Computing in the Biosciences

EECS researchers examine fundamental problems in biology and how principles and technologies of computing can be applied to life sciences. They advance computational methods and tools for applications in biological, biochemical, and medical fields. Interdisciplinary research in biosciences at KU includes modeling, analysis, data management, and algorithm optimization.

A server stack
EECS researchers are searching for the causes of cancer and Autism, among numerous other projects supported by the high performance computing facility. The Bioinformatics Computing Facility is undergoing a major renovation that will increase computing power by 20 fold.

Program Objectives

  • Understand basic concepts and fundamental problems in biology.
  • Understand fundamental principles and technologies of computing in biosciences.
  • Understand how to apply and adapt computational methods to address life sciences problems, particularly bioinformatics problems.
  • Have the ability to effectively communicate to impact technological decisions.

Associated Faculty

Arvin Agah

Primary Research Interests

  • Applied Artificial Intelligence
  • Autonomous Mobile Robots

Jerzy Grzymala-Busse

Primary Research Interests

  • Data mining
  • Knowledge discovery
  • Machine learning
  • Expert systems
  • Reasoning under uncertainty
  • Rough set theory

Jim Miller
Associate Professor Emeritus
 J. Miller's site
 2036 Eaton Hall

Primary Research Interests

  • Computer Graphics
  • Visualization
  • Geometric Modeling
  • Technology in Education

Suzanne Shontz

Primary Research Interests

  • High Performance Scientific Computing Algorithms
  • Parallel Unstructured Mesh and Optimization Algorithms
  • Model Order Reduction
  • Computational Medicine
  • Image Processing

Cuncong Zhong
Assistant Professor
 Cuncong Zhong's Website
 2026 Eaton Hall

Primary Research Interests

  • Bioinformatics and Computational Biology
  • Genomics
  • Metagenomics
  • Genetics
  • Non-coding RNA

Associated Facilities

Artificial intelligence development tools and languages:

  • Lisp, CLOS, CLIPS, Prolog, GBB, OPS, MEM-1

Computational clusters:

  • Linux cluster with 64 dual processor 3.2 Ghz Xeon processors and 64 dual core 2.8 Ghz Xeon processors for a total of 384 processors 37 TB of on-line storage Reconfigurable floating-point gate arrays

Bioinformatics software including:

  • ClustalW, Emboss GENSCAN, hmmer, Ncbi toolkit

Data mining tools:

  • SNOB, Cobweb, ID3, C4.5, statistical analysis packages, LERS Genomics Unified Schema installation

Information retrieval and Web tools:

  • KUIR Information Retrieval Library
  • Parallel development tools including MPI, Pfortran, and PC
  • Parallel GROMOS for molecular dynamics

Core Coursework (MS)


Elective Coursework (MS)