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

Professor
785-864-8821
1C Eaton Hall

Primary Research Interests

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

Primary Research Interests

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

Primary Research Interests

  • Scientific and Information Visualization
  • Visual Analytics
  • Geometric Modeling
  • Technology in Education
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
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)

EECS 730 Introduction to Bioinformatics
This course provides an introduction to bioinformatics. It covers computational tools and databases widely used in bioinformatics. The underlying algorithms of existing tools will be discussed. Topics include: molecular biology databases, sequence alignment, gene expression data analysis, protein structure and function, protein analysis, and proteomics. Prerequisite: Data Structures class equivalent to EECS 560, and Introduction to Biology equivalent to BIOL 150, or consent of instructor. LEC.

The class is not offered for the Spring 2018 semester.

EECS 738 Machine Learning
"Machine learning is the study of computer algorithms that improve automatically through experience" (Tom Mitchell). This course introduces basic concepts and algorithms in machine learning. A variety of topics such as Bayesian decision theory, dimensionality reduction, clustering, neural networks, hidden Markov models, combining multiple learners, reinforcement learning, Bayesian learning etc. will be covered. Prerequisite: Graduate standing in CS or CoE or consent of instructor. LEC.

The class is not offered for the Spring 2018 semester.

EECS 831 Introduction to Systems Biology
This course provides an introduction to systems biology. It covers computational analysis of biological systems with a focus on computational tools and databases. Topics include: basic cell biology, cancer gene annotation, micro RNA identification, Single Nucleotide Polymorphism (SNP) analysis, genetic marker identification, protein-DNA interaction, computational Neurology, vaccine design, cancer drug development, and computational development biology. Prerequisite: Introduction to Bioinformatics equivalent to EECS 730, or consent of instructor. LEC.

The class is not offered for the Spring 2018 semester.

EECS 837 Data Mining
Extracting data from data bases to data warehouses. Preprocessing of data: handling incomplete, uncertain, and vague data sets. Discretization methods. Methodology of learning from examples: rules of generalization, control strategies. Typical learning systems: ID3, AQ, C4.5, and LERS. Validation of knowledge. Visualization of knowledge bases. Data mining under uncertainty, using approaches based on probability theory, fuzzy set theory, and rough set theory. Prerequisite: Graduate standing in CS or CoE or consent of instructor. LEC.

The class is not offered for the Spring 2018 semester.

Elective Coursework (MS)

EECS 638 Fundamentals of Expert Systems
Basic information about expert systems: architecture of an expert system, building expert systems, uncertainty in expert systems, taxonomy of expert systems. Knowledge representation: first order logic, production systems, semantic nets, frames. Uncertainty in expert systems, one-valued approaches: probability theory, systems using Bayes' rule, and systems using certainty theory; two-valued approaches: systems using Dempster-Shafer theory and system INFERNO; set-valued approaches: systems using fuzzy set theory and systems using rough set theory. Prerequisite: EECS 560 or consent of instructor. LEC.

The class is not offered for the Spring 2018 semester.

EECS 649 Introduction to Artificial Intelligence
General concepts, search procedures, two-person games, predicate calculus and automated theorem proving, nonmonotonic logic, probabilistic reasoning, rule based systems, semantic networks, frames, dynamic memory, planning, machine learning, natural language understanding, neural networks. Prerequisite: Corequisite: EECS 368. LEC.

The class is not offered for the Spring 2018 semester.

EECS 660 Fundamentals of Computer Algorithms
Basic concepts and techniques in the design and analysis of computer algorithms. Models of computations. Simple lower bound theory and optimality of algorithms. Computationally hard problems and the theory of NP-Completeness. Introduction to parallel algorithms. Prerequisite: EECS 560 and either EECS 461 or MATH 526. LEC.
Spring 2018
Type Time/Place and Instructor Credit Hours Class #
LEC Zhong, Cuncong
TuTh 08:00-09:15 AM EATN 2 - LAWRENCE
3 51351
EECS 672 Introduction to Computer Graphics
Foundations of 2D and 3D computer graphics. Structured graphics application programming. Basic 2D and 3D graphics algorithms (modeling and viewing transformations, clipping, projects, visible line/surface determination, basic empirical lighting, and shading models), and aliasing. Prerequisite: EECS 448. LEC.

The class is not offered for the Spring 2018 semester.

EECS 718 Graph Algorithms
This course introduces students to computational graph theory and various graph algorithms and their complexities. Algorithms and applications covered will include those related to graph searching, connectivity and distance in graphs, graph isomorphism, spanning trees, shortest paths, matching, flows in network, independent and dominating sets, coloring and covering, and Traveling Salesman and Postman problems. Prerequisite: EECS 560 or graduate standing with consent of instructor. LEC.
Spring 2018
Type Time/Place and Instructor Credit Hours Class #
LEC Kong, Man
TuTh 09:30-10:45 AM LEA 2111 - LAWRENCE
3 63596
EECS 739 Parallel Scientific Computing
This course is concerned with the application of parallel processing to real-world problems in engineering and the sciences. State-of-the-art serial and parallel numerical computing algorithms are studied along with contemporary applications. The course takes an algorithmic design, analysis, and implementation approach and covers an introduction to scientific and parallel computing, parallel computing platforms, design principles of parallel algorithms, analytical modeling of parallel algorithms, MPI programming, direct and iterative linear solvers, numerical PDEs and meshes, numerical optimization, GPU computing, and applications of parallel scientific computing. Prerequisite: MATH 122 or MATH 126; MATH 290; experience programming in C, C++, or Fortran; EECS 639 (or equivalent.) Highly recommended: MATH 127 or MATH 223. LEC.

The class is not offered for the Spring 2018 semester.

EECS 740 Digital Image Processing
This course gives a hands-on introduction to the fundamentals of digital image processing. Topics include: image formation, image transforms, image enhancement, image restoration, image reconstruction, image compression, and image segmentation. Prerequisite: EECS 672 or EECS 744. LEC.
Spring 2018
Type Time/Place and Instructor Credit Hours Class #
LEC Wang, Guanghui
TuTh 01:00-02:15 PM LEA 1131 - LAWRENCE
3 63594
EECS 741 Computer Vision
This course gives a hands-on introduction to the fundamentals of computer vision. Topics include: image formation, edge detection, image segmentation, line-drawing interpretation, shape from shading, texture analysis, stereo imaging, motion analysis, shape representation, object recognition. Prerequisite: EECS 672 or EECS 744. LEC.

The class is not offered for the Spring 2018 semester.

EECS 767 Information Retrieval
This class introduces algorithms and applications for retrieving information from large document repositories, including the Web. Topics span from classic information retrieval methods for text documents and databases, to recent developments in Web search, including: text algorithms, indexing, probabilistic modeling, performance evaluation, web structures, link analysis, multimedia information retrieval, social network analysis. Prerequisite: EECS 647 or permission of instructor. LEC.
Spring 2018
Type Time/Place and Instructor Credit Hours Class #
LEC Luo, Bo
W 06:10-09:00 PM LEA 2112 - LAWRENCE
3 65770
EECS 773 Advanced Graphics
Advanced topics in graphics and graphics systems. Topics at the state of the art are typically selected from: photorealistic rendering; physically-based lighting models; ray tracing; radiosity; physically-based modeling and rendering; animation; general texture mapping techniques; point-based graphics; collaborative techniques; and others. Prerequisite: EECS 672 or permission of instructor. LEC.

The class is not offered for the Spring 2018 semester.

EECS 774 Geometric Modeling
Introduction to the representation, manipulation, and analysis of geometric models of objects. Implicit and parametric representations of curves and surfaces with an emphasis on parametric freeform curves and surfaces such as Bezier and Nonuniform Rational B-Splines (NURBS). Curve and surface design and rendering techniques. Introduction to solid modeling: representations and base algorithms. Projects in C/C++ using OpenGL. Prerequisite: EECS 672 or permission of instructor. LEC.

The class is not offered for the Spring 2018 semester.

EECS 775 Visualization
Data representations, algorithms, and rendering techniques typically used in Visualization applications. The emphasis is on Scientific Visualization and generally includes topics such as contouring and volumetric rendering for scalar fields, glyph and stream (integral methods) for vector fields, and time animations. Multidimensional, multivariate (MDMV) visualization techniques; scattered data interpolation; perceptual issues. Prerequisite: General knowledge of 3D graphics programming or instructor's permission. LEC.
Spring 2018
Type Time/Place and Instructor Credit Hours Class #
LEC Miller, James
MWF 03:00-03:50 PM LEA 3150 - LAWRENCE
3 65771
EECS 781 Numerical Analysis I
Finite and divided differences. Interpolation, numerical differentiation, and integration. Gaussian quadrature. Numerical integration of ordinary differential equations. Curve fitting. (Same as MATH 781.) Prerequisite: MATH 320 and knowledge of a programming language. LEC.

The class is not offered for the Spring 2018 semester.

EECS 782 Numerical Analysis II
Direct and interactive methods for solving systems of linear equations. Numerical solution of partial differential equations. Numerical determination of eigenvectors and eigenvalues. Solution of nonlinear equations. (Same as MATH 782). Prerequisite: EECS 781. LEC.
Spring 2018
Type Time/Place and Instructor Credit Hours Class #
LEC Tu, Xuemin
TuTh 11:00-12:15 PM SNOW 564 - LAWRENCE
3 58014
EECS 830 Advanced Artificial Intelligence
A detailed examination of computer programs and techniques that manifest intelligent behavior, with examples drawn from current literature. The nature of intelligence and intelligent behavior. Development of, improvement to, extension of, and generalization from artificially intelligent systems, such as theorem-provers, pattern recognizers, language analyzers, problem-solvers, question answerers, decision-makers, planners, and learners. Prerequisite: Graduate standing in the EECS department or Cognitive Science or permission of the instructor. LEC.

The class is not offered for the Spring 2018 semester.

EECS 838 Applications of Machine Learning in Bioinformatics
This course is introduction to the application of machine learning methods in bioinformatics. Major subjects include: biological sequence analysis, microarray interpretation, protein interaction analysis, and biological network analysis. Common biological and biomedical data types and related databases will also be introduced. Students will be asked to present some selected research papers. Prerequisite: EECS 730 and EECS 738. LEC.

The class is not offered for the Spring 2018 semester.

EECS 839 Mining Special Data
Problems associated with mining incomplete and numerical data. The MLEM2 algorithm for rule induction directly from incomplete and numerical data. Association analysis and the Apriori algorithm. KNN and other statistical methods. Mining financial data sets. Problems associated with imbalanced data sets and temporal data. Mining medical and biological data sets. Induction of rule generations. Validation of data mining: sensitivity, specificity, and ROC analysis. Prerequisite: Graduate standing in CS or CoE or consent of instructor. LEC.
Spring 2018
Type Time/Place and Instructor Credit Hours Class #
LEC Grzymala-Busse, Jerzy
TuTh 11:00-12:15 PM LEA 2133 - LAWRENCE
3 65778
EECS 844 Adaptive Signal Processing
This course presents the theory and application of adaptive signal processing. Topics include adaptive filtering, mathematics for advanced signal processing, cost function modeling and optimization, signal processing algorithms for optimum filtering, array processing, linear prediction, interference cancellation, power spectrum estimation, steepest descent, and iterative algorithms. Prerequisite: Background in fundamental signal processing (such as EECS 644.) Corequisite: EECS 861. LEC.

The class is not offered for the Spring 2018 semester.

EECS 861 Random Signals and Noise
Fundamental concepts in random variables, random process models, power spectral density. Application of random process models in the analysis and design of signal processing systems, communication systems and networks. Emphasis on signal detection, estimation, and analysis of queues. This course is a prerequisite for most of the graduate level courses in radar signal processing, communication systems and networks. Prerequisite: An undergraduate course in probability and statistics, and signal processing. LEC.

The class is not offered for the Spring 2018 semester.

EECS 940 Theoretic Foundation of Data Science
A review of statistical and mathematical principles that are utilized in data mining and machine learning research. Covered topics include asymptotic analysis of parameter estimation, sufficient statistics, model selection, information geometry, function approximation and Hilbert spaces. Prerequisite: EECS 738, EECS 837, EECS 844 or equivalent. LEC.

The class is not offered for the Spring 2018 semester.

EECS 965 Detection and Estimation Theory
Detection of signals in the presence of noise and estimation of signal parameters. Narrowband signals, multiple observations, signal detectability and sequential detection. Theoretical structure and performance of the receiver. Prerequisite: EECS 861. LEC.

The class is not offered for the Spring 2018 semester.

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