Intelligent Systems Laboratory

4624 Seamans Center

Department of Industrial and Systems Engineering
The University of Iowa
Lab Director: Andrew Kusiak
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The Intelligent Systems Laboratory conducts research in data science and computational intelligence leading to applications in manufacturing, energy, service industry, and healthcare. The current project focus on smart manufacturing, digital industry, cloud and edge modeling, service manufacturing, and autonomous systems. Many of the intelligent manufacturing concepts pursued globally have originated in the laboratory. The pioneering research has been marked with publishing the textbook Intelligent Manufacturing Systems (Prentice Hall) and launching the Journal of Intelligent Manufacturing (Springer Nature). A number of successful companies have been established based on the research conducted in the laboratory. Most of PhD graduates from the laboratory pursue academic careers. Dozens MS graduates and some former PhD students have joined high tech corporations, including Microsoft and Google. 


The research activities of the Intelligent Systems Laboratory are coordinated by Andrew Kusiak, Professor in the Department of Industrial and Systems Engineering at the University of Iowa. Ongoing industrial and government-funded research projects are conducted by graduate and undergraduate students from the Department of Industrial Engineering. The following students are actively involved in the research program:

Current Visiting Scholars

  • None at this time

Meet some of the Recent Graduates from the Intelligent Systems Laboratory

Illustrative Careers of ISL Graduates and Visiting Scholars

  • Academia and Industry
    Most of PhD graduates from the Intelligent Systems Laboratory have joined universities. They enjoyed careers from Professor to Department Chair, Dean, and University President. Some graduates work for large corporations in positions ranging from a manager to a partner and CEO.

  • Start-up Companies
    Three successful start-up companies have been established based on research in the Intelligent Systems Laboratory, Optimal Electronics (based on the research in decomposition), Teseon GmbH (based on the triangularization algorithm), and Jet-China company (based on the data science research in energy).  Optimal Electronics (Austin, TX) was established by the former PhD student R. Vujosevic initially in Iowa City and then moved to Austin, TX. Teseon GmbH was established by M. Maurer, a PhD graduate from the Technical University of Munich, Germany whom Professor Kusiak has co-advised with Prof. Lindemann, TUM. Dr. Maurer has extended the matrix topology concept developed in the Intelligent Systems Laboratory and implemented it a software solution for product complexity management. Dr. Maurer has later cofounded Spritz Technology, Inc., Boston, MA and subsequently joined Akamai Technologies, Boston, MA. Shortly after graduation from the University of Iowa, Z. Song has cofounded an energy management company, Jet-China in the Shanghai area, China while pursuing his academic career.


The Intelligent Systems Laboratory is furnished with the latest technology to support research on a variety of computing platforms. A variety of software is also available in the laboratory for modeling, design, and construction of intelligent systems; including the following:

  • Machine learning software
  • Evolutionary computation software
  • Process modeling software
  • Logic programming languages
  • Intelligent design software
  • Simulation software
  • Standard word processing, spreadsheet, presentation, and project management software
  • Software development environments
  • General purpose programming languages
  • Specialized programs for design of products and systems

Industrial Partners

The Intelligent Systems Laboratory maintains long-standing relationships with several area corporations; including the following:

In addition, the lab continues to actively seek new partnerships with area industry.

Current Research Areas

The Intelligent Systems Laboratory pursues a dynamic research program that reflects the progress of the industrial engineering profession, as well as the needs of the laboratory's industrial and healthcare partners. Current research include the following  topics:

  • Smart Manufacturing
    Optimization of design and operations of manufacturing systems, predictive engineering, and innovation.
  • Wind Energy
    Performance modeling,  monitoring, diagnostics, and prognostics of wind farms.
  • HVAC Systems
    Performance optimization,  monitoring, and diagnostics of heating, ventilation, and air condititoing systems.
  • Energy in Wastewater Processing
    Optimization of energy consumption and energy production in wastewater processing plants.
  • Big Data: Energy and Industrial Applications 
    Development of novel algorithms for knowledge discovery in energy and engineering applications. Diagnostic and predictive systems are researched.
  • Data Science: Medical Applications
    Development of novel algorithms for autonomous decision making in medical applications. One of the projects is concerned with diagnosis of solitary pulmonary nodules, lung abnormalities that may be cancerous. 
  • Data Science: Pharmaceutical Applications
    Novel algorithms for knowledge discovery and decision making. Collaborative projects in pharmacogenomics, prediction of drug adverse effects, and selection of drug dosage have been initiated.
  • Evolutionary Computation
    br Algorithms for applications of evolutionary computation in engineering design, manufacturing, process modeling, and healthcare.
  • Medical Decision Making
    Novel concepts are researched for diverse medical applications. This research is conducted in collaboration with numerous faculty from the University of  Iowa College of Medicine, in particular the Department of Surgery, Department of Internal Medicine, and Department of Radiology, and VA Hospital.
  • Tools for Supplier Evaluation
    This research seeks to identify the key characteristics in a supplier/customer relationship and exploit these characteristics in a system that fosters strong supply chain    alliances. To accommodate all commodity teams, the system must be flexible in its approach to supplier evaluation. Maintaining such flexibility is essential for broad-based acceptance of the proposed system and is, thus, emphasized in the objective of the research. This work is being done in cooperation with Rockwell International.
  • Risk Assessment in Concurrent Engineering
    This research seeks to develop an intelligent systems for risk assessment in concurrent engineering environments. The proposed strategy is based on the premise that a  holistic model of the design process can be used to completely define the design of any product in the domain of the firm. Therefore, the product can be defined in the context of the activities that must be performed to result in a successful design, rather than traditional methods of modeling based on the design object. Once the model has been developed, it can be used repeatedly to evaluate the design of different products. Customer requirements provide an initial summary of the activities that must be performed; however, the entire design scenario(i.e., path through the design process)may seldom, if ever, be realized. Therefore, the research problem is that of determining the remaining activities in a project plan that result in a successful design. The proposed research will make the determination of a final design scenario based on a variety of risk factors. As a result, the overall risk, considering the perspectives of many different functional areas, will be minimized.
  • Modeling and Design of Warehousing Systems
    This research explores methods for modeling and designing a variety of warehouse systems. In general, the research seeks to decrease the cost of warehouse operations by maximizing floor space utilization and minimizing material handling costs. Modeling methods for accomplishing this objectives in practice range from linear programming models to simulation. Alternative storage policies (i.e., randomized storage, dedicated storage) are also a focus of the research effort.

Contacting the Laboratory

Andrew Kusiak, Professor

Department of Industrial and Systems Engineering
4627 Seamans Center
The University of Iowa
Iowa City, Iowa 52242 - 1527

Phone:  319-335-5934

Fax:      319-335-6086