Department of Industrial & Management Systems Engineering

 

Dr. Hui Yang

 

Director

Complex System Monitoring, Modeling and Analysis Laboratory (COMMAN Lab)

 

Assistant Professor

Office: ENC2509

Tel: 813-974-5579
Industrial & Management Systems Engineering
University of South Florida
4202 East Fowler Avenue, ENB118
Tampa, FL 33620-5350

Email: Send an email to Professor Yang

I'm an assistant professor in the Department of Industrial & Management Systems Engineering at the University of South Florida. I received my Ph.D. degree in December 2008 from the Department of Industrial Engineering & Management at the Oklahoma State University.

Open position:

We are interested in recruiting 1 more PhD student. Research Assistantship (RA) positions will be generally offered to high profile students who have demonstrated research capabilities through their master’s study.

Sensor based computational modeling and analysis of complex systems with special focus on nonlinear stochastic dynamics, and the resulting chaos, multifractal, self-organization, long range dependence behaviors:

Sensor Based Modeling and Analysis of Complex Manufacturing Systems: Modeling of nonlinear and stochastic dynamics in complex manufacturing processes (e.g. multistage automotive assembly line, nano-manufacturing processes) from an array of wired and wireless (e.g., RF/RFID) sensor signals, and tracking features extracted from these models for the system quality improvement and integrity assurance.

Health Informatics and Biomedical Signal Processing: studying the origins of complicated patterns in physiological signals (e.g. ECG, VCG, and EEG), exploring the nonlinear dynamics of human cardiovascular systems, modeling the strategies and functionalities of autonomic organism control, and integrating medical doctor’s accumulated expertise for prognostic applications.

Nonlinear Dynamic Data Mining: Investigating complex dynamical behaviors and spatiotemporal patterns from multi-dimensional state space, in addition to time, frequency or time-frequency domains; dwelling deep into hidden information such as recurrence, multifractal, bifurcation patterns; and predicting the system’s future statuses.

Quality Engineering and Applied Statistics: Using the techniques of multivariate statistics and process control, design of experiments, non-parametric model, data mining, machine learning for model development and quality improvement in the manufacturing processes.

Refereed Journals

         [1]      H. Yang, S. T. S. Bukkapatnam and R. Komanduri, “Nonlinear adaptive wavelet analysis of electrocardiogram  signals,” Physical Review E 76, 026214 (2007)

         [2]      S. T. S. Bukkapatnam, R. Komanduri, H. Yang, P. Rao, W. Lih, M. Malshe, L. M. Raff, B. A. Benjamin and M. Rockley “Classification of atrial fibrillation (AF) episodes from sparse electrocardiogram (ECG) datasets,” Journal of Electrocardiology Vol. 41, No. 4, p. 292-299, 2008.7

         [3]      D. Dawson, H. Yang, M. Malshe, S. T. S. Bukkapatnam, B. A. Benjamin and R. Komanduri, “Linear affine transformations between 3-lead (Frank XYZ leads) vectorcardiogram and  12-lead electrocardiogram signals,” Journal of Electrocardiology, Vol. 42, No. 6, p. 622-630, 2009.11

         [4]      B. Wilkins, R. Komanduri, S. T. S. Bukkapatnam, H. Yang, G. Warta, B. Benjamin, “Recurrence quantification analysis (RQA) used for detection of ST segment deviation,” Journal of the Federation of American Societies for Experimental Biology, 23: LB89

Book Chapter

         [1]      S. T. S. Bukkapatnam, H. Yang and F. Modhavi, “Towards Prediction of Nonlinear and Nonstationary Evolution of Customer Preferences using Local Markov Models,” The Art and Science behind Successful Product Launches, Eds: N. R. S. Raghavan and J. Cafeo, p. 300, ISBN: 9789048128594, August 2009

Conference Proceedings

         [1]       H. Yang, M. Malshe, S. T. S. Bukkapatnam and R. Komanduri, “Recurrence quantification analysis and principal components in the detection of myocardial infarction from vectorcardiogram signals,”  Proceedings of the 3^rd INFORMS Workshop on Data Mining and Health Informatics (DM-HI 2008), Oct 11, Washington, DC, USA, session A2.2

         [2]       U. Mittal, H. Yang, S. T. Bukkapatnam and L. G. Barajas, “Dynamics and performance modeling of multistage manufacturing systems using nonlinear stochastic differential equation models,” Proceedings of the 4th Annual IEEE Conference in Automation Science and Engineering, Aug 23-26, Washington, DC, USA, p. 498-503

         [3]       H. Yang and S. T. Bukkapatnam, “Recurrence based performance prediction and prognostics in the complex manufacturing systems,” Proceedings of 2009 Industrial Engineering Research Conference, May 30, Miami, FL (Best Paper Award in the Manufacturing and Design Track)

Dissertation

Nonlinear Stochastic Modeling and Analysis of Cardiovascular System Dynamics - Diagnostic and Prognostic Applications

EGN 3443 Probability and Statistics for Engineers This course presents the theory and methods of probability and statistics models needed to support engineering decision making. The course objectives include:

To understand the basic concepts of probability and statistics.

To understand the data representation techniques.

To learn discrete and continuous random variables, probability distributions, measure of central tendency, and measure of dispersion.

To learn the statistical inference and hypothesis testing.

To understand the regression analysis using least square parameter estimation.

To develop the statistical way of thinking.

          - Fall 2009: 90 students (on campus)

Professional Activities:

Refree, IEEE Transactions on Automation Science and Engineering

            IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans

            Computer and Industrial Engineering

            Medical Engineering & Physics

INFORMS Future Academician Colloquium, Seattle, WA, 2007/11

NSF Research Program Development Workshop, Knoxville, TN, 2008/01

Member of INFORMS, APM, IEEE, IIE, and ASEE

Honors and Awards:

IERC Best Paper Award, Manufacturing and Design Division, Miami, FL, 2009

Phoenix Award Finalist, Oklahoma State University, 2009

Niblack Biomedical Research Assistantship, Dr. John Niblack (Pfizer Inc.) and Oklahoma State University, 2008

Graduate College Research Fellowship, Oklahoma State University, Stillwater, 2008

NSF Travel Grant, Division of Civil, Mechanical and Manufacturing Innovation, National Science Foundation (NSF), 2007

Last updated on 10/2009