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BEGIN:VEVENT
DTSTAMP:20260407T085603
DTSTART;TZID=America/Detroit:20260602T080000
DTEND;TZID=America/Detroit:20260602T170000
SUMMARY:Conference / Symposium:2026 Byrne Conference on Stochastic Analysis in Finance and Insurance
DESCRIPTION:Attendance is free\, but online registration is required for all attendees who are not speakers. sites.google.com/umich.edu/byrneconference2026\n\nSpeakers \nAgostino Capponi (Columbia University)\nRama Cont (University of Oxford)\nGiorgio Ferrari (Bielefeld University)\nAnran Hu (Columbia University)\nKasper Larsen (Rutgers University)\nMartin Larsson (Carnegie Mellon University)\nJin Ma (University of Southern California)\nAlpar Meszaros (Durham University)\nSergey Nadtochiy (Carnegie Mellon University)\nJustin Sirignano (University of Oxford)\nRenyuan Xu (Stanford University)\nPhillip Yam (Chinese University of Hong Kong)\nThaleia Zariphopoulou (University of Texas at Austin)\nYufei Zhang (Imperial College London)\n\nVenue \nAll the talks will be held in Helmut Stern Auditorium at University of Michigan Museum of Art\, located at 525 S State St\, Ann Arbor\, MI 48109.\n\nOrganizers \nErhan Bayraktar (University of Michigan)\nAsaf Cohen (University of Michigan)\nIbrahim Ekren (University of Michigan)\n\nAcknowledgement\nThis meeting is partially funded by the Department of Mathematics\, Jack Byrne Center for Financial Mathematics and Risk Management\, and Curtis E. Huntington Honorary fund.
UID:144581-21895511@events.umich.edu
URL:https://events.umich.edu/event/144581
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:conference,In Person,Mathematics,Networking
LOCATION:Museum of Art - Helmut Stern Auditorium
CONTACT:
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260407T085603
DTSTART;TZID=America/Detroit:20260603T080000
DTEND;TZID=America/Detroit:20260603T170000
SUMMARY:Conference / Symposium:2026 Byrne Conference on Stochastic Analysis in Finance and Insurance
DESCRIPTION:Attendance is free\, but online registration is required for all attendees who are not speakers. sites.google.com/umich.edu/byrneconference2026\n\nSpeakers \nAgostino Capponi (Columbia University)\nRama Cont (University of Oxford)\nGiorgio Ferrari (Bielefeld University)\nAnran Hu (Columbia University)\nKasper Larsen (Rutgers University)\nMartin Larsson (Carnegie Mellon University)\nJin Ma (University of Southern California)\nAlpar Meszaros (Durham University)\nSergey Nadtochiy (Carnegie Mellon University)\nJustin Sirignano (University of Oxford)\nRenyuan Xu (Stanford University)\nPhillip Yam (Chinese University of Hong Kong)\nThaleia Zariphopoulou (University of Texas at Austin)\nYufei Zhang (Imperial College London)\n\nVenue \nAll the talks will be held in Helmut Stern Auditorium at University of Michigan Museum of Art\, located at 525 S State St\, Ann Arbor\, MI 48109.\n\nOrganizers \nErhan Bayraktar (University of Michigan)\nAsaf Cohen (University of Michigan)\nIbrahim Ekren (University of Michigan)\n\nAcknowledgement\nThis meeting is partially funded by the Department of Mathematics\, Jack Byrne Center for Financial Mathematics and Risk Management\, and Curtis E. Huntington Honorary fund.
UID:144581-21895512@events.umich.edu
URL:https://events.umich.edu/event/144581
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:conference,In Person,Mathematics,Networking
LOCATION:Museum of Art - Helmut Stern Auditorium
CONTACT:
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260407T085603
DTSTART;TZID=America/Detroit:20260604T080000
DTEND;TZID=America/Detroit:20260604T170000
SUMMARY:Conference / Symposium:2026 Byrne Conference on Stochastic Analysis in Finance and Insurance
DESCRIPTION:Attendance is free\, but online registration is required for all attendees who are not speakers. sites.google.com/umich.edu/byrneconference2026\n\nSpeakers \nAgostino Capponi (Columbia University)\nRama Cont (University of Oxford)\nGiorgio Ferrari (Bielefeld University)\nAnran Hu (Columbia University)\nKasper Larsen (Rutgers University)\nMartin Larsson (Carnegie Mellon University)\nJin Ma (University of Southern California)\nAlpar Meszaros (Durham University)\nSergey Nadtochiy (Carnegie Mellon University)\nJustin Sirignano (University of Oxford)\nRenyuan Xu (Stanford University)\nPhillip Yam (Chinese University of Hong Kong)\nThaleia Zariphopoulou (University of Texas at Austin)\nYufei Zhang (Imperial College London)\n\nVenue \nAll the talks will be held in Helmut Stern Auditorium at University of Michigan Museum of Art\, located at 525 S State St\, Ann Arbor\, MI 48109.\n\nOrganizers \nErhan Bayraktar (University of Michigan)\nAsaf Cohen (University of Michigan)\nIbrahim Ekren (University of Michigan)\n\nAcknowledgement\nThis meeting is partially funded by the Department of Mathematics\, Jack Byrne Center for Financial Mathematics and Risk Management\, and Curtis E. Huntington Honorary fund.
UID:144581-21895513@events.umich.edu
URL:https://events.umich.edu/event/144581
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:conference,In Person,Mathematics,Networking
LOCATION:Museum of Art - Helmut Stern Auditorium
CONTACT:
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260407T124520
DTSTART;TZID=America/Detroit:20261007T160000
DTEND;TZID=America/Detroit:20261007T170000
SUMMARY:Workshop / Seminar:Robust and Risk-Sensitive Acceleration in Gradient Methods
DESCRIPTION:First-order methods such as gradient descent (GD) are foundational in optimization. In unconstrained problems with exact gradients\, momentum-based methods—most notably Nesterov’s accelerated gradient descent (AGD) and Polyak’s heavy-ball (HB) method—achieve faster convergence by improving dependence on the condition number. However\, this acceleration comes at a cost: momentum amplifies gradient noise\, making these methods less robust than GD under standard parameter choices and requiring more accurate gradient estimates to attain comparable accuracy. Similar challenges arise in convex and nonconvex min–max optimization.\nMotivated by applications in machine learning\, this talk studies unconstrained and min–max optimization under deterministic\, unbiased stochastic\, and biased stochastic gradient noise. I will present new algorithms that achieve optimal robustness against different noise types\, using control-theoretic tools such as the H_2​ norm\, the H_∞​ norm\, and the risk-sensitivity index\, together with coherent risk measures. I will also discuss worst-case noise constructions and high-probability convergence guarantees. This perspective builds a bridge between optimization and robust control theory and enables the design of noise-robust and risk-sensitive accelerated methods.\nRepresentative Publications:\nM. Gürbüzbalaban\, Y. Syed\, N. S. Aybat\, Accelerated gradient methods with biased gradient estimates: Risk sensitivity\, high-probability guarantees\, and large deviation bounds\, Journal of Nonlinear and Variational Analysis\, 2026 (Special Issue). https://jnva.biemdas.com/archives/2927\nM. Gürbüzbalaban\, Robustly Stable Accelerated Momentum Methods with a Near-Optimal L_2​ Gain and H_∞​ Performance\, Mathematics of Operations Research\, 2025.\nhttps://pubsonline.informs.org/doi/abs/10.1287/moor.2023.0321\nB. Can and M. Gürbüzbalaban\, Entropic risk-averse generalized momentum methods\, Optimization Methods and Software\, 2025. https://www.tandfonline.com/doi/abs/10.1080/10556788.2025.2549356
UID:141373-21888712@events.umich.edu
URL:https://events.umich.edu/event/141373
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Mathematics
LOCATION:East Hall - 1360
CONTACT:
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