Selected Publications

A Duration-Dependent Flood Depth-Damage Function Calibrated Using FEMA NFIP Claims from Three US Hurricanes

I Novikov, N Lazarichev, D Ayvazov, A Popov, Y Dorn, R Sultimov, et al.

Natural Hazards Research, 2026

Author manuscript

Author manuscript; may differ from the Natural Hazards Research publication.

Differentiable Grand-Canonical Monte Carlo with Importance-Sampled Insertions for Gas Adsorption in Porous Materials

EI Tuzharov, Y Maximov, AV Bochenkova

Journal of Chemical Theory and Computation, 2026

Author manuscript

Author manuscript; may differ from the Journal of Chemical Theory and Computation publication.

Efficient Contextual Bandit Learning via Reward-Space Sampling and Online Optimization (Student Abstract)

E Suraveikin, D Omirzak, R Sultimov, Y Maximov

Proceedings of the AAAI Conference on Artificial Intelligence 40 (48), 2026

Journal / proceedings paper

Importance-Sampling Estimation of Gaussian Molecular Shape Overlap: Exact Union Volumes and Confidence-Bounded Virtual Screening

EI Tuzharov, AV Bochenkova, Y Maximov

arXiv preprint arXiv:2607.20766, 2026

Preprint · arXiv:2607.20766v1

LLM-Guided Multi-Agent Evacuation Coordination via Episodic Memory and Cognitive Task Analysis

R Sultimov, A Volkov, M Mitrovic, Y Maximov

AAMAS 2026

Journal / proceedings paper

Neural network-based approach to evaluate convective hazards frequency shift under climate change

M Mozikov, D Taniushkina, A Bulkin, Y Liu, N Sotiriadi, A Osiptsov, et al.

Frontiers in Climate 8, 1779183, 2026

Journal / proceedings paper

RESPOND: Realistic Environment Simulation of Population and Natural Disasters with LLM-Driven Agents

R Sultimov, M Mozikov, D Abramov, M Kovalchuk, M Malykh, I Makarov, et al.

Proceedings of the AAAI Conference on Artificial Intelligence 40 (48), 2026

Journal / proceedings paper

RESPOND: Realistic Environment Simulation of Population and Natural Disasters with LLM-Driven Agents (Student Abstract)

R Sultimov, M Mozikov, D Abramov, M Kovalchuk, M Malykh, A Volkov, et al.

Proceedings of the AAAI Conference on Artificial Intelligence 40 (48), 2026

Journal / proceedings paper

SteelAgent: An LLM-Orchestrated System for Physics-Informed Steel Property Prediction and Generalization Auditing

A Volkov, R Sultimov, M Kuzin, Y Maximov

Conference manuscript

Unveiling Intrinsic Dimension of Texts: from Academic Abstract to Creative Story

Vladislav Pedashenko, Laida Kushnareva, Yana Khassan Nibal, Eduard Tulchinskii, Kristian Kuznetsov, Vladislav Zharchinskii, Yury Maximov, Irina Piontkovskaya

EACL 2026, Volume 1: Long Papers, 7916–7944

Journal / proceedings paper

UrbanMix: LLM-Guided Simulation of Mixed Autonomy Traffic with Heterogeneous Behavioral Profiles

R Sultimov, D Efimov, I Novikov, A Volkov, Y Maximov

Journal / proceedings paper

A Modular LLM-Agent System for Transparent Multi-Parameter Weather Interpretation

D Sukhorukov, A Zakharov, N Glazkov, K Yanchanka, V Kirilin, et al.

arXiv preprint arXiv:2512.11819, 2025

Preprint · arXiv:2512.11819v1

Hierarchical AI-Meteorologist: LLM-Agent System for Multi-Scale and Explainable Weather Forecast Reporting

D Sukhorukov, A Zakharov, N Glazkov, K Yanchanka, V Kirilin, et al.

arXiv preprint arXiv:2511.23387, 2025

Preprint · arXiv:2511.23387v1

Self-training: A survey

MR Amini, V Feofanov, L Pauletto, L Hadjadj, E Devijver, Y Maximov

Neurocomputing 616, 128904, 2025

Preprint · arXiv:2202.12040v6

Unveiling Intrinsic Dimension of Texts: from Academic Abstract to Creative Story

V Pedashenko, L Kushnareva, YK Nibal, E Tulchinskii, K Kuznetsov, et al.

arXiv preprint arXiv:2511.15210, 2025

Preprint · arXiv:2511.15210v1

The EACL 2026 proceedings version is also listed separately.

A-priori reduction of scenario approximation for automated generation control in high-voltage power grids with renewable energy

A Lukashevich, A Bulkin, Y Maximov

IEEE control systems letters 8, 1613-1618, 2024

Preprint · arXiv:2310.02509v2

Cascading blackout severity prediction with statistically-augmented graph neural networks

J Gorka, T Hsu, W Li, Y Maximov, L Roald

Electric power systems research 234, 110738, 2024

Preprint · arXiv:2403.15363v1

Case study on climate change effects and food security in Southeast Asia

D Taniushkina, A Lukashevich, V Shevchenko, IS Belalov, N Sotiriadi, et al.

Scientific reports 14 (1), 16150, 2024

Journal / proceedings paper

Climate change impact on agricultural land suitability: An interpretable machine learning-based Eurasia case study

V Shevchenko, A Lukashevich, D Taniushkina, A Bulkin, R Grinis, et al.

IEEE access 12, 15748-15763, 2024

Preprint · arXiv:2310.15912v1

Long-term drought prediction using deep neural networks based on geospatial weather data

A Marusov, V Grabar, Y Maximov, N Sotiriadi, A Bulkin, A Zaytsev

Environmental Modelling & Software 179, 106127, 2024

Preprint · arXiv:2309.06212v6

Accessing convective hazards frequency shift with climate change using physics-informed machine learning

M Mozikov, I Makarov, A Bulkin, D Taniushkina, R Grinis, Y Maximov

arXiv preprint arXiv:2310.03180, 2023

Preprint · arXiv:2310.03180v1

Assessing the Risk of Permafrost Degradation with Physics-Informed Machine Learning

Polina Pilyugina, Timofey Chernikov, Alexey Zaytsev, Alexander Bulkin, Evgeny Burnaev, Ilya Belalov, Nazar Sotiriadi, Yury Maximov, Oleg Anisimov

arXiv:2310.02525v1

Preprint · arXiv:2310.02525v1

Earlier manuscript related to “A physics-informed machine learning framework for permafrost stability assessment” (IEEE Access, 2025). The title and author list differ from the journal version.

Climate Change and Future Food Security: Predicting the Extent of Cropland Gain or Degradation

Daria Tanushkina, Valeriy Shevchenko, Aleksander Lukashevich, Aleksandr Bulkin, Roland Grinis, Kirill Kovalev, Veronika Narozhnaia, Nazar Sotiriadi, Alexander Krenke, Yury Maximov

arXiv preprint arXiv:2310.03261, 2023

Preprint · arXiv:2310.03261v2

CMIP X-MOS: Improving climate models with extreme model output statistics

V Morozov, A Galliamov, A Lukashevich, A Kurdukova, Y Maximov

arXiv preprint arXiv:2311.03370, 2023

Preprint · arXiv:2311.03370v1

Data-driven stochastic AC-OPF using Gaussian process regression

M Mitrovic, A Lukashevich, P Vorobev, V Terzija, S Budennyy, Y Maximov, et al.

International journal of electrical power & energy systems 152, 109249, 2023

Preprint · arXiv:2207.10781v1

Fast Data-Driven Chance Constrained AC-OPF using Hybrid Sparse Gaussian Processes

Mile Mitrovic, Aleksandr Lukashevich, Petr Vorobev, Vladimir Terzija, Yury Maximov, Deepjyoti Deka

2023 IEEE Belgrade PowerTech, pp. 1–7

Conference manuscript

Gp cc-opf: Gaussian process based optimization tool for chance-constrained optimal power flow

M Mitrovic, O Kundacina, A Lukashevich, S Budennyy, P Vorobev, et al.

Software impacts 16, 100489, 2023

Preprint · arXiv:2302.08454v1

Importance sampling approach to chance-constrained dc optimal power flow

A Lukashevich, V Gorchakov, P Vorobev, D Deka, Y Maximov

IEEE transactions on control of network systems 11 (2), 928-937, 2023

Preprint · arXiv:2111.11729v1

Efficient numerical methods to solve sparse linear equations with application to PageRank

Y Maximov, A Anikin, A Gasnikov, A Gornov, D Kamzolov, Y Nesterov

Optimization Methods and Software

Accepted manuscript

Efficient numerical methods to solve sparse linear equations with application to pagerank

A Anikin, A Gasnikov, A Gornov, D Kamzolov, Y Maximov, Y Nesterov

Optimization Methods and Software 37 (3), 907-935, 2022

Preprint · arXiv:1508.07607v6

The accepted manuscript is also listed separately.

Long-term hail risk assessment with deep neural networks

Ivan Lukyanenko, Mikhail Mozikov, Yury Maximov, Ilya Makarov

arXiv:2209.01191v1

Preprint · arXiv:2209.01191v1

Earlier manuscript. The 2023 IWANN publication has an expanded author team.

Ranking-Based Physics-Informed Line Failure Detection in Power Grids

A Burashnikova, W Li, M Amini, D Deka, Y Maximov

arXiv preprint arXiv:2209.01021, 2022

Preprint · arXiv:2209.01021v1

Recommender systems: when memory matters

A Burashnikova, M Clausel, MR Amini, Y Maximov, N Dante

European Conference on Information Retrieval, 56-63, 2022

Preprint · arXiv:2112.02242v1

Learning over no-preferred and preferred sequence of items for robust recommendation

A Burashnikova, Y Maximov, M Clausel, C Laclau, F Iutzeler, MR Amini

Journal of Artificial Intelligence Research 71, 121-142, 2021

Journal / proceedings paper

Power grid reliability estimation via adaptive importance sampling

A Lukashevich, Y Maximov

IEEE Control Systems Letters 6, 1010-1015, 2021

Preprint · arXiv:2105.08753v1

User preference and embedding learning with implicit feedback for recommender systems

S Sidana, M Trofimov, O Horodnytskyi, C Laclau, Y Maximov, MR Amini

Data Mining and Knowledge Discovery 35 (2), 568-592, 2021

Author manuscript

A bayesian framework for power system components identification

A Mikhalev, A Emchinov, S Chevalier, Y Maximov, P Vorobev

2020 IEEE Power & Energy Society General Meeting (PESGM), 1-5, 2020

Preprint · arXiv:2001.06704v1

Gauges, loops, and polynomials for partition functions of graphical models

M Chertkov, V Chernyak, Y Maximov

Journal of Statistical Mechanics: Theory and Experiment 2020 (12), 124006, 2020

Preprint · arXiv:1811.04713v6

Learning model of generator from terminal data

N Stulov, DJ Sobajic, Y Maximov, D Deka, M Chertkov

Electric power systems research 189, 106742, 2020

Journal / proceedings paper

Tractable minor-free generalization of planar zero-field Ising models

V Likhosherstov, Y Maximov, M Chertkov

Journal of Statistical Mechanics: Theory and Experiment 2020 (12), 124007, 2020

Preprint · arXiv:1910.11142v1

A new family of tractable Ising models

V Likhosherstov, Y Maximov, M Chertkov

arXiv preprint arXiv:1906.06431, 2019

Preprint · arXiv:1906.06431v1

Importance sampling the union of rare events with an application to power systems analysis

AB Owen, Y Maximov, M Chertkov

Preprint · arXiv:1710.06965v4

Inference and Sampling of K₃,₃-free Ising Models

V Likhosherstov, Y Maximov, M Chertkov

International Conference on Machine Learning, 3963-3972, 2019

Journal / proceedings paper

Learning a Generator Model from Terminal Bus Data

N Stulov, DJ Sobajic, Y Maximov, D Deka, M Chertkov

arXiv preprint arXiv:1901.00781, 2019

Preprint · arXiv:1901.00781v1

Sequential learning over implicit feedback for robust large-scale recommender systems

A Burashnikova, Y Maximov, MR Amini

ECML PKDD, 2019

Preprint · arXiv:1902.08495v1

Belief propagation min-sum algorithm for generalized min-cost network flow

A Riazanov, Y Maximov, M Chertkov

2018 Annual American Control Conference (ACC), 6108-6113, 2018

Preprint · arXiv:1710.07600v2

Entropy penalized semidefinite programming

M Krechetov, J Marecek, Y Maximov, M Takac

arXiv preprint arXiv:1802.04332, 2018

Preprint · arXiv:1802.04332v4

Heterogeneous dyadic multi-task learning with implicit feedback

S Moura, A Asarbaev, MR Amini, Y Maximov

International Conference on Neural Information Processing, 660-672, 2018

Author manuscript

Rademacher complexity bounds for a penalized multi-class semi-supervised algorithm

Y Maximov, MR Amini, Z Harchaoui

Journal of Artificial Intelligence Research 61, 761-786, 2018

Journal / proceedings paper

Aggressive sampling for multi-class to binary reduction with applications to text classification

B Joshi, MR Amini, I Partalas, F Iutzeler, Y Maximov

Advances in Neural Information Processing Systems 30, 2017

Journal / proceedings paper

Efficient rank minimization to tighten semidefinite programming for unconstrained binary quadratic optimization

R Pogodin, M Krechetov, Y Maximov

55th Annual Allerton Conference on Communication, Control, and Computing, 2017

Preprint · arXiv:1708.01690v1

Peculiar spectral statistics of ensembles of trees and star-like graphs

V Kovaleva, Y Maximov, S Nechaev, O Valba

Journal of Statistical Mechanics: Theory and Experiment 2017 (7), 073402, 2017

Preprint · arXiv:1612.01002v4

Representation learning and pairwise ranking for implicit feedback in recommendation systems

S Sidana, M Trofimov, O Horodnitskii, C Laclau, Y Maximov, MR Amini

arXiv preprint arXiv:1705.00105, 2017

Preprint · arXiv:1705.00105v4

Tight risk bounds for multi-class margin classifiers

Y Maximov, D Reshetova

Pattern Recognition and Image Analysis 26 (4), 673-680, 2016

Preprint · arXiv:1507.03040v3

Modern efficient numerical approaches to regularized regression problems in application to traffic demands matrix calculation from link loads

Anton Anikin, Pavel Dvurechensky, Alexander Gasnikov, Andrey Golov, Alexander Gornov, Yuri Maximov, Mikhail Mendel, Vladimir Spokoiny

arXiv preprint arXiv:1508.00858, 2015

Preprint · arXiv:1508.00858v9 · re-typeset

Re-typeset from arXiv:1508.00858v9 (2016 revision). Listed on Scholar as “Efficient numerical algorithms for regularized regression problem with applications to traffic matrix estimations.”

Correct algebras over estimation algorithms in the set of regular recognition problems with nonoverlapping classes

Yu. V. Maksimov

Computational Mathematics and Mathematical Physics 49 (7), 1264–1275

Journal / proceedings paper

Published English translation.