Computational Biology

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.

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.

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.

Giulia Guidi works on challenges at the intersection of large-scale computational biology and the algorithms and software infrastructures. She leverages high-performance computing techniques and computer systems and architectures to make genomics data processing faster and more flexible for the community, and to enable higher-quality bioinformatics and biomedical research. Another important focus of her work is developing software to make cloud infrastructures more accessible for high-performance scientific computing.

Jaehee Kim's research interests are in the general fields of population-genetic dynamical systems, statistical genetics, and mathematical phylogenetics. She also applies mechanistic understanding of population genetics and evolutionary biology to solve important questions in mathematical epidemiology and forensic genetics.

Education

A new graduate program in Computational Molecular Biology that crosses colleges was initiated with the participation of the computer science field.