The Industry Track is sponsored by the Practice Section of INFORMS
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.
Dr. Sheng-Wei Chen (a.k.a. Kuan-Ta Chen) is a Research Fellow at the Institute of Information Science and the Research Center for Information Technology Innovation (joint appointment) of Academia Sinica. He is currently the Chairman of Taiwan Data Science Association, the Director of Artificial Intelligence Foundation, the Director of Taiwan AI Academy, and the CTO of ESUN Financial Holding Company. He was an Assistant Research Fellow from 2006 to 2011 and an Associate Research Fellow from 2011 to 2015 at the Institute of Information Science, Academia Sinica. He received the Best Paper Award in IWSEC 2008 and K. T. Li Distinguished Young Scholar Award from ACM Taipei/Taiwan Chapter in 2009. He also received the Outstanding Young Electrical Engineer Award from The Chinese Institute of Electrical Engineering in 2010, the Young Scholar’s Creativity Award from Foundation for the Advancement of Outstanding Scholarship in 2013, and IEEE ComSoc MMTC Best Journal Paper Award in 2014. He was an Associate Editor of IEEE Transactions on Multimedia (IEEE TMM) during 2011 to 2014 and has been an Associate Editor of ACM Transactions on Multimedia Computing, Communications, and Applications (ACM TOMM) since 2015. He organized ACM Multimedia Systems 2017 in Taiwan and served the lead program chair of ACM Multimedia 2017. He is a Senior Member of ACM and a Senior Member of IEEE.
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.
Dr. Jen-Yao Chung received the M.S. and Ph.D. degrees in computer science from the Universityof Illinois at Urbana-Champaign. Since 2014, he has been with the Quanta Cloud Technology as an Associate VP where he is responsible for creating hyper converged cloud solutions. Before that, he was the Senior Research and Development Director Cloud System Software Institute, Institute for Information Industry. Before that, he was the senior manager for Industry Technology and Solutions, IBM T. J. Watson Research Center. Dr. Chung is co-Editor in Chief of the International Journal of Service Oriented Computing and Applications (Springer). Dr.Chung is the co-founder and co-chair of the IEEE Computer Society Technical Committee on Electronic Commerce. Dr. Chung co-founded IEEE International Conference on e-Commerce Technology in 1998 and IEEE International Conference on e-Business Engineering in 2004. He has served as general chair and program chair for over 30 international conferences. He has authored or coauthored over 180 technical papers in published journals or conference proceedings. He is an IEEE Fellow and a Distinguished Engineer of ACM.
National Taiwan University and NTU Hospital
SUNDAY AFTERNOON, JUNE 17
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.
Dr. Weichung Wang is a professor and deputy chair of the Institute of Applied Mathematical Sciences and Department of Mathematics at National Taiwan University. His research interests include high-performance matrix computing, computational sciences, artificial intelligence, and medical image analysis and applications. Prof. Wang serves on the editorial board of SIAM Journal on Mathematics of Data Science and actively involved major international conferences including Supercomputing as organizers and program committee members. He has been the secretary of EASIAM and TMS and an executive committee member of the TWSIAM. Prof. Wang received the Nian-Tsz Award, NTU Outstanding Professor Award, and MOST Outstanding Young Scholar Project. He is an Honorary Research Fellow of the National Center for Theoretical Sciences.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Akira Sakakibara is the Chief Technology Officer at Microsoft Japan. Before joining Microsoft, he spent almost 30 years in IBM Global Business Services and IBM Research as an IBM Distinguished Engineer. One of his current responsibilities is for solving Japan’s national issues by using IT including AI. He is a member of ACM, IEEE Computer Society and IPSJ.
Founder & CEO, Fusion$360.com, 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.
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.
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.
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.
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.
Tim Lin is a Data Scientist and Analytics Consultant with years of experience in IT Service and Digital Marketing industries. Today, Tim serves as an Analytics Manger in Experian, one of the Big 3 credit bureaus in the States that manages the data of over 200 million people. Tim currently leads an analytics team responsible for building a deep understanding of customer behavior and subscription/upsell business while leveraging the power of statistics and machine learning to optimize customer experience and enhance omni-channel marketing campaigns. His expertise includes Survival Analysis, Customer Segmentation, Recommendation Engine and Optimization grounded in the completion of his Master’s Degree in Industrial Engineering and Operation Research from National Tsing-Hua University, Taiwan.
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.
Dr. Yuehwern Yih received her B.S from National Tsing Hua University in Taiwan and Ph.D. from University of Wisconsin-Madison. She is a professor in Industrial Engineering, also the Associate Director of Regenstrief Center for Healthcare Engineering at Purdue. She published over 150 scientific articles and book chapters, four edited books, and a patent on system modeling, decision making, and healthcare engineering. Dr. Yih received the highest honor at Purdue in engagement, based on her work at AMPATH to establish a food distribution system for HIV patients in Kenya. She is NEC Faculty Fellow, IIE Fellow, and ELATE Fellow.
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.
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.
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.
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.
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.
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