BEGIN:VCALENDAR VERSION:2.0 PRODID:-//Drupal iCal API//EN X-WR-CALNAME:Events items teaser X-WR-TIMEZONE:America/Toronto BEGIN:VTIMEZONE TZID:America/Toronto X-LIC-LOCATION:America/Toronto BEGIN:DAYLIGHT TZNAME:EDT TZOFFSETFROM:-0500 TZOFFSETTO:-0400 DTSTART:20240310T070000 END:DAYLIGHT BEGIN:DAYLIGHT TZNAME:EDT TZOFFSETFROM:-0500 TZOFFSETTO:-0400 DTSTART:20210314T070000 END:DAYLIGHT BEGIN:DAYLIGHT TZNAME:EDT TZOFFSETFROM:-0500 TZOFFSETTO:-0400 DTSTART:20230312T070000 END:DAYLIGHT BEGIN:DAYLIGHT TZNAME:EDT TZOFFSETFROM:-0500 TZOFFSETTO:-0400 DTSTART:20200308T070000 END:DAYLIGHT BEGIN:DAYLIGHT TZNAME:EDT TZOFFSETFROM:-0500 TZOFFSETTO:-0400 DTSTART:20190310T070000 END:DAYLIGHT BEGIN:DAYLIGHT TZNAME:EDT TZOFFSETFROM:-0500 TZOFFSETTO:-0400 DTSTART:20180311T070000 END:DAYLIGHT BEGIN:DAYLIGHT TZNAME:EDT TZOFFSETFROM:-0500 TZOFFSETTO:-0400 DTSTART:20160313T070000 END:DAYLIGHT BEGIN:STANDARD TZNAME:EST TZOFFSETFROM:-0400 TZOFFSETTO:-0500 DTSTART:20241103T060000 END:STANDARD BEGIN:STANDARD TZNAME:EST TZOFFSETFROM:-0400 TZOFFSETTO:-0500 DTSTART:20211107T060000 END:STANDARD BEGIN:STANDARD TZNAME:EST TZOFFSETFROM:-0400 TZOFFSETTO:-0500 DTSTART:20221106T060000 END:STANDARD BEGIN:STANDARD TZNAME:EST TZOFFSETFROM:-0400 TZOFFSETTO:-0500 DTSTART:20191103T060000 END:STANDARD BEGIN:STANDARD TZNAME:EST TZOFFSETFROM:-0400 TZOFFSETTO:-0500 DTSTART:20181104T060000 END:STANDARD BEGIN:STANDARD TZNAME:EST TZOFFSETFROM:-0400 TZOFFSETTO:-0500 DTSTART:20171105T060000 END:STANDARD BEGIN:STANDARD TZNAME:EST TZOFFSETFROM:-0400 TZOFFSETTO:-0500 DTSTART:20151101T060000 END:STANDARD END:VTIMEZONE BEGIN:VEVENT UID:682923df3ddb5 DTSTART;TZID=America/Toronto:20241129T120000 SEQUENCE:0 TRANSP:TRANSPARENT DTEND;TZID=America/Toronto:20241129T133000 URL:/statistics-and-actuarial-science/events/mactsc-and -mqf-info-session SUMMARY:MActSc and MQF Info Session CLASS:PUBLIC DESCRIPTION:Summary \n\nJOIN US ON NOVEMBER 29TH TO LEARN MORE ABOUT OUR MA STER OF ACTUARIAL\nSCIENCE (MACTSC) AND MASTER OF QUANTITATIVE FINANCE (MQ F) PROGRAMS. \n\nRegistration is required for each session\, sign up at t he links below:\n\nMActSc session: Friday\, November 29th\, 12:00pm\n[http s://uwaterloo.ca/statistics-and-actuarial-science/form/mactsc-info-session ]\nMQF session: Friday\, November 29th\, 12:45pm\n[/st atistics-and-actuarial-science/form/mqf-info-session]\n DTSTAMP:20250518T000343Z END:VEVENT BEGIN:VEVENT UID:682923df41b13 DTSTART;TZID=America/Toronto:20241112T090000 SEQUENCE:0 TRANSP:TRANSPARENT DTEND;TZID=America/Toronto:20241112T100000 URL:/statistics-and-actuarial-science/events/mactsc-inf o-session SUMMARY:Master of Actuarial Science (MActSc) Info Session CLASS:PUBLIC DESCRIPTION:Summary \n\nJoin us on November 12th\, 2024\, to learn more abo ut how our program\ncan help you become a successful actuary. \n\nAM sess ion registration (9:00am EST)\n[https://www.ticketfi.com/event/5941/master -of-actuarial-science-info-session-am]\n\nPM session registration (2:30pm EST\n[https://www.ticketfi.com/event/5940/master-of-actuarial-science-info -session-pm])\n DTSTAMP:20250518T000343Z END:VEVENT BEGIN:VEVENT UID:682923df42524 DTSTART;TZID=America/Toronto:20241112T143000 SEQUENCE:0 TRANSP:TRANSPARENT DTEND;TZID=America/Toronto:20241112T153000 URL:/statistics-and-actuarial-science/events/mactsc-inf o-session SUMMARY:Master of Actuarial Science (MActSc) Info Session CLASS:PUBLIC DESCRIPTION:Summary \n\nJoin us on November 12th\, 2024\, to learn more abo ut how our program\ncan help you become a successful actuary. \n\nAM sess ion registration (9:00am EST)\n[https://www.ticketfi.com/event/5941/master -of-actuarial-science-info-session-am]\n\nPM session registration (2:30pm EST\n[https://www.ticketfi.com/event/5940/master-of-actuarial-science-info -session-pm])\n DTSTAMP:20250518T000343Z END:VEVENT BEGIN:VEVENT UID:682923df42c8c DTSTART;TZID=America/Toronto:20211210T120000 SEQUENCE:0 TRANSP:TRANSPARENT DTEND;TZID=America/Toronto:20211210T120000 URL:/statistics-and-actuarial-science/events/department -seminar-xiaowu-dai SUMMARY:Department seminar by Xiaowu Dai CLASS:PUBLIC DESCRIPTION:Summary \n\nPLEASE NOTE: This seminar will be given online.\n\ nDepartment seminar\n\nXIAOWU DAI\n_University of California\, Berkeley_\n \nLink to join seminar: Hosted on Zoom\n[https://uwaterloo.zoom.us/j/9293 9135090?pwd=YXExTXJEaytjWnNZNjFMU09nWUoxQT09]\n\nSTATISTICAL LEARNING AND MATCHING MARKETS\n DTSTAMP:20250518T000343Z END:VEVENT BEGIN:VEVENT UID:682923df435b2 DTSTART;TZID=America/Toronto:20211206T110000 SEQUENCE:0 TRANSP:TRANSPARENT DTEND;TZID=America/Toronto:20211206T110000 URL:/statistics-and-actuarial-science/events/department -seminar-lisa-gao SUMMARY:Department seminar by Lisa Gao CLASS:PUBLIC DESCRIPTION:Summary \n\nPLEASE NOTE: This seminar will be given online.\n\ nDepartment seminar\n\nLISA GAO\n_University of Wisconsin-Madison_\n\nLink to join seminar: Hosted on Zoom\n[https://uwaterloo.zoom.us/j/9854990083 2?pwd=Q2RZWFVJT08rWVZuM3V1L01FTGR2Zz09]\n\nA MARKED SPATIAL POINT PROCESS FOR INSURANCE CLAIMS MANAGEMENT\n DTSTAMP:20250518T000343Z END:VEVENT BEGIN:VEVENT UID:682923df43e4d DTSTART;TZID=America/Toronto:20231027T080000 SEQUENCE:0 TRANSP:TRANSPARENT DTEND;TZID=America/Toronto:20231028T170000 URL:/statistics-and-actuarial-science/events/student-co nference SUMMARY:À¶Ý®ÊÓƵ Student Conference in Statistics\, Actuarial Science and\n Finance CLASS:PUBLIC DESCRIPTION:Summary \n\nÀ¶Ý®ÊÓƵ Student Conference in Statistics\, Actuari al Science and\nFinance\n DTSTAMP:20250518T000343Z END:VEVENT BEGIN:VEVENT UID:682923df4462c DTSTART;TZID=America/Toronto:20200910T160000 SEQUENCE:0 TRANSP:TRANSPARENT DTEND;TZID=America/Toronto:20200910T160000 URL:/statistics-and-actuarial-science/events/department -seminar-emma-jingfei-zhang-miami-university SUMMARY:Department seminar by Emma Jingfei Zhang\, Miami University CLASS:PUBLIC DESCRIPTION:Summary \n\nNETWORK RESPONSE REGRESSION FOR MODELING POPULATION OF NETWORKS WITH\nCOVARIATES\n\n-------------------------\n\nMultiple-net work data are fast emerging in recent years\, where a\nseparate network ov er a common set of nodes is measured for each\nindividual subject\, along with rich subject covariates information.\nExisting network analysis metho ds have primarily focused on modeling a\nsingle network\, and are not dire ctly applicable to multiple networks\nwith subject covariates.\n\nIn this talk\, we present a new network response regression model\,\nwhere the obs erved networks are treated as matrix-valued responses\,\nand the individua l covariates as predictors. The new model\ncharacterizes the population-le vel connectivity pattern through a\nlow-rank intercept matrix\, and the pa rsimonious effects of subject\ncovariates on the network through a sparse slope tensor. We formulate\nthe parameter estimation as a non-convex optim ization problem\, and\ndevelop an efficient alternating gradient descent a lgorithm. We\nestablish the non-asymptotic error bound for the actual esti mator from\nour optimization algorithm. Built upon this error bound\, we d erive the\nstrong consistency for network community recovery\, as well as the edge\nselection consistency. We demonstrate the efficacy of our method \nthrough intensive simulations and two brain connectivity studies.\n\nJoi n Zoom Meeting\n[https://zoom.us/j/8442836948?pwd=MVdCUFFCbVFuSzduQjhDQnNN Z3J1QT09]\n\nMeeting ID: 844 283 6948\nPasscode: 318995\n DTSTAMP:20250518T000343Z END:VEVENT BEGIN:VEVENT UID:682923df4508b DTSTART;TZID=America/Toronto:20190425T160000 SEQUENCE:0 TRANSP:TRANSPARENT DTEND;TZID=America/Toronto:20190425T160000 URL:/statistics-and-actuarial-science/events/david-spro tt-distinguished-lecture-damir-filipovic-epfl-and SUMMARY:David Sprott Distinguished Lecture by Damir Filipovic\, EPFL and Sw iss\nFinance Institute Senior Chair CLASS:PUBLIC DESCRIPTION:Summary \n\nA MACHINE LEARNING APPROACH TO PORTFOLIO RISK MANAG EMENT\n\n-------------------------\n\nRisk measurement\, valuation and hed ging form an integral task in\nportfolio risk management for insurance com panies and other financial\ninstitutions. Portfolio risk arises because t he values of constituent\nassets and liabilities change over time in resp onse to changes in the\nunderlying risk factors. The quantification of thi s risk requires\nmodeling the dynamic portfolio value process. This boils down to\ncompute conditional expectations of future cash flows over long time\nhorizons\, e.g.\, up to 40 years and beyond\, which is computation ally\nchallenging. \n\nThis lecture presents a framework for dynamic port folio risk\nmanagement in discrete time building on machine learning theo ry. We\nlearn the replicating martingale of the portfolio from a finite\n sample of its terminal cumulative cash flow. The learned replicating\nmar tingale is in closed form thanks to a suitable choice of the\nreproducing kernel Hilbert space. We develop an asymptotic theory and\nprove\nconverg ence and a central limit theorem. We also derive finite sample\nerror boun ds and concentration inequalities. As application we\ncompute the value a t risk and expected shortfall of the one-year loss\nof some stylized port folios.\n DTSTAMP:20250518T000343Z END:VEVENT BEGIN:VEVENT UID:682923df45b66 DTSTART;TZID=America/Toronto:20181017T160000 SEQUENCE:0 TRANSP:TRANSPARENT DTEND;TZID=America/Toronto:20181017T160000 URL:/statistics-and-actuarial-science/events/david-spro tt-distinguished-lecture-speaker-dr-emery-brown SUMMARY:David Sprott Distinguished Lecture Speaker: Dr. Emery Brown\;\nAffi liation: Institute for Medical Engineering & Science CLASS:PUBLIC DESCRIPTION:Summary \n\nUNCOVERING THE MECHANISMS OF GENERAL ANESTHESIA: WH ERE NEUROSCIENCE\nMEETS STATISTICS\n\n-------------------------\n\nGeneral anesthesia is a drug-induced\, reversible condition involving\nunconsciou sness\, amnesia (loss of memory)\, analgesia (loss of pain\nsensation)\, a kinesia (immobility)\, and hemodynamic stability. I will\ndescribe a prima ry mechanism through which anesthetics create these\naltered states of aro usal. Our studies have allowed us to give a\ndetailed characterization of the neurophysiology of loss and recovery\nof consciousness​\, in the cas e of propofol\, and we have demonstrated\n​​ that the state of general anesthesia can be rapidly reversed by\nactivating specific brain circuits . The success of our research has\ndepended critically on tight coupling o f experiments\, ​statistical\nsignal processing​​ and mathematical m odeling.\n DTSTAMP:20250518T000343Z END:VEVENT BEGIN:VEVENT UID:682923df4654f DTSTART;TZID=America/Toronto:20180625T160000 SEQUENCE:0 TRANSP:TRANSPARENT DTEND;TZID=America/Toronto:20180625T160000 URL:/statistics-and-actuarial-science/events/david-spro tt-distinguished-lecture-dr-pauline-barrieu-london SUMMARY:David Sprott Distinguished Lecture by Dr. Pauline Barrieu\, London\ nSchool of Economics and Political Science CLASS:PUBLIC DESCRIPTION:Summary \n\nASSESSING FINANCIAL MODEL RISK\n\n----------------- --------\n\nModel risk has a huge impact on any financial or insurance ris k\nmeasurement procedure and its quantification is therefore a crucial\nst ep. In this talk\, we introduce three quantitative measures of model\nrisk when choosing a particular reference model within a given class:\nthe abs olute measure of model risk\, the relative measure of model risk\nand the local measure of model risk. Each of the measures has a\nspecific purpose and so allows for flexibility. We illustrate the\nvarious notions by study ing some relevant examples\, so as to emphasize\nthe practicability and tr actability of our approach.\n DTSTAMP:20250518T000343Z END:VEVENT BEGIN:VEVENT UID:682923df46e73 DTSTART;TZID=America/Toronto:20160512T160000 SEQUENCE:0 TRANSP:TRANSPARENT DTEND;TZID=America/Toronto:20160512T160000 URL:/statistics-and-actuarial-science/events/david-spro tt-distinguished-lecture-martin-wainwright SUMMARY:David Sprott Distinguished Lecture by Martin Wainwright\, Universit y of\nCalifornia\, Berkeley CLASS:PUBLIC DESCRIPTION:Summary \n\nSOME NEW PHENOMENA IN HIGH-DIMENSIONAL STATISTICS A ND OPTIMIZATION\n\nStatistical models in which the ambient dimension is of the same order\nor larger than the sample size arise frequently in differ ent areas of\nscience and engineering.  Examples include sparse regressio n in\ngenomics\; graph selection in social network analysis\; and low-rank \nmatrix estimation in video segmentation.  Although high-dimensional\nmo dels of this type date back to seminal work of Kolmogorov and\n DTSTAMP:20250518T000343Z END:VEVENT END:VCALENDAR