Dr. Yuan Zhou to Present Seminar

Our own Dr. Yuan Zhou will present at the IMSE seminar on Monday, April 29, 2019 at 1:00pm in Nedderman Hall, Room 105. Please note that this seminar is in the usual room, but we are moving the time up 15 minutes to accommodate faculty schedules. Dr. Zhou’s presentation title, abstract, and biographical sketch are below. 

 

Title: Modeling Complex Adaptive Systems: Agent-Based Simulation and Its Applications
Author: Yuan Zhou
Location: Nedderman Hall Room 105
Date: Monday, April 29, 2019
Time: 1:00pm 

Abstract: Modeling the behavior of complex adaptive systems, such as infectious disease transmissions and policing systems, plays an important role in management decision-making towards improving systems’ performance. However, it is often challenged by inherent complexities of the underlying systems: nonlinear interactions in between systems’ entities (e.g., contacts between humans), entities’ adaptive behaviors (e.g., criminals’ response to policing actions and other environmental factors), and dependent happenings of certain events (e.g., parent-offspring disease transmissions). Traditionally, equation-based models, such as differential equations and Markov models, have been used to represent the average system behavior, but they usually fail to capture those complexities appropriately. In recent years, agent-based simulation (ABS) has received growing attentions because it enables realistic representations of systems’ complexities at a micro-level. ABS is a class of computational models that is built upon the unique behaviors of individual entities, or agents, who are interacting with each other, autonomously making decisions, and collectively driving the macro-level behavior of the system. In this talk, we will discuss several projects involving ABS modeling in healthcare systems, policing systems, and traffic systems. Our goal is to address some critical issues in design and implementation of ABS models, including model granularity, data needs, and model validation, and provide some strategies to overcome these issues. 

Bio: Yuan Zhou is an Assistant Professor of Department of Industrial, Manufacturing and Systems Engineering at The University of Texas at Arlington. She received a B.S. degree in Mechanical and Electrical Engineering from Beijing Institute of Technology, Beijing, China and a Ph.D. degree in Industrial and Systems Engineering from The University at Buffalo, Buffalo, NY. Dr. Zhou’s primary research interests include healthcare delivery systems engineering, agent-based simulation, infectious disease modeling and policy development, health data analytics, and dynamic policing decision analytics. Currently, she is also working with local healthcare and law enforcement partners to develop analytical tools to support their management decision making and improve operations performance.

Dr. Beruvides to Present Seminar

AT&T Professor Mario Beruvides from the Whitacre College of Engineering’s Industrial, Manufacturing & Systems Engineering Department at Texas Tech University will present at the IMSE seminar on Wednesday, April 24, 2019 at 1:15pm in Nedderman Hall, Room 106.

Title: Systems Dynamics and its Role in Industrial & Engineering Management Research: A look at Minsky’s Financial Instability Hypothesis & Technology Diffusion Curves
Author: Mario Beruvides
Location: Nedderman Hall Room 106
Date: Wednesday, April 24, 2019
Time: 1:15pm

Abstract: The role of the industrial engineer has always been deeply entrenched in the analysis of industrial and social technical systems.  Systems theory and its off-shoot, systems dynamics, is a critical development encompassing a revolutionary theory of how we look at complex systems as well as how to model, analyze and ultimately practice industrial engineering knowledge.  In this talk, comprised of two parts – a look at Minsky’s Instability Hypothesis and Technology Diffusion curves, the speaker will provide some insights into the changing role of industrial engineering when addressing complex technical system.  With respect to the analysis of the Minsky Instability Hypothesis, the research analyzed eleven financial debt ratios related to the level of debt associated to the U.S. households, nonfinancial and financial businesses. The validation process utilized nonparametric statistical analysis of Page and binomial tests to provide statistical evidences that supported the validity of FIH. This confirmatory research found evidence to suggest FIH concepts were indeed applicable to the 1945-1980s era and remains relevant to the 1990-2017 periods.  In analyzing technology diffusion curves, the research looks at the potential of classifying and developing an economic procedure to optimize entrance and exit strategies for organizations with respect to their technology portfolios.

Bio: Mario Beruvides, Ph.D., P.E., is an AT&T Professor at Texas Tech University in the Whitacre College of Engineering’s Industrial, Manufacturing & Systems Engineering Department.  His current research interests include: Management of Technology, Engineering Management, Knowledge work Performance, Measurement, Production and Quality Systems Engineering, and Advanced Economic Analysis.  Dr. Beruvides has a Ph.D. in Industrial Engineering from the Virginia Polytechnic Institute and State University, a M.S. in Industrial Engineering from the University of Miami, and a B.S. in Mechanical Engineering from the University of Miami.

Dr. Leili Shahriyari to Present Seminar

Assistant Professor Leili Shahriyari from the Department of Mathematics will present at the IMSE seminar on today, April 15, 2019 at 1:15pm in Nedderman Hall, Room 105. Dr. Shahriyari’s presentation title, abstract, and biographical sketch are below.

Title: Data-Driven Models for Discovery of Effective Personalized Cancer Treatments

Author: Leili Shahriyari
Location: Nedderman Hall Room 105
Date: Monday, April 15, 2019
Time: 1:15pm

Abstract: Carcinogenesis is a complex stochastic evolutionary process. One of the key components of this process is evolving tumors, which interact with and manipulate their surrounding microenvironment in a dynamic spatio-temporal manner. Recently, several computational models have been developed to investigate such a complex phenomenon and to find potential therapeutic targets. In this talk, we present novel computational models to gain some insight about the evolutionary dynamics of cancer. Furthermore, we propose an innovative framework to systematically employ a combination of mathematical methods and bioinformatics techniques to arrive at unique personalized targeted therapies for cancer patients.

Bio: Leili has a Ph.D. degree in Mathematics and an M.S.E. degree in Computer Science from Johns Hopkins University (JHU). She studied Computer Science with a specific focus machine learning (ML) and data science, and Mathematics with focus on differential geometry. She conducted her first postdoctoral training in computational biology at the University of California Irvine (UCI). At UCI, she developed stochastic models to improve our understanding of cell dynamics during tumorigenesis and improved an artificial neural network model for obtaining gene regulatory networks. During her second postdoctoral training, as an NSF/MBI funded postdoc fellow at the Mathematical Biosciences Institute (MBI), she pursued an independent research program and established collaboration with biologists, physicians, and mathematicians. She is currently an assistant professor of Data Science at the University of Texas at Arlington, where she has been awarded STARs grant. Her lab, currently with three PhD, one Master, and four Undergraduate students, develops innovative frameworks to systematically employ a combination of machine learning and statistical methods as well as mathematical techniques to arrive at unique personalized therapies.