Oleg Smirnov

I’m a senior principal researcher at Microsoft Gaming and King studio, where I focus on behavior modeling and alignment of AI agents with human players. My broader research interests include geometric methods (particularly deep learning in non-Euclidean domains and Riemannian optimization), uncertainty quantification, and Bayesian learning approaches.

Prior to joining Microsoft, I was a senior applied scientist at Amazon and a member of technical staff at Spotify. My background is in Applied Mathematics from Kyiv Polytechnic Institute, where I was advised by Pavlo Maslyanko.

Outside of research, I enjoy applying gradient ascent in geographic settings—also known as alpinism.

Recent publications

S. Ennadir, O. Smirnov, Y. Abbahaddou, L. Cao, J.F. Lutzeyer. “Enhancing Graph Classification Robustness with Singular Pooling.” In NeurIPS, 2025
S. Ennadir, L. Zólyomi, O. Smirnov, T. Wang, J. Pertoft, F. Cornell, L. Cao. “Pool Me Wisely: On the Effect of Pooling in Transformer-Based Models.” In NeurIPS, 2025
F. Rietz, O. Smirnov, S. Karimi, L. Cao. “Prompt Tuning Decision Transformers with Structured and Scalable Bandits.” In NeurIPS, 2025
F. Cornell, O. Smirnov, G. Gandler, L. Cao. “Are We Really Measuring Progress? Transferring Insights from Evaluating Recommender Systems to Temporal Link Prediction.” In Temporal Graph Learning (TGL) workshop at KDD, 2025 (Oral)
F. Cornell, O. Smirnov, G. Gandler, L. Cao. “On the Power of Heuristics in Temporal Graphs.” In I Can’t Believe It’s Not Better (ICBINB) workshop at ICLR, 2025 (Didactic Award nomination)
F. Rietz, O. Smirnov, S. Karimi, L. Cao. “Prompt-Tuning Bandits: Enabling Few-Shot Generalization for Efficient Multi-Task Offline RL.” In Generalizing from Limited Resources in the Open World (GLOW) workshop at IJCAI, 2025 (Best Paper)
S. Ennadir, G. Gandler, F. Cornell, L. Cao, O. Smirnov, T. Wang, L. Zólyomi, S. Asadi. “Expressivity of Representation Learning on Continuous-Time Dynamic Graphs: An Information-Flow Centric Review.” In TMLR, 04/2025 (Survey Certification)
T. Wang, M. Honari-Jahromi, S. Katsarou, O. Mikheeva, T. Panagiotakopoulos, O. Smirnov, L. Cao, S. Asadi. “Understanding Players as If They Are Talking to the Game in a Customized Language.” In Customizable NLP (CustomNLP4U) workshop at EMNLP, 2024
J. Talur, O. Smirnov, P. Missault. “Few-Shot Out-of-Domain Intent Detection with Covariance-Corrected Mahalanobis Distance.” In Uncertainty Reasoning and Quantification in Decision Making (UDM) workshop at AAAI, 2023

Projects

TensorFlow RiemOpt:
A library for manifold-constrained optimization in TensorFlow

Mountaineering (outdated)

Mt. Rainier and Mt. Hood, 2022

Teaching (outdated)

Introduction to Algorithms at Kyiv Polytechnic Institute, 2011 (co-taught with Alex Polozov and Dmytro Korduban)
Introduction to Functional Programming at Kyiv Polytechnic Institute, 2011 (co-taught with Alex Polozov)
Introduction to Algorithms at GlobalLogic, 2008 (co-taught with Ivan Veselov)