Katerina Kechris, PhD


Program Director
Contact Katerina

David Pollock, PhD

David Pollock

Graduate Studies Director
Contact David

Welcome to the Computational Bioscience Program at the University of Colorado Anschutz Medical Campus!

The program was founded by Dr. Lawrence Hunter, founder of the International Society for Computational Biology, and the popular ISMB and PSB conferences. Now directed by Dr. Katerina Kechris, the CPBS Program is globally recognized for its research and teaching of computational biology and bioinformatics at the University of Colorado’s Anschutz Medical Campus. The Program is designed to produce graduates with depth in computational methods and molecular and clinical research, an intimate familiarity with the science and technology that synthesizes the two, and the skills necessary to pioneer novel computational approaches to significant biomedical questions.

Along with strong affiliations with the Pharmacology, Biostatistics and Informatics, and Biochemistry and Molecular Genetics departments, our faculty expertise is growing with the new Department of Biomedical Informatics. Our program now spans the entire spectrum of biomedical informatics research from the molecular to the bedside to the population. Along with our historical strength in bioinformatics, our faculty members have expanded into areas including imaging, clinical informatics, clinical research informatics, translational informatics, and implementation science. We are excited about these new growth areas, which will offer unprecedented opportunities for our trainees to work with rich data sources such as omics, images, electronic health records, personal sensors, and public health surveillance.

Outstanding biomedical research now requires inventive computational tools to harness the torrent of post–genomic data. Our computational innovations have led to significant insights across a broad spectrum of biomedicine. Deeper insight, arrived at more quickly, is what medicine needs today.

Our award-winning PhD in Computational Bioscience and post-doctoral training programs create productive, interdisciplinary scientists in a relatively short period of time. Our students begin supervised research immediately, collaborating with top scientists, working with the latest high–throughput instruments on critical biomedical problems.

Our History. The University of Colorado has a long tradition of outstanding research and training in computational bioscience. Several of the most important scientists in the field, including David Haussler and Gene Myers, received their graduate training at the University. The School of Medicine began offering a PhD in Computational Bioscience in 2001, and was awarded the prestigious National Library of Medicine Biomedical Informatics Training Grant in 2006.

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Goals of the Program

The Computational Bioscience Program of the University Of Colorado School Of Medicine is dedicated to training computational biologists who aspire to achieve excellence in research, education and service, and who will apply the skills they learn toward improving human health and deepening our understanding of the living world. The Computational Bioscience Program provides graduates with the foundation for a lifetime of continual learning. Our curriculum integrates training in computation and biomedical sciences with student research and teaching activities that grow increasingly independent through the course of the program. Our graduates are able to do independent computational bioscience research, to collaborate effectively with other scientists, and to communicate their knowledge clearly to both students and the broader scientific community.

Student Support

Students accepted in the PhD program are provided full tuition, health and dental insurance, and a stipend of $37,000 per year for living expenses. Continued support is contingent upon satisfactory academic and research performance by the student. When a student enters a thesis lab, the thesis mentor assumes complete responsibility for the student’s stipend, tuition, fees, and associated research costs.

The Student Handbook

The handbook is a document and guide for current students that includes parts of the Graduate School policies and the Computational Bioscience Graduate Program Guidelines.

Access the CompBio Student Handbook.

Learning Outcomes

The following four goals represent the foundation of the Computational Bioscience graduate education program at the University of Colorado Anschutz Medical Campus.

Educational Goals and Objectives

Knowledge Goals - Graduates demonstrate their knowledge of core concepts and principles of computational bioscience, and the ability to apply computation to gain insight into significant biomedical problems. This knowledge includes mastery of the fundamentals of biomedicine, statistics and computer science, as well as proficiency in the integration of these fields. Graduates contribute to the discovery and dissemination of new knowledge.

Knowledge Objectives
  1. Demonstrate knowledge of the scientific principles that underlie the current understanding of molecular biology, statistics and computer science.
  2. Demonstrate an ability to productively integrate knowledge from disparate fields to solve problems in biomedicine using computational methods.
  3. Demonstrate knowledge of the types and sources of data most commonly used in computational bioscience, including knowledge of all major public data repositories.
  4. Demonstrate the knowledge of the classes of algorithms most often applied in computational bioscience, and their domains of applicability.
  5. Demonstrate an understanding of the principles and practice of the scientific method as applied in computational bioscience, including experimental design, hypothesis testing, and evaluation of computational systems.

Communication Skills Communication Skills Goals - Graduates demonstrate interpersonal, oral and written skills that enable them to interact productively with scientists from both biomedical and computational domains, to clearly communicate the results of their work in appropriate formats, and to teach others computational bioscience skills. Graduates are able to bridge the gap between biomedical and computational cultures.

Communication Skills Objectives
  1. Communicate effectively, both orally and in writing, in an appropriate range of scientific formats, including formal presentations, collaborative interactions, and the critique of others’ work.
  2. Demonstrate familiarity with both biomedical and computational modes of expression, and be able to communicate clearly across disciplinary boundaries.
  3. Demonstrate commitment and skill in teaching to and learning from students, colleagues, and other members of the scientific community.

Professional Behavior Professional Behavior Goals - Graduates demonstrate the highest standards of professional integrity and exemplary behavior, as reflected by a commitment to the ethical conduct of research, continuous professional development, and thoughtfulness regarding the broader implications of their work.

Professional Behavior Objectives
  1. Act in an ethically responsible manner, displaying integrity, honesty, and appropriate conduct at all times.
  2. Recognize the limits of one’s knowledge, skills, and behavior through self-reflection and seek to overcome those limits.
  3. Always consider the broad significance of one’s professional actions, including their implications for society and the living world.
Self-Directed and Life Long Learning Skills Self-Directed and Life Long Learning Goals - Graduates demonstrate habits and skills for self-directed and life-long learning, and recognize that computational bioscience is a rapidly evolving discipline. Our focus is on the development of adaptive, flexible and curious scientists able to comfortably assimilate new ideas and technologies during the course of their professional development. Self-Directed and Life Long Learning Skills Objectives 1. Recognize the need to engage in lifelong learning to stay abreast of new technologies and scientific advances in multiple disciplines. 2. Locate, evaluate and assimilate relevant new knowledge and techniques from a wide variety of sources.

Program Committees

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