Digital Arts and Graphics (DA&G)

Computer graphics is a rapidly evolving field that has had significant impact on most scientific, artistic, and engineering fields, and graphics is today the most common and efficient means of man–machine communication. At Cornell, research and teaching in computer graphics are centered in CS and the closely affiliated PCG, one of the world’s leading computer-graphics laboratories and a dominant force in the international computer graphics community for more than thirty years.

The field of computer graphics relates to the broader area of digital arts and graphics, a new CIS program area that involves computer-graphics researchers, architects, artists, art historians, city planners, and information scientists. This program will explore the interaction of graphics and arts by considering the technical aspects of digital graphics, psychological aspects of vision and perception, and the creation of art in a time of digital reproduction.

Research in graphics requires a multidisciplinary team with knowledge in algorithms, systems, numerical simulation, machine vision, software and hardware engineering, physics, optics, and perception psychology. The PCG is particularly famous for its work on realistic rendering— simulating environments that are physically accurate and perceptually indistinguishable from real-world scenes.

Current graphics research at Cornell includes realistic interactive rendering, advanced material modeling, human visual perception in graphics, modeling complex scenes, image-based modeling and rendering, animation, and display technology.

For more information, see http://www.graphics.cornell.edu/

Information Science

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.

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 has been approved 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 in the coming year.

Students in the program will 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.

For more information, see http://www.infosci.cornell.edu.



Computational Science and Engineering

Many of the faculty members in engineering and the sciences engage in research that is computationally driven. 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 Dr. Andrew J. 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 attract students from across the university.

For more information, see http://www.cis.cornell.edu/cse/.


Computational Biology

Computation has been become essential to biological research. Genomic databases, protein structure databanks, MRIs of the human brain, and remote-sensing data on landscapes contain unprecedented amounts of detailed information that are transforming almost all of biology. Complex patterns, structures, and interactions raise fundamental and fascinating questions that can only be addressed using computational methods, and computational
biologists are creating new tools to analyze these new sources of data.

More than fifty Cornell professors in six university colleges, including the Weill Medical College in New York City, are involved in computational biology. They tend to be quantitatively-oriented biologists, biophysicists, doctors and medical researchers, mechanical engineers, and others, and they are looking at everything from the genetic differences between humans and chimpanzees, to how protein chains “fold” into three-dimensional structures, to
how hearts pump.

Problems investigated by computational biologists span a wide spectrum, including:

• the genetics of disease susceptibility
• comparing entire DNA genomes to uncover the secrets of evolution
• using protein structures to design new therapeutic drugs
• mathematical modeling of cellular signaling networks
• predicting how ecosystems will respond to climate change
• designing recovery plans for endangered species
• animal behavior and ecology
• cognitive psychology and neurobiology
• modeling the spread of diseases

Research at Cornell in these areas usually involves interdepartmental collaborations that take advantage of Cornell’s great breadth in the biological sciences and strength in computer science, mathematics, and the physical sciences.

For example:

Researchers in the Departments of Computer Science, Molecular Biology and Genetics, and Chemistry and Chemical Biology focus on macromolecular biology. Predicting the structures and functions of proteins is a primary aim of research supported by the National Science Foundation. The group is strong, well organized, and has ties to the Computational Biology Service Unit in the Cornell Theory Center. The group studying evolutionary genomics includes researchers from the Departments of Biological Statistics and Computational Biology, Molecular Biology and Genetics, and Ecology and Evolutionary Biology, and is one of the strongest of its kind in the country. This
group, like the macromolecular biology group, has strong ties to Cornell’s Genomics Initiative, which has
computational and statistical genomics as one of its major thrust areas.Studies of nonlinear systems bring together ecologists from the Departments of Ecology and Evolutionary Biology and Natural Resources, neurobiologists from the Department of Neurobiology and Behavior, and mathematicians from the Center for Applied Mathematics for
increasingly sophisticated analyses of some the highly complex coupled systems that arise in biology.
Intracellular signaling networks are studied by a collaboration involving biologists, physicists and
engineers from the Departments of Chemistry and Chemical Biology, Physics, and Chemical and
Biomolecular Engineering, and the Cornell Theory Center.

For more information, see http://www.cis.cornell.edu/cb/.