Programs of the Faculty of Computing and Information Science
Genomic databases, protein databanks, MRI images of the human brain, and remote-sensing data on landscapes contain unprecedentedly detailed information about biological systems that are transforming the way that we do almost all of biology. Problems investigated by computational biologists span a wide spectrum, including topics as diverse as the genetics of disease susceptibility, comparing entire DNA genomes to uncover the secrets of evolution, predicting protein structures and understanding their motions and interactions, designing new therapeutic drugs, mathematically modeling the complex signaling mechanisms within cells, predicting how ecosystems will respond to climate change, and designing recovery plans for endangered species. The computational biologist must have skills in mathematics, statistics, and the physical sciences, as well as in biology. A key goal in training is to develop the ability to relate biological processes to mathematical models that can be solved computationally.
Cornell faculty members work primarily in four subareas of computational biology: biomolecular structure and function, bioinformatics and data mining, ecology and evolutionary biology, and statistical and computational methods for modeling biological systems. These include the computational study of topics such as DNA databases, protein structure and function, computational neuroscience, biomechanics, population genetics, and management of natural and agricultural systems. Beyond the core skills in mathematics, physical sciences, and biology, the computational biology program of study requires additional coursework in mathematics, computer programming, a “bridging” course aimed at connecting biology to computation, and an advanced course where the theoretical/computational component of one aspect of biology will be studied.
The CIS–created undergraduate program of study in computational biology encourages students to gain fundamental skills and understanding that will allow them to focus on specific subareas and problems later in their careers. Computational biology is an emerging area that has applications as broad as biology itself. The problems of interest, as well as the tools available to study them, will undoubtedly change during the four years of an undergraduate program. The program is an excellent preparation for students who wish to specialize in one of these computational areas in graduate school.
There is great, and increasing, demand for research scientists and technical personnel who can bring mathematical and computational skills to the study of biological problems.
Computational Molecular Biology (CMB) is an interdisciplinary field that brings together numerous diverse research areas. A separate and isolated program in CMB will have difficulties in maintaining excellence in all fields, in teaching the diverse tools, and in providing the breadth of research topics that form the core of CMB. We therefore propose a different model of a multifield program in Computational Molecular Biology. For example, to meet the program conditions, a Ph.D. candidate in computer science can have supplementing studies in molecular biology. Alternatively, a Ph.D. student in the biophysics field can have supplementing studies in computer science and meet the CMB requirements. Hence, the students of this program may come from diverse fields such as molecular biology and genetics or computer science, creating the diverse community of researchers that we seek in CMB.
Through the Cornell Theory Center, two competitive IBM fellowships were granted to undergraduate students doing summer bioinformatic research. The research is a collaboration between the CBSU at the CTC and the Cornell faculty. It exposes the students to high-performance computing and its application to bioinformatics. The CBSU mission is to bridge the gap between molecular biology and mathematical sciences, by helping individual researchers or students, maintaining a computational-biology facility, and by conducting intensive training workshops.
Many of the faculty members in engineering and the sciences engage in research that is computationally driven. Computational science and engineering (CS&E) at Cornell continues to be as strong as ever. Critical to the overall environment is the Cornell Theory Center, whose Velocity Cluster supports lines of inquiry that require intensive, large-scale computation.
This year the CS&E subgroup began offering a series of four minicourses, taught by Andrew Pershing:
COM S 401 Applied Scientific Computing with MATLAB
COM S 402 Scientific Visualization with MATLAB
COM S 403 Development of Scientific Computing Programs in a Unix Environment
COM S 404 Survey and Use of Software Libraries for Scientific Computing
Designed primarily for first-year graduate students, these four-week courses provide an efficient introduction to important topics in applied scientific computing. The first two courses focus on the MATLAB programming environment and demonstrate how systems like MATLAB can be used to aid scientific research. The last two courses consider the process of developing scientific software and explore a range of techniques and tools to make this process more efficient. These well-received courses attracted students from across the university.
Graphics has been an area of strength at Cornell for many years, both in teaching and research. The latter is conducted mainly through the interdisciplinary Program in Computer Graphics and its associated laboratory. Computer science Ph.D. graduates of this program are leaders in academia and industry. Now, as with many areas of computer science, graphics is becoming broader as advances in hardware and software allow researchers to solve more difficult problems in realistic rendering, animation, scientific visualization, virtual reality, user interfaces, computer vision, and other areas. As it broadens, the subject touches the digital arts in more fundamental ways, and this has led us to explore a program in digital arts and graphics with the College of Architecture, Art, and Planning and the College of Arts and Sciences. We have made three critical faculty hires that now allow us to create a rich program in this area. Kavita Bala and Steve Marschner have joined CS to broaden our offerings in graphics and allow Don Greenberg to explore his lifelong passion for computer animation. During the next academic year we will be shaping an exciting new program in this area.
Information Science at Cornell is an interdisciplinary program of CIS that allows graduate and undergraduate students to study new theories, models, concepts, and design principles that incorporate an understanding of both social and technical information systems.
The field of information science combines aspects of computer science and human–computer interaction with an examination of the social, economic, political, and legal contexts in which information systems function.
During the past academic year, information science was approved as an official minor or concentration in six of the undergraduate schools or colleges at Cornell: the College of Agriculture and Life Sciences, the College of Arts and Sciences, the College of Engineering, the College of Human Ecology, the School of Industrial and Labor Relations, and students from the Architecture and Planning departments of Architecture, Art & Planning. We are excited by the enthusiasm with which information science has been received across campus thus far, and look forward to welcoming undergraduates into the information-science program this coming year.
Students in the program will obtain an understanding of the core topics of study emerging in this new and quickly growing field: the design and analysis of computing applications, information infrastructures, and human-centered systems; the legal, economic, and ethical issues that surround the construction of information systems; and the ways in which information technology is transforming society. Specific topics emphasized in the information-science program include electronic communication; knowledge networking; collaboration within and across groups, communities, organizations, and society; the Web and Web-information systems; natural-language processing; computational techniques in the collection, archiving, and analysis of social-science data; information privacy; methods of collecting, preserving, and distributing information; information-system design; cognition and learning; and human-interface design and evaluation.