Senior Lecturer Position Opening

Due to our exponential growth in the last few years, the Industrial, Manufacturing, and Systems Engineering Department at UT Arlington has openings for several senior lecturer positions.UT Arlington IMSE“Each position is a non-tenure track with 9-month academic year appointment and the possible opportunity of additional appointment during summers.  An earned doctoral degree in industrial engineering, engineering management, or systems engineering, or a closely related field is required. Successful candidates are expected to teach undergraduate and master’s-level courses in their respective disciplines.  To apply, please submit your cover letter, vita, and references on this form.  Review of the applications will begin June 22 and will continue until the positions are filled.

UT Arlington is a doctoral, research-extensive university with a current enrollment of over 48,000 students around the world, and is part of the University of Texas System.  The College of Engineering has more than 6,000 students and 25,000 alumni, the COE (uta.edu/engineering). The College offers ten baccalaureate, 14 masters, and nine Ph.D. programs.

The successful candidates will be required to complete an Employment Eligibility Verification form and provide documents to verify identity and eligibility to work in the U.S. UT Arlington is an Affirmative Action/Equal Opportunity Employer. Women, minorities, veterans, and individuals with disabilities are encouraged to apply. The use of tobacco products is prohibited on UT Arlington properties. A criminal background check will be conducted on finalists.” (Source of information: https://www.uta.edu/engineering/about/faculty-search/senior-lecturer-imse.php)

IMSE Welcomes New Faculty Member

Dr. Shouyi WangThe IMSE Department welcomes new Assistant Professor, Dr. Shouyi Wang. He comes to UTA from the University of Washington where he served as a Research Scientist.

Dr. Wang has interests in data mining, machine learning, pattern recognition, multivariate process monitoring and prediction, multivariate statistics, applied operation research, and human-centered computing. He has developed mathematical theories and algorithms to frame, model and optimize complex systems, and solve large-scale data mining and knowledge discovery problems in engineering and science. He has conducted research projects on intelligent learning control systems for humanoid walking robots, personalized healthcare online monitoring and decision-making systems using multivariate physiological signals, functional and diagnostic brain imaging analysis and network modeling (fMRI), clinical recommendation system for respiratory-gated PET/CT Imaging using patient classification and statistical association, real-time prediction/detection of mental states and cognitive activities using brain-computer interfaces, and personalized healthcare information systems with wearable body sensor networks. He is also the author of several articles that have appeared in publications such as the International Journal of Data Mining and Bioinformatics, Wiley Encyclopedia of Operations Research and Management Science, and  conference proceedings.

Dr. Wang received a Ph.D in Industrial and Systems Engineering from Rutgers in 2012. He is also a member of professional engineering organizations such as the Institute for Industrial Engineers (IIE), Institute for Operations Research and the Management Sciences (INFORMS), and Institute of Electronics Engineers (IEEE).