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ACM Chicago members are welcome to join the IEEE Chicago webinar on Supercomputing and Argonne Leadership Computing Facility where Michael Papka from Argonne National Lab will discuss about the new supercomputer Aurora at Argonne and what the Argonne Leadership Computing Facility is doing in supercomputing and what interesting applications that this enables. This is also a joint webinar with Chicago Section of Society Women Engineers (SWE). Remember to click on the Online event Zoom registration URL in the sidebar where you will be redirected to enter registration and then receive your link for the webinar. Abstract: Supercomputers are wonderfully flexible research instruments. They give us the ability to control the flow of time and perform experiments that are impossible in the real world. As the number of application fields that use supercomputing continues to expand with each new generation of machines, so do the possibilities to reach new knowledge levels. Forthcoming supercomputers will be able to perform a billion billion floating-point operations per second. The Argonne Leadership Computing Facility, a leading computer science research center, will operate such a machine, called Aurora. This talk will provide an overview of the facility and the exciting science it enables. Agenda: 6:00 - 6:05 PM Introduction 6:05 - 6:45 PM Talk 6:45 - 6:55 PM Q&A 6:55 - 7:00 PM Closing and Adjournment Join us and learn how Argonne is creating one of the world's fastest supercomputers! About the speaker: Michael E. Papka is a senior scientist and member of the senior leadership at Argonne National Laboratory. He has served as Argonne’s Deputy Associate Laboratory Director for Computing, Environment and Life Sciences since 2006, where he supports programmatic efforts that contribute to or benefit from high-performance computing. He also has served as director of the Argonne Leadership Computing Facility since 2010, with significant responsibility in delivering next-generation computing systems to the research community, including the future Aurora exascale system. His degrees include a Master's and a Ph.D. in computer science from the University of Chicago. His research interests span visualization, large-scale data analysis, and the development and deployment of research infrastructure in support of science. He is a Presidential Research, Scholarship and Artistry Professor in the Department of Computer Science at Northern Illinois University and leads the Data, Devices, and Interaction Laboratory (ddiLab). Contact him at [masked]. IMPORTANT: You must also click the online link to attend the meeting over Zoom! See the link in the right sidebar in a browser or the Attend Online button on a phone.
IMPORTANT: You must also click the online link in order to attend the meeting over Zoom! The link will be provided later. For our December and last meeting of 2020, we will have our Chair, Alvin Chin to give a presentation about machine learning for the connected car. Don't miss on this exciting meeting! Abstract: As cars are now being connected to the Internet and the car platforms become like computers with hardware and software, machine learning and AI become paramount for connected and autonomous driving as well as predictive vehicle maintenance. Data mining plays an important part in the evolutionary development of these systems. Connected and self-driving vehicles generate amazing amounts of data, including building internal user profiles for building predictive models of preferences. Machine learning can help connected and autonomous vehicles find optimal patterns to create safe, useful, and enjoyable passenger experiences. In this talk, I will explain three applications for applying machine learning in journey management, predictive vehicle maintenance and personal preferences. Agenda: 6:00 - 6:05 PM Introduction 6:05 - 6:45 PM Talk 6:45 - 6:55 PM Q&A 6:55 - 7:00 PM Closing and Adjournment Join us and learn how machine learning is being used to create the connected car! About the speaker: Dr. Alvin Chin is Chair of ACM Chicago, but his full time job is AI and Emerging Technology Researcher at BMW Technology Corporation where his research involves exploring AI and emerging technologies for potential use cases in production at BMW. Previously, he was Senior Machine Learning Researcher at BMW Technology Corporation in Chicago where he worked on big data and machine learning for improving driving behaviour. Prior to BMW, he was Senior Researcher at Microsoft and Nokia in Beijing working on big data and analytics for browsing behavior in Xpress Browser, and Senior Researcher at Nokia Research Center working on mobile social networking in particular proximity social networks for inferring social activity, collaboration and recommendation in real physical environments. Dr. Chin has authored more than 30 publications and 10 patents including those pending. He has a Bachelors and Masters degrees in Computer Engineering from the University of Waterloo and a PhD in Computer Science from the University of Toronto. Dr. Chin is a member of various program committees such as ACM KDD, ACM Hypertext, IEEE CPSCom, ACM Ubicomp, ACM CSCW, and IEEE VTC. He is an ACM Senior Member and IEEE Senior Member. Dr. Chin is also active in the Chicago community, as Chair of ACM Chicago, Chair of the IEEE VTS Chicago Chapter, and Chair of the IEEE Computer Society Chicago Chapter. He is the Publicity Co-Chair for IEEE Vehicular Technology Conference-Fall 2020 and Secretary of the IEEE VTS/Automated Vehicles Standards Committee. Alvin can be reached at [masked] and his website is http://www.alvinychin.com.
IMPORTANT: You must also click the online link in order to attend the meeting over Zoom! The link will be provided later. Don't miss this exciting meeting on machine learning using GPU computing! At the January Chicago ACM meeting (1/20/21 Online) learn how those of you who look at algorithms and see opportunities for massive parallelism can apply that skill to problem solving. Financial modelers can now use advanced computer architectures known as GPUs and a Python-based framework known as RAPIDS to speed up problems using big data and machine learning. Rather than taking minutes to run, these problems will be completed in seconds. We show a detailed analysis of several million loans where the contribution of each feature is discovered via their Shapley values along the lines of this article: https://developer.nvidia.com/blog/explaining-and-accelerating-machine-learning-for-loan-delinquencies/ Our speaker: Mark Bennett is from the Chicago area and has been working in large datasets and real-time high performance computing for over three decades. Mark is currently senior data scientist at Nvidia Corporation where he focuses on acceleration for financial machine learning. He has taught financial analytics at the University of Iowa and the University of Chicago. Mark holds a Ph.D. from UCLA, an M.S. from the University of Southern California, and a B.S. with Distinction from the University of Iowa, all in computer science. His early work experience was in applied mathematics at Argonne National Laboratory and as a research scientist at Unisys Corporation. Later he was a member of technical staff at AT&T Bell Laboratories, senior technical advisor and engineering manager at Northrop Grumman aerospace, senior technology specialist at XR Trading Securities and senior quantitative finance analyst at Bank of America Securities. He is also the co-author of Financial Analytics with R published by Cambridge University Press (ISBN:[masked]