- Diversity, Equity, and Inclusion
- Research News
- Department Life
- Oral History of Cornell CS
- Department Timeline
- Job Postings
- Ithaca Info
- Internal info
- Graduation Information
- Cornell Tech Colloquium
- Student Colloquium
- Student Recognition
- 2020 Celebratory Event
- CS Colloquium
- SoNIC Workshop
- Conway-Walker Lecture Series
- Salton Lecture Series
- Seminars / Lectures
- Big Red Hacks
- Cornell University High School Programming Contest
- Game Design Initiative
- CSMore: The Rising Sophomore Summer Program in Computer Science
- Explore CS Research
- Research Night Fall 2020
The recent completion of the human genome project underlines the need for new computational and theoretical tools in modern biology. The tools are essential for analyzing, understanding and manipulating the detailed information on life we now have at our disposal.
Problems in computational molecular biology vary from understanding sequence data to the analysis of protein shapes, prediction of biological function, study of gene networks, and cell-wide computations.
Cornell has a university-wide plan in the science of genomics; the Department of Computer Science is playing a critical role in this initiative. Researchers in the computer science department are engaged in a wide range of computational biology projects, from genetic mapping, to advanced sequence analysis, fold prediction, structure comparison algorithms, protein classification, comparative genomics, and long-time simulation of protein molecules.
Faculty and Researchers
Carla Gomes works on solutions to hard combinatorial problems, with an emphasis on planning and scheduling problems, combining techniques fromm Computer Science (CS), Artificial Intelligence (AI), and Operations Reserach (OR). Her research is leading to the creation of the new field of computational sustainability, which develops and applies computational methods to enable a sustainable environment, economy and society.
Alon Keinan studies how human genetic variation has arisen from evolutionary history, develops theoretical tools, and applies them to genomic data sets, bridging theoretical population genetics and empirical studies.
Volodymyr Kuleshov's research focuses on machine learning and its applications in health, personalized medicine, and genomics. His work includes the development of new sequencing technologies powered by machine learning and the creation of machine reading systems for biomedical and scientific literature. He also works on core machine learning problems, such as probabilistic methods, deep generative models, uncertainty estimation, and approximate inference.
David Shmoys is studying approximate algorithms for genetic linkage mapping (identifying the locations of markers on the genome) to reduce the cost of wet lab experiments and improve the accuracy of the resulting maps.
Amy Williams' research interests center on the development of algorithms for analyzing large scale gemomic data, and the application of these methods to study human genetic history, population and individual relationships, and the genetic basis of human disease.
Haiyuan Yu performs research research in the broad area of Biomedical Systems Biology with both high-throughput experimental (see Yu et al., Science 2008) and integrative computational (see Wang et al., Nature Biotechnology 2012) methodologies, aiming to understand gene functions and their relationships within complex molecular networks and how perturbations to such systems may lead to various human diseases. The complexity of biological systems calls for building experimentally-verified computational models based on high-quality large-scale datasets, which is truly the future of biomedical research and the main theme of the lab.
A new graduate program in Computational Molecular Biology that crosses colleges was initiated with the participation of the computer science field.