Industry Track & Special Panel Sessions

The Industry Track is sponsored by the Practice Section of INFORMS

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Chi-Yi Kuan

Analytics and Data Science Leader, LinkedIn

Sunday Morning, June 17

AI is here: Turning Advanced Analytics into Business Advantage

Data is driving business transformation. Advanced technologies are reshaping the business with new discoveries, better customer experiences, and improved products and services – enabled by AI. Chi-Yi Kuan shares examples of how LinkedIn unleashed intelligent & scalable insights to make better decisions, and explores best practices for incorporating data, advanced analytics and talent into your organization. 

  • Chi-Yi Kuan is a seasoned big data analytics leader with more than 15 years of industry experience in applying the-state-of-the-art big data analytics and solutions, data science, global risk management, and sales & marketing effectiveness at both Fortune 500 firms and startups. He combines deep expertise in analytics and data science with business acumen and dynamic technology leadership. Currently, Chi-Yi is director of analytics and data mining at LinkedIn. Previously, he worked at eBay and DemandTec (an IBM Company). He holds dual M.S. in Statistics and Engineering-Economic Systems & Operations Research from Stanford University.

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Pei-Yun S. Hsueh

IBM Academy of Technology Member, and Research Scientist, Center for Computational Health, IBM T.J. Watson Research Center

Sunday Morning, June 17

Integrating Data Science with Science of Care for Precision Healthcare: Applications of Computational Behavior Science in an Interpretable AI Fashion for Maximally Supported, Minimally Disruptive Medicine

Behavioral factors are the key contributors to mental health risk and morbidity, accounting for 41 percent of global disease burden. Recent studies documented the importance of accounting for individuality and heterogeneity in human health behavior through personalized approaches. In practice, varying behavioral responses are often revealed in patient care history. The rise of consumer awareness and the prevalence of personal health technologies (e.g., mobiles, sensors, wearables) have further enabled the accumulation of personal health data for interpretation. However, today’s care programs are structured around population-level evidence, but not personal understanding. What if healthcare professionals can take advantage of the revealed behavioral understanding to further engage target patients and personalize their care plans? To address the multi-level challenge, recently, in addition to traditional clinical and epidemiological methods, novel AI and machine learning algorithms are being proposed. The goal of this talk is to review the development of an interpretable behavioral learning pipeline that captures individual predictive pathways from observational behavior data. As the black-box nature of AI/ML has widened the gap between how humans and machines make decisions, we will also outline the lessons underlying current practice for making AI/ML more interpretable and actionable in health informatics. Example showcases will help illustrate how to support precision health applications that are maximally patient-centric yet minimally disruptive.

  • Pei-Yun (Sabrina) Hsueh is IBM Academy of Technology Member at IBM Watson Research Center. She is a thought leader in consumer and pervasive health informatics (CPHI) for health behavior understanding from observational data (including wearable sensors). She is a serial winner of IBM Inventor and Research awards and chairs IBM HI PIC. She authored 20+ patents and 50+ articles, served on scientific program committees in ACM, IEEE, AMIA and IMIA conferences, and is elected as Chair of AMIA CPHI Work Group. Prior to IBM, she worked in EU FP Augmented Multiparty Interaction projects and was an European Google Anita Borg Scholar. She obtained her BS from NTU, MIMS from Berkeley, and PhD from University of Edinburgh respectively.

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Ranganath Nuggehalli

Principal Scientist, UPS

Sunday Morning, June 17

UPS Optimizes Delivery Routes

UPS, the leading logistics provider in the world, and long known for its penchant for efficiency, embarked on a journey to streamline and modernize its pickup and delivery operations in 2003. This journey resulted in a suite of systems, including ORION (On Road Integrated Optimization and Navigation) optimization system. Every day, ORION provides an optimized route for each of UPS’ 55,000 U.S. drivers. The system creates routes that maintain the desired level of consistency from day to day. To bring this transformational system from concept to reality, UPS instituted extensive change management practices to ensure that both users and executives would accept the system. Costing more than $250 million to build and deploy, it is estimated that ORION is saving UPS $300 to $400 million annually. ORION is also contributing to the sustainability efforts of UPS by reducing its CO2 emissions by 100,000 tons annually. By providing a foundation for a new generation of advanced planning systems, ORION is transforming the pickup and delivery operations at UPS.

  • Ranganath Nuggehalli is a principal scientist in the Operations Research group of UPS. His group is responsible for developing advanced planning systems for use in UPS operations. He is a laureate of the Franz Edelman Academy and a member of the UPS team that won the 2016 Franz Edelman Prize. He is an active member of the Institute for Operations Research and Management Sciences. He joined UPS in 1990 after completing Ph.D. in Operations Management from Purdue University. His childhood interest in improving efficiency led him to operations research. Implementing optimization based planning systems is his passion. Unfortunately, he is yet to learn that life is not an optimization process. His research interests are routing and scheduling, network modeling, location analysis, system integration, and computer-human interfaces. If you go to his house, you may not get food; but you will always get good wine.

TJ Jia

Data & Insights, Risk management, Uber

Sunday Morning, June 17

Revolution in Fraud Risk Management Driven by Advancing Analytics and Technology

What you’ll learn: Functions and capabilities of fraud risk management systems from leading technology companies. Examples of best practices and use cases in large scale Risk decisions by leveraging rules engine/ data science and analytics. Description: Companies in many industries such as Financial/ Banking/ ecommerce & payment are facing unprecedented challenges from fraud risk and under constant pressure to innovate and keep up with a continuously moving target of threats to the bottom line of the business. Uber Risk team is at the forefront of one of the world’s biggest challenges working on delivering innovative risk management capability to maximize legitimate revenue and sustainable growth. Everyday we process millions of mobile transactions in over 75 different counties in real time through highly scaled decision platform with sophiscated machine learning models/ risk strategies. This talk will cover: 1. common functions and structure of risk management in technology companies; 2. evolution of concept, tools and techniques in fraud risk management; 3. importance of measurement/ test iterations and 4. Some emerging techniques and their applications at Uber Risk

  • T.J. Jiao has 15 years of extensive and successful experience in fraud and credit risk management, customer insight analytics, business & product strategy development and decision intelligence in e-Commerce / Payment / Financial / Technology industries. He is currently heading the Data & Insights function at Uber Risk Management delivering data driven risk strategies to maximize legitimate revenue and sustainable growth. Previously he was Sr. Director at Lending Club where he led the Fraud Risk, Decision Infrastructure, Alternative lending research and Strategies Optimizations. Before that he held various management roles in Trust & Safety/Fraud science/Marketplace Analytics at eBay and PayPal.

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Yan Xu

Director of the Numerical Optimization team, SAS Institute Inc.

Sunday Afternoon, June 17

Better Machine Learning Models by Derivative-free Optimization

Optimization is a key component in many machine learning (ML) or artificial intelligence algorithms.  Optimization is not only used to fit ML models, but also help to create better models in terms of accuracy and complexity. In this tutorial, we first introduce a number of derivative-free optimization (DFO) methods, which have been successfully used to improve ML models by optimizing their hyperparameters. We then present several real-world ML applications that significantly benefit from those DFO methods.

  • Yan Xu is the director of the Numerical Optimization team at SAS Institute Inc., where he leads the development of optimization solvers and linear algebra routines.  His team also design and implement optimization components for products related to Statistics, Data Mining, Econometrics, etc. In recently years, Dr. Xu has been focusing on the optimization methods for Machine Learning, Artificial Intelligence and IoT.  Dr. Xu is a full member of COIN-OR. He has published papers in optimization and machines learning journals and conferences, and won several contests in the area of computational optimization.

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Allen Butler

President & CEO, Daniel H. Wagner Associates, Inc., VP Practice, INFORMS Board of Directors

Sunday Afternoon, June 17

Applying Advanced Analytics to U.S. Naval Problems

Anti-Submarine Warfare (ASW) capabilities for the U.S. Navy and its allies.  In this talk, we discuss two Wagner Associates’ ASW-related projects for the U.S. Navy.  The first, entitled Coordinated ASW Mission Planning (CAMP), harkens back to the early days of the company, when Daniel H. Wagner, the founder of the company, focused on assisting the U.S. Navy in the search for threat submarines.  The second project, Fusion and Optimization for Command and Control of Unmanned Systems (FOCUS), is exploring the use of computer science formal methods in designing and recommending mission plans for mixed teams of manned and unmanned systems in complex contested environments.

  • Allen Butler received his PhD in Mathematics from the University of Illinois, Champaign-Urbana.  He has been employed at Daniel H. Wagner Associates, Inc. since 1987 and is currently President and CEO.  Dr. Butler has served as the principal investigator for DoD projects involving a variety of mathematical disciplines as applied to areas such as tracking, track correlation, data fusion, and search optimization.

    Dr. Butler was recently elected an INFORMS Fellow and currently serves on the INFORMS Board of Directors as VP of Practice.  He is also on the Industrial Advisory Board of Christopher Newport University’s Physics and Computer Science Department and is the chair of the Business Industry Government Special Interest Group of the Mathematical Association of America (BIG SIGMAA).

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Young M. Lee, Ph.D.

Director, Data Sciences & Product Development, Building Technologies & Solutions, Johnson Controls

Sunday Afternoon, June 17

ML/AI Analytics for Connected Equipment: An Example of Industrial IoT

With the advanced of IoT technology, more and more industrial equipment bare now connected with numerous sensors that collect the operational data in real time. Algorithms in Machine Leaning (ML), Deep Learning (DL) and Artificial Intelligence (AI) are becoming easily accessible to convert these data into business values and improvement of the quality of life. Johnson Controls is a global leader in buildings, energy and security market. We are shaping the future to create a world that’s safe, comfortable and sustainable. Our global team creates innovative, integrated solutions to make cities more connected, buildings more intelligent and environment safer. More and more our equipment that we sell and service around the world such as chillers, boilers, air handling unit, rooftop unit, refrigeration units, energy storage system and security systems, are being equipped with sensors and send real time data to the cloud, where ML/DL/AI tools analyze the data and make the operations safe and cost-effective. The IoT analytics we are developing include predictiveb asset management, operational optimization, risk analysis and energy optimization. This talk will describe Johnson Controls’ IoT analytics and how they are helping our clients reducing maintenance costs, reducing energy costs, improving the life of equipment, improving service level and improving security and comfort of occupants.

  • Dr. Young M. Lee is the Director of Data Science at Johnson Controls and is leading development of Industrial IoT solutions using ML/DL/AI and optimization technology.  Dr. Lee previously worked for 15 years at IBM T.J. Watson Research Center as a Research Staff Member, a Research Manager and an IBM Master Inventor, and developed industrial applications of mathematical modeling, optimization and machine learning.  Dr. Lee also worked for BASF for 14 years, founded and managed the Mathematical Modeling Group, and led development of numerous optimization and simulation models for various manufacturing and supply chain processes.  Dr. Lee received B.S., M.S. and Ph.D. degrees from Columbia University.

Markus Ettl

IBM Research

Sunday Afternoon, June 17

TBD

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Ko-Yang Wang

Founder & CEO, Fusion$360.com, Chair Professor, Asia University

Monday Morning, June 18

FinTech and the Financial Transformation

FinTech is disrupting the financial industry globally. McKinsey predicted that 40% of banking industry’s revenue and 60% of the profits will be taken away by non-financial companies. But what’s more important, is the much larger inclusive financial services of small and medium enterprises and individuals whom the financial institutes failed to serve. Since IT is disrupting every industry, enabling SMEs with inclusive financial services will power the growth of digital economy and accelerate the next wave of economy evolution.

In this talk, Dr. Wang will discuss the current state of FinTech revolution and financial transformation, areas where analytics and AI can have significant impacts on the future of finance, and approaches for establishing an open, global sharing ecosystem to democratize accesses to data and analytics and to enable flexible, low cost, scalable inclusive financial services for smart digital economy.

  • Dr. Ko-Yang Wang, Chairman of Taiwan FinTech Association, is an entrepreneur focusing on helping Taiwan develop its 1st FinTech Ecosystem for SMEs. He has extensive experience in strategic business management and IT consulting. Dr. Wang was an EVP and CTO at the Institute for Information Industry between 2011-2015, where he was responsible for its R&D strategy, industry partnership, and business development. Prior to 2011, Wang held various technical and business leadership roles at IBM in US, including Partner and Practice Leader for BPM Practice and CTO for various organizations in IBM Global Business Services; He was the Research & Innovation Executive for Business Transformation, IBM Global Service from 2002-2005 where he led innovation initiatives such as Enterprise of the Future, Future Service Business, Service Oriented Technology to help IBM’s service transformation. He has co-authored 10 patents and > 50 publications in compilers, KM, BPM, Cloud, Big Data, FinTech. He mentored many professionals, 13 of his mentees became IBM distinguished Engineers.

Westin Bank

Monday Morning, June 18

TBD

Akira Sakakibara

President, Microsoft Development, Japan

Monday Morning, June 18

MicroSoft AI Strategy and Initiative

Thomas Li

VP, Delta Electronic

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Richard Chen

Amazon

Monday Morning, June 18

TBD

Monday Afternoon, June 19

Panel: Industry 4.0 Opportunities for Integrative Decisions in Smart Manufacturing

Industry 4.0 with digitalization and Intent of Things (IoT) provides tremendous opportunities for achieving smart manufacturing through real time data sharing across different levels of enterprise operations. Meanwhile, it also brings about new research challenges on how to effectively utilize those data to make integrative decisions so that a smart manufacturing system can adaptively respond in real time to meet changing demands and conditions in the factory, in the supply network, and in customer needs. This panel session is especially organized to promote broad communication and interdisciplinary research for making integrative decisions across manufacturing process design, quality control, production system operations and logistics/supply chain management. The panelist is consisted of five invited speakers from North American, Europe, and Asia, whose research expertise crossly cover these areas. Each panelist will firstly give a 10mins talk to share his/her expert point of view on the research opportunities, challenges and strategies to achieve integrated decision-making for smart manufacturing under Industry 4.0. The remaining time of the session will be Q&A interactions between the panelist and audience.

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Panel Session Chair: Judy Jin

Judy Jin is Professor of the Industrial and Operations Engineering Department, the Director of Manufacturing Program in the Integrative Systems and Design Division, the Director of Data Fusion Lab, at the University of Michigan. Her research area is in data fusion and quality engineering. She received numerous awards including the NSF CAREER and Presidential Awards (PECASE), the Forging Achievement Award, 12 Best Paper Awards.  She is serving as Departmental Editor for IISE Transactions, was the Vice President of INFORMS-International. She is a Fellow of Institute of Industrial and Systems Engineers and a Fellow of the American Society of Mechanical Engineers.

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Panelist: Kai Hoberg

Kai Hoberg is Professor of Supply Chain & Operations Strategy at Kühne Logistics University in Hamburg. Before joining KLU, he was Assistant Professor of Supply Chain Management at the University of Cologne. After his studies, he worked as a strategy consultant and project manager for Booz & Company conducting supply chain and operations management projects. His research topics include supply chain analytics, role of technology in supply chains, inventory modelling, and the link between operations and finance. His research findings have been published in academic journals like JOM, POM or EJOR.

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Panelist: Dongni Li

Dongni Li is an associate professor in the School of Computer Science at Beijing Institute of Technology. She received her degrees of B.S., M.S., and Ph.D. in Computer Science from Northeastern University, Shenyang China. Her research interests include intelligent optimization approaches and their applications in manufacturing industry. She has authored more than 20 papers in journals including IEEE T-SMC, IEEE T-ASE, IJPR, C&OR, and several proceedings. She is the secretary general of two professional committees of AI and VR for the Alliance of Emerging Engineering Education of China. She served as a panel member for Manufacturing Informatization of Inner-Mongolia.

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Panelist: Leyuan Shi

Leyuan Shi is Professor in the Department of Industrial and Systems Engineering at University of Wisconsin-Madison. She received her Ph.D. in Applied Mathematics from Harvard University in 1992.  Her research interests include simulation modeling and large-scale optimization with applications to operational planning and scheduling and digital supply chain management. She has developed a novel optimization framework, the Nested Partitions Method that has been applied to many large-scale and complex systems optimization problems. Shi has published 3 books and more than 130 papers. She is currently serving as Editor for IEEE Trans on Automation Science and Engineering. She is an IEEE Fellow.

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Panelist: Fugee Tsung

Fugee Tsung is Professor and former Head of the Department of Industrial Engineering and Decision Analytics, Director of the Quality and Data Analytics Lab, at the Hong Kong University of Science & Technology, and Editor-in-Chief of the Journal of Quality Technology. He is Academician of the International Academy for Quality, Fellow of the American Society for Quality, American Statistical Association, Institute of Industrial and Systems Engineers, and Hong Kong Institution of Engineers. He received his PhD and MSc from University of Michigan, and BSc from National Taiwan University. His research interests include industrial big data and quality analytics.

Monday Afternoon, June 19

Panel: Meet the Editors

Panel Chair: Mabel Chou, NUS