[Turkmath:6446] 2023 Körezlioğlu Araştırma Ödülü / 2023 Körezlioğlu Research Award

[Alp Bassa]

(please scroll down for English)

Değerli liste üyeleri,

Matematik Vakfı'nın verdiği *2023 Körezlioğlu Araştırma Ödülü*'ne jüri
değerlendirmesi sonucunda Bilkent Üniversitesi'nden *Naci Saldı* layık
görüldü. Araştırmacıyı tebrik eder, başarılarının devamını dileriz.

Ödül töreni 26 Nisan 2024 tarihinde saat 15:40'da Orta Doğu Teknik
Üniversitesi Uygulamalı Matematik Enstitüsü Hayri Körezlioğlu Seminer
Salonu'nda gerçekleştirilecektir.

Ödül töreninde Naci Saldı aşağıda detaylarını bulacağınız konuşmayı
verecektir. Ödül sahibi ve araştırmaları ile ilgili kısa bilgi aşağıda
verilmiştir. Törenin duyuru posterini ekte bulabilirsiniz.

Törene herkes davetlidir ve sizi de aramızda görmekten mutluluk duyarız.

Saygılarımla,

Matematik Vakfı adına,
Alp Bassa

*********************
[ENGLISH]

Dear List Members,

It is our pleasure to announce that the *2023 Körezlioğlu Research Award*
has been awarded to *Naci Saldı* from Bilkent University.

The award ceremony will take place on April 26th 2024 at 15:40 in the Hayri
Körezlioğlu Seminar Room at the Institute of Applied Mathematics of the
Middle East Technical University.

On this occasion, the prize recipient will deliver a talk, whose details
you can find below. Please also find attached a poster of the event.

You are cordially invited to the award ceremony.

Your sincerely,

Alp Bassa

(on behalf of the Mathematics Foundation)

******************
*2023 Körezlioğlu Research Award Recipient Naci Saldı*
*Title of Talk:* A General Introduction to Mean-Field Games

*Abstract:* In this talk, we consider learning approximate Nash equilibria
for discrete-time mean-field games with nonlinear stochastic state dynamics
subject to both average and discounted costs. To this end, we introduce a
mean-field equilibrium (MFE) operator, whose fixed point is a mean-field
equilibrium (i.e. equilibrium in the infinite population limit). We first
prove that this operator is a contraction, and propose a learning algorithm
to compute an approximate mean-field equilibrium by approximating the MFE
operator with a random one. Moreover, using the contraction property of the
MFE operator, we establish the error analysis of the proposed learning
algorithm. We then show that the learned mean-field equilibrium constitutes
an approximate Nash equilibrium for finite-agent games.

*About Naci Saldı*
Naci Saldi received the B.Sc. and M.S. degrees in Electrical and
Electronics Engineering from Bilkent University in 2008 and 2010,
respectively and the Ph.D. degree in Department of Mathematics and
Statistics from Queen’s University in 2015. After completing his Ph.D., he
was a postdoctoral researcher at the University of Illinois at
Urbana-Champaign. He was an assistant professor at Department of Natural
and Mathematical Sciences at Özyeğin University from July 2017 to January
2022 before joining the Department of Mathematics at Bilkent University as
an Assistant Professor. He is a co-author of the book Finite Approximations
in Discrete-Time Stochastic Control, published by Springer. His research
interests include stochastic and decentralized control, information theory,
reinforcement learning, mean-field games, quantum information, and applied
probability. He received the BAGEP Prize in mathematics in 2021.
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