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TZID:America/Detroit
TZURL:http://tzurl.org/zoneinfo/America/Detroit
X-LIC-LOCATION:America/Detroit
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TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20070311T020000
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BEGIN:VEVENT
DTSTAMP:20260323T082306
DTSTART;TZID=America/Detroit:20260324T150000
DTEND;TZID=America/Detroit:20260324T170000
SUMMARY:Workshop / Seminar:MICDE State of AI & the Future of Institutions
DESCRIPTION:The State of AI & the Future of Institutions event is hosted by the Michigan Institute for Computational Discovery and Engineering (MICDE). We bring together scholars and institutional leaders to explore the current state and future trajectory of AI\; how it may reshape institutions and how we can be better prepared for its disruptive impact. This event aims to move beyond abstract debate and towards actionable insights and assess how institutions can more actively shape a more resilient and responsible future. We anticipate this event to recur every semester.
UID:146034-21898298@events.umich.edu
URL:https://events.umich.edu/event/146034
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Electrical And Computer Engineering
LOCATION:Palmer Commons - Forum Hall
CONTACT:
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260313T072550
DTSTART;TZID=America/Detroit:20260324T183000
DTEND;TZID=America/Detroit:20260324T193000
SUMMARY:Careers / Jobs:GEICO Corporate Information Sessions
DESCRIPTION:3/24/2026 | 6:30 pm | DOW 1018 (FOOD PROVIDED: Cottage Inn Pizza)\nMajors:  All Engineering Majors\nPositions: Full Time\, Intern\nDegrees: Bachelors\, Masters\n\nResumes Collected\nUS Citizenship or Permanent Resident\n\nGEICO (Government Employees Insurance Company) is a leading American auto insurer\, ranking as the second-largest in the U.S. A Berkshire Hathaway subsidiary founded in 1936\, it specializes in direct-to-consumer private passenger auto insurance\, offering policies online and by phone. GEICO also covers motorcycles\, RVs\, homeowners\, and renters.
UID:146554-21899265@events.umich.edu
URL:https://events.umich.edu/event/146554
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Electrical And Computer Engineering
LOCATION:Chemistry Dow Lab - 1018
CONTACT:
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260313T073041
DTSTART;TZID=America/Detroit:20260325T173000
DTEND;TZID=America/Detroit:20260325T183000
SUMMARY:Careers / Jobs:BASF Corporate Information Session
DESCRIPTION:3/25/2026 | 5:30 pm | GGBL 1025 (FOOD PROVIDED: Jerusalem Garden)\nMajors:  Chemical Engineering\, Computer Engineering\, Computer Science\, Data Science\, Electrical Engineering\, Mechanical Engineering\nPositions: Intern\, Full-time\nDegrees: Bachelors\, PhD\n\nUS Citizenship or Permanent Resident\n\nAt BASF\, we create chemistry for a sustainable future. Our ambition: We want to be the preferred chemical company to enable our customers’ green transformation. We combine economic success with environmental protection and social responsibility. Around 108\,000 employees in the BASF Group contribute to the success of our customers in nearly all sectors and almost every country in the world. Our portfolio comprises\, as core businesses\, the segments Chemicals\, Materials\, Industrial Solutions\, and Nutrition & Care\; our standalone businesses are bundled in the segments Surface Technologies and Agricultural Solutions. BASF generated sales of around €60 billion in 2025. BASF shares are traded on the stock exchange in Frankfurt (BAS) and as American Depositary Receipts (BASFY) in the United States.
UID:146555-21899266@events.umich.edu
URL:https://events.umich.edu/event/146555
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Electrical And Computer Engineering
LOCATION:GG Brown Laboratory - 1025
CONTACT:
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260224T151016
DTSTART;TZID=America/Detroit:20260326T110000
DTEND;TZID=America/Detroit:20260326T120000
SUMMARY:Workshop / Seminar:Quantum Research Institute | Have we seen a demonstration of experimental quantum advantage?
DESCRIPTION:In-Person: Michigan Memorial Phoenix Project\, 2301 Bonisteel Blvd\, Ann Arbor\, MI 48109\, USA\, PML2000\nZoom: https://umich.zoom.us/j/94764879233?jst=2\n\nAbstract: A major goal for the field of quantum computation is “quantum advantage\" -- the first experimental demonstration of a quantum computation that is beyond the capabilities of any classical computer.  While we have now seen many quantum advantage claims made by experimental groups around the world\, many of these claims have been disproven.  \n\nIn this talk\, we'll discuss the status quo regarding the latest experimental quantum advantage claims and the evidence for their classical hardness.  We’ll then discuss the classical verification problem\, and propose a new quantum advantage proposal that uses ideas from quantum error correction to enable a large gap between classical verification and simulation.\n\nBio:\nI am an Associate Professor in the Department of Computer Science at the University of Chicago.\nPreviously\, I held research positions at the University of California at Berkeley\, advised by Umesh Vazirani\, and in QuICS\, at the University of Maryland/NIST.\nI received my Ph.D. in computer science from the Department of Computer and Mathematical Sciences and the Institute for Quantum Information and Matter at Caltech\, co-advised by Alexei Kitaev and Chris Umans.
UID:142260-21890280@events.umich.edu
URL:https://events.umich.edu/event/142260
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Electrical And Computer Engineering
LOCATION:
CONTACT:
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260303T095838
DTSTART;TZID=America/Detroit:20260407T110000
DTEND;TZID=America/Detroit:20260407T120000
SUMMARY:Lecture / Discussion:Scalable quantum and classical photonics
DESCRIPTION:Abstract: \nThere is now a broad agreement that photonics is essential for reducing energy consumption of AI hardware through optical interconnects\, but quantum technologies also need photonics for scaling. This is true even for “non-photonic” quantum systems based on superconductors\, or trapped atoms and ions in vacuum. For example\, new types of spatial light modulators and switches are needed to trap and control atoms and ions\, microwave to optical quantum transducers are needed for networking superconducting processors\, chip-scale laser systems are required for controlling atoms or spin qubits in solids\, and very high efficiency integrated photonics is needed for quantum networks\, sensors\, and chip-based semiconductor quantum systems. Unfortunately\, the desired level of performance and some of the functionalities are not available even in today’s best integrated photonics.   We show how this can be addressed by photonics inverse design combined with emerging materials\, new nanofabrication and heterogenous integration approaches. Specific examples include development of miniaturized titanium:sapphire lasers and amplifiers on chip\, quantum network nodes in diamond\, and a quantum simulator with silicon carbide color centers. Classical photonic technologies that will be discussed  include fast\, compact and error-free  chip-scale optical interconnects\, as well as CMOS compatible laser isolators and frequency stabilizers. \nBio: \nJelena Vuckovic (PhD Caltech 2002) is the Jensen Huang Professor of Global Leadership\, Professor of Electrical Engineering and\, by courtesy\, of Applied Physics at Stanford. She is a member of the National Academy of Sciences and an External Scientific Member of the Max Planck Institute for Quantum Optics. Her awards include the Zeiss Award\, Vannevar Bush Faculty Fellowship\, Geoffrey Frew Fellowship from the Australian Academy of Sciences\, the IET A. F. Harvey Engineering Research Prize\, Mildred Dresselhaus Lectureship from MIT\, and the Humboldt Prize. She is a Fellow of the APS\, Optica\, and IEEE\, a lead editor of Physical Review Applied\, and an Editor of PNAS.
UID:146128-21898421@events.umich.edu
URL:https://events.umich.edu/event/146128
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Electrical And Computer Engineering
LOCATION:Herbert H. Dow  Building - 1010 Dow
CONTACT:
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260224T150916
DTSTART;TZID=America/Detroit:20260409T110000
DTEND;TZID=America/Detroit:20260409T120000
SUMMARY:Workshop / Seminar:Quantum Research Institute | Quantum Spin-Mechanics with Color Centers in Diamond: A Potential Platform for Quantum Computing
DESCRIPTION:In-Person: West Hall 411\nZoom: https://umich.zoom.us/j/91761768567?jst=2\n\nAbstract:\nIn a spin-mechanical system\, electron spins are coupled to vibrations of a nanomechanical resonator.  Coherent interactions between single spins and single phonons take place in the quantum regime of spin-mechanics.  A network of these resonators can enable phonon-mediated coupling between distant electron spin\, leading to a mechanical quantum network of spin qubits and providing an experimental platform for developing spin-based quantum computers.  \nIn this talk\, I will discuss our recent advance in achieving ultracoherent GHz diamond nanomechanical resonators and in developing mechanical quantum networks of spin qubits in diamond.  Localization and localization phase transitions induced by deterministic onsite potentials in a mechanical network are also exploited for the realization of extended network connectivity\, which is deemed essential for large-scale fault tolerant quantum computers. \n\nBio:\nHailin Wang received B.S. and Ph.D. degrees in physics from the University of Science and Technology of China and the University of Michigan in 1982 and 1990\, respectively. He was a research investigator at the University of Michigan and subsequently a staff consultant at AT&T Bell Laboratories. He joined the University of Oregon in 1995 where he is now a professor of physics. Dr. Wang has made important contributions to the current understanding of coherent as well as incoherent optical processes in semiconductor nanostructures. He also made the first experimental demonstration of amplitude squeezed light from an injection-locked diode laser and developed a fused silica optical resonator that feature highly directional evanescent tunneling. His work on exciton spin coherence and biexciton coherence has recently led to the first demonstration of electromagnetically induced transparency for interband optical transitions in semiconductors. His current research interest includes optical manipulation of quantum coherences in semiconductors and especially its application in both classical and quantum information processing. Dr. Wang is a recipient of an NSF-CAREER award and is a fellow of the Optical Society of America.
UID:142261-21890281@events.umich.edu
URL:https://events.umich.edu/event/142261
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Electrical And Computer Engineering
LOCATION:West Hall - 411
CONTACT:
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260324T092358
DTSTART;TZID=America/Detroit:20260413T153000
DTEND;TZID=America/Detroit:20260413T170000
SUMMARY:Lecture / Discussion:Realigning Incentives for a More Secure Internet Ecosystem
DESCRIPTION:Professor Mingyan Liu is receiving the T.C.Chang Professorship. Reception to immediately follow in the EECS Atrium.\n\nAbstract: \nMany of the cybersecurity issues facing the modern digital society can ultimately be traced to an array of misaligned incentives. For instance\, the vast majority of the cost of a data breach is not borne by the firm suffering the breach\, but by the users and consumers whose data were stolen\; similarly\, the harm caused by software problems is\, by and large\, shouldered by consumers\, not developers. Over the past decade\, an overarching goal of my research group has been to develop innovative data analytics methodologies and policy ideas to help realign these incentives. Within this context\, I will present our recent work in new approaches to quantifying cyber risk at an organizational level\, quantifying the social cost of data breaches\, and in developing mathematical models that capture the strategic interactions and decision making among parties driven by different incentives.\nBio: \nMingyan Liu is a leading expert in sequential decision and learning theory\, game theory and incentive mechanisms\, all within the context of large-scale networked systems and with applications to cybersecurity. Technologies she developed in this space have been successfully transitioned.  She co-founded the start-up company\, QuadMetrics\, Inc.\, commercializing predictive data analytics her team developed for cyber risk quantification that resulted in the first global enterprise cybersecurity ratings system\; it was acquired by the analytics software company Fair Isaac (FICO) in 2016.  This technology has been used for enterprise risk management\, vendor management\, cyber insurance underwriting\, and most recently\, in augmenting Environmental\, Social\, and Governance (ESG) ratings.  For this she received the “Crossing the Valley of Death” PI Excellence Award from the Department of Homeland Security in 2016.\nProf. Liu joined the University of Michigan\, Ann Arbor\, in September 2000\, as an assistant professor in Electrical Engineering and Computer Science.  She was the Peter and Evelyn Fuss Chair of ECE from 2018 to 2023\, and has been the Associate Dean for Academic Affairs since 2023.  She is the recipient of the 2002 NSF CAREER Award\, the University of Michigan Elizabeth C. Crosby Research Award in 2003 and 2014\, the 2010 EECS Department Outstanding Achievement Award\, the 2015 CoE Excellence in Education Award\, the 2017 CoE Excellence in Service Award\, and the 2018 Distinguished University Innovator Award.  She has received a number of Best Paper Awards and has served on the editorial boards of IEEE and ACM Transaction. She is a Fellow of the IEEE and a member of the ACM.\nProf. Liu received an MS degree in Systems Engineering and Ph.D in Electrical Engineering from the University of Maryland\, College Park\, in 1997 and 2000\, respectively.
UID:146961-21899848@events.umich.edu
URL:https://events.umich.edu/event/146961
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Electrical And Computer Engineering
LOCATION:Electrical Engineering and Computer Science Building - 1311
CONTACT:
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260410T094510
DTSTART;TZID=America/Detroit:20260421T130000
DTEND;TZID=America/Detroit:20260421T160000
SUMMARY:Conference / Symposium:2026 Electrical and Computer Engineering (ECE) Undergraduate Research Symposium
DESCRIPTION:Please join us for the 2026 Electrical and Computer Engineering (ECE) Undergraduate Research Symposium - an engaging afternoon celebrating the innovative work of ECE undergraduates\, including members of the 2026 PURE-ECE cohort. Students will present a wide range of cutting-edge research projects through posters and discussions.\n\nEvent details\nDate: Tuesday\, April 21\, 2026  \nTime: 1:00-4:00 PM  \nLocation: EECS Atrium  \nCost: Free and open to the public (all are welcome)\n\nSchedule\n1:00-3:30 PM: Student poster session  \n3:30-4:00 PM: Awards reception (immediately following the poster session)\n\nDuring the poster session\, attendees are encouraged to browse posters\, ask questions\, and connect with students as they share their discoveries. Local alumni and industry professionals will serve as judges\, offering feedback and encouragement as students take early steps into the broader research landscape.\n\nPlease consider stopping by to support our undergraduates and see the impressive work of the next generation of ECE leaders.\n\nWe hope to see you there!
UID:147613-21901344@events.umich.edu
URL:https://events.umich.edu/event/147613
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Electrical And Computer Engineering
LOCATION:Electrical Engineering and Computer Science Building - EECS Atrium
CONTACT:
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260408T104211
DTSTART;TZID=America/Detroit:20260713T090000
DTEND;TZID=America/Detroit:20260713T160000
SUMMARY:Workshop / Seminar:AI for Scientists and Engineers Summer Academy
DESCRIPTION:Academy Overview\nThe AI for Scientists and Engineers Summer Academy is designed for academic researchers\, including university faculty\, in a wide range of domains including biological sciences\, engineering\, environmental and earth science\, physical sciences\, and social sciences. Participants will learn the mathematical foundations of machine learning (ML)\, critically assess the data used in AI models\, evaluate and validate ML model outputs\, and understand strategic considerations for incorporating AI into research workflows. The prerequisites are college level math and statistics\; prior coding experience is not required. Specific topics include supervised and unsupervised learning\, neural networks\, causal inference\, and science-informed machine learning models.\n\nThe Summer Academy consists of three weeks of instructions\, with different focuses. One can choose to attend any or all weeks\; however\, weeks 2 and 3 require some prior knowledge of AI / ML.\n\nWeek 1 (Monday\, July 13 – Friday\, July 17\, 2026): The conceptual understanding of AI and its applications in domain research.\nWeek 2 (Monday\, July 20 – Friday\, July 24\, 2026): The implementation of ML models in a Python environment.\nWeek 3 (Monday\, July 27 – Friday\, July 31\, 2026): Advanced topics of AI and its applications in domain research.
UID:147530-21901187@events.umich.edu
URL:https://events.umich.edu/event/147530
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Electrical And Computer Engineering
LOCATION:
CONTACT:
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260408T104211
DTSTART;TZID=America/Detroit:20260714T090000
DTEND;TZID=America/Detroit:20260714T160000
SUMMARY:Workshop / Seminar:AI for Scientists and Engineers Summer Academy
DESCRIPTION:Academy Overview\nThe AI for Scientists and Engineers Summer Academy is designed for academic researchers\, including university faculty\, in a wide range of domains including biological sciences\, engineering\, environmental and earth science\, physical sciences\, and social sciences. Participants will learn the mathematical foundations of machine learning (ML)\, critically assess the data used in AI models\, evaluate and validate ML model outputs\, and understand strategic considerations for incorporating AI into research workflows. The prerequisites are college level math and statistics\; prior coding experience is not required. Specific topics include supervised and unsupervised learning\, neural networks\, causal inference\, and science-informed machine learning models.\n\nThe Summer Academy consists of three weeks of instructions\, with different focuses. One can choose to attend any or all weeks\; however\, weeks 2 and 3 require some prior knowledge of AI / ML.\n\nWeek 1 (Monday\, July 13 – Friday\, July 17\, 2026): The conceptual understanding of AI and its applications in domain research.\nWeek 2 (Monday\, July 20 – Friday\, July 24\, 2026): The implementation of ML models in a Python environment.\nWeek 3 (Monday\, July 27 – Friday\, July 31\, 2026): Advanced topics of AI and its applications in domain research.
UID:147530-21901188@events.umich.edu
URL:https://events.umich.edu/event/147530
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Electrical And Computer Engineering
LOCATION:
CONTACT:
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260408T104211
DTSTART;TZID=America/Detroit:20260715T090000
DTEND;TZID=America/Detroit:20260715T160000
SUMMARY:Workshop / Seminar:AI for Scientists and Engineers Summer Academy
DESCRIPTION:Academy Overview\nThe AI for Scientists and Engineers Summer Academy is designed for academic researchers\, including university faculty\, in a wide range of domains including biological sciences\, engineering\, environmental and earth science\, physical sciences\, and social sciences. Participants will learn the mathematical foundations of machine learning (ML)\, critically assess the data used in AI models\, evaluate and validate ML model outputs\, and understand strategic considerations for incorporating AI into research workflows. The prerequisites are college level math and statistics\; prior coding experience is not required. Specific topics include supervised and unsupervised learning\, neural networks\, causal inference\, and science-informed machine learning models.\n\nThe Summer Academy consists of three weeks of instructions\, with different focuses. One can choose to attend any or all weeks\; however\, weeks 2 and 3 require some prior knowledge of AI / ML.\n\nWeek 1 (Monday\, July 13 – Friday\, July 17\, 2026): The conceptual understanding of AI and its applications in domain research.\nWeek 2 (Monday\, July 20 – Friday\, July 24\, 2026): The implementation of ML models in a Python environment.\nWeek 3 (Monday\, July 27 – Friday\, July 31\, 2026): Advanced topics of AI and its applications in domain research.
UID:147530-21901189@events.umich.edu
URL:https://events.umich.edu/event/147530
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Electrical And Computer Engineering
LOCATION:
CONTACT:
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260408T104211
DTSTART;TZID=America/Detroit:20260716T090000
DTEND;TZID=America/Detroit:20260716T160000
SUMMARY:Workshop / Seminar:AI for Scientists and Engineers Summer Academy
DESCRIPTION:Academy Overview\nThe AI for Scientists and Engineers Summer Academy is designed for academic researchers\, including university faculty\, in a wide range of domains including biological sciences\, engineering\, environmental and earth science\, physical sciences\, and social sciences. Participants will learn the mathematical foundations of machine learning (ML)\, critically assess the data used in AI models\, evaluate and validate ML model outputs\, and understand strategic considerations for incorporating AI into research workflows. The prerequisites are college level math and statistics\; prior coding experience is not required. Specific topics include supervised and unsupervised learning\, neural networks\, causal inference\, and science-informed machine learning models.\n\nThe Summer Academy consists of three weeks of instructions\, with different focuses. One can choose to attend any or all weeks\; however\, weeks 2 and 3 require some prior knowledge of AI / ML.\n\nWeek 1 (Monday\, July 13 – Friday\, July 17\, 2026): The conceptual understanding of AI and its applications in domain research.\nWeek 2 (Monday\, July 20 – Friday\, July 24\, 2026): The implementation of ML models in a Python environment.\nWeek 3 (Monday\, July 27 – Friday\, July 31\, 2026): Advanced topics of AI and its applications in domain research.
UID:147530-21901190@events.umich.edu
URL:https://events.umich.edu/event/147530
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Electrical And Computer Engineering
LOCATION:
CONTACT:
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260408T104211
DTSTART;TZID=America/Detroit:20260717T090000
DTEND;TZID=America/Detroit:20260717T160000
SUMMARY:Workshop / Seminar:AI for Scientists and Engineers Summer Academy
DESCRIPTION:Academy Overview\nThe AI for Scientists and Engineers Summer Academy is designed for academic researchers\, including university faculty\, in a wide range of domains including biological sciences\, engineering\, environmental and earth science\, physical sciences\, and social sciences. Participants will learn the mathematical foundations of machine learning (ML)\, critically assess the data used in AI models\, evaluate and validate ML model outputs\, and understand strategic considerations for incorporating AI into research workflows. The prerequisites are college level math and statistics\; prior coding experience is not required. Specific topics include supervised and unsupervised learning\, neural networks\, causal inference\, and science-informed machine learning models.\n\nThe Summer Academy consists of three weeks of instructions\, with different focuses. One can choose to attend any or all weeks\; however\, weeks 2 and 3 require some prior knowledge of AI / ML.\n\nWeek 1 (Monday\, July 13 – Friday\, July 17\, 2026): The conceptual understanding of AI and its applications in domain research.\nWeek 2 (Monday\, July 20 – Friday\, July 24\, 2026): The implementation of ML models in a Python environment.\nWeek 3 (Monday\, July 27 – Friday\, July 31\, 2026): Advanced topics of AI and its applications in domain research.
UID:147530-21901191@events.umich.edu
URL:https://events.umich.edu/event/147530
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Electrical And Computer Engineering
LOCATION:
CONTACT:
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260408T104211
DTSTART;TZID=America/Detroit:20260720T090000
DTEND;TZID=America/Detroit:20260720T160000
SUMMARY:Workshop / Seminar:AI for Scientists and Engineers Summer Academy
DESCRIPTION:Academy Overview\nThe AI for Scientists and Engineers Summer Academy is designed for academic researchers\, including university faculty\, in a wide range of domains including biological sciences\, engineering\, environmental and earth science\, physical sciences\, and social sciences. Participants will learn the mathematical foundations of machine learning (ML)\, critically assess the data used in AI models\, evaluate and validate ML model outputs\, and understand strategic considerations for incorporating AI into research workflows. The prerequisites are college level math and statistics\; prior coding experience is not required. Specific topics include supervised and unsupervised learning\, neural networks\, causal inference\, and science-informed machine learning models.\n\nThe Summer Academy consists of three weeks of instructions\, with different focuses. One can choose to attend any or all weeks\; however\, weeks 2 and 3 require some prior knowledge of AI / ML.\n\nWeek 1 (Monday\, July 13 – Friday\, July 17\, 2026): The conceptual understanding of AI and its applications in domain research.\nWeek 2 (Monday\, July 20 – Friday\, July 24\, 2026): The implementation of ML models in a Python environment.\nWeek 3 (Monday\, July 27 – Friday\, July 31\, 2026): Advanced topics of AI and its applications in domain research.
UID:147530-21901194@events.umich.edu
URL:https://events.umich.edu/event/147530
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Electrical And Computer Engineering
LOCATION:
CONTACT:
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260408T104211
DTSTART;TZID=America/Detroit:20260721T090000
DTEND;TZID=America/Detroit:20260721T160000
SUMMARY:Workshop / Seminar:AI for Scientists and Engineers Summer Academy
DESCRIPTION:Academy Overview\nThe AI for Scientists and Engineers Summer Academy is designed for academic researchers\, including university faculty\, in a wide range of domains including biological sciences\, engineering\, environmental and earth science\, physical sciences\, and social sciences. Participants will learn the mathematical foundations of machine learning (ML)\, critically assess the data used in AI models\, evaluate and validate ML model outputs\, and understand strategic considerations for incorporating AI into research workflows. The prerequisites are college level math and statistics\; prior coding experience is not required. Specific topics include supervised and unsupervised learning\, neural networks\, causal inference\, and science-informed machine learning models.\n\nThe Summer Academy consists of three weeks of instructions\, with different focuses. One can choose to attend any or all weeks\; however\, weeks 2 and 3 require some prior knowledge of AI / ML.\n\nWeek 1 (Monday\, July 13 – Friday\, July 17\, 2026): The conceptual understanding of AI and its applications in domain research.\nWeek 2 (Monday\, July 20 – Friday\, July 24\, 2026): The implementation of ML models in a Python environment.\nWeek 3 (Monday\, July 27 – Friday\, July 31\, 2026): Advanced topics of AI and its applications in domain research.
UID:147530-21901195@events.umich.edu
URL:https://events.umich.edu/event/147530
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Electrical And Computer Engineering
LOCATION:
CONTACT:
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260408T104211
DTSTART;TZID=America/Detroit:20260722T090000
DTEND;TZID=America/Detroit:20260722T160000
SUMMARY:Workshop / Seminar:AI for Scientists and Engineers Summer Academy
DESCRIPTION:Academy Overview\nThe AI for Scientists and Engineers Summer Academy is designed for academic researchers\, including university faculty\, in a wide range of domains including biological sciences\, engineering\, environmental and earth science\, physical sciences\, and social sciences. Participants will learn the mathematical foundations of machine learning (ML)\, critically assess the data used in AI models\, evaluate and validate ML model outputs\, and understand strategic considerations for incorporating AI into research workflows. The prerequisites are college level math and statistics\; prior coding experience is not required. Specific topics include supervised and unsupervised learning\, neural networks\, causal inference\, and science-informed machine learning models.\n\nThe Summer Academy consists of three weeks of instructions\, with different focuses. One can choose to attend any or all weeks\; however\, weeks 2 and 3 require some prior knowledge of AI / ML.\n\nWeek 1 (Monday\, July 13 – Friday\, July 17\, 2026): The conceptual understanding of AI and its applications in domain research.\nWeek 2 (Monday\, July 20 – Friday\, July 24\, 2026): The implementation of ML models in a Python environment.\nWeek 3 (Monday\, July 27 – Friday\, July 31\, 2026): Advanced topics of AI and its applications in domain research.
UID:147530-21901196@events.umich.edu
URL:https://events.umich.edu/event/147530
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Electrical And Computer Engineering
LOCATION:
CONTACT:
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260408T104211
DTSTART;TZID=America/Detroit:20260723T090000
DTEND;TZID=America/Detroit:20260723T160000
SUMMARY:Workshop / Seminar:AI for Scientists and Engineers Summer Academy
DESCRIPTION:Academy Overview\nThe AI for Scientists and Engineers Summer Academy is designed for academic researchers\, including university faculty\, in a wide range of domains including biological sciences\, engineering\, environmental and earth science\, physical sciences\, and social sciences. Participants will learn the mathematical foundations of machine learning (ML)\, critically assess the data used in AI models\, evaluate and validate ML model outputs\, and understand strategic considerations for incorporating AI into research workflows. The prerequisites are college level math and statistics\; prior coding experience is not required. Specific topics include supervised and unsupervised learning\, neural networks\, causal inference\, and science-informed machine learning models.\n\nThe Summer Academy consists of three weeks of instructions\, with different focuses. One can choose to attend any or all weeks\; however\, weeks 2 and 3 require some prior knowledge of AI / ML.\n\nWeek 1 (Monday\, July 13 – Friday\, July 17\, 2026): The conceptual understanding of AI and its applications in domain research.\nWeek 2 (Monday\, July 20 – Friday\, July 24\, 2026): The implementation of ML models in a Python environment.\nWeek 3 (Monday\, July 27 – Friday\, July 31\, 2026): Advanced topics of AI and its applications in domain research.
UID:147530-21901197@events.umich.edu
URL:https://events.umich.edu/event/147530
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Electrical And Computer Engineering
LOCATION:
CONTACT:
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260408T104211
DTSTART;TZID=America/Detroit:20260724T090000
DTEND;TZID=America/Detroit:20260724T160000
SUMMARY:Workshop / Seminar:AI for Scientists and Engineers Summer Academy
DESCRIPTION:Academy Overview\nThe AI for Scientists and Engineers Summer Academy is designed for academic researchers\, including university faculty\, in a wide range of domains including biological sciences\, engineering\, environmental and earth science\, physical sciences\, and social sciences. Participants will learn the mathematical foundations of machine learning (ML)\, critically assess the data used in AI models\, evaluate and validate ML model outputs\, and understand strategic considerations for incorporating AI into research workflows. The prerequisites are college level math and statistics\; prior coding experience is not required. Specific topics include supervised and unsupervised learning\, neural networks\, causal inference\, and science-informed machine learning models.\n\nThe Summer Academy consists of three weeks of instructions\, with different focuses. One can choose to attend any or all weeks\; however\, weeks 2 and 3 require some prior knowledge of AI / ML.\n\nWeek 1 (Monday\, July 13 – Friday\, July 17\, 2026): The conceptual understanding of AI and its applications in domain research.\nWeek 2 (Monday\, July 20 – Friday\, July 24\, 2026): The implementation of ML models in a Python environment.\nWeek 3 (Monday\, July 27 – Friday\, July 31\, 2026): Advanced topics of AI and its applications in domain research.
UID:147530-21901198@events.umich.edu
URL:https://events.umich.edu/event/147530
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Electrical And Computer Engineering
LOCATION:
CONTACT:
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260408T104211
DTSTART;TZID=America/Detroit:20260727T090000
DTEND;TZID=America/Detroit:20260727T160000
SUMMARY:Workshop / Seminar:AI for Scientists and Engineers Summer Academy
DESCRIPTION:Academy Overview\nThe AI for Scientists and Engineers Summer Academy is designed for academic researchers\, including university faculty\, in a wide range of domains including biological sciences\, engineering\, environmental and earth science\, physical sciences\, and social sciences. Participants will learn the mathematical foundations of machine learning (ML)\, critically assess the data used in AI models\, evaluate and validate ML model outputs\, and understand strategic considerations for incorporating AI into research workflows. The prerequisites are college level math and statistics\; prior coding experience is not required. Specific topics include supervised and unsupervised learning\, neural networks\, causal inference\, and science-informed machine learning models.\n\nThe Summer Academy consists of three weeks of instructions\, with different focuses. One can choose to attend any or all weeks\; however\, weeks 2 and 3 require some prior knowledge of AI / ML.\n\nWeek 1 (Monday\, July 13 – Friday\, July 17\, 2026): The conceptual understanding of AI and its applications in domain research.\nWeek 2 (Monday\, July 20 – Friday\, July 24\, 2026): The implementation of ML models in a Python environment.\nWeek 3 (Monday\, July 27 – Friday\, July 31\, 2026): Advanced topics of AI and its applications in domain research.
UID:147530-21901201@events.umich.edu
URL:https://events.umich.edu/event/147530
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Electrical And Computer Engineering
LOCATION:
CONTACT:
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260408T104211
DTSTART;TZID=America/Detroit:20260728T090000
DTEND;TZID=America/Detroit:20260728T160000
SUMMARY:Workshop / Seminar:AI for Scientists and Engineers Summer Academy
DESCRIPTION:Academy Overview\nThe AI for Scientists and Engineers Summer Academy is designed for academic researchers\, including university faculty\, in a wide range of domains including biological sciences\, engineering\, environmental and earth science\, physical sciences\, and social sciences. Participants will learn the mathematical foundations of machine learning (ML)\, critically assess the data used in AI models\, evaluate and validate ML model outputs\, and understand strategic considerations for incorporating AI into research workflows. The prerequisites are college level math and statistics\; prior coding experience is not required. Specific topics include supervised and unsupervised learning\, neural networks\, causal inference\, and science-informed machine learning models.\n\nThe Summer Academy consists of three weeks of instructions\, with different focuses. One can choose to attend any or all weeks\; however\, weeks 2 and 3 require some prior knowledge of AI / ML.\n\nWeek 1 (Monday\, July 13 – Friday\, July 17\, 2026): The conceptual understanding of AI and its applications in domain research.\nWeek 2 (Monday\, July 20 – Friday\, July 24\, 2026): The implementation of ML models in a Python environment.\nWeek 3 (Monday\, July 27 – Friday\, July 31\, 2026): Advanced topics of AI and its applications in domain research.
UID:147530-21901202@events.umich.edu
URL:https://events.umich.edu/event/147530
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Electrical And Computer Engineering
LOCATION:
CONTACT:
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260408T104211
DTSTART;TZID=America/Detroit:20260729T090000
DTEND;TZID=America/Detroit:20260729T160000
SUMMARY:Workshop / Seminar:AI for Scientists and Engineers Summer Academy
DESCRIPTION:Academy Overview\nThe AI for Scientists and Engineers Summer Academy is designed for academic researchers\, including university faculty\, in a wide range of domains including biological sciences\, engineering\, environmental and earth science\, physical sciences\, and social sciences. Participants will learn the mathematical foundations of machine learning (ML)\, critically assess the data used in AI models\, evaluate and validate ML model outputs\, and understand strategic considerations for incorporating AI into research workflows. The prerequisites are college level math and statistics\; prior coding experience is not required. Specific topics include supervised and unsupervised learning\, neural networks\, causal inference\, and science-informed machine learning models.\n\nThe Summer Academy consists of three weeks of instructions\, with different focuses. One can choose to attend any or all weeks\; however\, weeks 2 and 3 require some prior knowledge of AI / ML.\n\nWeek 1 (Monday\, July 13 – Friday\, July 17\, 2026): The conceptual understanding of AI and its applications in domain research.\nWeek 2 (Monday\, July 20 – Friday\, July 24\, 2026): The implementation of ML models in a Python environment.\nWeek 3 (Monday\, July 27 – Friday\, July 31\, 2026): Advanced topics of AI and its applications in domain research.
UID:147530-21901203@events.umich.edu
URL:https://events.umich.edu/event/147530
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Electrical And Computer Engineering
LOCATION:
CONTACT:
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260408T104211
DTSTART;TZID=America/Detroit:20260730T090000
DTEND;TZID=America/Detroit:20260730T160000
SUMMARY:Workshop / Seminar:AI for Scientists and Engineers Summer Academy
DESCRIPTION:Academy Overview\nThe AI for Scientists and Engineers Summer Academy is designed for academic researchers\, including university faculty\, in a wide range of domains including biological sciences\, engineering\, environmental and earth science\, physical sciences\, and social sciences. Participants will learn the mathematical foundations of machine learning (ML)\, critically assess the data used in AI models\, evaluate and validate ML model outputs\, and understand strategic considerations for incorporating AI into research workflows. The prerequisites are college level math and statistics\; prior coding experience is not required. Specific topics include supervised and unsupervised learning\, neural networks\, causal inference\, and science-informed machine learning models.\n\nThe Summer Academy consists of three weeks of instructions\, with different focuses. One can choose to attend any or all weeks\; however\, weeks 2 and 3 require some prior knowledge of AI / ML.\n\nWeek 1 (Monday\, July 13 – Friday\, July 17\, 2026): The conceptual understanding of AI and its applications in domain research.\nWeek 2 (Monday\, July 20 – Friday\, July 24\, 2026): The implementation of ML models in a Python environment.\nWeek 3 (Monday\, July 27 – Friday\, July 31\, 2026): Advanced topics of AI and its applications in domain research.
UID:147530-21901204@events.umich.edu
URL:https://events.umich.edu/event/147530
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Electrical And Computer Engineering
LOCATION:
CONTACT:
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260408T104211
DTSTART;TZID=America/Detroit:20260731T090000
DTEND;TZID=America/Detroit:20260731T160000
SUMMARY:Workshop / Seminar:AI for Scientists and Engineers Summer Academy
DESCRIPTION:Academy Overview\nThe AI for Scientists and Engineers Summer Academy is designed for academic researchers\, including university faculty\, in a wide range of domains including biological sciences\, engineering\, environmental and earth science\, physical sciences\, and social sciences. Participants will learn the mathematical foundations of machine learning (ML)\, critically assess the data used in AI models\, evaluate and validate ML model outputs\, and understand strategic considerations for incorporating AI into research workflows. The prerequisites are college level math and statistics\; prior coding experience is not required. Specific topics include supervised and unsupervised learning\, neural networks\, causal inference\, and science-informed machine learning models.\n\nThe Summer Academy consists of three weeks of instructions\, with different focuses. One can choose to attend any or all weeks\; however\, weeks 2 and 3 require some prior knowledge of AI / ML.\n\nWeek 1 (Monday\, July 13 – Friday\, July 17\, 2026): The conceptual understanding of AI and its applications in domain research.\nWeek 2 (Monday\, July 20 – Friday\, July 24\, 2026): The implementation of ML models in a Python environment.\nWeek 3 (Monday\, July 27 – Friday\, July 31\, 2026): Advanced topics of AI and its applications in domain research.
UID:147530-21901205@events.umich.edu
URL:https://events.umich.edu/event/147530
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Electrical And Computer Engineering
LOCATION:
CONTACT:
END:VEVENT
END:VCALENDAR