EECS researchers work with interdisciplinary collaborators to develop computational capabilities enabling research in a variety of scientific disciplines including computational medicine, electronic circuit design, and climate science. Central to this effort is the development of algorithms of all types: numerical, non-numerical, sequential, and parallel. A variety of paradigms are exploited, including artificial intelligence for use with image processing, computer vision, and robotics as well as visualization which is now widely recognized as an integral part of the scientific discovery process, not just a method for displaying final results.

Associated Disciplines

 

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Associated Programs

Associated Faculty

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
785-864-7389
2038 Eaton Hall

Primary Research Interests

  • Algorithm Design and Analysis
  • Combinatorial Optimizations
  • Graph Algorithms
Associate Professor
785-864-7393
2044 Eaton Hall

Primary Research Interests

  • Information security and privacy, database security
  • Information retrieval, Web and online social networks
  • Security and privacy issues in smart grid systems
  • XML and conventional database systems, data management
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
Roy A. Roberts Distinguished Professor, Provost, Executive Vice Chancellor
785-864-4904

Primary Research Interests

  • Efficient Algorithms For External Memory
  • Compressed Text Indexes and Data Structures
  • Data Compression
  • Database Access and Data Mining
  • Machine Learning and Prediction
Assistant Professor
785-864-8800
3012 Eaton Hall

Primary Research Interests

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

Associated Facilities

Advanced Computing Facility cluster with over 350 nodes and 6000 CPU cores connected to 125TB of on-line storage. Some GPUs and phi co-processors are also available as part of the ACF cluster.

Program Objectives

  • Understand the design and analysis of numerical algorithms.
  • Understand the design and analysis of non-numerical algorithms.
  • Understand how to develop algorithms capable of artificial intelligence.
  • Understand how to develop algorithms for use in image processing and visualizations of scientific data.
  • Understand how to apply the various types of algorithms described above to provide efficient and numerically reliable solutions to real-world problems.
  • Have the ability to effectively communicate to impact technological decisions.

Core Coursework (MS)

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 2019
Type Time/Place and Instructor Credit Hours Class #
LEC Zhong, Cuncong
TuTh 08:00-09:15 AM EATN 2 - LAWRENCE
3 61233
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.
Spring 2019
Type Time/Place and Instructor Credit Hours Class #
LEC Williams, Andrew
W 06:10-09:00 PM LEEP2 2415 - LAWRENCE
3 75465
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 2019 semester.

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.
Spring 2019
Type Time/Place and Instructor Credit Hours Class #
LEC Shontz, Suzanne
MWF 03:00-03:50 PM LEA 3150 - LAWRENCE
3 75458
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 2019
Type Time/Place and Instructor Credit Hours Class #
LEC Wang, Guanghui
TuTh 01:00-02:15 PM LEA 1131 - LAWRENCE
3 72168

Note: Select two of the three 600-level core courses listed above.

Elective Coursework (MS)

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.

The class is not offered for the Spring 2019 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.
Spring 2019
Type Time/Place and Instructor Credit Hours Class #
LEC Kuehnhausen, Martin
M 05:15-07:45 PM LEA 2112 - LAWRENCE
3 75477
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 2019 semester.

EECS 743 Advanced Computer Architecture
This course will focus on the emerging technologies to build high-performance, low-power, and resilient microprocessors. Topics include multiprocessing, reliability-and-variability-aware computer architecture designs, energy-efficient computer systems, on-chip networks, 3D microprocessor designs, general-purpose computation on graphics processing units, and non-volatile computer memory. The course responds to VLSI technologies ability to provide increasing numbers of transistors and clock speeds to allow computer architects to build powerful microprocessors and computer systems and the challenges (e.g. resilience, energy-efficiency) that the growth in microprocessor performance is facing from the aggressive technology scaling. Prerequisite: EECS 643 or EECS 645, or equivalent. A good understanding of C/C++ and having basic Unix/Linux skills is required. LEC.
Spring 2019
Type Time/Place and Instructor Credit Hours Class #
LEC Aly, Esam Eldin
MWF 09:00-09:50 AM LEA 1136 - LAWRENCE
3 70914
EECS 764 Analysis of Algorithms
Models of computations and performance measures; asymptotic analysis of algorithms; basic design paradigms including divide-and-conquer, dynamic programming, backtracking, branch-and-bound, greedy method and heuristics; design and analysis of approximation algorithms; lower bound theory; polynomial transformation and the theory of NP-Completeness; additional topics may be selected from arithmetic complexity, graph algorithms, string matching, and other combinatorial problems. Prerequisite: EECS 660 or equivalent. LEC.
Spring 2019
Type Time/Place and Instructor Credit Hours Class #
LEC Zhong, Cuncong
TuTh 02:30-03:45 PM LEA 2111 - LAWRENCE
3 75478
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.
Spring 2019
Type Time/Place and Instructor Credit Hours Class #
LEC Miller, James
MWF 01:00-01:50 PM LEA 2133 - LAWRENCE
3 79222
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 2019 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.

The class is not offered for the Spring 2019 semester.

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 2019 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 or MATH 781. LEC.
Spring 2019
Type Time/Place and Instructor Credit Hours Class #
LEC Miedlar, Agnieszka
TuTh 01:00-02:15 PM SNOW 306 - LAWRENCE
3 67361
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 2019 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 2019 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.

Explore: EECS Courses


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