Description

Back to top

This is a follow up to the 2022 long program on Decision Making and Uncertainty. The aim is to bring together a group of participants who were speakers and long-term participants in the Spring 2022 long program, and discuss the progress and developments made since in the areas they presented and the collaborations that started at IMSI. The workshop will cover methodological advances in:

  • decision making under uncertainty and reinforcement learning,
  • model uncertainty and robustness,
  • sustainable finance and systemic vulnerabilities,
  • machine learning and automated markets,
  • and financial stability and stress testing.

Organizers

Back to top
R C
Rama Cont University of Oxford
J O
Jan Obloj University of Oxford
T Z
Thaleia Zariphopoulou University of Texas

Speakers

Back to top
B A
Beatrice Acciaio ETH Zurich
J B
Jose Blanchet Stanford University
I C
Igor Cialenco Illinois Institute of Technology (IIT)
S C
Samuel Cohen Oxford University
P G
Paul Glasserman Columbia University
X G
Xin Guo University of California Berkeley
L H
Lars Hansen University of Chicago
A H
Anran Hu Oxford University
T M
Thibaut Mastrolia University of California Berkeley
A M
Andreea Minca Cornell University
M N
Marcel Nutz Columbia University
H P
Huyên Pham University of Paris
C R
Christoph Reisinger Oxford University
S S
Susanna Saroyan INET Oxford
L T
Ludovic Tangpi Princeton University
P T
Peter Tankov ENSAE Paris
L V
Luitgard Veraart London School of Economics
R W
Ruodu Wang University of Waterloo
J W
Johannes Wiesel Carnegie Mellon University
R X
Renyuan Xu University of Southern California
L Z
Luhao Zhang Columbia University
Y Z
Yufei Zhang London School of Economics
X Z
Xunyu Zhou Columbia University

Registration

Back to top

IMSI is committed to making all of our programs and events inclusive and accessible. Contact to request accommodations.

In order to register for this workshop, you must have an IMSI account and be logged in. Please use one of the buttons below to login or create an account.