News & EventsDepartment Events
Events
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Oct29
EVENT DETAILS
lessBio
Harsha Honnappa is an Associate Professor in the Edwardson School of Industrial Engineering at Purdue University, where he runs the Stochastic Systems Lab. He is an applied probabilist with strong interests in the analysis of stochastic models, theoretical statistics, stochastic optimization and control. He is the recipient of the Lajos Takacs award for this PhD thesis on Transitory Queueing Theory. His research is supported by a number of grants from the National Science Foundation, including an NSF CAREER award, the Office of Naval Research, the Purdue Research Foundation, and through the Edwardson School of Industrial Engineering’s Frontiers awards
Talk Abstract
Doubly Stochastic Point Processes: From Representation to Nonparametric Maximum Likelihood Estimation
Doubly stochastic point process models are invaluable for modeling discrete-event systems exhibiting nonstationarities and high variability. The sample path measure of doubly stochastic models can be viewed as ‘infinite mixture’ models. The doubly stochastic Poisson (DSPP) or Cox process serves as exemplar in this talk. Statistical inference for these models is challenging due to the need to estimate the mixing probability measure from point process observations. This challenge is known as nonparametric maximum likelihood estimation (NPMLE) in the statistical literature. While much of the existing literature focuses on mixing measures supported on finite-dimensional subspaces, doubly stochastic processes often involve mixing measures supported on infinite-dimensional or path spaces, further complicating statistical estimation and inference.This talk addresses two key questions:
The “representation” power of DSPP or Cox models: What classes of point processes can be exactly represented using a Cox model? We will revisit JFC Kingman’s classic result on representing renewal processes as DSPPs and present a new, simplified proof of his main finding. Estimating the mixing measure from sample paths: After reviewing classical work on estimating finitely parameterized mixing models using the expectation-maximization (EM) algorithm, we will explore tight lower-bounding variational inference (VI) approximations to the NPMLE objective in more complicated settings that use neural networks to parametrize the mixing measure. We will provide approximation guarantees and insights into the accuracy of these approximate solutions.
TIME Tuesday, October 29, 2024 at 11:00 AM - 12:00 PM
LOCATION A230, Technological Institute map it
CONTACT Kendall Minta kendall.minta@gmail.com EMAIL
CALENDAR Department of Industrial Engineering and Management Sciences (IEMS)
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Nov19
EVENT DETAILS
lessTalk abstract: Benders decomposition is a mathematical decomposition technique designed to solve large-scale linear and mixed-integer programs. Since its introduction in 1962, the approach has been successfully applied to a wide variety of problems arising in supply chain management, transportation, telecommunications, and energy management. Despite its success, however, it has long been overshadowed by dual decomposition methods such as Lagrangian relaxation and Dantzig-Wolfe decomposition. Over the last two decades, one has witnessed a renewed interest in Benders decomposition with the introduction of several novel ideas to improve performance. The purpose of this talk is to give an overview of the main acceleration techniques by focusing on two families of problems where Benders decomposition has proven especially effective: facility location problems and network design problems. After briefly explaining the general methodology and practical enhancements, we will present examples of successful applications to set covering problems and fixed-charge network design problems. In each case, we will focus on strategies for generating strong cuts efficiently, including the application of unified cut generation frameworks and the use of normalization constraints in the dual subproblem.
Bio: Jean-François Cordeau obtained his Ph.D. in Applied Mathematics at École Polytechnique de Montréal in 1999. He is a professor of Operations Management at HEC Montréal, where he also holds the Chair in Logistics and Transportation. He has authored or co-authored more than 175 scientific articles in combinatorial optimization and mathematical programming, focusing primarily on vehicle routing and logistics network design. He has also supervised more than 75 M.Sc. and Ph.D. students. Dr. Cordeau is an Area Editor of Transportation Science and a member of the Editorial Board of Computers & Operations Research. He has worked as a consultant for several Canadian and European organizations in the private and public sectors. He is currently one of the scientific directors of IVADO Labs. He received the Canadian Operational Research Society (CORS) Award of Merit in 2016 and the Pierre-Laurin Award for Research Excellence at HEC Montréal in 2018. In 2023, he and ten of his colleagues won the CORS Practice Prize for their work on maritime vessel routing.
TIME Tuesday, November 19, 2024 at 11:00 AM - 12:00 PM
LOCATION Hive Annex, Ford Motor Company Engineering Design Center map it
CONTACT Kendall Minta kendall.minta@gmail.com EMAIL
CALENDAR Department of Industrial Engineering and Management Sciences (IEMS)
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Dec7
EVENT DETAILS
lessFall classes end
TIME Saturday, December 7, 2024
CONTACT Office of the Registrar nu-registrar@northwestern.edu EMAIL
CALENDAR University Academic Calendar
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Dec14
EVENT DETAILS
lessThe ceremony will take place on Saturday, December 14 in Pick-Staiger Concert Hall, 50 Arts Circle Drive.
*No tickets required
TIME Saturday, December 14, 2024 at 4:00 PM - 6:00 PM
LOCATION Pick-Staiger Concert Hall map it
CONTACT Andi Joppie andi.joppie@northwestern.edu EMAIL
CALENDAR McCormick School of Engineering and Applied Science