Nicolas Gutowski

@univ-angers.fr

Laboratoire d'étude et de recherche en informatique d'Angers (LERIA)
University of Angers

Nicolas Gutowski


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https://researchid.co/ngutowski
Nicolas Gutowski is an Associate Professor in the Department of Electrical Engineering and Industrial Computing at IUT of Angers (University of Angers, France) since 2020 and is a member of the LERIA Laboratory. He worked as an IT engineer in Paris (2006–2010) before teaching in four universities (Angers, Poitiers, La Rochelle, Rouen) in 2010–2011. He then served as lecturer and head of the computer engineering department at ESAIP (2011–2016) and as lecturer at ESEO (2016–2019), where he completed a PhD in Computer Science on Reinforcement Learning and Recommendation Systems (2019, supervised by T. Amghar and O. Camp). He continued as Associate Professor at ESEO (2019–2020) before joining the University of Angers. In March 2025, he obtained his H.D.R. (Accreditation to Supervise Research) in Computer Science on multicriteria optimization for machine learning and feature selection, physics-guided deep learning, and generative AI for symbolic music and molecular generation.

EDUCATION

31/03/2025 HDR - Habilitation à Diriger des Recherches - in Computer Science (i.e. habilitation to supervise research), University of Angers, LERIA, France.
Subject: "Machine Learning for generation and exploration : Adaptation and contextualization for complex HDR mentor: Pr. Frédéric Saubion.
04/11/2019 PhD in Computer Science, University of Angers, LERIA, France
Subject: "Context-aware recommendation systems for cultural events recommendation in Smart PhD Supervisor: Dr. Tassadit Amghar. PhD Co-supervisor: Olivier Camp

RESEARCH, TEACHING, or OTHER INTERESTS

Artificial Intelligence, Computer Science
22

Scopus Publications

371

Scholar Citations

11

Scholar h-index

11

Scholar i10-index

Scopus Publications

  • Physics-guided approach with transfer learning in vehicle lateral dynamics
    Fabien Lionti, Nicolas Gutowski, Sébastien Aubin, Philippe Martinet
    Journal of Intelligent Information Systems, 2026
  • Learning Direct Solution in Moving Horizon Estimation with Deep Learning Methods
    Fabien Lionti, Nicolas Gutowski, Sébastien Aubin, Philippe Martinet
    Proceedings IEEE International Conference on Robotics and Automation, 2025
    State estimation in the context of dynamical systems is crucial for various applications, including control and monitoring. Moving Horizon Estimation (MHE) is an optimization-based state estimation algorithm that leverages a known dynamical model integrated over a moving horizon. The MHE optimization criterion corresponds to identify the initial state that best aligns the integrated trajectory with the system observation. In MHE setting, the state estimation performance increases with the considered length of the moving horizon but it can become computationally intensive which is a limiting factor for its applicability to fast-varying dynamical systems or on hardware with restricted computational power. Deep Learning (DL) methods can learn solutions to complex optimization problems without incurring any additional online computational cost beyond the inference of the considered architecture. In the context of state estimation we propose to study different type of DL architecture in order to provide full state estimation from partial and noisy system observations. The novel proposed method is based on an end-to-end differentiable formulation of the MHE optimization problem, enabling the offline training of a DL model to provide a state estimation that minimizes the MHE optimization criterion. Once training is completed, state estimations are generated through an explicit relationship learned by the DL model. The proposed method is compared to the online MHE formulation in various case studies, including scenarios with partially observed state and model discrepancies in the context of lateral vehicle dynamics. The results highlight improved state estimation performance both in terms of reduced computational time and accuracy with respect to the online MHE algorithm.
  • Bias-Variance Analysis of Multi-Step Loss Functions for Dynamical System Identification
    Fabien Lionti, Nicolas Gutowski, Sébastien Aubin, Philippe Martinet
    Proceedings of the International Joint Conference on Neural Networks, 2025
    System identification is a fundamental task in understanding and modeling dynamical systems, with extensive applications in engineering. Traditional statistical estimators for system identification rely on loss functions based on single-step predictions of model state variables. However, these approaches often lack robustness and reliability in real-world scenarios characterized by noisy and imperfect data. Recent advancements have introduced multi-step loss functions for autoregressive neural network predictions, leading to significant improvements in system identification performance. These loss functions are optimized via gradient descent, leveraging backpropagation through the numerically integrated neural network architecture. Despite their potential, the statistical and mathematical properties of these gradient estimators, such as bias, variance and robustness, remain underexplored. This paper examines the statistical and mathematical characteristics of multi-step loss function estimators in the context of dynamical system identification. We provide a theoretical foundation for the bias-variance decomposition of these loss functions, enabling the separation of error contributions from disturbances and deterministic model parameterization. Theoretical insights are validated and extended through empirical analysis, allowing an exploration of the bias-variance decomposition dynamics across the training phase. Our results demonstrate both theoretically and practically the influence of the contractive properties of the underlying dynamical system and the autoregressive prediction horizon on training stability. By bridging the theoretical and practical gap in the exploration of multi-step loss functions, this work contributes to the understanding and development of more robust and reliable methods for dynamical system identification involving gradient descent.
  • A Transformer Model for Predicting Chemical Products from Generic SMARTS Templates with Data Augmentation
    Derin Ozer, Sylvain Lamprier, Nicolas Gutowski, Benoit Da Mota, Thomas Cauchy
    Proceedings International Conference on Tools with Artificial Intelligence Ictai, 2025
    The accurate prediction of chemical reaction outcomes is a major challenge in computational chemistry. Current models rely heavily on either highly specific reaction templates or template-free methods, both of which present limitations. To address these, this work proposes the Broad Reaction Set (BRS), a set featuring 20 generic reaction templates written in SMARTS, a pattern-based notation designed to describe substructures and reactivity. Additionally, we introduce ProPreT5, a T5-based model specifically adapted for chemistry and, to the best of our knowledge, the first language model capable of directly handling and applying SMARTS reaction templates. To further improve generalization, we propose the first augmentation strategy for SMARTS, which injects structural diversity at the pattern level. Trained on augmented templates, ProPreT5 demonstrates strong predictive performance and generalization to unseen reactions. Together, these contributions provide a novel and practical alternative to current methods, advancing the field of template-based reaction prediction.
  • Guiding Evolutionary Molecular Design: Adding Reinforcement Learning for Mutation Selection
    Gaëlle Milon-Harnois, Chaïmaâ Touhami, Nicolas Gutowski, Benoit Da Mota, Thomas Cauchy
    Proceedings International Conference on Tools with Artificial Intelligence Ictai, 2025
    The efficient exploration of chemical space remains a central challenge, as many generative models still produce unstable or non-synthesizable compounds. To address these limitations, we present EvoMol-RL, a significant extension of the EvoMol evolutionary algorithm that integrates reinforcement learning to guide molecular mutations based on local structural context. By leveraging Extended Connectivity Fingerprints (ECFPs), EvoMol-RL learns context-aware mutation policies that prioritize chemically plausible transformations. This approach significantly improves the generation of valid and realistic molecules, reducing the frequency of structural artifacts and enhancing optimization performance. The results demonstrate that EvoMol-RL consistently outperforms its baseline in molecular pre-filtering realism. These results emphasize the effectiveness of combining reinforcement learning with molecular fingerprints to generate chemically relevant molecular structures.
  • Rethinking NLP for Chemistry: A Critical Look at the USPTO Benchmark
    Derin Ozer, Nicolas Gutowski, Benoit Da Mota, Thomas Cauchy, Sylvain Lamprier
    Emnlp 2025 2025 Conference on Empirical Methods in Natural Language Processing Findings of Emnlp 2025, 2025
    International audience
  • Bandit algorithms: A comprehensive review and their dynamic selection from a portfolio for multicriteria top-k recommendation
    Alexandre Letard, Nicolas Gutowski, Olivier Camp, Tassadit Amghar
    Expert Systems with Applications, 2024
  • Bayesian Approach for Parameter Estimation in Vehicle Lateral Dynamics
    Fabien Lionti, Nicolas Gutowski, Sébastien Aubin, Philippe Martinet
    Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2024
  • IMPACT OF TIME AND NOTE DURATION TOKENIZATIONS ON DEEP LEARNING SYMBOLIC MUSIC MODELING
    24th International Society for Music Information Retrieval Conference Ismir 2023 Proceedings, 2023
  • IMPACT OF TIME AND NOTE DURATION TOKENIZATIONS ON DEEP LEARNING SYMBOLIC MUSIC MODELING
    Proceedings of the International Society for Music Information Retrieval Conference, 2023
  • Byte Pair Encoding for Symbolic Music
    Nathan Fradet, Nicolas Gutowski, Fabien Chhel, Jean-Pierre Briot
    Emnlp 2023 2023 Conference on Empirical Methods in Natural Language Processing Proceedings, 2023
  • A novel multi-objective medical feature selection compass method for binary classification
    Nicolas Gutowski, Daniel Schang, Olivier Camp, Pierre Abraham
    Artificial Intelligence in Medicine, 2022
  • COM-MABs: From Users’ Feedback to Recommendation
    Alexandre Letard, Tassadit Amghar, Olivier Camp, Nicolas Gutowski
    Proceedings of the International Florida Artificial Intelligence Research Society Conference Flairs, 2022
  • Gorthaur-EXP3: Bandit-based selection from a portfolio of recommendation algorithms balancing the accuracy-diversity dilemma
    Nicolas Gutowski, Tassadit Amghar, Olivier Camp, Fabien Chhel
    Information Sciences, 2021
  • Partial Bandit and Semi-Bandit: Making the Most out of Scarce Users' Feedback
    Alexandre Letard, Tassadit Amghar, Olivier Camp, Nicolas Gutowski
    Proceedings International Conference on Tools with Artificial Intelligence Ictai, 2020
  • Gorthaur: A portfolio approach for dynamic selection of multi-armed bandit algorithms for recommendation
    Nicolas Gutowski, Tassadit Amghar, Olivier Camp, Fabien Chhel
    Proceedings International Conference on Tools with Artificial Intelligence Ictai, 2019
  • Improving bandit-based recommendations with spatial context reasoning: An online evaluation
    Nicolas Gutowski, Olivier Camp, Fabien Chhel, Tassadit Amghar, Patrick Albers
    Proceedings International Conference on Tools with Artificial Intelligence Ictai, 2019
  • Using individual accuracy to create context for non-contextual multi-armed bandit problems
    Nicolas Gutowski, Olivier Camp, Tassadit Amghar, Fabien Chhel
    Rivf 2019 Proceedings 2019 IEEE Rivf International Conference on Computing and Communication Technologies, 2019
  • Global versus individual accuracy in contextual multi-armed bandit
    Nicolas Gutowski, Tassadit Amghar, Olivier Camp, Fabien Chhel
    Proceedings of the ACM Symposium on Applied Computing, 2019
  • Context enhancement for linear contextual multi-armed bandits
    Nicolas Gutowski, Tassadit Amghar, Olivier Camp, Fabien Chhel
    Proceedings International Conference on Tools with Artificial Intelligence Ictai, 2018
  • Mobility and Prediction: an Asset for Crisis Management
    Nicolas Gutowski, Tassadit Amghar, Olivier Camp, Slimane Hammoudi
    How Information Systems can Help in Alarm Alert Detection, 2018
  • Measuring the Energy Consumption of Massive Data Insertions: An Energy Consumption Assessment of the PL/SQL for LOOP and FORALL Methods
    Nicolas Gutowski, Olivier Camp, Eric Chauveau
    Proceedings 2017 IEEE International Conference on Internet of Things IEEE Green Computing and Communications IEEE Cyber Physical and Social Computing IEEE Smart Data Ithings Greencom Cpscom Smartdata 2017, 2017

RECENT SCHOLAR PUBLICATIONS

  • Physics-guided approach with transfer learning in vehicle lateral dynamics
    F Lionti, N Gutowski, S Aubin, P Martinet
    Journal of Intelligent Information Systems 64 (1), 145-160 , 2026
    2026
    Citations: 2
  • Rethinking NLP for Chemistry: A Critical Look at the USPTO Benchmark
    D Ozer, N Gutowski, B Da Mota, T Cauchy, S Lamprier
    Findings of the Association for Computational Linguistics: EMNLP 2025, 22813 … , 2025
    2025
  • A Transformer Model for Predicting Chemical Products from Generic SMARTS Templates with Data Augmentation
    D Ozer, S Lamprier, N Gutowski, B Da Mota, T Cauchy
    2025 IEEE 37th International Conference on Tools with Artificial … , 2025
    2025
  • Guiding Evolutionary Molecular Design: Adding Reinforcement Learning for Mutation Selection
    G Milon-Harnois, C Touhami, N Gutowski, B Da Mota, T Cauchy
    2025 IEEE 37th International Conference on Tools with Artificial … , 2025
    2025
  • Bias-Variance Analysis of Multi-Step Loss Functions for Dynamical System Identification
    F Lionti, N Gutowski, S Aubin, P Martinet
    2025 International Joint Conference on Neural Networks (IJCNN), 1-8 , 2025
    2025
  • Learning-Guided MCTS for Generalizable and Interpretable Synthesis Planning Using Generic Reactions
    D Ozer, B Da Mota, N Gutowski, S Lamprier, T Cauchy, GDR MaDICS
    2025
  • Learning Direct Solution in Moving Horizon Estimation with Deep Learning Methods
    F Lionti, N Gutowski, S Aubin, P Martinet
    2025 IEEE International Conference on Robotics and Automation (ICRA), 7067-7072 , 2025
    2025
  • A transformer model for predicting chemical reaction products from generic templates
    D Ozer, S Lamprier, T Cauchy, N Gutowski, B Da Mota
    arXiv e-prints, arXiv: 2503.05810 , 2025
    2025
    Citations: 5
  • Bandit algorithms: A comprehensive review and their dynamic selection from a portfolio for multicriteria top-k recommendation
    A Letard, N Gutowski, O Camp, T Amghar
    Expert Systems with Applications 246, 123151 , 2024
    2024
    Citations: 16
  • Bayesian approach for parameter estimation in vehicle lateral dynamics
    F Lionti, N Gutowski, S Aubin, P Martinet
    International Symposium on Methodologies for Intelligent Systems, 249-259 , 2024
    2024
    Citations: 4
  • Byte pair encoding for symbolic music
    N Fradet, N Gutowski, F Chhel, JP Briot
    Proceedings of the 2023 conference on empirical methods in natural language … , 2023
    2023
    Citations: 44
  • Approche Bayésienne pour l'Estimation des Paramètres de la Dynamique Latérale d'un Véhicule
    F Lionti, N Gutowski, S Aubin, P Martinet
    CAID 2023-5e Conference on Artificial Intelligence for Defense , 2023
    2023
    Citations: 1
  • MidiTok: A python package for MIDI file tokenization
    N Fradet, JP Briot, F Chhel, AEF Seghrouchni, N Gutowski
    arXiv preprint arXiv:2310.17202 , 2023
    2023
    Citations: 96
  • Impact of time and note duration tokenizations on deep learning symbolic music modeling
    N Fradet, N Gutowski, F Chhel, JP Briot
    arXiv preprint arXiv:2310.08497 , 2023
    2023
    Citations: 19
  • Bandit Algorithms for Multicriteria Top-K Recommendations
    A Letard, N Gutowski, O Camp, T Amghar
    Available at SSRN 4502483 , 2023
    2023
  • COM-MABs: From Users' Feedback to Recommendation
    A Letard, T Amghar, O Camp, N Gutowski
    The 35th International Conference of the Florida Artificial Intelligence … , 2022
    2022
    Citations: 3
  • A novel multi-objective medical feature selection compass method for binary classification
    N Gutowski, D Schang, O Camp, P Abraham
    Artificial Intelligence in Medicine 127, 102277 , 2022
    2022
    Citations: 23
  • Bandits-Manchots Combinatoires: du retour utilisateur à la recommandation
    A Letard, T Amghar, O Camp, N Gutowski
    CNIA 2021: Conférence Nationale en Intelligence Artificielle, 52--59 , 2021
    2021
    Citations: 2
  • Gorthaur-EXP3: Bandit-based selection from a portfolio of recommendation algorithms balancing the accuracy-diversity dilemma
    N Gutowski, T Amghar, O Camp, F Chhel
    Information Sciences 546, 378-396 , 2021
    2021
    Citations: 30
  • Partial Bandit and Semi-Bandit: Making the Most Out of Scarce Users' Feedback
    A Letard, T Amghar, O Camp, N Gutowski
    2020 IEEE 32nd International Conference on Tools with Artificial … , 2020
    2020
    Citations: 7

MOST CITED SCHOLAR PUBLICATIONS

  • MidiTok: A python package for MIDI file tokenization
    N Fradet, JP Briot, F Chhel, AEF Seghrouchni, N Gutowski
    arXiv preprint arXiv:2310.17202 , 2023
    2023
    Citations: 96
  • Byte pair encoding for symbolic music
    N Fradet, N Gutowski, F Chhel, JP Briot
    Proceedings of the 2023 conference on empirical methods in natural language … , 2023
    2023
    Citations: 44
  • Gorthaur-EXP3: Bandit-based selection from a portfolio of recommendation algorithms balancing the accuracy-diversity dilemma
    N Gutowski, T Amghar, O Camp, F Chhel
    Information Sciences 546, 378-396 , 2021
    2021
    Citations: 30
  • A framework for context-aware service recommendation for mobile users: A focus on mobility in smart cities
    N Gutowski, T Amghar, O Camp, S Hammoudi
    From data to decision, 1-17 , 2017
    2017
    Citations: 27
  • A novel multi-objective medical feature selection compass method for binary classification
    N Gutowski, D Schang, O Camp, P Abraham
    Artificial Intelligence in Medicine 127, 102277 , 2022
    2022
    Citations: 23
  • Impact of time and note duration tokenizations on deep learning symbolic music modeling
    N Fradet, N Gutowski, F Chhel, JP Briot
    arXiv preprint arXiv:2310.08497 , 2023
    2023
    Citations: 19
  • Bandit algorithms: A comprehensive review and their dynamic selection from a portfolio for multicriteria top-k recommendation
    A Letard, N Gutowski, O Camp, T Amghar
    Expert Systems with Applications 246, 123151 , 2024
    2024
    Citations: 16
  • Gorthaur: A portfolio approach for dynamic selection of multi-armed bandit algorithms for recommendation
    N Gutowski, T Amghar, O Camp, F Chhel
    2019 IEEE 31st international conference on tools with artificial … , 2019
    2019
    Citations: 16
  • Context enhancement for linear contextual multi-armed bandits
    N Gutowski, T Amghar, O Camp, F Chhel
    2018 IEEE 30th International Conference on Tools with Artificial … , 2018
    2018
    Citations: 15
  • Global versus individual accuracy in contextual multi-armed bandit
    N Gutowski, T Amghar, O Camp, F Chhel
    Proceedings of the 34th ACM/SIGAPP symposium on applied computing, 1647-1654 , 2019
    2019
    Citations: 14
  • Improving bandit-based recommendations with spatial context reasoning: An online evaluation
    N Gutowski, O Camp, F Chhel, T Amghar, P Albers
    2019 IEEE 31st international conference on tools with artificial … , 2019
    2019
    Citations: 12
  • Partial Bandit and Semi-Bandit: Making the Most Out of Scarce Users' Feedback
    A Letard, T Amghar, O Camp, N Gutowski
    2020 IEEE 32nd International Conference on Tools with Artificial … , 2020
    2020
    Citations: 7
  • Using individual accuracy to create context for non-contextual multi-armed bandit problems
    N Gutowski, O Camp, T Amghar, F Chhel
    2019 IEEE-RIVF international conference on computing and communication … , 2019
    2019
    Citations: 7
  • Context-aware recommendation systems for cultural events recommendation in Smart Cities
    N Gutowski
    < bound method Organization. get_name_with_acronym of< Organization: TEL … , 2019
    2019
    Citations: 6
  • A transformer model for predicting chemical reaction products from generic templates
    D Ozer, S Lamprier, T Cauchy, N Gutowski, B Da Mota
    arXiv e-prints, arXiv: 2503.05810 , 2025
    2025
    Citations: 5
  • Bayesian approach for parameter estimation in vehicle lateral dynamics
    F Lionti, N Gutowski, S Aubin, P Martinet
    International Symposium on Methodologies for Intelligent Systems, 249-259 , 2024
    2024
    Citations: 4
  • Bandit et Semi-Bandit avec Retour Partiel: Une Stratégie d'Optimisation du Retour Utilisateur
    A Letard, T Amghar, O Camp, N Gutowski
    5ème Conférence Nationale sur les Applications Pratiques de l’Intelligence … , 2020
    2020
    Citations: 4
  • Measuring the Energy Consumption of Massive Data Insertions: an energy consumption assessment of the PL/SQL FOR LOOP and FORALL methods
    N Gutowski, O Camp, E Chauveau
    2017 IEEE International Conference on Internet of Things (iThings) and IEEE … , 2017
    2017
    Citations: 4
  • COM-MABs: From Users' Feedback to Recommendation
    A Letard, T Amghar, O Camp, N Gutowski
    The 35th International Conference of the Florida Artificial Intelligence … , 2022
    2022
    Citations: 3
  • How information systems can help in alarm/alert detection
    F Sedes
    Elsevier , 2018
    2018
    Citations: 3