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                2014-15 
                  Working Lunch Seminar Series 
                   
                  at 
                  the Fields Institute, 222 College St., Toronto  
               
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          Part I: Timothy Chan 
           
             
              In this talk, Professor Timothy Chan will discuss some of his 
                recent sports analytics research in hockey and baseball. In hockey, 
                he and his students have developed a player classification system 
                for both NHL and junior hockey players. This system can be used 
                to estimate the contribution of different players to their team 
                and to predict future performance. In baseball, he and a collaborator 
                have developed a method to quantify the value of "flexible" 
                players  those who can play multiple positions  which 
                provides insight into which teams are more resilient to injury 
                risk.  
             
           
          
          
            
              Sport analytics is a rapid-growing field that 
                is changing the way athletes train, prepare, and compete. Many 
                professional sports (NBA, NFL, and soccer) are undergoing a "Moneyball 
                period" in which technologies such as GPS, accelerometry, 
                heart rate (HR) and video are used to monitor player positioning, 
                movement, and physiological responses. Rugby has recognized the 
                benefit of incorporating sport analytics to gain competitive edge 
                over other teams and have been collecting speed, acceleration, 
                and HR data in real-time with physiological data already being 
                measured using standard laboratory and field-based methodologies. 
                Discriminant analysis was used to identify performance indicators 
                between winning and losing in basketball, while clustering and 
                regression methods were used to characterize individual player's 
                contribution to team's overall performance in hockey. At the recent 
                Fields big data analysis workshop, researchers identified indicators 
                that contribute to winning/losing/performance, athlete types based 
                on their offensive and defensive behaviors, and athlete behavior 
                within game. Preliminary insights in connecting physiological 
                to tactical/technical data are being investigated as well. 
             
           
         
        
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          Timothy Chan is an Associate Professor in the Department of Mechanical 
            and Industrial Engineering at the University of Toronto and Director 
            of the Centre for Research in Healthcare Engineering. He received 
            his BSc in Applied Mathematics from the University of British Columbia 
            (2002), and his PhD in Operations Research from the Massachusetts 
            Institute of Technology (2007). Professor Chan was an Associate in 
            the Chicago office of McKinsey and Company, a global management consulting 
            firm (2007-2009). During that time, he advised leading companies in 
            the fields of medical device technology, travel and hospitality, telecommunications, 
            and energy on issues of strategy, organization, technology and operations. 
          Professor Chans primary research interests are in optimization 
            under uncertainty and the application of optimization methods to problems 
            in healthcare, medicine, global engineering, sustainability, and sports. 
            He received the George B. Dantzig Dissertation Award from INFORMS 
            (2007), an Early Researcher Award from the Ministry of Economic Development 
            and Innovation of Ontario (2012), an Early Career Teaching Award from 
            both the U of T Department of Mechanical and Industrial Engineering 
            (2012) and the U of T Faculty of Applied Science & Engineering 
            (2013), second place in the INFORMS Section on Public Programs, Service 
            and Needs best paper competition (2012), and first place in the MIT 
            Sloan Sports Analytics Conference research paper competition (2013). 
            His research has been featured by the CBC, CTV News, the Toronto Star, 
            and Canadian Business magazine. 
         
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          Ming-Chang Tsai 
          Ming-Chang Tsai is a researcher in the Faculty of Kinesiology and 
            Physical Education at the University of Toronto and a data analyst/sport 
            scientist at the Canadian Sport Institute Pacific. He received his 
            BASc in Engineering Science from the University of Toronto (1995) 
            and his PhD in Exercise Sciences from University of Toronto (2015). 
          Ming has been coaching for 20 years in rowing, cycling, running, 
            and triathlon. He was an elite rower competed around the world with 
            the Chinese Taipei national team at World Cups, World Championships, 
            Asian Championships, and Asian Games. After his elite rowing career 
            was over, he started racing multisport and has represented Canada 
            on several Age Group World Championship teams in Duathlon and Triathlon. 
           
         
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          Friday June 12, 2015 
             
            11 - 12:30 
            Room 210 at the Fields Institute  
            
         
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          Special Workshop: Optimization 
            of a novel purification process of solar grade silicon 
             
         
         
           
             
              We have developed a novel technique for purifying silicon that 
                does not require the conventional chemical treatment of the Siemens 
                process. Instead, we treat metallurgical grade silicon wafers 
                in solid state with 
                microwave radiation. This technique has been experimentally shown 
                to be effective in forcing the migration of transition metals, 
                which radically harm the performance of semiconductor devices. 
                We are trying to optimize the process for mass production of solar 
                grade silicon. 
               
             
           
          Mohammad Samani, Prised Solar Inc. 
           
         
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         Tuesday April 28, 2015  
          12 - 2 p.m. 
          Stewart Library, Fields Institute  
          
          
        Click for full size 
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       OpenCL: 
        The Hardware Evolution and the Software Revolution 
         
           
            Computer hardware is evolving in ways that prompt changes to how 
              software is written for performance. The era of a single increasingly 
              fast processor in a system gave way to the homogeneous parallel 
              programming era, with multiple cores and processors from the same 
              vendor in a system. The next evolutionary step upon us is the heterogeneous 
              parallel programming era, with multiple cores and processors from 
              different vendors in the same system. Heterogeneous compute systems 
              can be faster than homogeneous systems and may require a fraction 
              of the energy. 
            The Khronos OpenCL specification is a standard for parallel heterogeneous 
              computing that enables software to leverage CPUs, GPUs, FPGAs, or 
              other accelerators detected in a system. It provides a set of abstractions 
              that can obtain peak performance on physical hardware across processor 
              architectures. OpenCL standardizes a common device programming language 
              so that developers can write software to run on any supported processor. 
              Today, OpenCL is available on everything from a mobile device to 
              a supercomputer, opening a world of opportunities for business and 
              researcher. 
            This talk will motivate the OpenCL standard and present its opportunities 
              and challenges. A survey of performance gains and energy savings 
              will be provided so that the potential of the parallel heterogeneous 
              compute era can be understood. The recent announcements from Khronos 
              at GDC 2015, including Vulkan and OpenCL 2.1, will be echoed. 
            
           
         
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       AJ 
        Guillon 
         
         
          AJ Guillon is a Khronos member and actively contributed to the new 
            OpenCL C++ kernel language, provisionally released as part of OpenCL 
            2.1. He has dedicated himself to solving the hardest problems in parallel 
            programming and software engineering. AJ is the founder and CTO of 
            YetiWare Inc, a local startup company that is commercializing a distributed 
            heterogeneous compute operating system for next-generation cloud analytical 
            platforms. 
          AJ is an alumni of the University of Toronto where he completed his 
            Honors Bachelor of Science with a strong focus on mathematics, operating 
            system design, and computer science theory. His passions include big, 
            fast computers and the mathematics that powers them. AJ is a masters 
            swimmer, water polo player, and enjoys rock climbing when time permits. 
            
         
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        February 24, 2015  
        12:30 - 2 p.m. 
        Stewart Library, Fields Institute  | 
       Brain-CODE 
        - Ontario Brain Institute's Data Integration Platform: Opportunities for 
        Complex Data Analytics  
         
           
            As the production and use of data in research and healthcare increase 
              we are faced with a big data challenge and an array of opportunities 
              to make the most of this data. Not only is the size, volume, variety, 
              and potential privacy issues of this multi-dimensional data present 
              some unique challenges but our ability to efficiently standardize, 
              collect, store, manage, and process this data ultimately determines 
              its utility and the efficacy of the resulting analysis. It is an 
              exciting time for big data analytics for brain health and medicine 
              which holds promise for improved and faster diagnosis and discovery! 
           
         
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      Francis 
        Jeanson  
        Program Lead, Informatics, Ontario Brain Institute  
        Francis has joined OBI in the spring of 2013 to help design and implement 
          the Brain-CODE neuroscience informatics platform. Francis first pursued 
          an Honours Bachelors of Science at the University of Toronto in Cognitive 
          Science and Artificial Intelligence and developed a passion for embodied 
          cognition and robotics. After gaining experience as a software developer, 
          Francis pursued a Masters in Evolutionary and Adaptive Systems at the 
          University of Sussex which he completed in 2008. There, he honed his 
          skills in neural modelling and evolutionary robotics. He finally moved 
          on to pursue a PhD in Cognitive Science with a focus in neural coding 
          theory and application at Carleton University which he completed in 
          the winter of 2014. Francis possesses a long standing interest in complex 
          systems, cognitive robotics, artificial intelligence, computational 
          analysis, and modelling. He is excited to work with OBI and hopes to 
          see flourish the enormous potential that the Brain-CODE neuroinformatics 
          platform can offer for multidimensional brain research. 
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