2021 Opening Keynote Speaker<\/h2> <\/div>\n<\/div>\n \n \n \n\n \n ![\"Jane](\"https:\/\/meetings.informs.org\/wordpress\/wsc2021\/files\/2021\/06\/Jane_Macfarlane.jpg\" "\\"Jane_Macfarlane\\"")
\n <\/div>\n \n \n <\/div>\n <\/div>\n \n \n\n \n Jane Macfarlane<\/strong><\/h4>\nDirector, Smart Cities & Sustainable Mobility<\/em>
University of California at Berkeley <\/em>
Affiliate Scientist, Lawrence Berkeley National Laboratory<\/em><\/h5>\nBeyond Data: Data-Driven Digital Twins for Sustainable Future Cities<\/h3>\n
Urbanization is straining our transportation systems and road networks. The resulting congestion, delays and air pollution lead to a declined quality of life but also greatly contributes to climate change. The confluence of dramatic changes in the availability of data that measures mobility with significant increases in computational capabilities will allow us to develop next-generation traffic management systems that make it easier for cities to create safe and fluid traffic networks, while balancing the needs of a large variety of travelers. Advanced traffic management systems, founded on data-driven digital twins, will predict traffic congestion patterns and find alternative routing and control mechanisms to distribute mobility. This talk will highlight our urban-scale, parallel discrete event simulation platform \u2013 Mobiliti. Mobiliti is built on HPC and can simulate a day of travel in large urban networks in a matter of minutes, e.g., the Bay Area (1M road edges, 19M trip legs, ~4 minutes) and the full Los Angeles Basin (2M road edges, 40M trip legs, ~6 minutes). We will also describe our efforts in machine learning techniques to develop surrogate models for Mobiliti that not only provide predictive capabilities but will integrate into advanced control systems and orchestrate the movement of traffic in an efficient and effective manner. As important, this talk will highlight how we measure the improvements these technologies bring against the requirements of building livable, equitable cities.<\/p> <\/div>\n<\/div>\n\n
\n \n \n - \n \n \n Bio <\/a>\n <\/div>\n \n \n
Jane Macfarlane <\/strong>is the Director of Smart Cities and Sustainable Mobility at the University of California at Berkeley and an affiliate scientist at Lawrence Berkeley National Laboratory. Dr. Macfarlane has over 30 years of experience in high performance computing, data analytics and geospatial mapping. For a large part of her career, she has been responsible for directing industry research.\u00a0 Including: Chief Scientist and Head of Research for HERE \u2013 a leader in geospatial mapping and Director of Advanced Technology Planning for OnStar at General Motors \u2013 the first at-scale, telematics solution in the US. Her research focus is on semantic analytics, big data analytics and visualization, contextualization of data streams and spatially distributed computing. Her thesis work focused on Dynamic Systems Understanding using an assumption-based truth maintenance system and symbolic analysis. She has authored over 26 patents, primarily in the domain of geospatial data analytics. She holds a Ph.D. in Mechanical Engineering from the University of Minnesota. Currently she is leading a DOE National Laboratory effort across three national labs focused on the use of High-Performance Computing and big data in transportation systems.<\/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 2021 Titans of Simulation<\/h2> <\/div>\n<\/div>\n \n \n \n\n \n ![\"David](\"https:\/\/meetings.informs.org\/wordpress\/wsc2021\/files\/2021\/06\/David_nicol.jpg\" "\\"David_nicol\\"")
\n <\/div>\n \n \n <\/div>\n <\/div>\n \n \n\n \n David Nicol<\/strong><\/h4>\nDirector, Information Trust Institute<\/em>
Herman M. Dieckamp Endowed Chair in Engineering<\/em>
Department of Electrical and Computer Engineering<\/em>
University of Illinois at Urbana\u2010Champaign<\/h5>\nChallenges and Approaches to the Modeling and Simulation of Gargantuan Discrete Systems<\/h3>\n
One of the most impactful ways that simulation is used in the physical sciences is to apply relatively simple rules to really large data states, e.g., to simulate the evolution of star position and mass in galaxies, or to track the interaction of many molecules in simulations supporting drug design.\u00a0 When data sets become very very large the computations are organized to execute on specialized computing hardware, and models that operate on domain-specific abstractions of the data are developed.<\/o:p><\/span><\/p>\nModeling and simulation can (and is) being used in very large discrete systems (e.g., logical behavior of computer chips) <\/span>and, philosophically, very large models can be simulated using specialized hardware, specialized data abstractions, and <\/span>specialized algorithms applied to them. However there are important differences between these and physical models, <\/o:p><\/span>due to the fact that the foundational rules of discrete models are often complex whereas the foundational <\/o:p><\/span>rules for physical systems are comparatively simpler <\/o:p><\/span>physical laws. This talk outlines some of the challenges I’ve faced in modeling and simulating of gargantuan discrete models, and <\/o:p><\/span>application domains that give rise to them such as the global Internet, cyber-security of critical infrastructures, and <\/o:p><\/span>risk assessment of cyber-physical systems.<\/o:p><\/span><\/p> <\/div>\n<\/div>\n\n\n \n
\n <\/div>\n \n \n <\/div>\n <\/div>\n Jane Macfarlane<\/strong><\/h4>\nDirector, Smart Cities & Sustainable Mobility<\/em>
University of California at Berkeley <\/em>
Affiliate Scientist, Lawrence Berkeley National Laboratory<\/em><\/h5>\nBeyond Data: Data-Driven Digital Twins for Sustainable Future Cities<\/h3>\n
Urbanization is straining our transportation systems and road networks. The resulting congestion, delays and air pollution lead to a declined quality of life but also greatly contributes to climate change. The confluence of dramatic changes in the availability of data that measures mobility with significant increases in computational capabilities will allow us to develop next-generation traffic management systems that make it easier for cities to create safe and fluid traffic networks, while balancing the needs of a large variety of travelers. Advanced traffic management systems, founded on data-driven digital twins, will predict traffic congestion patterns and find alternative routing and control mechanisms to distribute mobility. This talk will highlight our urban-scale, parallel discrete event simulation platform \u2013 Mobiliti. Mobiliti is built on HPC and can simulate a day of travel in large urban networks in a matter of minutes, e.g., the Bay Area (1M road edges, 19M trip legs, ~4 minutes) and the full Los Angeles Basin (2M road edges, 40M trip legs, ~6 minutes). We will also describe our efforts in machine learning techniques to develop surrogate models for Mobiliti that not only provide predictive capabilities but will integrate into advanced control systems and orchestrate the movement of traffic in an efficient and effective manner. As important, this talk will highlight how we measure the improvements these technologies bring against the requirements of building livable, equitable cities.<\/p> <\/div>\n<\/div>\n\n
\n \n \n - \n \n \n Bio <\/a>\n <\/div>\n \n \n
Jane Macfarlane <\/strong>is the Director of Smart Cities and Sustainable Mobility at the University of California at Berkeley and an affiliate scientist at Lawrence Berkeley National Laboratory. Dr. Macfarlane has over 30 years of experience in high performance computing, data analytics and geospatial mapping. For a large part of her career, she has been responsible for directing industry research.\u00a0 Including: Chief Scientist and Head of Research for HERE \u2013 a leader in geospatial mapping and Director of Advanced Technology Planning for OnStar at General Motors \u2013 the first at-scale, telematics solution in the US. Her research focus is on semantic analytics, big data analytics and visualization, contextualization of data streams and spatially distributed computing. Her thesis work focused on Dynamic Systems Understanding using an assumption-based truth maintenance system and symbolic analysis. She has authored over 26 patents, primarily in the domain of geospatial data analytics. She holds a Ph.D. in Mechanical Engineering from the University of Minnesota. Currently she is leading a DOE National Laboratory effort across three national labs focused on the use of High-Performance Computing and big data in transportation systems.<\/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 2021 Titans of Simulation<\/h2> <\/div>\n<\/div>\n \n \n \n\n \n \n <\/div>\n \n \n <\/div>\n <\/div>\n \n \n\n \n David Nicol<\/strong><\/h4>\nDirector, Information Trust Institute<\/em>
Herman M. Dieckamp Endowed Chair in Engineering<\/em>
Department of Electrical and Computer Engineering<\/em>
University of Illinois at Urbana\u2010Champaign<\/h5>\nChallenges and Approaches to the Modeling and Simulation of Gargantuan Discrete Systems<\/h3>\n
One of the most impactful ways that simulation is used in the physical sciences is to apply relatively simple rules to really large data states, e.g., to simulate the evolution of star position and mass in galaxies, or to track the interaction of many molecules in simulations supporting drug design.\u00a0 When data sets become very very large the computations are organized to execute on specialized computing hardware, and models that operate on domain-specific abstractions of the data are developed.<\/o:p><\/span><\/p>\nModeling and simulation can (and is) being used in very large discrete systems (e.g., logical behavior of computer chips) <\/span>and, philosophically, very large models can be simulated using specialized hardware, specialized data abstractions, and <\/span>specialized algorithms applied to them. However there are important differences between these and physical models, <\/o:p><\/span>due to the fact that the foundational rules of discrete models are often complex whereas the foundational <\/o:p><\/span>rules for physical systems are comparatively simpler <\/o:p><\/span>physical laws. This talk outlines some of the challenges I’ve faced in modeling and simulating of gargantuan discrete models, and <\/o:p><\/span>application domains that give rise to them such as the global Internet, cyber-security of critical infrastructures, and <\/o:p><\/span>risk assessment of cyber-physical systems.<\/o:p><\/span><\/p> <\/div>\n<\/div>\n\n\n \n
University of California at Berkeley <\/em>
Affiliate Scientist, Lawrence Berkeley National Laboratory<\/em><\/h5>\n
Beyond Data: Data-Driven Digital Twins for Sustainable Future Cities<\/h3>\n
Urbanization is straining our transportation systems and road networks. The resulting congestion, delays and air pollution lead to a declined quality of life but also greatly contributes to climate change. The confluence of dramatic changes in the availability of data that measures mobility with significant increases in computational capabilities will allow us to develop next-generation traffic management systems that make it easier for cities to create safe and fluid traffic networks, while balancing the needs of a large variety of travelers. Advanced traffic management systems, founded on data-driven digital twins, will predict traffic congestion patterns and find alternative routing and control mechanisms to distribute mobility. This talk will highlight our urban-scale, parallel discrete event simulation platform \u2013 Mobiliti. Mobiliti is built on HPC and can simulate a day of travel in large urban networks in a matter of minutes, e.g., the Bay Area (1M road edges, 19M trip legs, ~4 minutes) and the full Los Angeles Basin (2M road edges, 40M trip legs, ~6 minutes). We will also describe our efforts in machine learning techniques to develop surrogate models for Mobiliti that not only provide predictive capabilities but will integrate into advanced control systems and orchestrate the movement of traffic in an efficient and effective manner. As important, this talk will highlight how we measure the improvements these technologies bring against the requirements of building livable, equitable cities.<\/p> <\/div>\n<\/div>\n\n
- \n
- \n \n \n Bio <\/a>\n <\/div>\n\n\n
Jane Macfarlane <\/strong>is the Director of Smart Cities and Sustainable Mobility at the University of California at Berkeley and an affiliate scientist at Lawrence Berkeley National Laboratory. Dr. Macfarlane has over 30 years of experience in high performance computing, data analytics and geospatial mapping. For a large part of her career, she has been responsible for directing industry research.\u00a0 Including: Chief Scientist and Head of Research for HERE \u2013 a leader in geospatial mapping and Director of Advanced Technology Planning for OnStar at General Motors \u2013 the first at-scale, telematics solution in the US. Her research focus is on semantic analytics, big data analytics and visualization, contextualization of data streams and spatially distributed computing. Her thesis work focused on Dynamic Systems Understanding using an assumption-based truth maintenance system and symbolic analysis. She has authored over 26 patents, primarily in the domain of geospatial data analytics. She holds a Ph.D. in Mechanical Engineering from the University of Minnesota. Currently she is leading a DOE National Laboratory effort across three national labs focused on the use of High-Performance Computing and big data in transportation systems.<\/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\n2021 Titans of Simulation<\/h2> <\/div>\n<\/div>\n
\n\n \n\n\n \n <\/div>\n \n \n <\/div>\n <\/div>\n \n \n\n\nDavid Nicol<\/strong><\/h4>\n
Director, Information Trust Institute<\/em>
Herman M. Dieckamp Endowed Chair in Engineering<\/em>
Department of Electrical and Computer Engineering<\/em>
University of Illinois at Urbana\u2010Champaign<\/h5>\nChallenges and Approaches to the Modeling and Simulation of Gargantuan Discrete Systems<\/h3>\n
One of the most impactful ways that simulation is used in the physical sciences is to apply relatively simple rules to really large data states, e.g., to simulate the evolution of star position and mass in galaxies, or to track the interaction of many molecules in simulations supporting drug design.\u00a0 When data sets become very very large the computations are organized to execute on specialized computing hardware, and models that operate on domain-specific abstractions of the data are developed.
Modeling and simulation can (and is) being used in very large discrete systems (e.g., logical behavior of computer chips) <\/span>and, philosophically, very large models can be simulated using specialized hardware, specialized data abstractions, and <\/span>specialized algorithms applied to them. However there are important differences between these and physical models,
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![\"Andreas](\"https:\/\/meetings.informs.org\/wordpress\/wsc2021\/files\/2021\/06\/Andreas_Tolk.jpg\" "\\"Andreas_Tolk\\"")
\n <\/div>\n \n \n <\/div>\n <\/div>\n ![\"Tae](\"https:\/\/meetings.informs.org\/wordpress\/wsc2021\/files\/2021\/06\/Tae_Eog.jpg\" "\\"Tae_Eog\\"")
\n <\/div>\n \n \n <\/div>\n <\/div>\n ![\"T\"](\"https:\/\/meetings.informs.org\/wordpress\/wsc2021\/files\/2021\/11\/Litton_Profile-3-002.jpg\" "\\"Sean")
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