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Industry Track & Special Panel Sessions

The Industry Track is sponsored by the Practice Section of INFORMS

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Sheng-Wei Chen

Research Fellow, Institute of Information Science and the Research Center for Information Technology Innovation of Academia Sinica

Sunday Morning, June 17

From Project Theta to Taiwan AI Academy

Taiwan AI Academy was founded in January 2018. It is unique in many ways, in particular, its close collaborations with industry in order to empower domain experts from various fields by machine learning and deep learning techniques within a short 3-4 month. In this talk, I will elaborate the story to start from the achievements of the Project Theta to the foundation of Taiwan AI Academy, and how we will transform the AI talent development in Taiwan starting from these efforts.

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Jen-Yao Chung

Quanta Cloud Technology

Sunday Morning, June 17

Process to Identify Innovation and Cloud Services

We are approaching technology shift that will drive new paradigms for software and systems. Software complexity is driving a rethinking of software development. Creating innovative solutions and services is not about doing something new, it is about creatively solving business problems with reusable assets or building blocks. It is about using a systematic process to solve business and infrastructure problems for speed and quality. IT-enabled service plays a key role of boosting the economics by integrating IT with different domain knowledge to create innovative values from existing business services. Cloud Computing is a model of shared network-delivered services, both public and private, in which the user sees only the service, and need not worry about the implementation or infrastructure. Changing business environments require quick changes, new business models and new solutions. In this talk, lessons learned and future innovation drivers will be presented. We will present how to transition services from an ad-hoc practice to a more innovative and systematic discipline. We will present building applications based on cloud and everything as a service approach.

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Wei-Chung Wang

National Taiwan University and NTU Hospital


Artificial Intelligence for Medical Image Analyses and Applications

We lay out our plan to build a platform called Artificial Intelligence for Medical Image Analysis (AIMIA). The AIMIA platform consists of Artificial Intelligent Engine (AI Engine) and Augmented Intelligence Workflows (AI Workflows). The AI Engine consists of high-performance algorithms and software modules aiming to extract insightful information from a large volume of medical image datasets accurately, efficiently, and robustly. In particular, the AI Engine includes Image Processing, Quantitative Analytics, Deep Learning, Machine Learning, and High Dimensional Data Analysis Toolboxes to analyze medical images. By taking these algorithms and software modules as the building blocks, we further build up innovative AI Workflows in various clinical applications. AI Workflows examples include precision cancer treatments in a lung, hypopharyngeal, hepatocellular carcinoma, digital pathology whole slide image analysis for prostate cancers, pancreatic masses classification and detection, radiotherapy treatment planning in lung cancer, and psychiatric disorders phenotyping. These examples illustrate how we apply the AI Engine to configure AI Workflows in clinical medical cares and biomedical research. AIMIA is also a platform allowing international experts from academia and industry in medical, mathematical, statistical, computational, and information sciences to work together to ensure the research and development efforts can benefit the society broadly.

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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 Afternoon, June 17

Integrating Data Science with Science of Care for Precision

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.

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Yan Xu

Director, Numerical Optimization team, SAS Institute Inc

Monday Morning, June 18

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.

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Chi-Yi Kuan

Analytics and Data Science Leader, LinkedIn

Monday Morning, June 18

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. 

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Young M. Lee, Ph.D.

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

Monday Morning, June 18

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.

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Ranganath Nuggehalli

Principal Scientist, UPS

Monday Morning, June 18

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.

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Akira Sakakibara

President, Microsoft Development, Japan

Monday Afternoon, June 18

AI Research at Microsoft Research

Microsoft Research (MSR), the research division of Microsoft, celebrated its 25th anniversary last year. MSR has been conducting AI research since its founding. In this talk, I will discuss the current direction of the AI-related research at MSR amidst the new AI boom we are experiencing, along with some research examples.

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Ko-Yang Wang

Founder & CEO, Fusion$, Chair Professor, Asia University

Monday Afternoon, 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.

T.J. Jiao

Data & Insights, Risk management, Uber

Tuesday Morning, June 19

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 sophisticated 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.

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Tim Lin

Data Scientist and Analytics Consultant

Tuesday Morning, June 19

Marketing with Data During the Digital Revolution

Empowered by the digital revolution, marketing has emerged as one of the key functions in leading companies. As this paradigm shift ensues, the success of the marketing will be determined by the ability to combine physical, digital, sensorial and emotional experiences to personalize and individualize target audience interactions. As businesses make the transition away from siloed experiences, they will be able to deploy data-driven solutions that understand user behavior in order to connect with customers deeper than ever before. As a B2C department in one of the biggest credit bureau in the world that manages the data of over 200 million people, Experian Consumer Service is always brainstorming new methodologies on customer service and seek to evolve the experience as we collect data and learn more and more about customer needs. This talk will frame the proper marketing mindset needed to take advantage of the modern digital world. Tim will reveal real use case examples Experian has had in leveraging the full potential of data for customers on their e-commerce site as well as in email marketing and online advertising to accomplish the conversion and retention goals. He invites you today in learning how to build a frictionless customer journey that drives authentic and measurable engagement.

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Yuehwern Yih, PhD

Professor of Industrial Engineering, Associate Director of Regenstrief Center for Healthcare Engineering, Purdue University

Tuesday Morning, June 19

Health Supply Chain for improving Maternal and Child Health: A Case Study in Uganda

Many healthcare systems, such as in Uganda, implementing standardized data capture registers, lack responsiveness due to paper-based reporting and requisition systems, which impede access to data for timely decision-making. At the district level, a lack of such data results in pharmaceutical supply stock-outs and expired medications and negatively impacts system responsiveness to the needs of lower-level health facilities. In this scenario, one of the key vulnerable populations is pregnant women. The UN has identified 9 commodities that could potentially save 6 million lives though timely availability and use across the MCH ‘continuum of care’. This project is targeting this area.

We proposed a Diagnosis-Based Demand sensing and Digital tracking (DBDD) approach to use last mile data, which is currently captured in paper-based formats, to improve the availability and reduce stock-outs of essential maternal health supplies in primary care facilities. DBDD will triangulate patient data, consumption data and laboratory data to optimize ordering practices in primary care facilities. This includes the digitization of those data so as to greatly simplify its capture and management at primary care facility level. In this talk I will present our current work and the challenges and barriers we encounter so far.

Sunday Morning, June 17

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.

Sunday Afternoon, June 17

Panel: Meet the Editors

Panel Chair: Mabel Chou, NUS