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DTSTART:20070311T020000
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BEGIN:VEVENT
DTSTAMP:20231215T073302
DTSTART;TZID=America/Detroit:20240111T110000
DTEND;TZID=America/Detroit:20240111T150000
SUMMARY:Exhibition:Investigate Labs
DESCRIPTION:Step into our two Investigate Labs\, where you can use scientific tools and museum specimens to answer questions and solve problems. Our labs offer activities most appropriate for ages 6 and up. Schedule subject to change.
UID:96857-21836263@events.umich.edu
URL:https://events.umich.edu/event/96857
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Museum,natural history museum,Natural Sciences,Science,Children
LOCATION:Museum of Natural History
CONTACT:
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BEGIN:VEVENT
DTSTAMP:20240126T063224
DTSTART;TZID=America/Detroit:20240111T110000
DTEND;TZID=America/Detroit:20240111T150000
SUMMARY:Careers / Jobs:NGIC January 2024 VEC Hiring Fair
DESCRIPTION:NGIC January 2024 VEC Hiring Fair\nThursday\, January 11\, 2024\n11:00 AM – 3:00 PM\n\nNorthside Library\n705 Rio Road West\nCharlottesville\, VA\n\nIn-person applications accepted on January 11\, 2024 11:00 AM – 3:00 PM\n\nOnline submissions accepted on January 10-16\, 2024\nJobannouncement details available here: https://www.usainscom.army.mil/MSCs/NGIC/\n\nVisit Facebook for announcement information (more details added soon):\nhttps://www.facebook.com/NGIC.gov\n\nApplications should include your resume\, supporting documentation\, and announcement number\n\nCandidates must be U.S. citizens and able to obtain and maintain a Top Secret clearance.\n\nNGIC provides foundational all-source intelligence on foreign ground force capabilities\, related military technologies\, and targeting support to ensure that U.S. Army\, DoD\, joint\, and national-level decisionmakers maintain decision advantage and prevent strategic surprise.\n\nWe will be hiring the following positions:\nGeneral Intelligence Specialists\, Administrative Assistants\, Computer Scientists\, Engineers\, Intelligence Analyst\, C4 Systems and Network Analysts\, and more.\n\nBenefits include:\n- Competitive salaries\n- Flexible work schedules\n- Retirement contribution matching\n- Paid fitness time\n- Professional development\n- Travel opportunities\n- Unmatched job security
UID:116470-21837004@events.umich.edu
URL:https://events.umich.edu/event/116470
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:
LOCATION:Charlottesville, Virginia, United States
CONTACT:
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BEGIN:VEVENT
DTSTAMP:20240111T150304
DTSTART;TZID=America/Detroit:20240111T113000
DTEND;TZID=America/Detroit:20240111T130000
SUMMARY:Workshop / Seminar:CSCS Seminar | Methods in networks and machine learning: from hate speech to personalized predictive medicine
DESCRIPTION:Snacks and coffee will be served. \n\n*Abstract:* In this talk I will discuss some aspects at the intersection of machine learning and networks\, and introduce interdisciplinary methods for data-driven analysis of social and biological complex systems. \n\nFirst\, I will present some nuanced aspects of social media. In recent work\, we study\, using knowledge graphs\, sentiment analysis\, and natural language processing\, how online hate speech and public sentiment evolves for different marginalized groups\, and how this leads to hate crimes and reactions that is different between e.g. Black Vs LGBTQ+ groups.\n\nNext\, in order to investigate this more rigorously I introduce a new mathematical model of opinion dynamics\, which (unlike previous models) captures phenomena such as temporary consensus (eventually falling out of consensus) and opinion crossing. Human behaviour (e.g. masking vs not masking) is also tied to health outcomes and disease spreading. \n\nI will use these ideas and heterogeneous behaviour\, symptoms\, and genes\, to present a new method in multilayer networks for early and personalized prediction of disease subtype with remarkable success in prediction of Parkinson's subtype five years in advance.\n\nHuman opinions evolve\, and lead to changing alliances\, behavior\, and outcomes. Higher order networks such as simplicial complexes (where more than two people interact simultaneously) are particularly useful in capturing properties of higher-order human interaction effectively. I will present theory of simplicial complexes and then some applications on (a) studying strategies and influence in passing of bills in Congress\, and (b) the privilege of structural position in coauthorship network on hiring using graph neural networks. \n\nLastly\, I will discuss topics in explainable machine learning\, especially for prediction on graphs. Machine learning is tremendously successful but often a black box. Here\, I will present some recent rigorous results on understanding what precisely are the weight dynamics in the graph neural network during 'learning'\, where we show how properties of the graph (the spectrum of the Laplacian) plays a role in training. I will also briefly discuss work on more 'biological' ways of training machine learning models of cognition. \n\n*Bio: *\nSanjukta's research interests are multidisciplinary\, with the goal of developing tools to answer questions about real world systems. She received her PhD from the University of Maryland where she was also a fellow of the Combine (Computational and Mathematics in Biological Networks) program. She was then a postdoc at University College London\, where she worked with Google DeepMind on computational models of cognition. She is currently a UC Presidential postdoc with a joint appointment at UC Berkeley CS and UCLA Math. She enjoys traveling to remote corners of the world and has lived on four continents. In her free time she enjoys dancing\, diving and hiking.
UID:116699-21837825@events.umich.edu
URL:https://events.umich.edu/event/116699
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Research,Biomath,Biological Networks,Computational Modeling,Natural Sciences,Maching Learning,Biosciences
LOCATION:Weiser Hall - 747
CONTACT:
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