CIS Programs CIS Programs
Computational Biology
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
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.