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X-WR-CALDESC:Events for Columbia Entrepreneurship
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DTSTART;TZID=UTC:20191001T110000
DTEND;TZID=UTC:20191001T123000
DTSTAMP:20260503T074309
CREATED:20190930T143447Z
LAST-MODIFIED:20190930T143447Z
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SUMMARY:Data Science Institute-Industry-Innovation Seminar: Goldman Sachs
DESCRIPTION:Free\, open to the public\, and does not require registration.\n\n\n\n\n\nTuesday\, October 1\, 2019\n\n11:00 AM – 12:30 PM\n\n \nSchapiro CEPSR – Davis Auditorium\n\n530 West 120th Street\, 4th Floor\nNew York\, NY 10027\n\n \nSpeaker #1: Mayur Thakur\, Head of the Data Analytics Group\, Global Compliance Division \nBiography: Mayur Thakur is head of the Data Analytics Group in the Global Compliance Division. He joined Goldman Sachs as a managing director in 2014. Prior to joining the firm\, Mayur worked at Google\, where he designed search algorithms for more than seven years. Previously\, he was an assistant professor of computer science at the University of Missouri. Mayur earned a PhD in Computer Science from the University of Rochester in 2004 and a BTech in Computer Science and Engineering from the Indian Institute of Technology\, Delhi\, in 1999.\n \nSpeaker #2: Suruchi Deodhar\, Vice President\, Goldman Sachs \nBiography: Suruchi is a Vice President at Goldman Sachs\, leading the Communication Risk Engineering team in the Global Compliance Division. She joined the firm in 2015 after completing her PhD in Computer Science from Virginia Tech. Her PhD research focused on developing large scale data integration techniques and big-data systems for evaluation and forecasting of epidemics.
URL:https://entrepreneurship.columbia.edu/event/data-science-institute-industry-innovation-seminar-goldman-sachs/
CATEGORIES:Columbia Data Science Institute
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BEGIN:VEVENT
DTSTART;TZID=UTC:20191009T173000
DTEND;TZID=UTC:20191009T183000
DTSTAMP:20260503T074309
CREATED:20190930T143616Z
LAST-MODIFIED:20190930T143616Z
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SUMMARY:NYC DS3 Seminar - How Can Machine-Learning Methods Help to Make Scientific Inferences?
DESCRIPTION:Free\, open to the public\, and does not require registration. \n\n\nWednesday\, October 9\, 2019\n\n5:00 PM – 5:30 PM: Welcome\n5:30 PM – 6:30 PM: Talk\n\n \nSchapiro CEPSR – Davis Auditorium\n\n530 West 120th Street\, 4th Floor\nNew York\, NY 10027\n\n \nTalk Abstract: Machine learning in the form of standard supervised classification algorithms is not all that useful or productive in the natural sciences\, because the (effective) goals of these algorithms aren’t very similar to the goals of scientific inquiry. However\, the ML community has delivered great ideas and methods for building\, fitting\, and validating extremely flexible models. I argue that if we want to exploit the good things about ML but achieve truly scientific goals\, we need to do two things: We need to augment or modify the (currently trivial) causal structure of the ML methods to represent our very strong domain-specific beliefs about how the data are generated. And we need to be careful to use ML methods only in the parts of our problems for which we don’t care about the latent structure or parameters (that is\, use them to model nuisances\, not use them to do everything). I give examples from stellar astrophysics where adding ML components into larger causal models has created new scientific capabilities.\n \nSpeaker: Professor David W. Hogg\, NYU|MPIA|Flatiron\n \nSpeaker Biography: After a Ph.D. in Physics from Caltech and a few years at the Institute for Advanced Study\, Hogg came to New York University in 2001 and was granted tenure there in 2007. His work at NYU has ranged from fundamental cosmological measurements to stellar dynamics to planet search and characterization. His work includes a significant engineering component. He spends a part of each year at the Max Planck Institute for Astronomy in Heidelberg\, Germany\, where he is a visiting member of the research staff\, and a part of each week at the Flatiron Institute of the Simons Foundation\, where he is a group leader.
URL:https://entrepreneurship.columbia.edu/event/nyc-ds3-seminar-how-can-machine-learning-methods-help-to-make-scientific-inferences/
CATEGORIES:Columbia Data Science Institute
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BEGIN:VEVENT
DTSTART;TZID=UTC:20191025T140000
DTEND;TZID=UTC:20191025T153000
DTSTAMP:20260503T074309
CREATED:20190930T143738Z
LAST-MODIFIED:20190930T143738Z
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SUMMARY:FinTech Lecture Series - Where on Earth is AI Headed?
DESCRIPTION:Free\, open to the public\, and does not require registration.\n\n\n\nWhere on Earth is AI Headed?\n\n\nFriday\, October 25\, 2019\n\n2:00 PM – Lecture\n\n3:00 PM – Reception\n\n \nSchapiro CEPSR – Davis Auditorium\n\n530 West 120th Street\, 4th Floor\nNew York\, NY 10027\n\n \nSpeaker: Tom M. Mitchell – Founders University Professor\, School of Computer Science\n\n \nSpeaker Biography: Tom M. Mitchell is the Founders University Professor in the School of Computer Science at Carnegie Mellon University\, where he created the world’s first academic Machine Learning Department. Mitchell’s research explores machine learning theory\, algorithms and applications\, as well as the impact of AI on society. He has testified to the U.S. Congress several times on AI impacts on society\, and he co-chaired the 2017 U.S. National Academy study on “Information Technology\, Automation\, and the U.S. Workforce.” Mitchell advises a variety of young and old companies internationally on their AI product and business strategies\, and his research has been featured in popular press from the New York Times\, to CCTV (China’s national television network)\, to CBS’s 60 Minutes. He is a member of the U.S. National Academy of Engineering\, a member of the American Academy of Arts and Sciences\, and a Fellow and Past President of the Association for the Advancement of Artificial Intelligence (AAAI).
URL:https://entrepreneurship.columbia.edu/event/fintech-lecture-series-where-on-earth-is-ai-headed/
CATEGORIES:Columbia Data Science Institute
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