Final Appraisal Seminar for Tenure Track Assistant Professor Sadegh Talebi
Join the Final Appraisal Seminar for Tenure Track Assistant Professor Sadegh Talebi. The seminar will feature presentations on research achievements during the tenure track period, future research directions, and reflections on teaching, followed by questions from external opponents and the appraisal committee.
Programme
| Arrival | |
| Short welcome and introduction by the Associate Dean for Research | |
45-minute presentation by the candidate:
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| Short break | |
| Research questions by external opponent, Professor Aurélien Garivier | |
| Research questions by external opponent, Professor Gergely Neu | |
| Questions on teaching experience and future teaching visions by Deputy Head for Education, Boris Düdder | |
| Questions regarding future ideas, collaboration and international networking by Head of Department and Associate Dean for Research | |
| Evaluation by Tenure Track Appraisal Committee (closed session) | |
| Concluding evaluation and conversation between candidate and committee in front of audience | |
| Reception |
Title:
Provably Efficient Reinforcement Learning Beyond the Markov and Risk-Neutral Formulations
Abstract:
My research focuses on the theoretical foundations of reinforcement learning (RL), with the goal of developing learning algorithms with rigorous performance guarantees in terms of regret and sample complexity. My work advances the state of the art for classical RL while extending its theoretical foundations to settings where its standard assumptions do not hold. More recently, my research has focused on risk-sensitive RL, going beyond the standard risk-neutral objective by incorporating risk measures such as entropic risk. In this setting, I develop algorithms and theoretical guarantees for RL with risk-aware objectives. I have also investigated RL beyond the classical Markov assumption, extending these guarantees to environments where reward and transition dynamics are governed by an underlying (possibly latent) finite-state automaton, capturing memory and partial observability. My work spans both online and offline RL settings. I will present recent developments along these directions and conclude by presenting an overview of my teaching and service contributions to the department, together with my vision for future research on the theory of RL.