Please join the Webex virtual seminar using the following link:
https://uconn-cmr.webex.com/uconn-cmr/j.php?MTID=mcff3996e1bfb542702cef27de6fba0e6

(Meeting number: 2620 464 0564
Password: tpW8pD7KZx6)

Abstract: We introduce a flexible risk decomposition method for life insurance contracts associated with exposure to several risk factors. Hedging can be naturally included in the framework. Although the method is applied to variable annuities in this work, it is also applicable in general to other insurance or financial contracts. The approach relies on applying an allocation principle to components of a Shapley decomposition of the gain and loss. The implementation of the allocation method requires a stochastic on stochastic algorithm.
Numerical examples studying the relative impact of equity, interest rate and mortality risk for guaranteed minimal maturity benefit (GMMB) policies are also presented. (Joint work with Emmanuel Hamel, Patrice Gaillardetz and Edwin Hon-Man Ng)


Speaker's short bio: Frederic is an associate professor in the Department of Mathematics and Statistics at Concordia University (Canada). He obtained his PhD from HEC Montreal in 2014. His research interests include Mathematical and Computational Finance, Actuarial Science, Risk Management, Dynamic Programming, and Statistics. For more information on his research, please visit https://www.researchgate.net/profile/Frederic-Godin.

Please join the Webex virtual seminar using the following link:
https://uconn-cmr.webex.com/uconn-cmr/j.php?MTID=m4843051786397a32038771f6b19eafdb

(Meeting number: 2623 958 5994
Password: 33sWs6ZJAp2)

Abstract: We consider an n-player symmetric stochastic game with weak interaction between the players. Time is continuous and the horizon and the number of states are finite. We show that the value function of each of the players under Nash equilibrium can be approximated by the solution of a partial differential equation called the master equation. For this, we prove the regularity of the master equation. (Based on joint work with Erhan Bayraktar and Ethan Zell).

Speaker's short bio: Dr. Cohen is an assistant professor in the Department of Mathematics at the University of Michigan at Ann Arbor. He obtained his PhD in 2014 from Tel-Aviv University, Israel, and was an assistant professor at the University of Haifa. His research interests are applied probability and control theory and their applications to mean-field games, mathematical finance, and stochastic networks. Please visit his website for more information: https://sites.google.com/site/asafcohentau/home

This talk is based on joint work with Véronique Bazier-Matte in which we found a relation between cluster algebras and knot theory. To every knot diagram (or link diagram), we associate a cluster algebra by constructing a quiver with potential. The rank of the cluster algebra is \(2n\), where \(n\) is the number of crossing points in the knot diagram. We then construct \(2n\) indecomposable modules \(T(i)\) over the Jacobian algebra of the quiver with potential. For each \(T(i)\), we show that the submodule lattice of \(T(i)\) is isomorphic to the corresponding lattice of Kauffman states of the knot. Furthermore, the Alexander polynomial of the knot is a specialization of the \(F\)-polynomial of \(T(i)\), for every \(i\).

Cryptography is the practice and study of techniques used for secure communication in the presence of adverse third parties. Although cryptography is of interest to computer scientists, there is a lot of mathematics being done behind the scenes. In this talk, we will explain the difference between private key cryptography and public key cryptography. We will also talk about some computationally hard math problems that are used in public key cryptography.


Note: Free refreshments. The talk starts at 5:40.

Join us in the Logic Colloquium!

Zoe Ashton (OSU)

"How the Standard View of Rigor and the Standard Practice of Mathematics Clash"

Mathematical proofs are rigorous – it’s part of what distinguishes proofs from other argument types. But the quality of rigor, relatively simple for the trained mathematician to spot, is difficult to explicate. The most common view, often referred to as the standard view of rigor, is that “a mathematical proof is rigorous iff it can be converted into a formal derivation” (Burgess & De Toffoli (2022)). Each proponent of the standard view interprets “conversion” differently. For some, like Hamami (2022), conversion means algorithmic translation while others, like Burgess (2015), interpret it as just revealing enough steps of the formal derivation.

In this talk, I aim to present an overarching concern for the standard view. I’ll argue that no extant version of the standard view makes sense of how mathematicians make rigor judgments. Both Hamami (2022) and Tatton-Brown (2021) have both attempted to account for mathematicians’ rigor judgments using the standard view. I’ll argue that both still fail to adequately account for mathematical practice by positing that mathematicians engage in either algorithmic proof search and/or extensive training in formal rigor.

We seem to be left with two options: continue trying to amend the standard view or introduce a new account of rigor which is practice-focused. I’ll argue that issues with the two accounts are general issues that will likely occur for future formulations of the standard view. Thus, we should aim to introduce a new account of informal, mathematical rigor. I’ll close by sketching a new account of rigor related to the audience-based view of proof introduced in Ashton (2021).

https://logic.uconn.edu/calendar/

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The UCSAS is coming back in-person on Saturday, Oct. 8, 2022.

The demand for data scientists has grown rapidly over the last decade and this trend is projected to continue. There are, however, not enough highly qualified data scientists to meet the increasing demand. The talent shortfall extends to existing job classifications from the executive suite to frontline jobs---all of which are increasingly enabled by data analytics. The overarching goal of the UCSAS is to engage more students outside of traditional data science programs into data science through sports analytics.

While there are many well established sports analytics conferences, they are often not accessible to students due to technical level, cost, or space limitations. UConn, recognized nationally for its teams in sports such as basketball, baseball, and hockey, among others, hosts the UCSAS, which focuses specifically on undergraduate and graduate students who are interested in sports analytics. UCSAS, started in 2019 and organized by the Statistical Data Science Lab at UConn, aims to:

showcase sports analytics to students at an accessible level;
train students in data analytics with application to sports data; and
foster collaboration between academic programs and the sports industry.

This is an in-person seminar talk.

Abstract: I will discuss several interrelated projects on the use of Gaussian Process (GP) models for longevity analysis. The unifying objective is to capture the structure of age-specific mortality rates, with an emphasis on jointly modeling multiple mortality surfaces. Examples include modeling mortality across different countries, jointly for males and females, and analyzing cause-of-death datasets. I will review the GP spatial covariance framework in the Age-Period-Cohort context and the resulting features of uncertainty quantification, non-parametric forecasts, long-term coherence, and transparent information fusion and extrapolation. I will then discuss multi-output GPs, especially coregionalization dimension reduction techniques for handling multiple populations. The developed scalable GP models can handle joint analysis of several dozen populations, hierarchically arranged along nationalities, genders and causes-of-death. Illustrations using Human Mortality Database datasets and corresponding insights into mortality patterns and differences will be given. This is joint work with Nhan Huynh, Doris Padilla and Jimmy Risk.

Speaker's short bio: Mike Ludkovski is Professor at the Department of Statistics and Applied Probability at University of California Santa Barbara where he co-directs the Center for Financial Mathematics and Actuarial Research. Among his research interests are longevity modeling, renewable energy markets, and quantitative finance. He has over 70 publications and his Simulation and Control in Finance and Insurance (SCiFI) lab is supported by NSF, SOA and ARPA-E grants. He has broad interests in financial mathematics and actuarial science. Please visit his website http://ludkovski.faculty.pstat.ucsb.edu/ for more information.

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