Hi!

I am a tenure-track assistant professor at CIMeC and DISI, University of Trento (Italy). I spend my time pondering open problems in AI and Machine Learning and coming up with practical (and sometimes sound) solutions for them. I have previously worked as a post-doc with Andrea Passerini and Fausto Giunchiglia at DISI, with Luc De Raedt at KU Leuven (Belgium), and with Luciano Serafini at FBK (Trento, Italy). I obtained my Ph.D. from the University of Trento in 2014.

On Google Scholar, my pen name is “uaQCyXkAAAAJ”. All the code I write is available on GitHub. Sometimes I tweet and retweet AI and ML-related content @looselycorrect.

I maintain a curated list of literature on interacting with Machine Learning models via explanations in the awesome explanatory supervision repo. Contributions are welcome!

Contacting Me

Feel free to contact me at: name.surname@unitn.it

Research Topics

I am interested in AI and Machine Learning, and specifically in ensuring that models learned from data are aligned with the specifications, requirements, and preferences of their users, also out-of-distribution, in interactive and lifelong settings. I am attacking this problem through a combination of:

  • xai Explainable Artificial Intelligence,
  • cbms Self-Explainable / Concept-Based Models,
  • nesy Neuro-Symbolic Integration / Statistical Relational Learning,
  • iml Interactive Machine Learning,
  • llm Large Language Models / Foundation Models,
  • cl Constraints and Constraint Learning,
  • pe Preference Elicitation.

If you are a student and you are interested in these topics, have a look at the theses and projects page.

Publications & Preprints

2025

  • Learning to Reject Low-Quality Explanations via User Feedback Luca Stradiotti, Dario Pesenti, Stefano Teso, Jesse Davis. [preprint] xai iml

  • Personalized Interpretability–Interactive Alignment of Prototypical Parts Networks Tomasz Michalski, Adam Wróbel, Andrea Bontempelli, Jakub Luśtyk, Mikolaj Kniejski, Stefano Teso, Andrea Passerini, Bartosz Zieliński, Dawid Rymarczyk. [preprint] xai cbms iml

  • If Concept Bottlenecks are the Question, are Foundation Models the Answer? Nicola Debole, Pietro Barbiero, Francesco Giannini, Andrea Passerini, Stefano Teso, Emanuele Marconato. [preprint] xai cbms llm

  • Shortcuts and Identifiability in Concept-based Models from a Neuro-Symbolic Lens Samuele Bortolotti, Emanuele Marconato, Paolo Morettin, Andrea Passerini, Stefano Teso. [preprint] xai cbms nesy

  • MedGellan: LLM-Generated Medical Guidance to Support Physicians Debodeep Banerjee, Burcu Sayin, Stefano Teso, Andrea Passerini. [preprint] Workshop on Hybrid Human-Machine Learning and Decision Making (HLDM’25) @ ECMLPKDD 2025 llm

  • Time Can Invalidate Algorithmic Recourse Giovanni De Toni, Stefano Teso, Bruno Lepri, Andrea Passerini. FACCT 2025. [paper, code] xai

  • Beyond Topological Self-Explainable GNNs: A Formal Explainability Perspective Steve Azzolin, Sagar Malhotra, Andrea Passerini, Stefano Teso. ICML 2025. [paper, code] xai

  • Reconsidering faithfulness in regular, self-explainable and domain invariant GNNs Steve Azzolin, Antonio Longa, Stefano Teso, Andrea Passerini. ICLR 2025. [paper, code] xai

2024

  • A neuro-symbolic benchmark suite for concept quality and reasoning shortcuts Samuele Bortolotti, Emanuele Marconato, Tommaso Carraro, Paolo Morettin, Emile van Krieken, Antonio Vergari, Stefano Teso, Andrea Passerini. NeurIPS 2024 Datasets & Benchmarks Track [paper, website] nesy cbms]

  • Logically Consistent Language Models via Neuro-Symbolic Integration Diego Calanzone, Stefano Teso, Antonio Vergari. [preprint] nesy llm

  • Benchmarking in Neuro-Symbolic AI Robin Manhaeve, Francesco Giannini, Mehdi Ali, Damiano Azzolini, Alice Bizzarri, Andrea Borghesi, Samuele Bortolotti, Luc De Raedt, Devendra Dhami, Michelangelo Diligenti, Sebastijan Dumancic, Boi Faltings, Elisabetta Gentili, Alfonso Gerevini, Marco Gori, Tias Guns, Martin Homola, Kristian Kersting, Jens Lehmann, Michele Lombardi, Luca Lorello, Emanuele Marconato, Stefano Melacci, Andrea Passerini, Debjit Paul, Fabrizio Riguzzi, Stefano Teso, Neil Yorke-Smith, Marco Lippi International Joint Conference on Learning & Reasoning nesy

  • Spurious Correlations in Concept Drift: Can Explanatory Interaction Help? Cristiana Lalletti, Stefano Teso. xai iml

  • Perks and Pitfalls of Faithfulness in Regular, Self-Explainable and Domain Invariant GNNs Steve Azzolin, Antonio Longa, Stefano Teso, Andrea Passerini. [preprint] xai

  • Towards Logically Consistent Language Models via Probabilistic Reasoning. Diego Calanzone, Stefano Teso, Antonio Vergari. ICLR 2024 Workshop on Reliable and Responsible Foundation Models. [preprint] nesy llm

  • Learning To Guide Human Decision Makers With Vision-Language Models. Debodeep Banerjee, Stefano Teso, Burcu Sayin, Andrea Passerini. [preprint] xai llm

  • BEARS Make Neuro-Symbolic Models Aware of their Reasoning Shortcuts. Emanuele Marconato, Samuele Bortolotti, Emile van Krieken, Antonio Vergari, Andrea Passerini, Stefano Teso. Spotlight at UAI 2024 [preprint, code] nesy iml

2023

  • Not all neuro-symbolic concepts are created equal: Analysis and mitigation of reasoning shortcuts. Emanuele Marconato, Stefano Teso, Antonio Vergari, Andrea Passerini. NeurIPS 2023. [paper, code] nesy

  • Interpretability Is in the Mind of the Beholder: A Causal Framework for Human-Interpretable Representation Learning Emanuele Marconato, Andrea Passerini, Stefano Teso. Entropy. [paper, preprint] iml xai cbms

  • How Faithful are Self-Explainable GNNs? Marc Christiansen, Lea Villadsen, Zhiqiang Zhong, Stefano Teso, Davide Mottin. LOG 2023. [paper, code] xai

  • Learning to Guide Human Experts via Personalized Large Language Models. Debodeep Banerjee, Stefano Teso, Andrea Passerini. Workshop @ Conference. [paper] iml xai

  • To Transfer or Not to Transfer and Why? Meta-Transfer Learning for Explainable and Controllable Cross-Individual Activity Recognition. Qiang Shen, Stefano Teso, Fausto Giunchiglia, Hao Xu. Electronics. [paper] other

  • Neuro-Symbolic Reasoning Shortcuts: Mitigation Strategies and their Limitations. Emanuele Marconato, Stefano Teso, Andrea Passerini. NeSy Workshop 2023. [paper] nesy

  • Neuro Symbolic Continual Learning: Knowledge, Reasoning Shortcuts and Concept Rehearsal. Emanuele Marconato, Gianpaolo Bontempo, Elisa Ficarra, Simone Calderara, Andrea Passerini, Stefano Teso. ICML 2023. [paper], code] nesy

  • Semantic Loss Functions for Neuro-Symbolic Structured Prediction. Kareem Ahmed, Stefano Teso, Paolo Morettin, Luca Di Liello, Pierfrancesco Ardino, Jacopo Gobbi, Yitao Liang, Eric Wang, Kai-Wei Chang, Andrea Passerini, Guy Van den Broeck. Compendium of Neurosymbolic Artificial Intelligence. [book chapter] nesy

  • Personalized Algorithmic Recourse with Preference Elicitation. Giovanni De Toni, Paolo Viappiani, Stefano Teso, Bruno Lepri, Andrea Passerini. TMLR. [paper, code] iml xai

  • Leveraging Explanations in Interactive Machine Learning: An Overview. Stefano Teso, Öznur Alkan, Wolfgang Stammer, Elizabeth Daly. Frontiers in Artificial Intelligence. [paper, preprint] iml xai

2022

  • Machine Learning for Combinatorial Optimisation of Partially-Specified Problems: Regret Minimisation as a Unifying Lens. Stefano Teso, Laurens Bliek, Andrea Borghesi, Michele Lombardi, Neil Yorke-Smith, Tias Guns, Andrea Passerini. [preprint] cl

  • Concept-level Debugging of Part-Prototype Networks. Andrea Bontempelli, Stefano Teso, Fausto Giunchiglia, Andrea Passerini. [preprint, code] iml xai

  • Learning Max-SAT from Contextual Examples for Combinatorial Optimisation. Mohit Kumar, Samuel Kolb, Stefano Teso, Luc De Raedt. Artificial Intelligence. [paper] cl

  • Human-in-the-loop Handling of Knowledge Drift. Andrea Bontempelli, Fausto Giunchiglia, Andrea Passerini, Stefano Teso. Data Mining and Knowledge Discovery. [pdf, code] iml

  • GlanceNets: Interpretabile, Leak-proof Concept-based Models. Emanuele Marconato, Andrea Passerini, Stefano Teso. NeurIPS 2022. [pdf, code] cbms xai

  • Semantic Probabilistic Layers for Neuro-Symbolic Learning. Kareem Ahmed, Stefano Teso, Kai-Wei Chang, Guy Van den Broeck, Antonio Vergari. NeurIPS 2022. [pdf] nesy

  • Machine Learning for Utility Prediction in Argument-Based Computational Persuasion. Ivan Donadello, Anthony Hunter, Stefano Teso, Mauro Dragoni. AAAI 2022. [pdf] other

  • Federated Multi-Task Attention for Cross-Individual Human Activity Recognition. Qiang Shen, Haotian Feng, Rui Song, Stefano Teso, Fausto Giunchiglia, Hao Xu. IJCAI 2022. [pdf] other

  • Catastrophic Forgetting in Continual Concept Bottleneck Models. Emanuele Marconato, Gianpaolo Bontempo, Stefano Teso, Elisa Ficarra, Simone Calderara, Andrea Passerini. ICIAP 2021. [paper] cbms

  • Toward a Unified Framework for Debugging Concept-based Models. Andrea Bontempelli, Fausto Giunchiglia, Andrea Passerini, Stefano Teso. Workshop on Interactive Machine Learning @ AAAI 2022. [pdf] iml xai cbms

  • Efficient and Reliable Probabilistic Interactive Learning with Structured Outputs. Stefano Teso, Antonio Vergari. Workshop on Interactive Machine Learning @ AAAI 2022. [pdf] iml nesy

  • Lifelong Personal Context Recognition. Andrea Bontempelli, Marcelo Rodas Britez, Xiaoyue Li, Haonan Zhao, Luca Erculiani, Stefano Teso, Andrea Passerini, Fausto Giunchiglia. Workshop on Human-Centered Design of Symbiotic Artificial Intelligence @ HHAI 2022. [pdf] iml nesy

2021

  • Bandits for Learning to Explain from Explanations Frey Behrens, Stefano Teso, Davide Mottin. [preprint, code] iml xai

  • Learning Modulo Theories for Constructive Preference Elicitation. Paolo Campigotto, Stefano Teso, Roberto Battiti, Andrea Passerini. Artificial Intelligence. [paper] pe cl

  • Putting Human Behavior Predictability in Context. Wanyi Zhang, Qiang Shen, Stefano Teso, Bruno Lepri, Andrea Passerini, Ivano Bison, Fausto Giunchiglia. EPJ Data Science. [pdf] other

  • Interactive Label Cleaning with Example-based Explanations. Stefano Teso, Andrea Bontempelli, Fausto Giunchiglia, Andrea Passerini. NeurIPS 2021. [pdf, code] iml xai

  • A Compositional Atlas of Tractable Circuit Operations: From Simple Transformations to Complex Information-Theoretic Queries. Antonio Vergari, YooJung Choi, Anji Liu, Stefano Teso, Guy Van den Broeck. NeurIPS 2021. [pdf] srl

  • Learning Mixed-Integer Linear Programs from Contextual Examples. Mohit Kumar, Stefano Kolb, Luc De Raedt, Stefano Teso. Data Science Meets Optimization Workshop @ IJCAI 2021. [pdf] cl

  • Neuro-Symbolic Constraint Programming for Structured Prediction. Paolo Dragone, Stefano Teso, Andrea Passerini. Workshop on Neural-Symbolic Learning and Reasoning @ IJCLR 2021. [pdf] nesy

  • Co-creating Platformer Levels with Constrained Adversarial Networks. Paolo Morettin, Andrea Passerini, Stefano Teso. Workshop on Human-AI Co-Creation with Generative Models @ IUI 2021. [pdf] nesy

2020

  • Machine Guides, Human Supervises: Interactive Learning with Global Explanations. Teodora Popordanoska, Mohit Kumar, Stefano Teso. [preprint] iml xai

  • Making Deep Neural Networks Right for the Right Scientific Reasons by Interacting with their Explanations Patrick Schramowski, Wolfgang Stammer, Stefano Teso, Anna Brugger, Franziska Herbert, Xiaoting Shao, Hans-Georg Luigs, Anne-Katrin Mahlein, Kristian Kersting. Nature Machine Intelligence. [paper] iml xai

  • Predictive Spreadsheet Autocompletion with Constraints Samuel Kolb, Stefano Teso, Anton Dries, Luc De Raedt. Machine Learning. [pdf] srl cl

  • Challenges in Interactive Machine Learning. Stefano Teso, Oliver Hinz. Künstliche Intelligenz. [pdf] iml

  • Learning Weighted Model Integration Distributions. Paolo Morettin, Samuel Kolb, Stefano Teso, Andrea Passerini. AAAI 2020. [pdf] srl cl

  • Learning Max-SAT from contextual examples for combinatorial optimisation. Mohit Kumar, Samuel Kolb, Stefano Teso, Luc De Raedt. AAAI 2020 [pdf] cl

  • Efficient Generation of Structured Objects with Constrained Adversarial Networks Luca Di Liello, Piero Ardino, Jacopo Gobbi, Paolo Morettin, Stefano Teso, Andrea Passerini. NeurIPS 2020. [pdf] nesy

  • Learning in the Wild with Incremental Skeptical Gaussian Processes. Andrea Bontempelli, Stefano Teso, F Giunchiglia, Andrea Passerini. IJCAI 2020 [pdf, code] iml

  • Human-Machine Collaboration for Democratizing Data Science. Clement Gautrais, Yann Dauxais, Stefano Teso, Samuel Kolb, Gust Verbruggen, Luc De Raedt. Human-Like Machine Intelligence. [pdf] cl

  • VisualSynth: Democratizing Data Science in Spreadsheets. Clement Gautrais, Yann Dauxais, Samuel Kolb, Architt Jain, Mohit Kumar, Stefano Teso, Elia Van Wolputte, Gust Verbruggen, Luc De Raedt. ECML-PKDD 2020 Applied Data Science and Demo Track. [paper] cl

  • Toward Machine-Guided, Human-Initiated Explanatory Interactive Learning. Teodora Popordanoska, Mohit Kumar, Stefano Teso. TAILOR Workshop @ ECAI 2020. [pdf] iml xai

  • Does Symbolic Knowledge Prevent Adversarial Fooling? Stefano Teso. Workshop on Statistical Relational AI @ NeurIPS 2020. [pdf] nesy

  • Multi-Modal Subjective Context Modelling and Recognition. Qiang Shen, Stefano Teso, Wanyi Zhang, Hao Xu, Fausto Giunchiglia. Workshop on Modelling and Reasoning in Context @ ECAI 2020. [pdf] other

2019

  • Combining learning and constraints for genome-wide protein annotation Stefano Teso, Luca Masera, Michelangeli Diligenti, Andrea Passerini. BMC Bioinformatics. [paper] nesy

  • Constraint Learning: An Appetizer. Stefano Teso. Reasoning Web. Explainable Artificial Intelligence. [paper] cl

  • Explanatory Interactive Machine Learning. Stefano Teso, Kristian Kersting. AIES 2019 [pdf, code] iml xai

  • Acquiring Integer Programs from Data. Mohit Kumar, Stefano Teso, Luc De Raedt. IJCAI 2019. [pdf] cl

  • Learning Linear Programs from Data. Elias Arnold Schede, Samuel Kolb, Stefano Teso. ICTAI 2019. [paper] cl

  • Automating Personnel Rostering by Learning Constraints Using Tensors. Mohit Kumar, Stefano Teso, P De Causmaecker, Luc De Raedt. ICTAI 2019. [paper] cl

  • SynthLog: A Language for Synthesising Inductive Data Models. Yann Dauxais, Clement Gautrais, Anton Dries, Architt Jain, Samuel Kolb, Mohit Kumar, Stefano Teso, Elia Van Wolputte, Gust Verbruggen, Luc De Raedt. ECML-PKDD 2019. [paper] cl

  • Acquiring Non-Linear Constraints. Mohit Kumar, Stefano Teso, Luc De Raedt. EURO 2019. cl

  • Toward Faithful Explanatory Active Learning with Self-explainable Neural Nets. Stefano Teso. Workshop on Interactive Adaptive Learning @ ECML-PKDD 2019. [pdf, code] iml xai

2018

  • Constructive Preference Elicitation. Paolo Dragone, Stefano Teso, Andrea Passerini Frontiers in Robotics and AI. [pdf] pe cl

  • Learning Constraints from Examples. Luc De Raedt, Andrea Passerini, Stefano Teso. AAAI 2018. [pdf] cl

  • Constructive preference elicitation over hybrid combinatorial spaces. Paolo Dragone, Stefano Teso, Andrea Passerini. AAAI 2018. [pdf] pe cl

  • Decomposition Strategies for Constructive Preference Elicitation. Paolo Dragone, Stefano Teso, Mohit Kumar, Andrea Passerini. AAAI 2018. [pdf] pe cl

  • Learning SMT(LRA) constraints using SMT solvers. Samuel Kolb, Stefano Teso, Andrea Passerini, Luc De Raedt. IJCAI 2018. [pdf] cl

  • Elements of an Automatic Data Scientist. Luc De Raedt, Hendrick Blockeel, Samuel Kolb, Stefano Teso, Gust Verbruggen. IDA 2018. [paper] cl

  • Automating Layout Synthesis with Constructive Preference Elicitation. Luca Erculiani, Paolo Dragone, Stefano Teso, Andrea Passerini. ECML-PKDD 2018. [paper] pe cl

  • Pyconstruct: constraint programming meets structured prediction Paolo Dragone, Stefano Teso, Andrea Passerini. IJCAI 2018 (Demo Track). [pdf] srl pe cl

  • Constraint Learning using Tensors. Mohit Kumar, Stefano Teso, Patrick De Causmaecker, Luc De Raedt. EURO 2018. cl

2017

  • Structured Learning Modulo Theories. Stefano Teso, Roberto Sebastiani, Andrea Passerini. Artificial Intelligence. [paper] srl

  • Investigating the Association between Social Interactions and Personality States Dynamics D Gundogdu, AN Finnerty, J Staiano, Stefano Teso, Andrea Passerini, F Pianesi, … Royal Society Open Science. [paper] other

  • Coactive Critiquing: Elicitation of Preferences and Features. Stefano Teso, Paolo Dragone, Andrea Passerini. AAAI 2017. [pdf] pe cl

  • Constructive Preference Elicitation for Multiple Users with Setwise Max-margin. Stefano Teso, Andrea Passerini, Paolo Viappiani. Algorithmic Decision Theory 2017. [paper] pe cl

2016

  • Rule Mining in Feature Space. Stefano Teso, Andrea Passerini. [preprint] nesy cl

  • Constructive Preference Elicitation by Setwise Max-Margin Learning. Stefano Teso, Andrea Passerini, Paolo Viappiani. IJCAI 2016. [pdf] pe cl

  • Structured feedback for preference elicitation in complex domains. Stefano Teso, Paolo Dragone, Andrea Passerini. BeyondLabeler Workshop @ IJCAI 2016. [pdf] pe cl

  • Constructive layout synthesis via coactive learning Paolo Dragone, Luca Erculiani, Maria Teresa Chietera, Stefano Teso, Andrea Passerini. Constructive Machine Learning Workshop @ NeurIPS 2016. [pdf] pe cl

2015

  • Inducing Sparse Programs for Learning Modulo Theories. Stefano Teso, Andrea Passerini. Constructive Machine Learning Workshop @ ICML 2015. [pdf] srl

  • Constructive Learning Modulo Theories. Stefano Teso, Roberto Sebastiani, Andrea Passerini. Constructive Machine Learning Worshop @ ICML 2015. [pdf] srl

  • Predicting Virus Mutations through Statistical Relational Learning. Elisa Cilia, Stefano Teso, Sergio Ammendola, Tom Lenaerts, Andrea Passerini. BMC Bioinformatics. [pdf] srl

2014

  • Joint Probabilistic-Logical Refinement of Multiple Protein Feature Predictors. Stefano Teso, Andrea Passerini. BMC bioinformatics. [paper] srl

  • Improved Multi-level Protein–Protein Interaction Prediction with Semantic-Based Regularization. Claudo Sacca, Stefano Teso, Michelangeli Diligenti, Andrea Passerini BMC bioinformatics. [paper] srl

  • Hybrid Statistical Relational Learning with Optimization Modulo Theories. Stefano Teso, Roberto Sebastiani, Andrea Passerini. Constructive Machine Learning Workshop @ NeurIPS 2014. [pdf] srl

2013

  • Statistical relational learning for proteomics: function, interactions and evolution. Stefano Teso. Ph.D. Thesis. [pdf] srl

  • Ego-centric graphlets for personality and affective states recognition. Stefano Teso, Jacopo Staiano, Bruno Lepri, Andrea Passerini, Fabio Pianesi. International Conference on Social Computing 2013. [paper] other

2012

  • Predicting virus mutations through relational learning. Elisa Cilia, Stefano Teso, Sergio Ammendola, Tom Lenaerts, Andrea Passerini. AIMM 2012. [pdf] srl

2010

  • An on/off lattice approach to protein structure prediction from contact maps. Stefano Teso, Cristina Di Risio, Andrea Passerini, Roberto Battiti. IAPR International Conference on Pattern Recognition in Bioinformatics 2010. [pdf] other

  • From on-going to complete activity recognition exploiting related activities. Carlo Nicolini, Bruno Lepri, Stefano Teso, Andrea Passerini. International Workshop on Human Behavior Understanding 2010. [paper] other

2008

  • Notes on stochastic simulation of chemical kinetics with cycle-leaping. Stefano Teso, Paola Lecca. Technical Report. [pdf] other