Krisztian Balog is a Professor of Computer Science at the University of Stavanger and a Staff Research Scientist at Google DeepMind. His work focuses on advancing AI-driven information retrieval, natural language processing, and machine learning for user-centric systems. Key affiliations include co-organizing the Sim4IA 2025 Workshop at SIGIR, leading tutorials on user simulation in generative AI, and directing the NorwAI research center’s PhD project on LLMs for recommendations. Research Interests: User simulation, conversational AI, transparency in recommender systems, and simulation frameworks like SimIIR 3 . Scientific Recognition: Recipient of the Karen Spärck Jones Award (2018) and Best Resource Paper Award at CIKM’23 . Leadership: Serves on program committees for SIGIR, WSDM, WWW, and ECIR. Co-organized workshops and tutorials at SIGIR, AAAI, and WWW. Recent Publications highlight advancements in user simulation methodologies, generative AI applications, and open web infrastructure (e.g., SimIIR 3 , OpenWebSearch.eu ). His work bridges theoretical models (e.g., Markov Decision Processes) with practical toolkits for synthetic data generation and system evaluation.
Carlo D'Eramo is Professor and Head of the Reinforcement Learning and Computational Decision-Making professorship at the Center for Artificial Intelligence and Data Science (CAIDAS) of University of Würzburg. He additionally serves as an independent group leader of hessian.AI, focusing on developing lightweight methods to obtain adaptive autonomous agents that can handle real-world complexity. His academic journey includes: B.Sc. in Computer Engineering from Politecnico di Milano (2011) M.Sc. in Computer Engineering from Politecnico di Milano (2015) Double degree in Computer Science from University of Illinois at Chicago (2015) Ph.D. in Information Technology from Politecnico di Milano (2019) Postdoctoral research at TU Darmstadt's Intelligent Autonomous Systems group (2019-2022) D'Eramo leads the LiteRL research group investigating how agents can efficiently acquire expert skills accounting for real-world complexity. His research spans multiple reinforcement learning domains including multi-task, curriculum, adversarial, options, and multi-agent RL. His work bridges theoretical advances with practical applications, particularly in robotics and decision-making systems. His recent publications (2023-2025) demonstrate significant contributions across exploration strategies, neural network architectures, multi-agent coordination, and physics-informed machine learning. His work frequently appears in top-tier venues including TMLR, RLJ, IEEE PAMI, ICML, and ICLR, with multiple papers receiving spotlight or oral presentation designations. Professional activities include: Senior area chair for RLC Area chair for AAAI, ACML, AISTATS, NeurIPS, and ICLR Reviewer for DFG and ERC proposals Creator of MushroomRL reinforcement learning framework D'Eramo actively contributes to academic community service while mentoring researchers in his group. His work on lightweight methods aims to make reinforcement learning more practical for real-world applications across various domains.
PD Dr. Kaspar Riesen is the Head of the Pattern Recognition Group at the Institute of Computer Science, University of Bern. His research focuses on graph-based methods for pattern recognition, with applications in document analysis, environmental modeling, and healthcare. Key interests include graph matching, neural networks, and spatio-temporal modeling. His work spans structural pattern recognition, graph embeddings, and keyword spotting in historical documents. Recent projects involve river network analysis using graph regression and hypoglycemia prediction via LSTM-GNN hybrid models. Publications emphasize graph theory advancements, such as normalized graph compression and geometric similarity learning. Collaborations include developing specialized algorithms for automated error detection and improving decision-making in simulated sports. Labs/Teams: Pattern Recognition Group (PRG) at the University of Bern.
Affiliations and Roles Michael Bronstein is a Professor at the Università della Svizzera italiana (USI) in the Faculty of Informatics and the Institute of Computational Science . He holds the Chair in Machine Learning and Pattern Recognition at Imperial College London and serves as Head of Graph Learning Research at Twitter . Previously, he was affiliated with the Dalle Molle Institute for Artificial Intelligence (IDSIA USI-SUPSI) as an Adjunct Professor. Education Ph.D. in Computer Science, Technion–Israel Institute of Technology (2007) Visiting appointments at Stanford University, MIT, Harvard University, and Tel Aviv University Research Interests Bronstein's work focuses on geometric deep learning , graph representation learning , and non-rigid shape analysis . He pioneered methods for extending machine learning to non-Euclidean domains like graphs and manifolds. His research combines theoretical advancements in spectral geometry with practical applications in computer vision, robotics, and medical imaging. Publications Trends His articles emphasize geometric deep learning frameworks, functional maps for shape correspondence, and spectral methods for manifold analysis. Key themes include invariant representations, partial shape matching, and applications in 3D reconstruction and graph neural networks. Awards and Honors Five ERC Grants Royal Society Wolfson Research Merit Award IEEE and IAPR Fellowships World Economic Forum Young Scientist Advising and Entrepreneurship Bronstein is a serial entrepreneur, founding companies like Novafora , Invision (acquired by Intel), and Fabula AI (acquired by Twitter). His academic advising spans PhD and Master’s students in machine learning and geometry processing. Labs and Teams Active in the Institute of Computational Science at USI and leads Twitter’s Graph Learning Research team, focusing on real-world applications of geometric deep learning.
Antonio Rago is a Researcher in the Department of Computing at Imperial College London. He specializes in Explainable Artificial Intelligence (XAI) with a focus on computational argumentation frameworks and their integration with data-driven AI systems. His work bridges symbolic AI and machine learning to enhance transparency and societal benefit in AI applications. Education: PhD in Computing (2019) from Imperial College London, supervised by Prof. Francesca Toni and Dr. Marco Aurisicchio, with an MEng in Automotive Engineering from Loughborough University (2012). Research: Explores explainable AI through argumentation semantics, counterfactual reasoning, and hybrid symbolic-statistical methods. Application domains include e-learning, Formula One race strategy, healthcare, and mechanical engineering. Workshops: Organizer of international workshops like Arg&App 2025 and ArgXAI-25, and co-organizer of previous events at KR, ECAI, and COMMA conferences. Publications: Active in top AI venues (KR, IJCAI, AAAI, AAMAS) with over 25 publications since 2016, emphasizing argumentation-based explanations and robust AI systems. Industry Experience: Former Race Strategy Engineer at Mercedes AMG Petronas F1 Team (2012-2014) and Project Manager at Green Lifting Ltd. (2014-2017).
Renata Borovica-Gajic is an Associate Professor in Data Analytics and an ARC DECRA Fellow at the School of Computing and Information Systems (CIS), University of Melbourne. She also serves as Associate Dean (Diversity and Inclusion) for the Faculty of Engineering and IT, demonstrating leadership in both research and academic community development. Her research lies at the intersection of database systems, machine learning, and artificial intelligence, with a vision of creating adaptive, self-driving database engines that optimize query execution in real-time. Her work spans learned indexes, query optimization, data quality, and data-driven traffic optimization, aiming to reduce costs and improve performance in data analytics. The recent publications reflect a strong trend toward integrating machine learning into core database operations—particularly through learned indexes, bandit-based tuning, and reinforcement learning for traffic systems. These works emphasize automation, provable guarantees, and real-time adaptation, showcasing a cohesive research agenda focused on intelligent, self-optimizing data systems. Her scientific excellence is recognized by numerous awards, including: L'Oréal-UNESCO for Women in Science Fellowship (2023) Victorian Young Tall Poppy (2024) Test of Time Award at SIGMOD 2022 Multiple Research and Teaching Excellence Awards from the University of Melbourne Google Research Inclusion Award (2021) She actively mentors PhD students and leads significant research projects funded by the Australian Research Council, Google, and Telstra. Her service includes roles as Associate Editor for SIGMOD Record, conference organization (e.g., aiDM, ADC, VLDB), and leadership in diversity and inclusion initiatives. She has also contributed to influential publications such as a chapter in the 7th edition of Database System Concepts . Her research lab focuses on AI-powered databases, traffic optimization via reinforcement learning, and self-healing data systems, positioning her at the forefront of next-generation data management.
Debabrota Basu is a tenured faculty member (Inria Starting Faculty Position - ISFP) at the Scool team (previously called SequeL) of Inria Centre at University of Lille in France. He teaches postgraduate-level courses on privacy, responsible machine learning, and research methods in AI at École normale supérieure-PSL University, Université de Lille, and Centrale Lille. He is also a member of the ELLIS Society (European Laboratory for Learning and Intelligent Systems) and the Paris unit of ELLIS. Dr. Basu earned his PhD in Computer Science from the Department of Computer Science, School of Computing, National University of Singapore, advised by Stéphane Bressan and Pierre Senellart. Prior to that, he obtained a B.E. degree with Honours in Electronics and Telecommunication Engineering from Jadavpur University. Before joining Inria, he was a postdoctoral researcher at Chalmers University of Technology's Data Science and AI Division. Dr. Basu's research focuses on constructing algorithms for developing efficient, robust, private, and ethical learning machines that solve real-world problems. His methodological approach blends statistics, machine learning, and optimization. His application interests span sustainable agro-ecology, medical and pharmaceutical applications, energy-efficient autonomous systems, and algorithmic audits. Recent collaborations include the Inria-Indian Statistical Institute associate team SeRAI for developing Sequential Testing and Learning Algorithms for Verifiably Robust and Responsible AI, and the Inria-INRAE collaboration on Resilient Agricultural Decision Making under Environmental Risks. His publication record demonstrates expertise in bandit algorithms, reinforcement learning, and privacy-preserving machine learning. Recent work shows a strong theoretical foundation with practical applications, particularly in pure exploration bandits, differential privacy mechanisms, constrained optimization, and fairness verification. His research bridges the gap between theoretical guarantees and real-world deployment challenges across multiple domains. Dr. Basu has received notable recognition for his contributions: Best Student Paper Award at ACM EAAMO 2022 for 'On Meritocracy in Optimal Set Selection' Young researcher (JCJC) grant from the French National Research Agency (ANR) in 2022 in 'Artificial Intelligence and Data Science' He leads the project 'RL under Real-life Constraints: Regrets and Algorithms' and supervises PhD students and postdoctoral researchers. His research is supported by multiple projects including REPUBLIC ('Vers l'IA responsable avec l'apprentissage par renforcement sous contraintes') and 'Foundations of robustness and reliability in artificial intelligence.' Dr. Basu actively collaborates with institutions worldwide, including establishing the RELIANT associate team with Kyoto University for investigating structured multi-armed bandit problems.
Prof. Dr. Martin Raubal is a Full Professor at the Institute for Cartography and Geoinformation, Department of Civil, Environmental and Geomatic Engineering, ETH Zurich. He leads research on spatial decision-making, mobile GIS, and location-based services, with applications in transportation, energy, and aviation. His work integrates cognitive engineering and eye-tracking to analyze human mobility and interaction with geospatial systems. Doctorate in Geoinformation (with distinction), Vienna University of Technology (2001) MS in Spatial Information Science and Engineering, University of Maine (1997) Dipl.-Ing. in Surveying Engineering and Geoinformation, Vienna University of Technology (1998) His research focuses on spatiotemporal human mobility, cognitive models for GIS, and sustainable urban systems. Key projects include the Digital Underground initiative at the Singapore-ETH Centre and the Empirical Use and Impact Analysis of Mobility-as-a-Service (EIM). Awards include the UV-Helava Prize and multiple best paper recognitions. Recent publications emphasize 3D spatial analysis, AI-driven geospatial tools, and causal inference in mobility modeling. Collaborations span institutions like Lufthansa Systems, SBB, and ETH-IVT. His lab investigates applications such as electric vehicle charging networks, bikeability indexes, and augmented reality in transportation planning.
Vinitra Swamy is a Postdoctoral Researcher at École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the ML4ED Lab (Machine Learning for Education) and the MLO Lab (Machine Learning and Optimization Group). She holds a PhD in Computer Science from EPFL and a Master's and Bachelor's from UC Berkeley, graduating at 20 as the youngest recipient in UC Berkeley's history. Her research focuses on explainable AI, human-centric machine learning, and education technology. She has held roles as a lead engineer at Microsoft AI (ONNX framework) and served as a lecturer at UC Berkeley and UW Seattle. Vinitra's work bridges technical innovation with societal impact, exemplified by projects like MEDITRON-70B (medical LLMs) and iLLuMinaTE (actionable explanations for students). She has received multiple awards, including the 2024 G-Research PhD Prize and Rising Stars in Data Science recognition. Key contributions include interpretable neural architectures (InterpretCC), bias analysis in LLMs, and multimodal learning systems (MultiModN). Her research emphasizes practical applications in education and healthcare, with a focus on user-centered design and ethical AI practices.
Laura Pozzi is a Full Professor at the Faculty of Informatics, Università della Svizzera italiana (USI), Switzerland, since 2015. She previously held positions as Associate Professor (2011–2015) and Assistant Professor (2005–2011) at USI. Prior to joining USI, she was a postdoctoral researcher at EPFL's Processor Architecture Laboratory (2001–2005), a research engineer at STMicroelectronics (2000), and an Industrial Visitor at UC Berkeley (2000). Education: MS and PhD in Computer Engineering from Politecnico di Milano, Italy (1996–2000). Her research focuses on the interaction between compiler and architecture design, particularly in embedded systems , with key areas including approximate computing , coarse-grained reconfigurable arrays (CGRAs) , high-level synthesis (HLS) , and fuzz testing . She has led projects on automated design space exploration, compiler optimizations for reconfigurable architectures, and error estimation in approximate circuits. Recent Publications span topics like SAT-based mapping for CGRAs , grammar-based fuzzing of shell interpreters , and approximate logic synthesis , reflecting her interdisciplinary work bridging hardware/software co-design and software verification. Scientific Awards: Credit Swiss Best Teaching Award IEEE DAC Best Paper Award Leadership Roles: Co-Program Chair, IEEE Symposium on Application Specific Processors (SASP) Editorial Board Member, IEEE Design and Test Students: Current: Rodrigo Otoni (Postdoc), Morteza Rezaalipour (PhD), Riccardo Felici (PhD), Cristian Tirelli (PhD) Alumni: Ilaria Scarabottolo (PhD/Postdoc), Lorenzo Ferretti (PhD/Postdoc), Georgios Zacharopoulos (PhD), Giovanni Ansaloni (PhD/Postdoc), Paolo Bonzini (PhD)
Giovanna Di Marzo Serugendo is a researcher affiliated with the University of Geneva (Faculty of Social Sciences, Centre for Informatics) and the Institute of Information Service Science (ISS) . Her work spans semantic technologies, agent-based modeling, and sustainable systems. Research interests focus on Semantic knowledge graphs for regulatory compliance Ontology-driven resource management Self-organizing systems inspired by biological models AI applications in smart grids and urban mobility Digital agriculture platforms for smallholder farmers Recent publications highlight trends in ontology automation using LLMs, agent-based simulations for urban planning, and KG-enhanced compliance frameworks . She leads projects integrating digital twins with smart energy systems and develops bio-inspired coordination paradigms. Supervised works include 17 research projects in these domains. Current technical reports and conference papers explore cybersecurity-safety interdependencies in autonomous vehicles and decentralized event source detection in sensor networks.
Francesco Regazzoni is a Senior Researcher at the Faculty of Informatics, Università della Svizzera italiana (USI), and affiliated with the Dalle Molle Institute for Artificial Intelligence (IDSIA USI-SUPSI). His work bridges embedded systems, cybersecurity, and artificial intelligence, with a focus on securing hardware and cyber-physical systems. Research Interests: His expertise spans embedded and cyber-physical systems security, side-channel attacks, post-quantum cryptography, hardware trojans, random number generators, and the security of AI and approximate computing. He also contributes to hardware/software co-design and operating systems security. The analysis of his recent publications reveals a consistent focus on hardware and system-level security , particularly in resource-constrained environments like IoT and embedded devices. His work integrates machine learning for attack detection and applies formal methods to ensure trust in hardware. A growing emphasis is placed on securing AI systems from physical and adversarial threats. Scientific Contributions: Over 100 peer-reviewed publications One book and one patent Extensive international collaboration (Belgium, Netherlands, USA, Switzerland, Singapore) Advising and Grants: While specific advisees and grants are not listed, his leadership in funded research projects and involvement with ALaRI and IDSIA suggest active mentorship and project coordination. His work has been supported by industry (e.g., ST Microelectronics, HP), the Swiss National Foundation, and the European Union. Labs and Teams: He is part of the Graph Machine Learning Group (GMLG) at IDSIA, which evolved from the Advanced Learning and Research Institute (ALaRI). This group focuses on graph machine learning, reinforcement learning, and dynamical systems, particularly in non-stationary environments.
Leon Bungert is a Professor of Mathematics of Machine Learning at the University of Würzburg, working in applied analysis and numerics with a particular focus on data science and machine learning. His research investigates PDEs and variational models on graphs, adversarial robustness of machine learning, variational regularization, and nonlinear optimization. Dr. Bungert serves as a guest editor for the European Journal of Applied Mathematics, an associate editor for Advances in Continuous and Discrete Models: Theory and Applications, and is a member of the program committee at SSVM 2025. He is also an ELLIS member and actively organizes conferences and workshops, including "MIA'25" at IHP in Paris (January 13-15, 2025), "Synergies of Machine Learning and Numerics" in Osaka (March 11-13, 2025), and "Mathematical Analysis of Adversarial Machine Learning" in Oaxaca (August 17-22, 2025). Research Interests Dr. Bungert's primary research areas include: PDEs on graphs Adversarial robustness in machine learning Inverse problems Optimization Variational problems in L-infinity Nonlinear eigenvalue problems Image reconstruction with structural priors His work bridges theoretical mathematics with practical applications in machine learning, particularly focusing on the mathematical foundations of deep learning and developing robust algorithms that can withstand adversarial attacks. He has made significant contributions to understanding the connections between partial differential equations and machine learning algorithms. Research Trends Analysis of Dr. Bungert's recent publications reveals a strong focus on the intersection of machine learning and mathematical analysis. A key theme is the application of variational methods and partial differential equations to machine learning problems, particularly in understanding and improving the robustness of neural networks against adversarial examples. His work on Lipschitz learning on graphs has established important theoretical foundations for graph-based semi-supervised learning. Additionally, his research on the infinity Laplacian and p-Laplacian equations provides deep insights into the mathematical structure of machine learning algorithms. The development of Bregman learning frameworks for sparse neural networks represents a significant contribution to efficient deep learning model training. Professional Activities Dr. Bungert is actively involved in the academic community through editorial roles and conference organization. His current professional activities include: Guest editor for the European Journal of Applied Mathematics Associate editor for Advances in Continuous and Discrete Models: Theory and Applications Member of the program committee at SSVM 2025 ELLIS member Co-organizer of multiple international conferences and workshops Technical Contributions Dr. Bungert has developed several open-source software packages that implement his theoretical contributions, including: Code for convergence rates of Lipschitz learning on graphs A Bregman training framework for sparse neural networks CLIP: Cheap Lipschitz Training of Neural Networks Nonlinear Power Method for Proximal Operators and Neural Networks Robust Image Reconstruction with Misaligned Structural Information These implementations are primarily in Python and MATLAB, demonstrating his commitment to making theoretical advances accessible for practical applications.
Bastian Alexander Grossenbacher is a Full Professor of Machine Learning at the University of Fribourg, where he leads the AIDOS Lab within the Department of Informatics, Faculty of Science and Medicine. He holds secondary appointments at the Institute of AI for Health and the Helmholtz Pioneer Campus of Helmholtz Munich. He is also a TUM Junior Fellow and a member of ELLIS and AI-LIFE. He co-directs the Applied Algebraic Topology Research Network (AATRN) and is involved in the Topology, Algebra, and Geometry in Data Science (TAG DS) initiative. His research lies at the intersection of geometry, topology, and machine learning, with a focus on Geometric Deep Learning, Topological Deep Learning, and Topological Data Analysis. He develops novel machine learning methods that use topological and geometric priors to improve model robustness and interpretability, particularly in biomedical applications. His work spans theoretical foundations and practical implementations in areas such as single-cell analysis, medical imaging, and network science. The 15 most recent publications (2023–2025) reveal a strong trend toward integrating algebraic and differential topology into deep learning architectures. Key themes include the use of Euler Characteristic and magnitude transforms, curvature-based analysis of graphs and data, simplicial and manifold-based neural representations, and the application of topological methods to biomedical data. His articles appear in top-tier venues such as Nature Communications, ICML, NeurIPS, ICLR, and IEEE conferences, reflecting both theoretical depth and high-impact applications. ERC Starting Grant Bastian Rieck is actively involved in mentoring and academic leadership. He supervises PhD students and postdoctoral researchers in the AIDOS Lab and is open to new collaborations, particularly with candidates from interdisciplinary backgrounds. He has secured significant research funding, including the ERC Starting Grant, and leads multiple collaborative research initiatives. He emphasizes open science, sharing code, data, and educational materials publicly. He leads the AIDOS Lab, which focuses on advancing machine learning through topological and geometric principles. He is also a co-director of the Applied Algebraic Topology Research Network (AATRN), which hosts a large online seminar series. Additionally, he maintains DONUT, a curated database of non-theoretical applications of topology, and is active in the ELLIS and AI-LIFE networks, fostering international collaboration in AI and health.
Michail Vlachos serves as a Lecturer in the Department of Management of Technology and Entrepreneurship (MTE) within the College of Management of Technology (CDM) at EPFL (École Polytechnique Fédérale de Lausanne). His current status is listed as "Invited guest," indicating a non-permanent academic appointment focused on teaching within the MTE-ENS unit. His research and pedagogical expertise centers on practical applications of Data Science and Machine Learning , with specialized focus areas including Recommender Systems , Generative AI , and Natural Language Processing . He instructs a comprehensive hands-on course covering regression, classification, clustering, dimensionality reduction, text analytics, and neural networks through Python-based coding sessions, emphasizing real-world implementation of techniques like chatbots and graph analytics.
