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DTSTART;TZID=UTC:20180216T120000
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SUMMARY:Data for Good
DESCRIPTION:OPEN TO ALL\n\nFriday\, February 16\, 2018\n12:00PM-1:30PM\nCEPSR 750\n\n\nIn this talk\, we discuss a systematic evaluation of the impact of financial regulations concerning the collateralization of derivative trades on systemic risk – a topic that has been vigorously discussed since the financial crisis in 2007/08. Experts often disagree on the efficacy of these regulations. Compounding this problem banks regard their trade data required for a full analysis as proprietary. We adapt a simulation technology combining advances in graph theory to randomly generate entire financial systems sampled from realistic distributions with a novel open source risk engine to compute risks in financial systems under different regulations. This allows us to consistently evaluate\, predict and optimize the impact of financial regulations on all levels – from a single trade to systemic risk – before it is implemented. The resulting data set is accessible to contemporary data science techniques like data mining\, anomaly detection and visualization. We find that collateralization reduces the costs of resolving a financial system in crisis\, yet it does not change the distribution of those costs and can have adverse effects on individual participants in extreme situations. \nRemote participants can register for online streaming in advance at: \nhttps://columbiauniversity.zoom.us/meeting/register/4e9e74be7f16abb08c34be5db4a05ad8 \nJointly sponsored by the Data Science Institute and the Institute for Social and Economic Research and Policy
URL:https://entrepreneurship.columbia.edu/event/data-for-good/
CATEGORIES:Columbia Data Science Institute
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BEGIN:VEVENT
DTSTART;TZID=UTC:20180220T173000
DTEND;TZID=UTC:20180220T193000
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SUMMARY:Data for Good
DESCRIPTION:LOCATION\nBloomberg Center\, Cornell Tech\n2 West Loop Road\nNew York\, NY 10044 \nFebruary 20\, 2018\nReception: 5:30pm-6:30pm\nTalk: 6:30pm-7:30pm  \nEvery field has data. We use data to discover new knowledge\, to interpret the world\, to make decisions\, and even to predict the future. The recent convergence of big data\, cloud computing\, and novel machine learning algorithms and statistical methods is causing an explosive interest in data science and its applicability to all fields. This convergence has already enabled the automation of some tasks that better human performance. The novel capabilities we derive from data science will drive our cars\, treat disease\, and keep us safe. At the same time\, such capabilities risk leading to biased\, inappropriate\, or unintended action. The design of data science solutions requires both excellence in the fundamentals of the field and expertise to develop applications which meet human challenges without creating even greater risk. \nThe Data Science Institute at Columbia University promotes “Data for Good”: using data to address societal challenges and bringing humanistic perspectives as—not after—new science and technology is invented. Started in 2012\, the Institute is now a university-level institute representing over 250 affiliated faculty from 12 different schools across campus. Data science literally touches every corner of the university. \nIn this talk\, Jeannette Wing\, Director of the Data Science Institute\, will present the vision on how the Institute plans to address some of the key challenges and opportunities of data science\, highlighting educational and research activities\, as well as future initiatives that may directly impact the data science community at Columbia\, New York City\, and beyond.
URL:https://entrepreneurship.columbia.edu/event/data-for-good-2/
CATEGORIES:Columbia Data Science Institute
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DTSTART;TZID=UTC:20180227T110000
DTEND;TZID=UTC:20180227T121500
DTSTAMP:20260509T111815
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SUMMARY:Data-Driven Discovery and Decision Making - A Paradigm Shift for Large-Scale Experimental Science
DESCRIPTION:Tuesday\, February 27\, 2018\n11:00AM-12:15PM\nCEPSR 414\, DAVIS AUDITORIUM\n\n“Data-Driven Discovery and Decision Making – A Paradigm Shift for Large-Scale Experimental Science” \nNew instrument technologies are enabling a new generation of in-situ and in-operando experiments\, with extremely fine spatial and temporal resolution\, that allows researchers to observe as physics\, chemistry and biology are happening. These new methodologies go hand in hand with an exponential growth in data volumes and rates – petabyte scale data collections and terabyte/sec. At the same time\, scientists are pushing for a paradigm shift. As they can now observe processes in intricate details\, they want to analyze\, interpret and control those processes. Given the multitude of voluminous\, heterogenous data streams involved in every single experiment\, novel real-time\, data-driven analysis and decision support approaches are needed to realize their vision. This talk will discuss state-of-the-art streaming analysis for experimental facilities\, its challenges and early successes. It will present work currently carried out at Brookhaven National Laboratory and identify areas for collaboration. \nKerstin Kleese van Dam is the director of the Computational Science Initiative (CSI) at the Department of Energy’s Brookhaven National Laboratory (BNL)\, in Long Island\, NY. With 100 petabytes\, BNL hosts the second largest scientific data archive in the US and the fourth largest in the world\, it processes annually in access of 400 petabytes of scientific results. \nCSI conducts leading edge computer science and applied mathematics research to address the associated analysis challenges\, specific focus areas are machine learning\, visual analytics and programming models. \n \nBefore she joined BNL Kerstin was associated division director at Pacific Northwest National Laboratory\, Director of Computing at University College London Medical School and Data Management Group Lead at the Science and Technology Facilities Council in the UK.\n \nHosted by Prof. Steven Nowick (Dept. of Computer Science)
URL:https://entrepreneurship.columbia.edu/event/data-driven-discovery-decision-making-paradigm-shift-large-scale-experimental-science/
CATEGORIES:Columbia Data Science Institute
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