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        "date_end":"2022-07-21",
        "time_start":"12:00:00",
        "time_end":"14:00:00",
        "time_zone":"America\/Detroit",
        "event_title":"Capital One Product Chats",
        "occurrence_title":"",
        "combined_title":"Capital One Product Chats",
        "event_subtitle":"",
        "event_type":"Careers \/ Jobs",
        "event_type_id":"2",
        "description":"Capital One is excited to meet students looking for a role where they can develop innovative solutions to meet customers\u2019 needs and positively impact their lives!\n\nWe invite you to register for Capital One Product Chats to ask questions about our full-time Product Development Program with current or recent grads of the program. You can also learn more about #LifeatCapitalOne, our culture, project areas, and more.\n\nBe sure to register! We look forward to seeing you soon!",
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        "event_title":"PHYSICS GRADUATE SUMMER SYMPOSIUM (PGSS) | Learn to Design: From Optimization to Deep Learning and Reinforcement Learning",
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        "combined_title":"PHYSICS GRADUATE SUMMER SYMPOSIUM (PGSS) | Learn to Design: From Optimization to Deep Learning and Reinforcement Learning: Taigao Ma (UM Physics)",
        "event_subtitle":"Taigao Ma (UM Physics)",
        "event_type":"Conference \/ Symposium",
        "event_type_id":"5",
        "description":"Designing physical structures and devices to achieve desirable performance is an important study in many disciplines, including physics and engineering. However, the design process is non-trivial because of the large design spaces as well as the non-unique optimal design. Usually, the design process requires an iterative trial-and-error process conducted by human experts through extensive simulations or experiments, which wastes much time and effort. The recent development of computer science has reshaped this research domain. In this talk, I will give a brief overview of these design methods, with a special focus on designing optical and photonic structures and devices. In this talk, I will briefly discuss three parts: 1) Traditional optimization methods; 2) Deep learning methods that use the neural networks as function approximators to speed up the evaluations and guide for optimization; 3) Reinforcement learning methods that can efficiently extrapolate in the design space and provide new design thoughts. One specific example will be discussed in each part.",
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        "tags":["Free","Graduate"],
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        "event_title":"Expedition Reef",
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        "event_type":"Presentation",
        "event_type_id":"16",
        "description":"Learn the secrets of the \u201crainforests of the sea\u201d as you embark on an oceanic safari of the world\u2019s most vibrant\u2014and endangered\u2014marine ecosystems. Expedition Reef immerses you in an undersea adventure. Along the way, discover how corals grow, feed, reproduce, and support over 25% of all marine life on Earth.",
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        "event_title":"An Introduction to Big Data and Machine Learning for Survey Researchers and Social Scientists - Summer Institute in Survey Research Techniques",
        "occurrence_title":"",
        "combined_title":"An Introduction to Big Data and Machine Learning for Survey Researchers and Social Scientists - Summer Institute in Survey Research Techniques: Course presented by Trent Buskirk",
        "event_subtitle":"Course presented by Trent Buskirk",
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        "event_type_id":"8",
        "description":"An Introduction to Big Data and Machine Learning for Survey Researchers and Social Scientists\r\n\r\nCourse open for registration!\r\nOpen to all!\r\n\r\nJuly 20-22, 2022\r\n1:00pm \u2013 5:00pm\r\nM\/Th\/F\r\n\r\nThe amount of data generated as a by-product in society is growing fast including data from satellites, sensors, transactions, social media and smartphones, just to name a few. Such data are often referred to as \u201cbig data\u201d, and can be used to create value in different areas such as health and crime prevention, commerce and fraud detection.  An emerging practice in many areas is to append or link big data sources with more specific and smaller scale sources that often contain much more limited information.  This practice has been used for some time by survey researchers in constructing frames by appending auxiliary information that is often not directly available on the frame, but can be obtained from an external source.   Using Big Data has the potential to go beyond the sampling phase for survey researchers and in fact has the potential to influence the social sciences in general.  Big Data is of interest for public opinion researchers and agencies that produce statistics to find alternative data sources either to reduce costs, to improve estimates or to produce estimates in a more timely fashion. However, Big Data pose several interesting and new challenges to survey researchers and social scientists among others who want to extract information from data. As Robert Groves (2012) pointedly commented, the era is \u201cappropriately called Big Data and not Big Information\u201d, because there is a lot of work for analysts before information can be gained from \u201cauxiliary traces of some process that is going on in society.\u201d\r\n\r\nThis course offers participants a broad overview of big data sources, opportunities and examples motivated within the survey and social science contexts including the use of social media data, para data and other such sources.  This course also offers a detailed, practical introduction to four common machine learning methods that can be applied to big and small data alike at various aspects of a study\u2019s lifecycle from design to nonresponse adjustments to propensity score matching to weighting and evaluation and analysis.  The machine learning methods will be demonstrated in R and we will provide several different examples of using these methods along with multiple packages in R that offer these methods.\r\n\r\nNot for academic credit\r\n\r\nInstructor: Trent Buskirk\r\n\r\nAll 2022 courses will be held in an alternative remote format.",
        "occurrence_notes":"Registration and payment required.",
                "guid":"95710-21790764@events.umich.edu",
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        "tags":["Anthropology","Data","Data Curation","Data Management","Data Science","Economics","Graduate and Professional Students","Health Data","Interdisciplinary","Mathematics","Political Science","Psychology","Research","Social Sciences","Survey Research"],
        "website":"https:\/\/si.isr.umich.edu\/",
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        "datetime_modified":"20220630T153053",
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        "event_title":"Certifiable Autonomous Systems Through Online Verification \u2014 CCAT Distinguished Lecture Series",
        "occurrence_title":"",
        "combined_title":"Certifiable Autonomous Systems Through Online Verification \u2014 CCAT Distinguished Lecture Series: Professor Matthias Althoff",
        "event_subtitle":"Professor Matthias Althoff",
        "event_type":"Livestream \/ Virtual",
        "event_type_id":"24",
        "description":"Engineers and computer scientists are currently developing autonomous systems whose entire set of behaviors in future, untested situations is unknown. For instance, how can a designer foresee all situations that an autonomous vehicle will face? Keeping in mind that many autonomous systems are safety-critical or operation-critical, it is irresponsible to deploy such systems without testing all possible situations---this, however, seems impossible since even the most important possible situations are often unmanageably many. This presentation proposes a paradigm shift that will make it possible to guarantee safety in unforeseeable situations (under mild model assumptions): Instead of verifying the correctness of a system before deployment, we propose online verification, a new verification paradigm where a system continuously checks the correctness of its next action by itself in its current environment (and only in it) in a just-in-time manner. The usefulness of this method will be demonstrated primarily for autonomous driving and robotics.\r\n\r\n---\r\n\r\nAbout the speaker: Matthias Althoff received a diploma in Mechatronics and Information Technology from the Department of Mechanical Engineering at the Technical University of Munich, Germany, in 2005. He received his Ph.D. degree in Electrical Engineering from the same university under the supervision of Univ.-Prof. Dr.-Ing.\/Univ. Tokio Martin Buss in 2010. From 2010 - 2012 he was a postdoctoral researcher at Carnegie Mellon University, USA, with a joint appointment in electrical engineering and the Robotics Institute. He joined the Computer Science Department at Ilmenau University of Technology, Germany, in 2012 as Assistant Professor for Automation Systems. Since 2013 Matthias Althoff is a Professor in Computer Science at the Technical University of Munich. \r\n\r\nHis research interests include the design and analysis of cyber-physical systems, formal verification of continuous and hybrid systems, reachability analysis, planning algorithms, and robust and fault-tolerant control. The main applications of his research are automated vehicles, robotics, and power systems.",
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        "tags":["conference","Discussion","Education","Electrical Engineering and Computer Science","Engineering","Engineering Academic Calendar","Faculty","Free","Graduate and Professional Students","Graduate Students","Lecture","Mechanical Engineering","Michigan Engineering","Research","Robotics","seminar","Talk","Virtual","Webcast"],
        "website":"https:\/\/AV-Verification.eventbrite.com\/?aff=Happening",
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                "website":"http:\/\/www.umtri.umich.edu\/"                }                    ],
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        "datetime_modified":"20220503T122215",
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        "date_end":"2022-07-21",
        "time_start":"14:00:00",
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        "time_zone":"America\/Detroit",
        "event_title":"CoderSpaces, Virtual Office Hours (Thursdays, 2-3:30pm)",
        "occurrence_title":"",
        "combined_title":"CoderSpaces, Virtual Office Hours (Thursdays, 2-3:30pm)",
        "event_subtitle":"",
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        "event_type_id":"14",
        "description":"Are you grappling with a piece of code, trying to compute on a University cluster, or just getting started with a new method such as machine learning? Then we might have just the right space for you.\r\n\r\nAll members of the U-M community are invited to join our weekly virtual CoderSpaces, Tuesdays and Thursdays, during the Summer 2022 term to get research support and connect with others.\r\n\r\nThe virtual sessions are designed to assist faculty, staff, and students with research methodology, statistics, data science applications, and computational programming for research.\r\n\r\nOur hosts have a wide set of methodological and technological expertise. They come to you from a variety of departments and disciplines and are looking forward to serving the U-M community in their research endeavors.\r\n\r\nCoderSpaces provide a casual, productive and inclusive environment. Everyone is welcome regardless of skill level.\r\n\r\nJoin via Zoom* (https:\/\/umich.zoom.us\/j\/96475145533)\r\n*Users will have to sign in with their UMICH (Level-1) credentials.\r\n\r\nWith Bennet Fauber (ARC), Meghan Dailey (ARC), Sara Lafia (ICPSR)\r\n\r\nExpertise at this session includes: ARC Armis, ARC Great Lakes, Bash, bash shell scripting, C , C++, conda (anaconda\/miniconda), CUDA, data analysis, data management, data visualization, git, GitHub, GitLab, GPU, high performance computing (HPC), Java, keras, Linux, Lmod modules, machine learning, macOS, MATLAB, natural language processing, pandas, Python, SLURM, social sciences, software compilation & installation on Linux, software development, spatial data analysis, Stata, tensorflow, text analysis, Windows",
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