of generous gifts from alumni, the Department of Computer Science has recently completed the Landgraf Center for Computer Vision, Graphics, and Robotics. Theoretical and applied research carried out in the center is supported by its experimental facilities, including a light-duty machine shop, a Barrett Whole-Arm Manipulator with integral Barrett Hand, an Adept industrial robot arm, many cameras with image processing systems, a Leica HDS 3000 LiDAR scanner, and a small fleet of Garcia and RPI-designed Ratbot mobile robots.

In addition, the Department of Computer Science has its own networks and laboratories, allowing computer science students the opportunity to work on some of the most advanced scientific computing equipment available. These include special-purpose computers with unique architectures, advanced-function workstations, and a variety of general-purpose computers. The laboratory’s facilities include over 12 terabytes of network attached storage, a multi-gigabit switch fabric network backbone, and multiple computing labs and clusters for a wide exposure to new and different technologies. The network itself provides both traditional networking services, such as a Class B IPv4 (Internet protocol), as well as advanced services such as WiFi, IPsec, and connection to the next-generation IPv6 Internet– all over dedicated 100-megabit or gigabit connections to the desktop. Cluster access includes ccNUMA design computers as well as clusters of x86, AMD64, IA64, POWER, and UltraSparc processors in a heterogeneous grid environment. In addition to office machines, the laboratory provides a Department public-access lab for work and collaboration.

Financial Aid

Financial aid is available in the forms of teaching and research assistantships and fellowships, which include tuition scholarships and stipends. Rensselaer assistantships cover the academic year, with summer support available in many departments. University, corporate, or national fellowships fund many of Rensselaer’s full-time graduate students. Outstanding students may qualify for university-sponsored Rensselaer Graduate Fellowship Awards, which carry a minimum stipend of $22,000 and a full tuition and fees scholarship. All fellowship awards are calendar-year awards for full-time graduate students. Low-interest, deferred-repayment graduate loans are available to U.S. citizens with demonstrated need.

Cost of Study

Full-time graduate tuition for the 2008–09 academic year is $36,950. Other costs (estimated living expenses, insurance, etc.) are projected to be about $13,680. Therefore, the cost of attendance for full-time graduate study is approximately $50,630. Part-time study and cohort programs are priced differently. Students should contact Rensselaer for specific cost information related to the program they wish to study.

Living and Housing Costs

Graduate students at Rensselaer may choose from a variety of housing options. On campus, students can select one of the many residence halls and immerse themselves in campus life or choose from a select number of apartments designed for graduate students only. There are abundant, affordable options off campus as well, many within easy walking distance.

Student Group

Of the 1,176 graduate students, 29 percent are women, and 92 percent are full-time with 75 percent of fulltime graduate students studying at the doctoral level.

Student Outcomes

Rensselaer’s graduate students are hired in a variety of industries and sectors of the economy and by private and public organizations, the government, and institutions of higher education. Their starting salaries average $74,807 for master’s degree recipients and $82,750 for Ph.D. recipients.

Location

Located just 10 miles northeast of Albany, New York State’s capital city, Rensselaer’s historic 275-acre campus sits on a hill overlooking the city of Troy, New York, and the Hudson River. The area offers a relaxed lifestyle with many cultural and recreational opportunities, with easy access to both the high-energy metropolitan centers of the Northeast–such as Boston, New York City, and Montreal, Canada–and the quiet beauty of the neighboring Adirondack mountains.

The Institute

Recognized as a leader in interactive learning and interdisciplinary research, Rensselaer continues a tradition of excellence and technological innovation dating back to 1824. Rensselaer has five schools–Architecture, Engineering, Management, Science, and Humanities and Social Sciences–that offer more than 100 graduate programs in over forty-eight disciplines that attract top students, researchers, and professors. The discovery of new scientific concepts and technologies, especially in emerging interdisciplinary fields, is the lifeblood of Rensselaer’s culture and a core goal for the faculty, staff, and students. Fueled by significant support from government, industry, and private donors, Rensselaer provides a world-class education in an environment tailored to the individual.

Applying

Students with significant prior computer science experience are encouraged to apply for admission to the program. To be considered, an applicant must have a bachelor’s degree in a technical field, preferably related to computer science. Applicants must know how to program in at least three higher-level languages and must have a thorough working knowledge of computer organization and data structures. The applicant also must have substantial mathematics background at the college level, including a year of calculus and knowledge of linear algebra and discrete mathematics. Preference for admission and aid is given to applicants with research experience in an area related to the Department’s research.

Application materials are available from the Rensselaer Graduate Admissions Office. A complete application file consists of the application itself, plus official scores for the Graduate Record Examination General Test (waived for Rensselaer undergraduate CS majors), transcripts from all prior undergraduate and graduate work, a statement of background and goals, and letters of recommendation. International applicants are also required to include official scores for the Test Of English as a Foreign Language (TOEFL) examination. The Computer Science Subject Exam that is an optional part of the Graduate Record Examination is not required. The application deadline is January 1.

The Faculty and Their Research

• Faculty members’ Web pages can be accessed from http://www.cs.rpi.edu/people/faculty.html
• Sibel Adali, Associate Professor; Ph.D., Maryland. Multimedia database systems, information integration, query optimization. (adalis@rpi.edu)
• Elliot Anshelevich, Assistant Professor; Ph.D., Cornell. Design and analysis of algorithms, especially for large decentralized networks; strategic agents in networks and algorithmic game theory; approximation algorithms. (eanshel@cs.rpi.edu)
• Kristin Bennett, Professor (joint with Mathematical Sciences); Ph.D., Wisconsin–Madison. Mathematical programming, optimization, machine learning, data mining, support vector machines, and applications of data mining to bioinformatics, cheminformatics, finance, science, and engineering. (bennek@rpi.edu)
• Selmer Bringsjord, Professor (joint with Cognitive Science) and Director of Rensselaer AI and Reasoning Laboratory; Ph.D., Brown. Artificial intelligence, specifically including logical, mathematical, and philosophical foundations of AI; AI and creativity; reasoning-based systems for homeland defense/intelligence analysis; automated reasoning; automatic story generation and narrative control; intelligent tutoring systems. (selmer@rpi.edu)
• Christopher Bystroff, Associate Professor (joint with biology); Ph.D., California, San Diego. Bioinformatics, Markov models, protein structure prediction and design, molecular simulations. (bystrc@rpi.edu)
• Christopher Carothers, Associate Professor; Ph.D., Georgia Tech. Parallel and distributed systems, simulation, networking and real-time systems. (chrisc@cs.rpi.edu)
• Barbara Cutler, Assistant Professor; Ph.D., MIT. Computer graphics, geometry processing, algorithms, design tools for architecture. (cutler@cs.rpi.edu)
• Sanmay Das, Assistant Professor; Ph.D., MIT. Machine learning, computational finance and economics, multi-agent systems, information retrieval and biomedical informatics.
• Petros Drineas, Assistant Professor; Ph.D., Yale. Design and analysis of algorithms, in particular randomized and approximation algorithms; linear algebra algorithms and their applications in data mining. (drinep@cs.rpi.edu)
• W. Randolph Franklin, Professor (joint with Electrical, Computer, and Systems Engineering); Ph.D., Harvard. Computational cartography, computational geometry, computer graphics, geographic information science, computer security. (mail@wrfranklin.org)
• Daniel Freedman, Associate Professor; Ph.D., Harvard. Computer vision, image processing, computational geometry, computational topology. (freedman@cs.rpi.edu)
• Mark Goldberg, Professor; Ph.D., Russian Academy of Science (Novosibirsk). Experimental design and analysis of algorithms, combinatorics and graph theory, applications to social networks. (goldberg@cs.rpi.edu)
• Martin Hardwick, Professor; Ph.D., Bristol. Database systems for engineering and manufacturing applications. (hardwick@cs.rpi.edu)
• Jim Hendler, Professor; Ph.D., Brown. Artificial intelligence, semantic web, agent-based computing, high-performance processing. (hendler@cs.rpi.edu)
• David Isaacson, Professor (joint with Mathematical Sciences); Ph.D., NYU. Mathematical and computational problems arising in the diagnosis and treatment of heart disease and breast cancer. (isaacd@rpi.edu)
• Kenneth E. Jansen, Associate Professor (joint with Mechanical, Aerospace, and Nuclear Engineering); Ph.D., Stanford. Large-scale, parallel scientific computing with emphasis on fluid dynamics; topics include cardiovascular system modeling, turbulence modeling, level set methods, finite element formulations, error estimation, design of software frameworks, and parallel computing. (kjansen@scorec.rpi.edu)
• Shivkumar Kalyanaraman, Associate Professor (joint with Electrical, Computer, and Systems Engineering); Ph.D., Ohio State. Computer networking, concentrated around the theme of traffic management and high performance wireless networking. (shivkuma@ecse.rpi.edu)
• Mukkai Krishnamoorthy, Associate Professor; Ph.D., Indian Institute of Technology. Programming environments, design and analysis of combinatorial algorithms, performance issues in Internet, analysis of Web documents, network visualization. (moorthy@cs.rpi.edu)
• Malik Magdon-Ismail, Associate Professor; Ph.D., Caltech. Theory, algorithms, and applications of computational learning systems; computational finance; bioinformatics, social and communication network analysis. (magdon@cs.rpi.edu)
• Deborah L. McGuinness, Professor; Ph.D., Rutgers. Semantic Web, explanation of intelligent systems, semantically-enabled informatics, semantic eScience.
• Harry McLaughlin, Professor (joint with Mathematical Sciences); Ph.D., Maryland. Applied geometry, computational geometry, complex systems. (mclauh@rpi.edu)
• Ana Milanova, Assistant Professor; Ph.D., Rutgers. Software engineering, programming languages, compilers, program analysis, software testing, verification, reliable software systems. (milanova@cs.rpi.edu)
• Lee Newberg, Research Associate Professor; Ph.D., Berkeley. Algorithmic, statistical, and combinatorial approaches to molecular biology, currently using cross-species DNA multisequence alignments for phylogeny and the detection of conserved regions such as transcription factor binding sites; significance and credibility of sequence alignments. (leen@cs.rpi.edu)
• Mark Shephard, Professor (joint with Mechanical, Aerospace, and Nuclear Engineering); Ph.D., Cornell. Scientific computation, mesh generation, adaptive and parallel finite elements and multiscale methods. (shephard@scorec.rpi.edu)
• David Spooner, Professor and Acting Dean of Science; Ph.D., Penn State. Information security, computer science and information technology education. (spoond@rpi.edu)
• Charles Stewart, Professor; Ph.D., Wisconsin–Madison. Computer vision, medical applications. (stewart@cs.rpi.edu)
• Boleslaw Szymanski, Claire and Roland Schmitt Distinguished Professor and Director of the Center for Pervasive Computing and Networking; IEEE Fellow, Ph.D., Polish Academy of Sciences (Warsaw). Computer and sensor networks, distributed and parallel computing, distributed simulation, computational biology. (szymab@rpi.edu)
• Jeff Trinkle, Professor and Chair; Ph.D., Pennsylvania. Robotics, manufacturing automation, physics engines, multibody dynamics, computational topology, human-machine interaction. (trink@cs.rpi.edu)
• Carlos Varela, Assistant Professor; Ph.D., Illinois at Urbana-Champaign. Internet computing, middleware, concurrent and distributed systems, programming languages, coordination models, computational science. (cvarela@cs.rpi.edu)
• Michael Wozny, Professor (joint with Electrical, Computer, and Systems Engineering); Ph.D., Arizona. Computer graphics, computer-aided geometric design, information systems in engineering design and manufacturing. (woznym@ecse.rpi.edu)
• Bülent Yener, Associate Professor; Ph.D., Columbia. Computer networks, biological networks, bioinformatics, security, combinatorial optimization. (yener@cs.rpi.edu)
• Mohammed Zaki, Associate Professor; Ph.D., Rochester. Data mining and knowledge discovery, bioinformatics, generic programming, high performance computing. (zaki@cs.rpi.edu)
• COMPUTER SCIENCE RESEARCH GROUPS
• Bioinformatics: Bioinformatics is the science of managing, retrieving, analyzing, and interpreting biological data. Research is being carried out on topics such as sequence assembly, protein and RNA structure prediction, sequence/structure motifs, comparative genomics, and regulatory networks. Research also spans emerging areas such as microarray data analysis, protein design, high-dimensional indexing, database support, information integration, and data mining. Faculty: Chris Bystroff, Sanmay Das, Malik Magdon-Ismail, Lee Newberg, Bülent Yener, Mohammed Zaki.
• Computational Geometry: Current research in computational geometry concentrates on algorithms for the reconstruction of smooth geometric objects from their samples. Problems of interest include characterizing the conditions on sampling density, which allow a curve to be reconstructed from its samples. The reconstruction is homeomorphic and sufficiently close to the original and the algorithms developed to achieve the reconstruction. Also involved are the dependence of such algorithms on the dimension of the embedding space, related algorithms for the reconstruction of surfaces and manifolds, and finding the most concise representation of a manifold in terms of its samples. A second research track focuses on applications of computational geometry, particularly in robotic motion planning. Faculty: Barbara Cutler, W. Randolph Franklin, Daniel Freedman, Charles Stewart.
• Computational Science and Engineering: Students and faculty members work on computational approaches and algorithms to solve large-scale problems that arise in natural science and engineering. Current research includes adaptive methods for solving partial differential equations, multiscale computations, scientific software libraries, algorithms for medical imaging and tomography, high-performance matrix algorithms, computational biology, and adaptive software for high-performance computation over dynamic parallel and distributed environments. Faculty: David Isaacson, Ken Jansen, Malik Magdon-Ismail, Mark Shephard, Boleslaw Szymanski, Carlos Varela.
• Computer Graphics: Faculty members and students are interested in a wide variety of rendering, geometry, simulation, and visualization problems motivated by computer games, special effects in movies, architectural design and previsualization, and many other exciting applications. Topics that are studied include physically-based digital sculpting, efficient high-quality photo-realistic rendering, new data representations and algorithms, and the use of modern graphics hardware for interactive applications. Faculty: Barbara Cutler, W. Randolph Franklin.
• Computer Vision: Computer vision and biomedical image analysis research in the Department of Computer Science covers a wide range of topics. Developing algorithms for registration and change detection, especially in the diagnosis and treatment of diseases of the human retina, is the largest current project; a related project studies the theory and application of robust estimation techniques in computer vision. A second research area focuses on the tracking and segmentation of objects, both in two- and three-dimensional images, using model-based algorithms. The techniques developed are general and may be used in a variety of computer vision tasks; the applications pursued at RPI are mainly focused on biomedical problems, such image-guided radiation therapy. A third track involves the development of stochastic models for the interpretation of video data, for example traffic video. Faculty: Daniel Freedman, Charles Stewart.
• Data Science: Data Mining; Machine and Computational Learning; Algorithms for Massive Data Sets: This research area deals with the theoretical and applied aspects of automated information extraction (knowledge discovery) from data. For large data sets, emphasis is placed on developing efficient, scalable, and parallel algorithms for various data mining techniques in addition to the data management itself. Examples include association rules, classification, clustering, and sequence mining. For small data sets, the emphasis is on robust computational learning systems (supervised, unsupervised and reinforcement) and their theoretical properties. Application areas include combinatorial optimization, computational biology (bioinformatics, computational genomics), Web mining, geographic information systems, and computational finance. Faculty: Sanmay Das, Petros Drineas, Mark Goldberg, Malik Magdon-Ismail, Mohammed J. Zaki.
• Database Systems: This research area deals with the efficient and effective methods for storing, querying, and maintaining data from possibly disparate and heterogeneous resources. Data is used in many different applications, from scientific data sets, sensor data, images, video, and audio to hypertext documents and data on stock market behavior. Research focuses on methods for caching data, querying large and distributed databases, and supporting applications such as computer-aided design and manufacturing and collaborative engineering. Faculty: Sibel Adali, Martin Hardwick, David Spooner.
• Logic-Based Artificial Intelligence (RAIR Lab): Researchers in the RAIR Lab design and build intelligent agents, software, robots, etc., on the basis of formal logic. R&D has been and is sponsored by NSF, DARPA, ARDA/DTO/IARPA, and AFOSR as well as other organizations. Ph.D. students need to have some background in logic, AI, and relevant programming paradigms. Faculty: Selmer Bringsjord.
• Pervasive Computing and Networking: Researchers investigate computer networks and their protocols, with a focus on wireless and sensor networks through the International Technology Alliance, a new ten-year research consortium led by the IBM Research Division and funded jointly by the U.S. and U.K. Research topics include distributed sensing and delivery, quality of information of sensor date, and adapting sensor networks to dynamic demands. Another area of activity is the security of computers, networks, and sensors. Security concerns are quickly becoming a significant barrier to the wide-spread acceptance of pervasive computing. The research topics include trust in Internet communications, identity of groups on the Internet, and cryptographic and systemic challenges in sensor networks. Finally, in the area of high-performance pervasive computing, the focus is on computational environments in which task allocation, migration, and fault tolerance are supported automatically and on application of such environments to computations relevant to different scientific disciplines. Faculty: Christopher Carothers, Boleslaw Szymanski, Carlos Varela, Bülent Yener.
• Programming Languages and Software Engineering: The Programming Languages and Software Engineering research group investigates programming models, languages, concepts, methodologies, and tools to enable the development of correct, efficient, reliable, and maintainable software. Faculty: Ana Milanova, Carlos Varela.
• Robotics: Research is being conducted in the areas of dexterous manipulation, pursuit-evasion, multi-robot coordination, and human-robot collaboration. Dexterous manipulation is important because currently robots are able to sense various important aspects of the world around them, but they have great difficulty performing physical work. Robots cannot fulfill their potential until they can perform common manipulation tasks (such as making a bed or repairing a tile floor) that currently only humans can. Pursuit-evasion is important to efficient monitoring of the world around us. In particular, this area of research combines traditional robotics topics with some from computer vision. Multi-robot coordination enables a group of robots to perform tasks that individual robots will not be able to do. Developing coordination and control strategies is fundamental to this goal. Finally, human-robot collaboration is important to enabling personal robot assistants to work safely with humans. In all of these areas, the Department is making research contributions of both theoretical and applied nature. Faculty: Jeff Trinkle.
• Security: Researchers in the security group focus on security problems at the systems level including discoverng hidden networks in social networks; network camouflaging; and privacy protection in data mining systems. Faculty: Mark Goldberg, Mukkai Krishnamoorthy, Malik Magdon-Ismail, Boleslaw Szymanski, Bülent Yener.
• Theory: Theory of Computation provides the foundation needed for effective applications. The theory group brings together researchers in many areas of computer science to develop novel approaches and solutions to problems in information technology. The research is characterized by close collaboration with researchers in diverse application areas, such as networking, bioinformatics, visualization, pattern recognition, physics and astronomy, digital library, data mining, and experimental algorithmics. Faculty: Elliot Anshelevich, Petros Drineas, Daniel Freedman, Mark Goldberg, Mukkai Krishnamoorthy, Malik Magdon-Ismail, Bülent Yener.

Correspondence and Information

Rensselaer Polytechnic Institute
Terry Hayden
Lally 208
110 8th Street
Troy, New York 12180
Telephone: 518-276-8419
Email: haydent@cs.rpi.edu