Computing in the Biosciences

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.

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.

Associated Disciplines

 
 

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Associated Programs

Arvin Agah
Professor, Dean of Engineering
785-864-8821
1C Eaton Hall

Primary Research Interests

  • Intelligent Systems
  • Robotics
  • Medical Applications of Artificial Intelligence
  • Software Engineering
Jerzy Grzymala-Busse
785-864-4488
3014 Eaton Hall

Primary Research Interests

  • Knowledge Discovery
  • Data Mining
  • Machine Learning
  • Expert Systems
  • Reasoning Under Uncertainty
James Miller
Associate Professor
785-864-7384
2036 Eaton Hall

Primary Research Interests

  • Computer Graphics
  • Visualization
  • Geometric Modeling
  • Technology in Education
Suzanne Shontz
Associate Professor
785-864-8816
3016 Eaton Hall

Primary Research Interests

  • High Performance Scientific Computing Algorithms
  • Parallel Unstructured Mesh and Optimization Algorithms
  • Model Order Reduction
  • Computational Medicine
  • Image Processing
Guanghui Wang
Assistant Professor
785-864-8800
3012 Eaton Hall

Primary Research Interests

  • Computer vision
  • Image processing
  • Pattern recognition
  • Artificial intelligence
  • Robotics

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

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.

Core Coursework (MS)

Elective Coursework (MS)

Explore: EECS Courses


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