Genomic databases, protein databanks, MRIs 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 computer science, 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.
Undergraduates can major in computational biology through the new CIS–created undergraduate program of study, which 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.
Recently Cornell announced a combined graduate program in computational biology with Sloan–Kettering and Rockefeller University. This tri-institutional effort provides three fellowships for Cornell computer science graduate students.
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.
Computational Science and Engineering
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.
The CS&E subgroup continues to offer a series of four minicourses, taught by Andrew Pershing:
COM S 401 Introduction to Applied Scientific Computing with MATLAB
COM S 402 Scientific Visualization with MATLAB
COM S 403 Development of Scientific Computing Programs
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.
Digital Arts and Graphics
The digital arts have strong ties to computer graphics at Cornell. With these ties we are building a new academic program. Research in computer graphics includes algorithms, computation, computer architecture, computer vision, physics, and perception psychology.
Cornell’s Program of Computer Graphics (PCG), an interdisciplinary research center with close ties to CS, was one of the pioneering laboratories in computer graphics. Established in 1974, the program made breakthrough contributions in radiosity and other aspects of high-quality rendering. Its algorithms for realistic rendering opened the way for computer-aided architectural design, computergenerated motion picture animation, scientific visualization, and more.
Research topics in PCG include reflectance models, physics-based accurate rendering, visual perception for graphics, sketching and modeling, medical visualization, digital photography, and computer animation. The program’s modern facility includes many tools for advanced research, including a sophisticated light measurement laboratory, a 128-processor PC cluster, and a high-resolution tiled projection display.
Digital Arts and Graphics (DA&G) involves architects, artists, art historians, city planners, and information scientists. It will explore technical aspects of digital graphics, psychological aspects of vision and perception, the relationship of human senses and robotics, and creating art in a time of digital reproduction.
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, cultural, and legal contexts in which information systems function. Information science has been available since 2002 as an official minor or concentration in all seven of the undergraduate schools or colleges at Cornell. An undergraduate major in information science is currently in the approval process in Arts and Sciences, Engineering, and CALS. 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 programs obtain an understanding of the core topics of study emerging in this new and rapidly 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.