2020 Opening Plenary Speaker<\/h2> <\/div>\n<\/div>\n \n \n \n\n \n ![\"Ben](\"https:\/\/meetings.informs.org\/wordpress\/wsc2020\/files\/2020\/01\/ben_amada.jpg\" "\\"ben_amada\\"")
\n <\/div>\n \n \n <\/div>\n <\/div>\n \n \n\n \n Ben Amaba, PhD, PE, CPIM\u00ae, LEED\u00ae AP<\/h4>\nChief Technology Officer for the Industrial Sector<\/em>
IBM Data Sciences and Artificial Intelligence Team Elite<\/h5>\nThe Rapid Democratization and Integration of Data with Simulation, Optimization and Artificial Intelligence<\/h3>\n
Many CEOs, CTOs, senior executives, and other decision-makers are seeing an advantage from the rise of big data and faster computing power.\u00a0 Data is now a board responsibility. The question arises: How can all this data drive innovation?\u00a0 Being able to harness the power of data through simulation, optimization, artificial intelligence (AI) and machine learning (ML) can help to improve financial, sales, manufacturing and supply-chain operations; enable a better, more intimate customer experience; or reduce downtime if done correctly. Waiting for the perfect environment is no longer a strategy, but an agile learning culture is the priority.<\/p>\n
With open source, hybrid clouds, high speed networks, increased computing power, and responsive platforms; simulation, optimization, AI and ML are being democratized and intertwined at a rapid pace. McKinsey estimates that AI could potentially deliver additional economic output of around $13 trillion by 2030, boosting global GDP by about 1.2 percent a year. Advancements in computing power and open source technologies have become a competitive advantage of day-to-day business by fundamentally improving the way the industry operates. Recent progress seeks to radically change our operations and workflows.\u00a0 To enable and maximize the creation of value, the integration and utilization of these data science technologies coupled with a rigorous approach is required. The overhead of data preparation, model governance, bias, trust, and deployment continue to inhibit democratization. The shortage of talent to promote and apply the interdisciplinary computer, mathematical and domain knowledge places many projects in pilot purgatory.\u00a0 The session will explore the future of the Digital Transformation and the use of advanced analytics to create an integrated business model where data becomes more than just a single source of truth, but a strategic asset.<\/p> <\/div>\n<\/div>\n\n
\n \n \n - \n \n \n Bio <\/a>\n <\/div>\n \n \n
Dr. Ben Amaba<\/strong> holds a PhD. degree in Industrial & Systems Engineering, a M.B.A.\/M.S. degree in Engineering and Operations, and a B.S. degree in Electrical Engineering. Dr. Amaba is a registered and licensed Professional Engineer in several states with International Registry; certified in Production, Operations, and Inventory Management by APICS \u00ae<\/span><\/b>; LEED<\/span>\u00ae <\/span><\/b>Accredited Professional (Leadership in Energy & Environmental Design); and certified in Corporate Strategy by Massachusetts Institute of Technology in Cambridge, Massachusetts. He is responsible for industrial manufacturing, infrastructure, engineering, and supply chain solutions. Dr. Amaba is the Chief Technology Officer for the Industrial Sector, IBM Data Sciences and Artificial Intelligence Team Elite. Dr. Amaba\u2019s focus and interest is in artificial intelligence, data sciences, and cloud technology. <\/span><\/p>\nDr. Amaba holds a copyright for Process Activity Flow Framework\u00ae, framework for requirements, design, architecture, simulation, and application development techniques. Dr. Amaba is prominently featured in \u201cGiving 2.0,\u201d by Laura Arrillaga-Andreessen with Stanford Business School, which details contributions to bolster and promote STEM education and careers, and is an Alexis de Tocqueville Society\/United Way Member, which recognizes local philanthropic leaders.<\/p>\n
Dr. Amaba holds positions as Industry Council Advisor to Project Production Institute, Board of Director of Whoop Wireless Corporation, UCX Corporation, and RANCS Group LLC, Executive Board Member of Applied Human Factors and Ergonomics (AHFE) and Editorial Board Member to IEEE (Institute of Electrical and Electronics Engineers) IT Professionals, Board Member to the Council on Industrial and Systems Engineering, founding member to the Institute of Advanced Systems Engineering, Founding member to the Center of Advanced Supply Chain Management, Editorial Board of The Open Cybernetics and Systemics Journal, and Executive Advisory Board Member to the University of Miami, University of Houston, and University of Central Florida.<\/p> <\/div>\n <\/div>\n <\/li>\n <\/ul>\n\n<\/div> <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n
\n \n \n \n\n \n Tuesday Keynote Speaker<\/h2> <\/div>\n<\/div>\n \n \n \n\n \n ![\"Steve](\"https:\/\/meetings.informs.org\/wordpress\/wsc2020\/files\/2020\/04\/Steve-Chick-2016.jpg\" "\\"Steve")
\n <\/div>\n \n \n <\/div>\n <\/div>\n \n \n\n \n Stephen E. Chick<\/h4>\nProfessor of Technology and Operations Management<\/em>
INSEAD – Europe Campus<\/h5>\nHealth Resource Allocation: Lessons for Today from Past Outbreaks<\/h3>\n
Recent events have brought mathematical modelling of infectious disease transmission and control to the forefront. An appropriate choice of mathematical model depends of course on the decision problem to be informed, yet there can be uncertainties about technical, social, and operational parameters of a model. And each model necessarily makes assumptions, for better or for worse. In this talk, we discuss different types of models for supporting decisions for cost-effective disease management decisions, touching on stochastic models, Bayesian methods, and simulation optimization. We then illustrate some obvious and some not-so-obvious ways that the choice of model is important, by drawing upon examples from the presenter\u2019s experience with projects to address influenza, vaccination, waterborne infections, Creutzfeldt-Jakob Disease and clinical trial design. We highlight the importance of problem selection and collaboration.<\/p> <\/div>\n<\/div>\n\n
\n \n \n - \n \n \n Bio <\/a>\n <\/div>\n \n \n
Steve Chick\u00a0<\/strong>is a Professor of Technology and Operations Management at INSEAD – Europe Campus. He holds the Novartis Chair of Healthcare Management and is academic director of INSEAD’s Healthcare Management Initiative. His academic research has focused on stochastic processes, Bayesian modelling for learning and uncertainty assessment, and healthcare modelling, particularly as it relates to epidemic modelling or to highly adaptive clinical trials. His research collaborations in healthcare modelling have been funded by the CDC, EPA, NIH, EU and the UK NIHR, and four of his papers have been finalist or winner of the Pierskalla Best Paper Award for INFORMS Healthcare. In executive teaching he has worked with numerous pharmaceutical companies and health care delivery organizations to support innovative and effective models for delivery health care.<\/p> <\/div>\n <\/div>\n <\/li>\n <\/ul>\n\n<\/div> <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n \n \n \n \n\n \n MASM Keynote<\/h2> <\/div>\n<\/div>\n <\/div>\n <\/div>\n <\/div>\n \n \n \n \n\n \n ![\"Sabine](\"https:\/\/meetings.informs.org\/wordpress\/wsc2020\/files\/2020\/02\/img_chenFu_chien.jpg\" "\\"img_chenFu_chien\\"")
\n <\/div>\n \n \n <\/div>\n <\/div>\n \n \n\n \n Chen-Fu Chien, Ph.D.<\/h4>\nTsinghua Chair Professor & Micron Chair Professor<\/em>
Department of Industrial Engineering & Engineering Management
National Tsing Hua University, Hsinchu 30013, Taiwan<\/h5>\nIndustry 3.5 as Hybrid Strategy empowered by AI & Big Data Analytics and Collaborative Research with Micron Taiwan for Smart Manufacturing <\/strong><\/h3>\nThe paradigm of global manufacturing is shifting as leading nations proposing next phase of industrial revolution for Industry 4.0 by Germany and reemphasizing the importance of advanced manufacturing such as AMP in USA. Driven by Moore\u2019s Law, semiconductor manufacturing is one of the most complex industries for continuous migration of advanced technologies for manufacturing excellence. Micron Technology is a world leading producer of semiconductor memory and computer data storage that has established one of her largest manufacturing bases in Taiwan through a number of acquisitions of local fabs as well as investments of new fabs. Industry 3.5 was proposed as a hybrid strategy between the best practice of the existing Industry 3.0 and to-be Industry 4.0 to address fundamental objectives for smart manufacturing while employing artificial intelligence and big data analytics as means objectives for manufacturing intelligence solutions. This speech will introduce Industry 3.5 and use a number of empirical studies under the existing infrastructure for validation. Furthermore, collaborative research with Micron for smart manufacturing will be used to illustrate our continuous efforts employing artificial intelligence, big data analytics, optimization, and intelligent decision for smart manufacturing and digital transformation. This talk will conclude with discussions of the implications of Industry 3.5 as alternative for Industry 4.0 to empower humanity in the ongoing industrial revolution.<\/p> <\/div>\n<\/div>\n\n
\n \n \n - \n \n \n Bio <\/a>\n <\/div>\n \n \n
Dr. Chen-Fu Chien is Tsinghua Chair Professor, in the Department of Industrial Engineering & Engineering Management, National Tsing Hua University (NTHU), Hsinchu, Taiwan. He is also a Micron Chair Professor sponsored by Micron Foundation, USA. Professor Chien is the Director of Artificial Intelligence for Intelligent Manufacturing Systems (AIMS) Research Center that is one of four national AI centers sponsored by Ministry of Science & Technology (MOST), Taiwan. He is the founder and chairing professor for Decision Analysis Laboratory (DALab), the NTHU-TSMC Center for Manufacturing Excellence, and the Semiconductor Technologies Empowerment Partners Consortium (STEP Consortium), Taiwan. He received B.S. with double majors in Industrial Engineering and Electrical Engineering with the Phi Tau Phi Honor from NTHU in 1990. He received M.S. in Industrial Engineering and Ph.D. of Decision Sciences and Operations Research at UW-Madison, in 1994 and 1996, respectively. He was a Fulbright Scholar in the Department of Industrial Engineering and Operations Research, UC Berkeley, from 2002 to 2003. From 2005 to 2008, he had been on-leave as the Deputy Director of Industrial Engineering Division in Taiwan Semiconductor Manufacturing Company (TSMC). He received the Executive Training of PCMPCL from Harvard Business School in 2007. He was a Visiting Professor in Institute for Manufacturing, Cambridge University (sponsored by Royal Society, UK), Visiting Professor in Beijing Tsinghua University (sponsored by Chinese Development Foundation), Visiting Professor in Waseda University (sponsored by Japan Interchange Association Young Scholar Fellowship), and Visiting Professor in Tianjin University and Zhejiang University, China.<\/p>\n
His research efforts center on decision analysis, big data analytics, modeling and analysis for semiconductor manufacturing, manufacturing strategy, and manufacturing intelligence. Dr. Chien and his DALab Associates have conducted in-depth university-industry collaborative research projects with the leaders of different industrial segments to validate developed solutions and served as senior consultant for leading companies including TSMC, MediaTek, Delta, and AUO. Dr. Chien has received 12 USA invention patents on intelligent manufacturing and published 6 books, more than 170 journal papers (1 HiCi) and 12 case studies in Harvard Business School. He has been invited to give keynote speech in various conferences including APIEMS, C&IE, FAIM, IEEE, IEEM, IML, ISMI, ISSM, leading universities and international companies worldwide. He is a Fellow of APIEMS, CIIE, and CSMOT. Dr. Chien received the National Quality Award, the Executive Yuan Award for Outstanding Science & Technology, three Distinguished Research Awards and Tier 1 Principal Investigator (Top 3%) from MOST, Distinguished University-Industry Collaborative Research Award from the Ministry of Education, University Industrial Contribution Awards from the Ministry of Economic Affairs, the TECO Award, the 2011 Best Paper Award of IEEE Transactions on Automation Science and Engineering<\/em>, and the 2015 Best Paper Award of IEEE Transactions on Semiconductor Manufacturing<\/em>. He is a member of the Board of Directors for BoardTek (TWSE: 5349) and Uniflex (TWSE: 3321) and Just College UNISON Foundation.<\/p>\n\u00a0<\/p> <\/div>\n <\/div>\n <\/li>\n <\/ul>\n\n<\/div> <\/div>\n <\/div>\n <\/div>\n
\n \n \n \n\n \n Military Keynote<\/h2> <\/div>\n<\/div>\n <\/div>\n <\/div>\n <\/div>\n \n \n \n \n\n \n ![\"Peter](\"https:\/\/meetings.informs.org\/wordpress\/wsc2020\/files\/2020\/08\/Peter-Schwartz.jpg\" "\\"Peter")
\n <\/div>\n \n \n <\/div>\n <\/div>\n \n \n\n \n Dr. Peter Schwartz<\/h4>\nPrincipal Artificial Intelligence Engineer
MITRE Corporation<\/i><\/h5>\nCombining AI with M&S to Meet Emerging Military Challenges<\/h3>\n
The U.S. is returning to a state of great power competition. The U.S. military must once again contend with near-peer adversaries that can bring to bear advanced weapon systems that are used in coordination with diplomatic, information, military, and economic (DIME) instruments of national power. In response to these challenges, the U.S. military is turning to new concepts of warfare such as Multi-Domain Operations (MDO) and Joint All-Domain Command and Control (JADC2). These concepts seek to orchestrate capabilities more tightly across domains (land, air, maritime, space, and cyberspace) as a means to converge effects rapidly and dynamically. This approach to warfare can provide U.S. commanders with a greater variety of options while presenting an adversary with multiple simultaneous dilemmas; however, it can also present U.S. commanders and their staffs with a far more complex battlespace and much shorter planning and decision timelines than they have faced in the past.<\/p>\n
The U.S. Department of Defense is looking to artificial intelligence (AI) and machine learning (ML) as potential technologies to support the execution of MDO and JADC2. AI and ML are often combined with models and simulations (M&S) to provide enhanced capabilities. This talk will present different configurations that combine AI\/ML with M&S and discuss their potential military applications. It will conclude with a presentation of a prototype course of action (COA) analysis tool that has been developed for the Army, including the specific way this tool combines AI with M&S and future work that will enable it to better support MDO and JADC2.<\/p> <\/div>\n<\/div>\n\n
\n \n \n - \n \n \n Bio <\/a>\n <\/div>\n \n \n
Dr. Peter Schwartz<\/strong> is a Principal Artificial Intelligence Engineer for the MITRE Corporation in the Artificial Intelligence & Autonomous Systems department where he serves as the Artificial Intelligence Joint & Services Domain SME. He supports multiple Department of Defense sponsors as an AI subject matter expert, leads MITRE\u2019s Army AI Community of Practice, and serves as the AI Domain Capability Area Lead for MITRE\u2019s Army Program. He has taught AI for the MITRE Institute and has served on a variety of AI panels and conference committees. Before joining MITRE in 2016, he was a Senior Data Scientist at Penn Medicine, and before that he was a Senior Mathematician at ORSA Corporation. He received a Ph.D. in computer science and intelligent systems from the University Michigan in 2007, and a B.S. in computer science and a B.A. in psychology from the University of Maryland in 2001.<\/p>\n\u00a0<\/p> <\/div>\n <\/div>\n <\/li>\n <\/ul>\n\n<\/div> <\/div>\n <\/div>\n <\/div>\n
\n \n \n \n\n \n ![\"Carrie](\"https:\/\/meetings.informs.org\/wordpress\/wsc2020\/files\/2020\/11\/Carrie_Beam.jpg\" "\\"Carrie_Beam\\"")
\n <\/div>\n \n \n <\/div>\n\n\n \n ![\"Ken](\"https:\/\/meetings.informs.org\/wordpress\/wsc2020\/files\/2020\/11\/Ken_Buxton.jpg\" "\\"Ken_Buxton\\"")
\n <\/div>\n \n \n <\/div>\n\n\n \n ![\"Traci](\"https:\/\/meetings.informs.org\/wordpress\/wsc2020\/files\/2020\/11\/Traci_McIntyre.jpg\" "\\"Traci_McIntyre\\"")
\n <\/div>\n \n \n <\/div>\n\n\n \n ![\"Jeffrey](\"https:\/\/meetings.informs.org\/wordpress\/wsc2020\/files\/2020\/11\/Jeffrey_Smith.jpg\" "\\"Jeffrey_Smith\\"")
\n <\/div>\n \n \n <\/div>\n <\/div>\n \n \n\n \n <\/div>\n<\/div>\n\n\n \n Panel Discussion:
Being a Simulation Professional in this Virtual World<\/h2> <\/div>\n<\/div>\n\n\n \n 2020 has forced many of us into uncharted waters in terms of how we teach and do business online. This panel will share their experiences working and teaching virtually: challenges and successes, best practices and maybe a few horror stories. Join us for a lively exchange on the advantages and disadvantages of working from home<\/p>\n
Panelists:
Carrie Beam, Carrie Beam Consulting and University of Arkansas<\/p> <\/div>\n<\/div>\n\n
\n \n \n - \n \n \n Bio <\/a>\n <\/div>\n \n \n
Dr. Carrie Beam<\/strong> holds a Ph.D. and an M.S. in Industrial Engineering and Operations Research, and a B.S.E. degree in Civil Engineering and Operations Research. \u00a0She is a Teaching Associate Professor in the fully online Master of Science in Operations Management Program at the University of Arkansas, where she teaches courses in statistics, risk management, operations management, data analytics, and other quantitative methods to online graduate and undergraduate students. \u00a0She has been teaching online for over a decade, and has spoken widely on how to move face-to-face classes online. \u00a0Two of her most popular talks are “Moving Online: How Exactly to Repackage a Face to Face Class into an Awesome Online Experience,” given at the INFORMS Annual meeting in 2018, and “Moving Online in a Weekend: \u00a0Distance Education in the Nick of Time,” given in March 2020 as the COVID-19 pandemic unfolded. \u00a0She is also the chair of the 2021 Franz Edelman Award committee.<\/p>\n\u00a0<\/p> <\/div>\n <\/div>\n <\/li>\n <\/ul>\n\n<\/div>\n
\n \n Ken Buxton, Accenture Strategy<\/p> <\/div>\n<\/div>\n\n
\n \n
\n <\/div>\n \n \n <\/div>\n <\/div>\n Ben Amaba, PhD, PE, CPIM\u00ae, LEED\u00ae AP<\/h4>\nChief Technology Officer for the Industrial Sector<\/em>
IBM Data Sciences and Artificial Intelligence Team Elite<\/h5>\nThe Rapid Democratization and Integration of Data with Simulation, Optimization and Artificial Intelligence<\/h3>\n
Many CEOs, CTOs, senior executives, and other decision-makers are seeing an advantage from the rise of big data and faster computing power.\u00a0 Data is now a board responsibility. The question arises: How can all this data drive innovation?\u00a0 Being able to harness the power of data through simulation, optimization, artificial intelligence (AI) and machine learning (ML) can help to improve financial, sales, manufacturing and supply-chain operations; enable a better, more intimate customer experience; or reduce downtime if done correctly. Waiting for the perfect environment is no longer a strategy, but an agile learning culture is the priority.<\/p>\n
With open source, hybrid clouds, high speed networks, increased computing power, and responsive platforms; simulation, optimization, AI and ML are being democratized and intertwined at a rapid pace. McKinsey estimates that AI could potentially deliver additional economic output of around $13 trillion by 2030, boosting global GDP by about 1.2 percent a year. Advancements in computing power and open source technologies have become a competitive advantage of day-to-day business by fundamentally improving the way the industry operates. Recent progress seeks to radically change our operations and workflows.\u00a0 To enable and maximize the creation of value, the integration and utilization of these data science technologies coupled with a rigorous approach is required. The overhead of data preparation, model governance, bias, trust, and deployment continue to inhibit democratization. The shortage of talent to promote and apply the interdisciplinary computer, mathematical and domain knowledge places many projects in pilot purgatory.\u00a0 The session will explore the future of the Digital Transformation and the use of advanced analytics to create an integrated business model where data becomes more than just a single source of truth, but a strategic asset.<\/p> <\/div>\n<\/div>\n\n
\n \n \n - \n \n \n Bio <\/a>\n <\/div>\n \n \n
Dr. Ben Amaba<\/strong> holds a PhD. degree in Industrial & Systems Engineering, a M.B.A.\/M.S. degree in Engineering and Operations, and a B.S. degree in Electrical Engineering. Dr. Amaba is a registered and licensed Professional Engineer in several states with International Registry; certified in Production, Operations, and Inventory Management by APICS \u00ae<\/span><\/b>; LEED<\/span>\u00ae <\/span><\/b>Accredited Professional (Leadership in Energy & Environmental Design); and certified in Corporate Strategy by Massachusetts Institute of Technology in Cambridge, Massachusetts. He is responsible for industrial manufacturing, infrastructure, engineering, and supply chain solutions. Dr. Amaba is the Chief Technology Officer for the Industrial Sector, IBM Data Sciences and Artificial Intelligence Team Elite. Dr. Amaba\u2019s focus and interest is in artificial intelligence, data sciences, and cloud technology. <\/span><\/p>\nDr. Amaba holds a copyright for Process Activity Flow Framework\u00ae, framework for requirements, design, architecture, simulation, and application development techniques. Dr. Amaba is prominently featured in \u201cGiving 2.0,\u201d by Laura Arrillaga-Andreessen with Stanford Business School, which details contributions to bolster and promote STEM education and careers, and is an Alexis de Tocqueville Society\/United Way Member, which recognizes local philanthropic leaders.<\/p>\n
Dr. Amaba holds positions as Industry Council Advisor to Project Production Institute, Board of Director of Whoop Wireless Corporation, UCX Corporation, and RANCS Group LLC, Executive Board Member of Applied Human Factors and Ergonomics (AHFE) and Editorial Board Member to IEEE (Institute of Electrical and Electronics Engineers) IT Professionals, Board Member to the Council on Industrial and Systems Engineering, founding member to the Institute of Advanced Systems Engineering, Founding member to the Center of Advanced Supply Chain Management, Editorial Board of The Open Cybernetics and Systemics Journal, and Executive Advisory Board Member to the University of Miami, University of Houston, and University of Central Florida.<\/p> <\/div>\n <\/div>\n <\/li>\n <\/ul>\n\n<\/div> <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n
\n \n \n \n\n \n Tuesday Keynote Speaker<\/h2> <\/div>\n<\/div>\n \n \n \n\n \n \n <\/div>\n \n \n <\/div>\n <\/div>\n \n \n\n \n Stephen E. Chick<\/h4>\nProfessor of Technology and Operations Management<\/em>
INSEAD – Europe Campus<\/h5>\nHealth Resource Allocation: Lessons for Today from Past Outbreaks<\/h3>\n
Recent events have brought mathematical modelling of infectious disease transmission and control to the forefront. An appropriate choice of mathematical model depends of course on the decision problem to be informed, yet there can be uncertainties about technical, social, and operational parameters of a model. And each model necessarily makes assumptions, for better or for worse. In this talk, we discuss different types of models for supporting decisions for cost-effective disease management decisions, touching on stochastic models, Bayesian methods, and simulation optimization. We then illustrate some obvious and some not-so-obvious ways that the choice of model is important, by drawing upon examples from the presenter\u2019s experience with projects to address influenza, vaccination, waterborne infections, Creutzfeldt-Jakob Disease and clinical trial design. We highlight the importance of problem selection and collaboration.<\/p> <\/div>\n<\/div>\n\n
\n \n \n - \n \n \n Bio <\/a>\n <\/div>\n \n \n
Steve Chick\u00a0<\/strong>is a Professor of Technology and Operations Management at INSEAD – Europe Campus. He holds the Novartis Chair of Healthcare Management and is academic director of INSEAD’s Healthcare Management Initiative. His academic research has focused on stochastic processes, Bayesian modelling for learning and uncertainty assessment, and healthcare modelling, particularly as it relates to epidemic modelling or to highly adaptive clinical trials. His research collaborations in healthcare modelling have been funded by the CDC, EPA, NIH, EU and the UK NIHR, and four of his papers have been finalist or winner of the Pierskalla Best Paper Award for INFORMS Healthcare. In executive teaching he has worked with numerous pharmaceutical companies and health care delivery organizations to support innovative and effective models for delivery health care.<\/p> <\/div>\n <\/div>\n <\/li>\n <\/ul>\n\n<\/div> <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n \n \n \n \n\n \n MASM Keynote<\/h2> <\/div>\n<\/div>\n <\/div>\n <\/div>\n <\/div>\n \n \n \n \n\n \n \n <\/div>\n \n \n <\/div>\n <\/div>\n \n \n\n \n Chen-Fu Chien, Ph.D.<\/h4>\nTsinghua Chair Professor & Micron Chair Professor<\/em>
Department of Industrial Engineering & Engineering Management
National Tsing Hua University, Hsinchu 30013, Taiwan<\/h5>\nIndustry 3.5 as Hybrid Strategy empowered by AI & Big Data Analytics and Collaborative Research with Micron Taiwan for Smart Manufacturing <\/strong><\/h3>\nThe paradigm of global manufacturing is shifting as leading nations proposing next phase of industrial revolution for Industry 4.0 by Germany and reemphasizing the importance of advanced manufacturing such as AMP in USA. Driven by Moore\u2019s Law, semiconductor manufacturing is one of the most complex industries for continuous migration of advanced technologies for manufacturing excellence. Micron Technology is a world leading producer of semiconductor memory and computer data storage that has established one of her largest manufacturing bases in Taiwan through a number of acquisitions of local fabs as well as investments of new fabs. Industry 3.5 was proposed as a hybrid strategy between the best practice of the existing Industry 3.0 and to-be Industry 4.0 to address fundamental objectives for smart manufacturing while employing artificial intelligence and big data analytics as means objectives for manufacturing intelligence solutions. This speech will introduce Industry 3.5 and use a number of empirical studies under the existing infrastructure for validation. Furthermore, collaborative research with Micron for smart manufacturing will be used to illustrate our continuous efforts employing artificial intelligence, big data analytics, optimization, and intelligent decision for smart manufacturing and digital transformation. This talk will conclude with discussions of the implications of Industry 3.5 as alternative for Industry 4.0 to empower humanity in the ongoing industrial revolution.<\/p> <\/div>\n<\/div>\n\n
\n \n \n - \n \n \n Bio <\/a>\n <\/div>\n \n \n
Dr. Chen-Fu Chien is Tsinghua Chair Professor, in the Department of Industrial Engineering & Engineering Management, National Tsing Hua University (NTHU), Hsinchu, Taiwan. He is also a Micron Chair Professor sponsored by Micron Foundation, USA. Professor Chien is the Director of Artificial Intelligence for Intelligent Manufacturing Systems (AIMS) Research Center that is one of four national AI centers sponsored by Ministry of Science & Technology (MOST), Taiwan. He is the founder and chairing professor for Decision Analysis Laboratory (DALab), the NTHU-TSMC Center for Manufacturing Excellence, and the Semiconductor Technologies Empowerment Partners Consortium (STEP Consortium), Taiwan. He received B.S. with double majors in Industrial Engineering and Electrical Engineering with the Phi Tau Phi Honor from NTHU in 1990. He received M.S. in Industrial Engineering and Ph.D. of Decision Sciences and Operations Research at UW-Madison, in 1994 and 1996, respectively. He was a Fulbright Scholar in the Department of Industrial Engineering and Operations Research, UC Berkeley, from 2002 to 2003. From 2005 to 2008, he had been on-leave as the Deputy Director of Industrial Engineering Division in Taiwan Semiconductor Manufacturing Company (TSMC). He received the Executive Training of PCMPCL from Harvard Business School in 2007. He was a Visiting Professor in Institute for Manufacturing, Cambridge University (sponsored by Royal Society, UK), Visiting Professor in Beijing Tsinghua University (sponsored by Chinese Development Foundation), Visiting Professor in Waseda University (sponsored by Japan Interchange Association Young Scholar Fellowship), and Visiting Professor in Tianjin University and Zhejiang University, China.<\/p>\n
His research efforts center on decision analysis, big data analytics, modeling and analysis for semiconductor manufacturing, manufacturing strategy, and manufacturing intelligence. Dr. Chien and his DALab Associates have conducted in-depth university-industry collaborative research projects with the leaders of different industrial segments to validate developed solutions and served as senior consultant for leading companies including TSMC, MediaTek, Delta, and AUO. Dr. Chien has received 12 USA invention patents on intelligent manufacturing and published 6 books, more than 170 journal papers (1 HiCi) and 12 case studies in Harvard Business School. He has been invited to give keynote speech in various conferences including APIEMS, C&IE, FAIM, IEEE, IEEM, IML, ISMI, ISSM, leading universities and international companies worldwide. He is a Fellow of APIEMS, CIIE, and CSMOT. Dr. Chien received the National Quality Award, the Executive Yuan Award for Outstanding Science & Technology, three Distinguished Research Awards and Tier 1 Principal Investigator (Top 3%) from MOST, Distinguished University-Industry Collaborative Research Award from the Ministry of Education, University Industrial Contribution Awards from the Ministry of Economic Affairs, the TECO Award, the 2011 Best Paper Award of IEEE Transactions on Automation Science and Engineering<\/em>, and the 2015 Best Paper Award of IEEE Transactions on Semiconductor Manufacturing<\/em>. He is a member of the Board of Directors for BoardTek (TWSE: 5349) and Uniflex (TWSE: 3321) and Just College UNISON Foundation.<\/p>\n\u00a0<\/p> <\/div>\n <\/div>\n <\/li>\n <\/ul>\n\n<\/div> <\/div>\n <\/div>\n <\/div>\n
\n \n \n \n\n \n Military Keynote<\/h2> <\/div>\n<\/div>\n <\/div>\n <\/div>\n <\/div>\n \n \n \n \n\n \n \n <\/div>\n \n \n <\/div>\n <\/div>\n \n \n\n \n Dr. Peter Schwartz<\/h4>\nPrincipal Artificial Intelligence Engineer
MITRE Corporation<\/i><\/h5>\nCombining AI with M&S to Meet Emerging Military Challenges<\/h3>\n
The U.S. is returning to a state of great power competition. The U.S. military must once again contend with near-peer adversaries that can bring to bear advanced weapon systems that are used in coordination with diplomatic, information, military, and economic (DIME) instruments of national power. In response to these challenges, the U.S. military is turning to new concepts of warfare such as Multi-Domain Operations (MDO) and Joint All-Domain Command and Control (JADC2). These concepts seek to orchestrate capabilities more tightly across domains (land, air, maritime, space, and cyberspace) as a means to converge effects rapidly and dynamically. This approach to warfare can provide U.S. commanders with a greater variety of options while presenting an adversary with multiple simultaneous dilemmas; however, it can also present U.S. commanders and their staffs with a far more complex battlespace and much shorter planning and decision timelines than they have faced in the past.<\/p>\n
The U.S. Department of Defense is looking to artificial intelligence (AI) and machine learning (ML) as potential technologies to support the execution of MDO and JADC2. AI and ML are often combined with models and simulations (M&S) to provide enhanced capabilities. This talk will present different configurations that combine AI\/ML with M&S and discuss their potential military applications. It will conclude with a presentation of a prototype course of action (COA) analysis tool that has been developed for the Army, including the specific way this tool combines AI with M&S and future work that will enable it to better support MDO and JADC2.<\/p> <\/div>\n<\/div>\n\n
\n \n \n - \n \n \n Bio <\/a>\n <\/div>\n \n \n
Dr. Peter Schwartz<\/strong> is a Principal Artificial Intelligence Engineer for the MITRE Corporation in the Artificial Intelligence & Autonomous Systems department where he serves as the Artificial Intelligence Joint & Services Domain SME. He supports multiple Department of Defense sponsors as an AI subject matter expert, leads MITRE\u2019s Army AI Community of Practice, and serves as the AI Domain Capability Area Lead for MITRE\u2019s Army Program. He has taught AI for the MITRE Institute and has served on a variety of AI panels and conference committees. Before joining MITRE in 2016, he was a Senior Data Scientist at Penn Medicine, and before that he was a Senior Mathematician at ORSA Corporation. He received a Ph.D. in computer science and intelligent systems from the University Michigan in 2007, and a B.S. in computer science and a B.A. in psychology from the University of Maryland in 2001.<\/p>\n\u00a0<\/p> <\/div>\n <\/div>\n <\/li>\n <\/ul>\n\n<\/div> <\/div>\n <\/div>\n <\/div>\n
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Being a Simulation Professional in this Virtual World<\/h2> <\/div>\n<\/div>\n\n\n \n 2020 has forced many of us into uncharted waters in terms of how we teach and do business online. This panel will share their experiences working and teaching virtually: challenges and successes, best practices and maybe a few horror stories. Join us for a lively exchange on the advantages and disadvantages of working from home<\/p>\n
Panelists:
Carrie Beam, Carrie Beam Consulting and University of Arkansas<\/p> <\/div>\n<\/div>\n\n
\n \n \n - \n \n \n Bio <\/a>\n <\/div>\n \n \n
Dr. Carrie Beam<\/strong> holds a Ph.D. and an M.S. in Industrial Engineering and Operations Research, and a B.S.E. degree in Civil Engineering and Operations Research. \u00a0She is a Teaching Associate Professor in the fully online Master of Science in Operations Management Program at the University of Arkansas, where she teaches courses in statistics, risk management, operations management, data analytics, and other quantitative methods to online graduate and undergraduate students. \u00a0She has been teaching online for over a decade, and has spoken widely on how to move face-to-face classes online. \u00a0Two of her most popular talks are “Moving Online: How Exactly to Repackage a Face to Face Class into an Awesome Online Experience,” given at the INFORMS Annual meeting in 2018, and “Moving Online in a Weekend: \u00a0Distance Education in the Nick of Time,” given in March 2020 as the COVID-19 pandemic unfolded. \u00a0She is also the chair of the 2021 Franz Edelman Award committee.<\/p>\n\u00a0<\/p> <\/div>\n <\/div>\n <\/li>\n <\/ul>\n\n<\/div>\n
\n \n Ken Buxton, Accenture Strategy<\/p> <\/div>\n<\/div>\n\n
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IBM Data Sciences and Artificial Intelligence Team Elite<\/h5>\n
The Rapid Democratization and Integration of Data with Simulation, Optimization and Artificial Intelligence<\/h3>\n
Many CEOs, CTOs, senior executives, and other decision-makers are seeing an advantage from the rise of big data and faster computing power.\u00a0 Data is now a board responsibility. The question arises: How can all this data drive innovation?\u00a0 Being able to harness the power of data through simulation, optimization, artificial intelligence (AI) and machine learning (ML) can help to improve financial, sales, manufacturing and supply-chain operations; enable a better, more intimate customer experience; or reduce downtime if done correctly. Waiting for the perfect environment is no longer a strategy, but an agile learning culture is the priority.<\/p>\n
With open source, hybrid clouds, high speed networks, increased computing power, and responsive platforms; simulation, optimization, AI and ML are being democratized and intertwined at a rapid pace. McKinsey estimates that AI could potentially deliver additional economic output of around $13 trillion by 2030, boosting global GDP by about 1.2 percent a year. Advancements in computing power and open source technologies have become a competitive advantage of day-to-day business by fundamentally improving the way the industry operates. Recent progress seeks to radically change our operations and workflows.\u00a0 To enable and maximize the creation of value, the integration and utilization of these data science technologies coupled with a rigorous approach is required. The overhead of data preparation, model governance, bias, trust, and deployment continue to inhibit democratization. The shortage of talent to promote and apply the interdisciplinary computer, mathematical and domain knowledge places many projects in pilot purgatory.\u00a0 The session will explore the future of the Digital Transformation and the use of advanced analytics to create an integrated business model where data becomes more than just a single source of truth, but a strategic asset.<\/p> <\/div>\n<\/div>\n\n
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Dr. Ben Amaba<\/strong> holds a PhD. degree in Industrial & Systems Engineering, a M.B.A.\/M.S. degree in Engineering and Operations, and a B.S. degree in Electrical Engineering. Dr. Amaba is a registered and licensed Professional Engineer in several states with International Registry; certified in Production, Operations, and Inventory Management by APICS \u00ae<\/span><\/b>; LEED<\/span>\u00ae <\/span><\/b>Accredited Professional (Leadership in Energy & Environmental Design); and certified in Corporate Strategy by Massachusetts Institute of Technology in Cambridge, Massachusetts. He is responsible for industrial manufacturing, infrastructure, engineering, and supply chain solutions. Dr. Amaba is the Chief Technology Officer for the Industrial Sector, IBM Data Sciences and Artificial Intelligence Team Elite. Dr. Amaba\u2019s focus and interest is in artificial intelligence, data sciences, and cloud technology. <\/span><\/p>\n
Dr. Amaba holds a copyright for Process Activity Flow Framework\u00ae, framework for requirements, design, architecture, simulation, and application development techniques. Dr. Amaba is prominently featured in \u201cGiving 2.0,\u201d by Laura Arrillaga-Andreessen with Stanford Business School, which details contributions to bolster and promote STEM education and careers, and is an Alexis de Tocqueville Society\/United Way Member, which recognizes local philanthropic leaders.<\/p>\n
Dr. Amaba holds positions as Industry Council Advisor to Project Production Institute, Board of Director of Whoop Wireless Corporation, UCX Corporation, and RANCS Group LLC, Executive Board Member of Applied Human Factors and Ergonomics (AHFE) and Editorial Board Member to IEEE (Institute of Electrical and Electronics Engineers) IT Professionals, Board Member to the Council on Industrial and Systems Engineering, founding member to the Institute of Advanced Systems Engineering, Founding member to the Center of Advanced Supply Chain Management, Editorial Board of The Open Cybernetics and Systemics Journal, and Executive Advisory Board Member to the University of Miami, University of Houston, and University of Central Florida.<\/p> <\/div>\n <\/div>\n <\/li>\n <\/ul>\n\n<\/div> <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n
\n\n\n \n\n\nTuesday Keynote Speaker<\/h2> <\/div>\n<\/div>\n
\n\n \n\n\n \n <\/div>\n \n \n <\/div>\n <\/div>\n \n \n\n\nStephen E. Chick<\/h4>\n
Professor of Technology and Operations Management<\/em>
INSEAD – Europe Campus<\/h5>\nHealth Resource Allocation: Lessons for Today from Past Outbreaks<\/h3>\n
Recent events have brought mathematical modelling of infectious disease transmission and control to the forefront. An appropriate choice of mathematical model depends of course on the decision problem to be informed, yet there can be uncertainties about technical, social, and operational parameters of a model. And each model necessarily makes assumptions, for better or for worse. In this talk, we discuss different types of models for supporting decisions for cost-effective disease management decisions, touching on stochastic models, Bayesian methods, and simulation optimization. We then illustrate some obvious and some not-so-obvious ways that the choice of model is important, by drawing upon examples from the presenter\u2019s experience with projects to address influenza, vaccination, waterborne infections, Creutzfeldt-Jakob Disease and clinical trial design. We highlight the importance of problem selection and collaboration.<\/p> <\/div>\n<\/div>\n\n
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Steve Chick\u00a0<\/strong>is a Professor of Technology and Operations Management at INSEAD – Europe Campus. He holds the Novartis Chair of Healthcare Management and is academic director of INSEAD’s Healthcare Management Initiative. His academic research has focused on stochastic processes, Bayesian modelling for learning and uncertainty assessment, and healthcare modelling, particularly as it relates to epidemic modelling or to highly adaptive clinical trials. His research collaborations in healthcare modelling have been funded by the CDC, EPA, NIH, EU and the UK NIHR, and four of his papers have been finalist or winner of the Pierskalla Best Paper Award for INFORMS Healthcare. In executive teaching he has worked with numerous pharmaceutical companies and health care delivery organizations to support innovative and effective models for delivery health care.<\/p> <\/div>\n <\/div>\n <\/li>\n <\/ul>\n\n<\/div> <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n
\n\n\n \n\n\nMASM Keynote<\/h2> <\/div>\n<\/div>\n <\/div>\n <\/div>\n <\/div>\n
\n\n\n \n\n\n \n <\/div>\n \n \n <\/div>\n <\/div>\n \n \n\n\nChen-Fu Chien, Ph.D.<\/h4>\n
Tsinghua Chair Professor & Micron Chair Professor<\/em>
Department of Industrial Engineering & Engineering Management
National Tsing Hua University, Hsinchu 30013, Taiwan<\/h5>\nIndustry 3.5 as Hybrid Strategy empowered by AI & Big Data Analytics and Collaborative Research with Micron Taiwan for Smart Manufacturing <\/strong><\/h3>\n
The paradigm of global manufacturing is shifting as leading nations proposing next phase of industrial revolution for Industry 4.0 by Germany and reemphasizing the importance of advanced manufacturing such as AMP in USA. Driven by Moore\u2019s Law, semiconductor manufacturing is one of the most complex industries for continuous migration of advanced technologies for manufacturing excellence. Micron Technology is a world leading producer of semiconductor memory and computer data storage that has established one of her largest manufacturing bases in Taiwan through a number of acquisitions of local fabs as well as investments of new fabs. Industry 3.5 was proposed as a hybrid strategy between the best practice of the existing Industry 3.0 and to-be Industry 4.0 to address fundamental objectives for smart manufacturing while employing artificial intelligence and big data analytics as means objectives for manufacturing intelligence solutions. This speech will introduce Industry 3.5 and use a number of empirical studies under the existing infrastructure for validation. Furthermore, collaborative research with Micron for smart manufacturing will be used to illustrate our continuous efforts employing artificial intelligence, big data analytics, optimization, and intelligent decision for smart manufacturing and digital transformation. This talk will conclude with discussions of the implications of Industry 3.5 as alternative for Industry 4.0 to empower humanity in the ongoing industrial revolution.<\/p> <\/div>\n<\/div>\n\n
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Dr. Chen-Fu Chien is Tsinghua Chair Professor, in the Department of Industrial Engineering & Engineering Management, National Tsing Hua University (NTHU), Hsinchu, Taiwan. He is also a Micron Chair Professor sponsored by Micron Foundation, USA. Professor Chien is the Director of Artificial Intelligence for Intelligent Manufacturing Systems (AIMS) Research Center that is one of four national AI centers sponsored by Ministry of Science & Technology (MOST), Taiwan. He is the founder and chairing professor for Decision Analysis Laboratory (DALab), the NTHU-TSMC Center for Manufacturing Excellence, and the Semiconductor Technologies Empowerment Partners Consortium (STEP Consortium), Taiwan. He received B.S. with double majors in Industrial Engineering and Electrical Engineering with the Phi Tau Phi Honor from NTHU in 1990. He received M.S. in Industrial Engineering and Ph.D. of Decision Sciences and Operations Research at UW-Madison, in 1994 and 1996, respectively. He was a Fulbright Scholar in the Department of Industrial Engineering and Operations Research, UC Berkeley, from 2002 to 2003. From 2005 to 2008, he had been on-leave as the Deputy Director of Industrial Engineering Division in Taiwan Semiconductor Manufacturing Company (TSMC). He received the Executive Training of PCMPCL from Harvard Business School in 2007. He was a Visiting Professor in Institute for Manufacturing, Cambridge University (sponsored by Royal Society, UK), Visiting Professor in Beijing Tsinghua University (sponsored by Chinese Development Foundation), Visiting Professor in Waseda University (sponsored by Japan Interchange Association Young Scholar Fellowship), and Visiting Professor in Tianjin University and Zhejiang University, China.<\/p>\n
His research efforts center on decision analysis, big data analytics, modeling and analysis for semiconductor manufacturing, manufacturing strategy, and manufacturing intelligence. Dr. Chien and his DALab Associates have conducted in-depth university-industry collaborative research projects with the leaders of different industrial segments to validate developed solutions and served as senior consultant for leading companies including TSMC, MediaTek, Delta, and AUO. Dr. Chien has received 12 USA invention patents on intelligent manufacturing and published 6 books, more than 170 journal papers (1 HiCi) and 12 case studies in Harvard Business School. He has been invited to give keynote speech in various conferences including APIEMS, C&IE, FAIM, IEEE, IEEM, IML, ISMI, ISSM, leading universities and international companies worldwide. He is a Fellow of APIEMS, CIIE, and CSMOT. Dr. Chien received the National Quality Award, the Executive Yuan Award for Outstanding Science & Technology, three Distinguished Research Awards and Tier 1 Principal Investigator (Top 3%) from MOST, Distinguished University-Industry Collaborative Research Award from the Ministry of Education, University Industrial Contribution Awards from the Ministry of Economic Affairs, the TECO Award, the 2011 Best Paper Award of IEEE Transactions on Automation Science and Engineering<\/em>, and the 2015 Best Paper Award of IEEE Transactions on Semiconductor Manufacturing<\/em>. He is a member of the Board of Directors for BoardTek (TWSE: 5349) and Uniflex (TWSE: 3321) and Just College UNISON Foundation.<\/p>\n
\u00a0<\/p> <\/div>\n <\/div>\n <\/li>\n <\/ul>\n\n<\/div> <\/div>\n <\/div>\n <\/div>\n
\n\n\n \n\n\nMilitary Keynote<\/h2> <\/div>\n<\/div>\n <\/div>\n <\/div>\n <\/div>\n
\n\n\n \n\n\n \n <\/div>\n \n \n <\/div>\n <\/div>\n \n \n\n\nDr. Peter Schwartz<\/h4>\n
Principal Artificial Intelligence Engineer
MITRE Corporation<\/i><\/h5>\nCombining AI with M&S to Meet Emerging Military Challenges<\/h3>\n
The U.S. is returning to a state of great power competition. The U.S. military must once again contend with near-peer adversaries that can bring to bear advanced weapon systems that are used in coordination with diplomatic, information, military, and economic (DIME) instruments of national power. In response to these challenges, the U.S. military is turning to new concepts of warfare such as Multi-Domain Operations (MDO) and Joint All-Domain Command and Control (JADC2). These concepts seek to orchestrate capabilities more tightly across domains (land, air, maritime, space, and cyberspace) as a means to converge effects rapidly and dynamically. This approach to warfare can provide U.S. commanders with a greater variety of options while presenting an adversary with multiple simultaneous dilemmas; however, it can also present U.S. commanders and their staffs with a far more complex battlespace and much shorter planning and decision timelines than they have faced in the past.<\/p>\n
The U.S. Department of Defense is looking to artificial intelligence (AI) and machine learning (ML) as potential technologies to support the execution of MDO and JADC2. AI and ML are often combined with models and simulations (M&S) to provide enhanced capabilities. This talk will present different configurations that combine AI\/ML with M&S and discuss their potential military applications. It will conclude with a presentation of a prototype course of action (COA) analysis tool that has been developed for the Army, including the specific way this tool combines AI with M&S and future work that will enable it to better support MDO and JADC2.<\/p> <\/div>\n<\/div>\n\n
\n \n- \n
- \n \n \n Bio <\/a>\n <\/div>\n\n\n
Dr. Peter Schwartz<\/strong> is a Principal Artificial Intelligence Engineer for the MITRE Corporation in the Artificial Intelligence & Autonomous Systems department where he serves as the Artificial Intelligence Joint & Services Domain SME. He supports multiple Department of Defense sponsors as an AI subject matter expert, leads MITRE\u2019s Army AI Community of Practice, and serves as the AI Domain Capability Area Lead for MITRE\u2019s Army Program. He has taught AI for the MITRE Institute and has served on a variety of AI panels and conference committees. Before joining MITRE in 2016, he was a Senior Data Scientist at Penn Medicine, and before that he was a Senior Mathematician at ORSA Corporation. He received a Ph.D. in computer science and intelligent systems from the University Michigan in 2007, and a B.S. in computer science and a B.A. in psychology from the University of Maryland in 2001.<\/p>\n
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Being a Simulation Professional in this Virtual World<\/h2> <\/div>\n<\/div>\n\n\n\n2020 has forced many of us into uncharted waters in terms of how we teach and do business online. This panel will share their experiences working and teaching virtually: challenges and successes, best practices and maybe a few horror stories. Join us for a lively exchange on the advantages and disadvantages of working from home<\/p>\n
Panelists:
Carrie Beam, Carrie Beam Consulting and University of Arkansas<\/p> <\/div>\n<\/div>\n\n\n \n- \n
- \n \n \n Bio <\/a>\n <\/div>\n\n\n
Dr. Carrie Beam<\/strong> holds a Ph.D. and an M.S. in Industrial Engineering and Operations Research, and a B.S.E. degree in Civil Engineering and Operations Research. \u00a0She is a Teaching Associate Professor in the fully online Master of Science in Operations Management Program at the University of Arkansas, where she teaches courses in statistics, risk management, operations management, data analytics, and other quantitative methods to online graduate and undergraduate students. \u00a0She has been teaching online for over a decade, and has spoken widely on how to move face-to-face classes online. \u00a0Two of her most popular talks are “Moving Online: How Exactly to Repackage a Face to Face Class into an Awesome Online Experience,” given at the INFORMS Annual meeting in 2018, and “Moving Online in a Weekend: \u00a0Distance Education in the Nick of Time,” given in March 2020 as the COVID-19 pandemic unfolded. \u00a0She is also the chair of the 2021 Franz Edelman Award committee.<\/p>\n
\u00a0<\/p> <\/div>\n <\/div>\n <\/li>\n <\/ul>\n\n<\/div>\n
\n\nKen Buxton, Accenture Strategy<\/p> <\/div>\n<\/div>\n\n
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