ISE Seminars

Dr. Denis Cormier, Rochester Institute of Technology
Metal Additive Manufacturing Via Jetting of Molten Metal Droplets

📆 Friday, October 24, 2025
⏰ 12:20 p.m. EST
📍Durham Hall 261 (or via Zoom ID: 540 231 0261)
👉 ISE Invited Seminar Series

ABSTRACT: In contrast to the rapid rise in usage of inexpensive desktop 3D printers, industrial adoption of metal additive manufacturing (AM) technologies has largely been limited to the aerospace and biomedical sectors over the past 20 years. The primary reasons for this are the high cost of equipment (often >$1M), the high cost of metal powder (5X-10X higher than ingot/bar material), the relatively low print speeds (hours to days per part), and environmental health and safety concerns about metal powders. This talk will present an emerging metal AM technology, called molten metal jetting (MMJ), which has the potential to address many of these challenges. MMJ ejects droplets of molten metal from nozzles towards a moving build platform where they land, spread out, cool down, and solidify to form the shape of the desired component. The process can use inexpensive ingot, rod, or wire as the feedstock material, thus significantly lowering part cost. This talk will highlight several multidisciplinary MMJ R&D efforts aimed at increasing print speed and part quality while lowering overall fabrication costs. One such effort involves the recent demonstration of coordinated multi-nozzle droplet jetting that enables significant reductions in part fabrication time with no loss of achievable feature size. Another research thrust involves multi-physics simulations of molten metal droplet ejection with an aim towards increasing the achievable jetting frequency and print speed. Lastly, part quality is of utmost concern for industrial applications. Research involving closed-loop process monitoring and control with high-speed multi-nozzle droplet jetting will be presented.

BIOGRAPHY: Denis Cormier is the Earl W. Brinkman Professor of Industrial and Systems Engineering at Rochester Institute of Technology (RIT) where he also directs the New York State funded AMPrint Center. He was on the ISE faculty at NC State University from 1994-2009. While at NC State, his research efforts focused on design and fabrication of engineered lattice structures, development of electron beam melting (EBM) process parameters for aerospace alloys, and novel applications of AM technologies. Since joining RIT, he has worked extensively in areas such as multifunctional 3D printing, molten metal jetting, and additive manufacturing with carbon fiber composite materials. Denis is a Fellow of IISE, and he was the 2024 recipient of the International FAME award, which recognizes career achievements related to additive manufacturing R&D. He has been the PI for >$30M in sponsored research, and has advised over 70 PhD and MS students to date.

Dr. Jim Luedtke, University of Wisconsin-Madison
Probing-enhanced stochastic programming

📆 Friday, October 10, 2025
📍Blacksburg, Va.
👉 ISE Invited Seminar Series

ABSTRACT: We consider a two-stage stochastic program where the decision-maker has the opportunity to obtain information about the distribution of the random variables X through a set of discrete actions that we refer to as probing. Probing allows the decision-maker to observe components of a random vector Y that is jointly-distributed with X. We propose a three-stage optimization model for this problem, where the first-stage variables select components of Y to observe. In the case that X and Y have finite support, a model of Goel and Grossmann can be applied to obtain a formulation of this problem whose size is proportional to the square of cardinality of the sample space of the random variables. We propose to solve the model using bounds obtained from an information-based relaxation, combined with a branching scheme that enforces the consistency of decisions with observed information. The branch-and-bound approach can naturally be combined with sampling in order to estimate both lower and upper bounds on the optimal solution value even for problems with continuous distribution. We demonstrate the approach on instances of a stochastic facility location problem. This is joint work with Zhichao Ma, Jeff Linderoth, Youngdae Kim, and Logan Matthews.

BIOGRAPHY: Jim Luedtke is a Professor in the department of Industrial and Systems Engineering at the University of Wisconsin-Madison. Luedtke earned his Ph.D. at Georgia Tech and did postdoctoral work at the IBM T.J. Watson Research Center. Luedtke’s research is focused on methods for solving stochastic and mixed-integer optimization problems, as well as applications of such models. Luedtke is a recipient of an NSF CAREER award, was a finalist in the INFORMS JFIG Best Paper competition, and was awarded the INFORMS Optimization Society Prize for Young Researchers. Luedtke serves on the editorial board of Mathematical Programming Computation, is chair of the Mathematical Optimization Society Publications Committee, and serves as Vice-Chair for Optimization under Uncertainty for the INFORMS Optimization Society.

Dr. Young-Jun Son, Purdue University
Multi-paradigm, Online, Hierarchical Simulation and Decision Models for Planning and Control of Complex Systems

📆 September 5, 2025
📍 Blacksburg, Va. 
👉 ISE Invited Seminar Series

ABSTRACT: In this talk, multi-paradigm, hierarchical, and online simulations will be introduced and discussed to support planning and control of complex systems. First, an overview of multi-paradigm simulations, such as discrete event simulation (DES), agent-based modeling (ABM), system dynamics (SD), and physics-based simulation will be provided. While the goal of these modeling paradigms is the same (i.e. representing a real system validly and credibly), key characteristics and differences will be explained. Second, an online simulation-based planning and control (SPC) approach is introduced, where a fast-running DES simulation is used as a predictive tool to evaluate decision alternatives at the planning stage, and the same DES model (a twin-simulation running in real-time) is used as a task generator to drive a real system at the control stage. Third, an extension of SPC to a highly complex system is discussed, which involves a dynamic data-driven adaptive multi-scale simulation (DDDAMS) framework. A key module in this framework enhances the computational efficiency of the system-level simulation considering available data, computational resources, and model validity/credibility via dynamic switching of fidelity of component simulations and information gathering during the simulation execution over time. In this talk, a few case studies (i.e. M/M/1 service, smart manufacturing, unmanned aerial/ground vehicles) will be used to illustrate the above-mentioned concepts and facilitate discussions.

BIOGRAPHY: Dr. Young-Jun Son is the James J. Solberg Head and Ransburg Professor of Edwardson School of Industrial Engineering at Purdue University. He is a Department Editor of the Institute of Industrial and Systems Engineers (IISE) Transactions, and serve on the editorial board for six other international journals. He is a Fellow of Institute of Industrial and Systems Engineers (IISE), and has received the Society of Manufacturing Engineers (SME) 2004 Outstanding Young ME Award, the IIE 2005 Outstanding Young IE Award, the IISE Annual Meeting Best Paper Awards (2005, 2008, 2009, 2016, 2018, 2019), and the Best Paper of the Year Award (2007) in International Journal of Industrial Engineering. His research works have been sponsored by NSF, AFOSR, USDOT, USDA, USDOE, NIST, among others. He can be reached at yjson@purdue.edu.

Dr. Lavanya Marala, University of Illinois at Urbana-Champagne 
Sensing in Airspace for Sequential O-D Aircraft Routing

 📆 Thursday, April 24, 2025
📍 Blacksburg, Va. 
👉 ISE Invited Seminar Series

ABSTRACT: Aircraft in today's National Aviation Systems (NASs) worldwide rely on wind predictions from the National Oceanic and Atmospheric Administration (NOAA) to calculate favorable paths. However, wind conditions are dynamic and the NOAA information becomes quickly outdated because it is based upon sparse sampling both in space and time. This leads to inefficient, slower, paths used in practice. A goal of the Federal Aviation Administration's (FAA) NextGen program is to use dynamic information to reduce inefficiencies. One such way to obtain high quality dynamic information and reduce inefficiency is to use en-route aircraft as 'sensors'. This raises a natural question, "if a subset of aircraft can be used for sampling, how should aircraft be routed to collect information most useful to minimize costs for itself and other future aircraft?" To answer this question, we begin with a stylized model of the aircraft routing problem, and capture the uniquely spatial and temporal correlations in wind dynamics. This allows us to model spatial and temporal correlation between the travel time along different paths, and formulate the travel time as a Brownian surface. Under this uncertainty structure, we answer two questions: (i) if a state-independent routing of paths to be sampled is desired, what is the optimal sampling policy? and (ii) if the paths to be sampled are to be chosen in real time according to flight schedules, what is a near-optimal sampling policy? We provide answers to these questions with provable guarantees. We also generate a comprehensive testbed from real-world flight data and computationally evaluate the performance of our sampling policies. Our testbed consists of seventeen origin-destination airport pairs, with five short-haul, seven medium-haul and five long-haul pairs. Our results show that collecting the right information and utilizing it to plan future aircraft routes could reduce a flight's travel time and associated fuel burn by 5% on average. 

BIOGRAPHY: Dr. Lavanya Marla is an Associate Professor in Industrial and Enterprise Systems Engineering at the University of Illinois at Urbana-Champaign. Her research interests are in robust and dynamic decision-making for large-scale networks subject to operating stochasticity. Her research builds advanced resource allocation tools for these systems by bridging aspects of data-driven optimization, statistics, simulation and artificial intelligence. Application areas of interest include aviation planning, operations and pricing; logistics, emergency medical services, and shared transportation systems. Prior to the University of Illinois, she was a Systems Scientist with the Heinz College at Carnegie Mellon University. She earned her PhD from the Massachusetts Institute of Technology and Bachelors degree from the Indian Institute of Technology Madras. Her work has been recognized through multiple awards including the prestigious Center for Advanced Study award from the University of Illinois, IISE Outstanding Innovation in Service Systems award, a semi-finalist at the INFORMS Innovative Applications in Analytics Award, Honorable mention for the Anna Valicek award from AGIFORS, KDD Startup Research award, and multiple best paper awards. Her research is funded by grants from the US National Science Foundation, the Department of Homeland Security, the Department of Transportation, the US-India Educational Foundation, and multiple industry grants. 

Dr. Maged Dessouky, University of Southern California
Cost-Sharing Transportation Systems

📆 Friday, March 28, 2025 
📍 Blacksburg, Va. 
👉 ISE Invited Seminar Series

ABSTRACT: A set of nascent industries focusing on cost-sharing transportation systems such as ridesharing/carsharing have recently emerged. These types of cost-sharing transportation systems are also being introduced in freight delivery through horizontal cooperation of their logistic systems to reduce costs and delay times. Horizontal cooperation achieved through pooling of freight transportation networks reduces total shipping costs, and alleviates the impact on traffic congestion. One major impediment for successful implementation of these types of transportation systems is the determination of the cost-share amount for each participant. The cost-sharing problem has largely been neglected in the literature and is the focus of this talk. One crucial component of a cost sharing transportation system is the allocation of costs and/or savings to each participant in the system. Without a model to allocate costs and/or savings to each participant in the system, there is no basis to allocate the costs in a fair manner to the participants, thus making it less of an incentive to participate. In this talk we give two examples of models, one for ridesharing and the other for freight consolidation, for determining the cost-share of each participant.

BIOGRAPHY: Maged M. Dessouky is Tryon Chair in Industrial and Systems Engineering and Professor and Chair in the Daniel J. Epstein Department of Industrial and Systems Engineering. His research area is transportation system optimization where he has authored over 115 refereed publications. His paper “Optimal Slack Time for Schedule Based Transit Operations” was awarded the INFORMS Transportation Science and Logistics Best Paper Prize. He is a Fellow of IISE and INFORMS and serves as Associate Director of METRANS, a center focused on solving important urban transportation problems. He is currently associate editor of Transportation Research Part B: Methodological and on the editorial board of Transportation Research Part E: Logistics and Transportation Review, and previously served as area editor of the ACM Transactions of Modeling and Computer Simulation, department editor of IISE Transactions, area editor of Computers and Industrial Engineering, and associate editor of IEEE Transactions on Intelligent Transportation Systems. He has won numerous teaching awards including USC Associates Award for Excellence in Teaching. He received his Ph.D. in Industrial Engineering from the University of California, Berkeley, and M.S. and B.S. degrees from Purdue University.

Dr. Joe Wilck, Bucknell University
Let’s Have a Chat: Applying ChatGPT and Other Large Language Models to ISE

📆 Monday, March 23, 2025 
📍 Blacksburg, Va. 
👉 ISE Invited Seminar Series

ABSTRACT: Artificial Intelligence large language models (LLMs) like ChatGBT can improve the efficiency of common language generation tasks. In this presentation, we draw upon emerging academic research to suggest LLMs’ industrial and systems engineering applications. Our demonstrations will showcase the immense capabilities and the significant risks involved in using these tools. Our session includes six specific practical principles to effectively and safely leverage LLMs. The session will also include commentary concerning the use of LLMs at the university level in terms of teaching and learning. 

BIOGRAPHY: Dr. Joe Wilck has been teaching analytics, operations research, data science, and engineering since 2006.  His research is in the area of applied optimization and analytics, and it has been funded by the National Science Foundation, Department of Energy, Defense Advanced Research Projects Agency (DARPA), and North Carolina Department of Transportation; among others.  He is a registered Professional Engineer.  He is an active member of the Institute of Industrial and Systems Engineers (IISE), Institute for Operations Research and the Management Sciences (INFORMS), and Military Operations Research Society (MORS).  Prior to joining the faculty at Bucknell University, he was a faculty member for William & Mary and United States Air Force Academy; among others. He graduated with his B.S. and M.S. in Industrial & Systems Engineering from Virginia Tech, and his Ph.D. in Industrial Engineering and Operations Research from Penn State. He is originally from Farmville, VA.

Dr. Peihua Qiu, University of Florida
Statistical Quality Control

📆 Wednesday, December 11, 2024
📍 Blacksburg, Va. 
👉 ISE Invited Seminar Series

ABSTRACT: Statistical process control (SPC) charts are powerful analytical tools for the online monitoring of data streams across diverse applications, including manufacturing, environmental monitoring, disease surveillance, and more. Traditional SPC charts are typically designed for scenarios where the in-control (IC) process observations are independent and identically distributed (i.i.d.) and follow a pre-specified parametric distribution. However, when these assumptions are violated, the reliability of such charts diminishes significantly. Over the past 25 years, my research in SPC has focused on advancing methodologies that remain robust and effective even when these traditional assumptions are invalid. Specifically, my research team has developed a range of innovative concepts and methods, including nonparametric SPC charts, dynamic screening systems (DySS) for monitoring dynamic processes, transparent sequential learning (TSL) approaches for correlated data, and spatio-temporal process monitoring techniques, among others. In this talk, I will present an intuitive overview of these developments, aiming to make the content accessible and engaging for a general audience.

BIOGRAPHY: Dr. Peihua Qiu is the Dean’s Professor and founding chair of the Department of Biostatistics at the University of Florida. He earned his PhD in Statistics from the University of Wisconsin-Madison in 1996. Dr. Qiu has made significant contributions to various fields, including jump regression analysis, image processing, statistical process control, survival analysis, dynamic disease screening, and spatio-temporal disease surveillance. To date, he has authored three books and published over 170 research papers in leading refereed journals. Dr. Qiu is an elected fellow of the American Association for the Advancement of Science (AAAS), the American Statistical Association (ASA), the American Society for Quality (ASQ), and the Institute of Mathematical Statistics (IMS), as well as an elected member of the International Statistical Institute (ISI). He served as Editor of Technometrics from 2014 to 2016 and is the 2024 recipient of the prestigious Shewhart Medal.

Dr.  Pierre Patie, Cornell University
Exact Isospectral Algorithms for Stochastic Processes and PDEs 

📆 Thursday, November 21, 2024
📍 Blacksburg, Va. 
👉 ISE Invited Seminar Series

ABSTRACT: This talk presents a novel isospectral framework that transforms continuous-time stochastic processes and parabolic PDEs into exact, computationally efficient  discrete models. Leveraging gateway and interweaving relations, we achieve precise  simulation algorithms that bypass traditional approximation errors. We illustrate our algorithms with classical models in mathematical finance, such as the squared Bessel and CR processes, as well as their Dyson analogues prominent in random matrix theory. Their approach offers substantial efficiency gains, providing powerful tools for exact solutions in stochastic modeling and applied mathematics. 

BIOGRAPHY: Pierre Patie is a Professor at the School of ORIE at Cornell University. He earned his Ph.D. in mathematics from the Swiss Federal Institute of Technology in Zurich. Pierre Patie has broad interests in mathematics, spanning probability theory, operator theory, algebra, and numerical analysis, aimed at solving problems in applied fields such as a mathematic finance, insurance mathematics, and neurology., His recent work focuses on scaling and university problems,using advanced algebraic and analytical methods to uncover fundamental principles across diverse applications.