Sunday, October 15, 9:30-10:35am
IFORS Distinguished lecture
Distributionally Robust Optimization: The Science of Underpromising and Overdelivering
Many decision problems in science, engineering and economics are affected by uncertain parameters whose distribution is only indirectly observable through samples. The goal of data-driven decision-making is to learn a decision from finitely many training samples that will perform well on unseen test samples. This learning task is difficult even if all training and test samples are drawn from the same distribution—especially if the dimension of the uncertainty is large relative to the training sample size. Wasserstein distributionally robust optimization (DRO) seeks data-driven decisions that perform well under the most adverse distribution within a certain Wasserstein distance from a nominal distribution constructed from the training samples. It has a wide range of conceptual, statistical and computational benefits. Most prominently, the optimal decisions can often be computed efficiently, and they enjoy provable out-of-sample and asymptotic consistency guarantees. This talk will highlight two recent advances in Wasserstein DRO. First, we will develop a principled approach to leveraging samples from heterogeneous data sources for making better decisions. In addition, we will prove the optimality of linear policies in Wasserstein distributionally robust linear-quadratic control problems with imperfect state observations, and we will show that these policies can be computed efficiently using dynamic programming, Kalman filtering and automatic differentiation.
About Daniel Kuhn
Daniel Kuhn is a Professor of Operations Research in the College of Management of Technology at EPFL, where he holds the Chair of Risk Analytics and Optimization. His research interests revolve around stochastic, robust and distributionally robust optimization, and his principal goal is to develop efficient algorithms as well as statistical guarantees for data-driven optimization problems. This work is primarily application-driven, the main application areas being energy systems, machine learning, business analytics and finance. Before joining EPFL, Daniel Kuhn was a faculty member in the Department of Computing at Imperial College London and a postdoctoral researcher in the Department of Management Science and Engineering at Stanford University. He holds a PhD degree in Economics from the University of St. Gallen and an MSc degree in Theoretical Physics from ETH Zurich. He is an INFORMS fellow and the recipient of several research and teaching prizes including the Friedrich Wilhelm Bessel Research Award by the Alexander von Humboldt Foundation and the Frederick W. Lanchester Prize by INFORMS. He is the editor-in-chief of Mathematical Programming and the area editor for continuous optimization of Operations Research.
Monday, October 16, 9:45-10:35am
PASCAL VAN HENTENRYCK
Panel on Harnessing the data revolution in Supply Chains
Supply chains vast scope and impact generate huge amounts of data, which represent a treasure trove of insights. Some areas are amply explored with techniques well known to our field, such as inventory optimization, demand forecasting, production planning and scheduling, and transportation. And yet with the advent of technical advances in the ability to store and calculate data, we can collect and compute in ways never before possible. The internet of things generates signals across the supply chain, and far more external data options exist, ranging from federated sources like microeconomic data, weather, and consumer demand to social media sentiment. Machine learning can ingest these signals to augment insights across the supply chain network. Algorithmic research also provides modern approaches that integrate machine learning and optimization in new ways. All of these changes offer unparalleled opportunity to harness the data revolution in the practice of supply chains, which is fortunate given that disruptions show no sign of ceasing and supply chains are on the board room agenda like never before. This panel of experts bring decades of experience looking at these issues and will share their perspectives on what areas represent the most promise and which the most peril.
About Anne Robinson
As Chief Strategy Officer, Anne is responsible for advancing Kinaxis strategic development to add continued value to customers. Her team delivers the strategic roadmap, extensive thought leadership, as well as internal communications and change management. Recognized in analytics and digital transformation, Dr. Robinson has extensive experience managing supply chains for global organizations. At Verizon, she was responsible for the strategic vision of the global supply chains, driving excellence through analytics and process innovation. Previously, Anne managed analytics and business performance teams for Cisco’s supply chain. Dr. Robinson is a past president of INFORMS, seasoned industry speaker, and recipient of the 2020 Starr Excellence in Production and Operations Management Practice Award. In 2021, she joined the Creative Destruction Lab as a Supply Chain Mentor. Anne has a BScH from Acadia University, MASc from the University of Waterloo and MSc and PhD from Stanford University.
About Derrick Fournier
Derrick is a passionate advocate for using the power of imagination and digital capabilities to drive innovation and value. He is the Vice President of Digital Transformation and Innovation for the Global Product Development and Supply Chain organization at Bristol Myers Squibb, a leading Bio Pharmaceutical company. Derrick was a founding member of BMS’ Business Insights and Analytics organization and prior to joining BMS, was a Strategy Consultant with Accenture. Derrick is a Professional Engineer and holds an MBA from the Richard Ivey School of Business and an Electrical Engineering Degree from Western University in Ontario Canada.
About Pascal Van Hentenryck
Pascal Van Hentenryck is the A. Russell Chandler III Chair and
Professor in the H. Milton Stewart School of Industrial and Systems
Engineering at the Georgia Institute of Technology. He is also the
director of the NSF AI Institute for Advances in Optimization. His
current research focuses on machine learning, optimization, and
privacy with applications in energy, manufacturing, mobility, and
supply chains. Van Hentenryck is an INFORMS Fellow and a Fellow
of the Association for the Advancement of Artificial Intelligence (AAAI).
About Kelly Thomas
Kelly Thomas has more than 35 years of experience in leading teams in design, development, sales, and delivery of supply chain management and manufacturing execution software solutions. He currently leads Worldlocity, a research firm that helps supply chain software companies develop and execute strategy. He is a member of the board of directors of Kinaxis and sits on the executive advisory board of EverStream Analytics. He is also an executive advisor to Shippeo, a supply chain visibility software company, and aThingz, a supply chain software company specializing in logistics analytics and logistics optimization.
Previously, he was Chief Product Officer of JDA Software (Blue Yonder), a leading supply chain management software company. Prior to that, he was with i2 Technologies, where he held a number of executive positions, including SVP of product strategy and SVP and GM of the manufacturing sector. Mr. Thomas has consulted with dozens of Fortune 500 companies on supply chain strategy and has published numerous papers on strategic issues related to supply chain strategy and technology. He has been recognized as a Supply Chain Pro to Know and is a former board member of the Supply Chain Council. Mr. Thomas holds a degree in chemical engineering from Rutgers University, where he was a Slade Scholar.
About Feryal Erhun
Feryal is the Professor of Operations and Technology Management at Cambridge Judge Business School. She is a co-director of the Centre for Health Leadership & Enterprise (CCHLE) and the Academic Director of the Centre for Health Leadership & Enterprise (CCHLE).
Her research interests include strategic interactions between stakeholders in supply chains, socially responsible operations, and healthcare operations. She is a strong proponent of practice-based research. Through collaborations with Intel Corporation, Cisco, Stanford University Medical Center, Public Health England and others, she has combined her academic interests with stakeholders’ needs to deliver insights for both communities.
Feryal is an editorial board member of Manufacturing and Service Operations Management and Management Science. She has served as a board member of the Production and Operations Management Society and has served as the President-elect (2020-2021) and the President (2021-2022) of the Manufacturing and Service Operations Management Society. She was previously an instructor in medicine at Stanford School of Medicine. She was a faculty member in the Management Science and Engineering Department of Stanford University from 2002 until 2013. She holds bachelor’s and master’s degrees from Bilkent University and a PhD in industrial administration from the Carnegie Mellon University Tepper School of Business.
Tuesday, October 17, 9:45-10:35am
The Power of Platforms to Transform HealthCare
True transformation of the healthcare sector requires us to move from pipeline thinking to a platform approach. To do this, we must capture diverse data sources at scale, constantly derive new insights from that data and create closed-loop solutions in a highly repeatable model. This presentation will focus on the work of Mayo Clinic Platform to create this model through privacy-protecting, federated, deidentified data behind glass that protects both data and intellectual property and the partnerships required to enable the greatest impact.
About John Halamka
John D. Halamka, M.D., M.S., president of the Mayo Clinic Platform, leads a portfolio of platform businesses focused on transforming healthcare by leveraging artificial intelligence, connected healthcare devices and a network of trusted partners.
Trained in emergency medicine and medical informatics, Dr. Halamka has been developing and implementing healthcare information strategy and policy for more than 25 years.
Prior to his appointment at Mayo Clinic, he was chief information officer at Beth Israel Deaconess Medical Center, where he served governments, academia, and industry worldwide. He is a practicing emergency medicine physician.
As the International Healthcare Innovation Professor at Harvard Medical School, Dr. Halamka helped the George W. Bush administration, Obama administration, and governments around the world plan their healthcare information strategies.
Dr. Halamka completed his undergraduate studies at Stanford University, earned his medical degree at the University of California, San Francisco, and pursued graduate work in bioengineering at the University of California, Berkeley. He completed his residency at Harbor – UCLA Medical Center in the Department of Emergency Medicine. Dr. Halamka has written a dozen books about technology-related issues, hundreds of articles, and thousands of posts on the Geekdoctor blog. He was elected to the National Academy of Medicine in 2020. He and his wife also run Unity Farm Sanctuary in Sherborn, Massachusetts – the largest animal sanctuary in New England, which includes 300 animals, 30 acres of agricultural production, and a cidery.
Wednesday, October 18, 11:05-11:55am
Reinventing Operations Management’s Research and Practice with Data Science
Machine learning is playing increasingly important roles in decision making, with key applications ranging from dynamic pricing and recommendation systems to personalized medicine and clinical trials. While supervised machine learning traditionally excels at making predictions based on i.i.d. offline data, many modern decision-making tasks, in particular in operations management, require making sequential decisions based on data collected online. Such discrepancy gives rise to important challenges of bridging offline supervised learning and online interactive learning to unlock the full potential of data – driven decision making.
The presentation will focus on the integration of online and offline learning to improve decision making in operations management. We highlight three examples. In the first, we consider the challenges of reducing difficult online decision-making problems to well-understood offline supervised learning problems. In the second, we show the impact of offline data on online decision making. Finally, in clinical trials, we show how to convert offline randomized control trials into adaptive, online, experimental design.
About David Simchi-Levi
David Simchi-Levi is a Professor of Engineering Systems at MIT and serves as the head of the MIT Data Science Lab. He is considered one of the premier thought leaders in supply chain management and business analytics.
His Ph.D. students have accepted faculty positions in leading academic institutes including University of California, Berkeley, Carnegie Mellon University, Columbia University, Cornell University, Duke University, Georgia Tech, Harvard University, University of Illinois Urbana-Champaign, University of Michigan, Purdue University, and Virginia Tech.
Professor Simchi-Levi is the current editor-in-chief of Management Science, one of the two flagship journals of INFORMS. He served as the editor-in-chief ofOperations Research (2006-2012), the other flagship journal of INFORMS and Naval Research Logistics (2003-2005).
In 2023, he was elected a member of the National Academy of Engineering. In 2020, he was awarded the prestigious INFORMS Impact Prize for playing a leading role in developing and disseminating a new highly impactful paradigm for the identification and mitigation of risks in global supply chains.
He is an INFORMS Fellow and MSOM Distinguished Fellow and the recipient of the 2020 INFORMS Koopman Award given to an outstanding publication in military operations research; Ford Motor Company 2015 Engineering Excellence Award; 2014 INFORMS Daniel H. Wagner Prize for Excellence in Operations Research Practice; 2014 INFORMS Revenue Management and Pricing Section Practice Award; and 2009 INFORMS Revenue Management and Pricing Section Prize.
He was the founder of LogicTools, which provided software solutions and professional services for supply chain optimization. LogicTools became part of IBM in 2009. In 2012 he co-founded OPS Rules, an operations analytics consulting company. The company became part of Accenture in 2016. In 2014, he co-founded Opalytics, a cloud analytics platform company focusing on operations and supply chain decisions. The company became part of the Accenture Applied Intelligence in 2018.
Sunday, October 15, 5:25-6:15pm
2023 Edelman Award Winner Reprise
Optimizing Walmart’s Outbound Supply Chain from Strategy to Execution – A Grocery Case Study Walmart
As the largest retail distribution operation in the US, Walmart has always been at the forefront of building optimization capabilities to empower its supply chain from strategy to execution. A set of scalable optimization models have been adopted to determine the optimal network and transformational roadmap for the next decade. For daily operations, a truck routing and load planning optimization system was built. With grocery network alone, the strategy work impacts at least $10 billion investment in transformation, and the system contributes $60 million direct savings in year 2022.
The Electric Grid in Evolution: Data, Optimization, and Risk-Taking
Power systems i.e., power grids around the world are now in the midst of an accelerating change that will deliver far more knowledge- and data-aware operations in the coming decade. This evolution is spurred by a need for more investment as equipment ages, by increased consumption of power around the world, and, principally, by increased volatility in real-time behavior due to ramped-up renewable penetration and to participation in power systems by prosumers, i.e., entities that can both generate and consume power. The latter two factors contribute
correlation patterns that may be opaque to traditional power systems operators.
In this talk we will discuss the state-of-the-art of a number of critical technical areas in this domain, including the computation of power flow patterns (a nonlinear, nonconvex optimization problem), financial engineering aspects already seen today, and appropriate data science that can be harnessed to effectively stress test power system operations.
About Daniel Bienstock
Daniel Bienstock is Liu Family Professor at the IEOR Department at Columbia University, with a joint appointment in applied mathematics, and a courtesy appointment in electrical engineering. His research focuses on all aspects of optimization, in particular computational issues in nonconvex and discrete optimization; and on algorithms for and analysis of electrical power systems. He was awarded the Khachiyan Prize in Optimization in 2022 and became an INFORMS Fellow in 2013. He received the PhD in operations research from MIT.
Making Good Decisions with Wrong Models
Our capacity to access massive data sets and computing resources at scale has enabled the application of OR/MS methods in data driven decision making. But what is the impact of decisions that are made when the data is corrupted or possibly contains anomalies? Or when the deployment/prescription environment is different from the training/learning environment? These situations appear similar in the sense that direct data driven OR/MS approaches lead to making decisions based on wrong models, but, as we shall discuss, they are fundamentally different conceptually. In this talk, we will discuss the differences and provide a disciplined yet practical approach to dealing with these types of problems.
About Jose Blanchet
Jose Blanchet is a Professor of Management Science and Engineering (MS&E) at Stanford. Prior to joining MS&E, he was a Professor at the Departments of IEOR and Statistics at Columbia and before that a Professor of Statistics at Harvard. Jose is a recipient of the 2010 Erlang Prize and has won several best publication awards in areas such as applied probability, simulation, operations management, and revenue management. He also received a Presidential Early Career Award for Scientists and Engineers in 2010. He currently leads a Department of Defense sponsored Multi-University Research Initiative involving teams from Duke, Harvard, Maryland, MIT and Stanford on rare event analysis. He was the President of the INFORMS Applied Probability Society during 2020-2022 and has participated in various INFORMS committees. He is an area editor of Stochastic Models in Mathematics of Operations Research. He has served on the editorial board of Advances in Applied Probability, Bernoulli, Extremes, Insurance: Mathematics and Economics, Journal of Applied Probability, Queueing Systems: Theory and Applications, and Stochastic Systems, among others.
The Model Made Me Do It – Ethical ORMS in a Data-Driven World
ORMS has the power to help the world but with great power comes great responsibility. How are we considering the impact of our models and using that information to create better models?
About Julie Ivy
Invited by the INFORMS Diversity, Equity, and Inclusion Committee, Julie Simmons Ivy, Ph.D., is a Professor and Chair of the Department of Industrial and Operations Engineering (IOE) at University of Michigan. Julie has an extensive background in decision-making under conditions of uncertainty using stochastic and statistical modeling. She received her B.S. and Ph.D. in Industrial and Operations Engineering from the University of Michigan. She also received her M.S. in Industrial and Systems Engineering from Georgia Tech. She is an active member of the Institute of Operations Research and Management Science (INFORMS), Dr. Ivy served as the 2007 Chair (President) of the INFORMS Health Applications Society and the 2012 – 13 President for the INFORMS Minority Issues Forum. Recently, Dr. Ivy was elected as a 2022 INFORMS Fellow. In 2023, she was selected for the National Academies Board on Mathematical Sciences and Analytics (BMSA). Dr. Ivy’s research seeks to model complex interactions and quantitatively capture the impact of different factors, objectives, system dynamics, intervention options and policies on outcomes with the goal of improving decision quality. In particular, Dr. Ivy has extensive background in the application of systems science methods, including the analysis and modeling of large data sets, to hunger relief and health decision making. This research has made an impact on how researchers and practitioners address complex societal issues, such as health disparities, public health preparedness, hunger relief, student performance, and personalized medical decision-making and has been funded by the CDC, NSF, and NIH.
Monday, October 16, 5:25-6:15pm
Omega-Rho Lecture: Reflections on the Profession of Operations Research and Some Thoughts on its Future
In this lecture, we will reflect on the speaker’s 40+ years of experience in operations research, including such notable milestones as the merger of ORSA and TIMS, the Science of Better marketing campaign, the analytics movement, the emergence of big data and data science, and now the realization of artificial intelligence. What did we get right and what were the missed opportunities? We will reflect on the past, suggest some key learnings, and share some ideas on how the profession and INFORMS can thrive in the future.
About Jeff Camm
Jeffrey D. Camm is Senior Associate Dean for Faculty, the Inmar Presidential Chair in Analytics, and the Academic Director of the Center for Analytics Impact at the Wake Forest University School of Business. His area of research is applied optimization. His research has appeared in Science, Management Science, Operations Research, the Informs Journal on Applied Analytics, Sloan Management Review (online) and a variety of other journals and has been funded by the USAF Office of Scientific Research, the U.S. EPA, the U.S. Office of Naval Research, and Argonne National Labs. A firm believer in practicing what he preaches, he has consulted for numerous corporations including among others, Procter and Gamble, Owens Corning, GE, Duke Energy, Tyco, Ace Hardware, Corning, Boar’s Head, Starbucks, Road Runner Sports, Brooks Running Shoes and Kroger. He previously served for six years as the editor in chief of the INFORMS journal on Applied Analytics.
Jeff earned a PhD in Management Science from Clemson University and a BS in Mathematics from Xavier University (Ohio). Prior to joining Wake Forest, he held the Joseph S. Stern Chair in Business Analytics in the Lindner College of Business at the University of Cincinnati and he has been a visiting professor at the Tuck School of Business at Dartmouth College and the School of Engineering at Stanford University. In 2016, he received the Kimball Medal for his service to the Operations Research profession and in 2017 he was named an INFORMS Fellow. He is coauthor of nine textbooks, including the best-selling Business Analytics 4e with Cengage.
Network Flows and Minimum Cuts in Ranking, Clustering, Machine Learning, Imaging and Diversity Problems
A significant category of integer programming problems, called “monotone” (IPM, Integer Programming Monotone), finds its solution by employing a minimum cut algorithm on an associated graph. Within this category, numerous well-known clustering problems fall under the umbrella of ratio IPM problems. Remarkably, it has been demonstrated that these and the respective ratio problems can all be resolved using a parametric cut procedure, which exhibits the same computational complexity as a single minimum cut procedure. Other applications of IPM include drug ranking, the identification of active neurons in calcium imaging movies, and a wide array of machine learning and classification tasks.
For several problems classified as NP-hard, the incorporation of modeling flexibility transforms them into efficiently solvable IPM problems. This modeling flexibility is exemplified in scenarios such as the threat detection problem, co-segmentation problem, and text summarization problem. In the context of text summarization, for instance, this flexibility is manifested by replacing the concept of minimum similarity with maximum dissimilarity.
A specialized subset within the realm of NP-hard problems is the “budgeted” IPM problems, which incorporate an additional budget constraint. In the case of budgeted IPM problems, it is established that the entire efficient frontier can be generated through the parametric cut procedure. Furthermore, the breakpoints within the efficient frontier are optimal and facilitate the derivation of high-quality solutions for the respective problem. These problems find applications in diverse fields, including facility dispersion, quadratic knapsack, maximum diversity, and text summarization.
The Markov Random Field (MRF) problem originally emerged in the context of machine vision. For convex deviation functions and bilinear separation, the MRF can be efficiently solved through a parametric cut algorithm with a “best possible” runtime. MRF with general convex functions is equivalent to the convex dual of the minimum cost network flow problem, which is also solved efficiently. Applications of the MRF model span various domains, encompassing image segmentation, customer segmentation, credit risk assessment for countries, student paper competitions, semiconductor yield prediction, isotonic regression, general ranking, and recently, the aggregation of voter preference rankings.
About Dorit Hochbaum
Dorit S. Hochbaum is a distinguished professor in Industrial Engineering and Operations Research (IEOR) at UC Berkeley. Professor Hochbaum holds a PhD from the Wharton school of Business at the University of Pennsylvania. Her research interests are in areas of discrete optimization, network flow techniques, data mining, image segmentation, supply chain management and efficient utilization of resources. Her work contributed to the analysis of heuristics and approximation algorithms in the worst case, and on average, to the complexity analysis of algorithms in general, and nonlinear optimization algorithms in particular. Her theoretical work focuses on particularly efficient techniques using network flow for data mining and image segmentation including parametric flow for the convex Markov Random Fields problem establishing it as polynomial; the PseudoFlow algorithm for the maximum flow problem and the parametric flow and cut algorithms. Her recent research is on problems relating to machine learning on recognizing bias in labeled data; reducing dependence in training data; using pairwise relationships to enhance clustering methods. Applications include improved yield prediction in semi-conductor manufacturing; devising balanced covariates for experimental design; the maximum diversity and dispersion problem and group rankings and aggregate decision problems. Professor Hochbaum is the author of over 190 papers that appeared in the Operations Research, Management Science and Theoretical Computer Science literature. She served as department editor for Management Science department of optimization and modeling, and on a number of editorial boards.
Professor Hochbaum was named in 2004 as honorary doctorate of Sciences of the University of Copenhagen, for her work on approximation algorithms. She was appointed the Pinhas Naor lecturer of the Technion for 2013, and a Research Excellence professor at the University of Vienna in 2007. She is the winner of the 2011 INFORMS Computing Society prize for her work on algorithms for image segmentation. Professor Hochbaum is a fellow of INFORMS and a fellow of SIAM (Society of Industrial and Applied Mathematics).
Jonathan Owen, CAP
Leveraging Analytics in Automotive
About Jonathan H. Owen, PhD, CAP
Jon Owen has served as director of GM’s Advanced Analytics Center of Expertise (AACE) in the Enterprise Data, Analytics, and Insights (EDAI) organization since January 1, 2019. He also serves as Chief Scientist for OR/MS and Analytics at GM since being named to the role in 2017.
In his current position, Jon leads strategic innovation for prescriptive analytics and applied data science activities across the company. His team partners with internal stakeholders to grow revenue, profit and operational effectiveness through improved data-driven decision making in diverse areas such as revenue management, portfolio planning, vehicle technology selection and content optimization, supply chain and logistics, market demand modeling, and dealer effectiveness.
Prior to joining EDAI, Jon served as director of the Global R&D Operations Research Lab since 2013. In this role, Jon led internal research activities as well as collaboration with university partners, external labs, and other organizations to tackle GM’s most significant technical challenges and advance the state-of-the-art knowledge in applied OR/MS and analytics.
Jon began his career at GM in 1999 as a member of the research staff in R&D and Strategic Planning. He advanced through several roles, attaining the rank of Technical Fellow, GM’s highest technical classification, before being promoted to an executive position. He earned a BS degree from University of North Carolina, MS and PhD degrees from Northwestern University, and is a graduate of Harvard Business School’s General Management Program.
Jon’s contributions have been recognized by GM’s highest internal awards, as well as external awards from IISE, SME, and INFORMS. He is a recipient of Northwestern University’s IE/MS Distinguished Alumni Award and was inducted as an INFORMS Fellow in 2018. In addition to serving on several advisory boards, Jon currently serves on the Board of Directors for MATHCOUNTS (www.mathcounts.org), a non-profit organization that provides engaging math programs to middle school students of all ability levels to build confidence and improve attitudes about math and problem solving.
Harnessing data for Operations Forecasting
About Yael Grushka-Cockayne
Professor Yael Grushka-Cockayne’s research and teaching activities focus on decision analysis, data science, business analytics, forecasting, forecast aggregation and the wisdom of crowds, decision analysis, project management, and behavioral decision-making. Yael is an award-winning teacher and was named one of “21 Thought-Leader Professors” in data science. At Darden Yael teaches courses on decision analysis, project management, and data science in business. Yael’s “Fundamentals of Project Planning and Management” Coursera MOOC and “Data Science for Business” HarvardX course have over 300,000 enrolled, across 200 countries worldwide. Yael is an associate editor at Management Science, Operations Research, Decision Analysis, and INFORMS Journal on Data Science.
Before starting her academic career, she worked in San Francisco as a marketing director of an ERP company. As an expert in the areas of decision analysis and critical thinking, project management, and digital transformation, she has served as a consultant to international firms in the ed-tech, aerospace and pharma industries, such as Merck Serono, Pfizer, Eli Lilly, 2U, High Speed 2 Rail, PPL Electric Utilities, Heathrow Airport and Eurocontrol, Network Rail UK and the Department for Transport UK, and Dunlop Aerospace.
UPS George D. Smith Prize Winner Reprise
Purdue University has a long history of preparing analytical business leaders. Our Business Analytics & Information Management (BAIM) programs are a catalyst for our new Mitchell E. Daniels, Jr. School of Business. U.S. News & World Report ranked us the #9 Best Business Analytics MBA program in 2022, and CIO ranked our MSBAIM program #1 in data science. Students are challenged by top companies to develop solutions that have measurable impact and to present their work at analytics conferences and competitions. 100% of this year’s MSBAIM class will be aCAPs.
Tuesday, October 17, 5:25-6:15pm
2023 Daniel H. Wagner Prize Winner Reprise
The Daniel H. Wagner Prize is awarded for a paper and presentation that describe a real-world, successful application of operations research or advanced analytics. The prize criteria emphasizes innovative, elegant mathematical modeling and clear exposition. To learn more about the prize, visit the information page.
Advanced Air Mobility: Are We There Yet?
Advanced Air Mobility (AAM)—characterized by electric and hybrid aircraft, and highly-automated operations—has the potential to dramatically transform the way in which we transport people and goods, as well as our ability to sense our world from the sky. The excitement around these vehicles and the services they could enable has led to the investment of billions of dollars in their development. However, the deployment of such new aircraft and fleet operators will increase competition for limited airspace resources. Furthermore, in contrast to conventional air traffic that is managed by centralized Air Navigation Service Providers like the FAA, AAM operations are expected to be managed by third-party service providers. In this talk, I will discuss some of the key traffic management challenges to realizing the promise of AAM, and our initial work in overcoming them.
About Hamsa Balakrishnan
Hamsa Balakrishnan is the William E. Leonhard (1940) Professor of Aeronautics and Astronautics at the Massachusetts Institute of Technology (MIT), where she is also affiliated with the Operations Research Center and the Institute for Data, Systems, and Society. She was previously the Associate Department Head of Aeronautics and Astronautics at MIT. She received her PhD from Stanford University, and a B.Tech. from the Indian Institute of Technology Madras. Her research is in the design, analysis, and implementation of control and optimization algorithms for cyber-physical infrastructures, with an emphasis on air transportation. She is the co-founder and chief scientist of Lumo, a Boston-based travel startup.
Prof. Balakrishnan is an Associate Fellow of the American Institute of Aeronautics and Astronautics (AIAA), and the recipient of an NSF CAREER Award in 2008, the inaugural CNA Award for Operational Analysis in 2012, the AIAA Lawrence Sperry Award in 2012, the American Automatic Control Council’s Donald P. Eckman Award in 2014, the MIT AIAA Undergrad Advising (2014) and Undergraduate Teaching (2019) Awards, and multiple best paper awards.
Operations Research and National Security
The main goal of this keynote is to entice the audience to consider engaging in the area of National Security. After briefly describing my journey from a non-military background interested in game theory and telecommunication to one dealing with National Security issues and policy, I’ll argue why national security needs your help, describe some remarkable achievements of our field to make our world safer and briefly describe my technical journey with five projects I undertook. This talk will be liberally interjected with my observations about our field more generally and will tie very serious issues starting with the Pearl Harbor attack to apt references to pirate attacks, the Manchester United Football Club, the tactic of salami slicing, and the film “Charlie Wilson’s War”. The talk will cite relevant quotations from very high levels of our government, from INFORMS/ORSA’s first president, but also from Steve Jobs and Dr. Seuss. I hope some in the audience will leave with a curiosity to attend their first National Security talk during the conference and perhaps think how they might use their unique skills to help make smarter decisions for a better world.
About Les Servi
Les Servi is the Chief Scientist for Cyber Operations Research at The MITRE Corporation and the Immediate Past President of the Military Operations Research Society (MORS). In 2020 he led the optimization modeling effort for the Joint Acquisition Task Force established by USD(A&S) for a multi-tier Covid-19 medical supply chain. He previously served on a Defense Science Board task force on Counterinsurgency and another on Constrained Military Operations both briefed out to the USD(I). He received a Certificate of Appreciation from the Director of National Intelligence, James Clapper in 2017. The projects he leads currently support multiple parts of the government.
Previously he received his PhD from Harvard University, worked at Bell Laboratories and GTE (now Verizon) Laboratories pursuing telecommunication research, served 1 year as a visiting scientist at Harvard University and MIT, and worked at MIT Lincoln Lab.
He is an INFORM Fellow, former chair of the INFORMS Social Media Analysis, Telecommunication, and Applied Probability, and Boston subdivision and Board member of INFORMS for six years and MORS for four years. He is a former assistant editor of Operations Research, Management Science, and INFORMS Journal on Computing. He is currently a member of the WPI Data Science Executive Advisory Board and Board member and mentor for the Notre Dame Cristo Rey High School.