Prof. Dr. Daniel Hoffmann is a Professor in the Group Bioinformatics and Computational Biophysics within the Faculty of Biology at the University of Duisburg-Essen. His research focuses on quantitative biology, developing mathematical and computational models to analyze biological systems and data, particularly addressing variability through probabilistic methods like Bayesian analyses. Collaborations span diverse fields including virology, immunology, cancer research, and ecology. Research interests emphasize computational tools for viral evolution studies (e.g., HAMdetector, IgGeneUsage), immune repertoire analysis, and drug delivery mechanisms. Recent work explores hepatitis virus adaptation to host immune responses and bone vascular networks. His publications reflect expertise in bioinformatics, virology, and biophysical systems. Notable contributions include modeling T-cell evasion in hepatitis viruses and developing computational frameworks for optimizing antiviral therapies. His interdisciplinary approach bridges biology with quantitative methodologies, fostering advancements in both basic science and translational medicine.
Jianwen Su is a Professor at the University of California, Santa Barbara, USA, with a distinguished career spanning over three decades in Computer Science , particularly in Database Systems , Business Process Management , and Web Services . Their research bridges theoretical foundations with practical applications, focusing on data-centric process modeling , formal verification , and spatio-temporal data analysis . Key contributions include the design of artifact-centric workflow models , temporal constraint languages , and query systems for uncertain data . Recent work integrates machine learning with proteomic analysis for stress biomarker discovery and LLM-based extraction of structured data from clinical reports. Notable scientific recognition includes the 2019 ACM PODS Alberto O. Mendelzon Test-of-Time Award . Collaborations span institutions globally, with frequent co-authorship in journals like Information Systems , ACM Transactions on Management Information Systems , and conferences such as BIBM and TIME .
Prof. Christoph Berkholz is a University Professor and head of the Algorithms Group at the Institute for Theoretical Computer Science within the Department of Computer Science and Automation at Humboldt-Universität zu Berlin since August 2022. He holds a PhD from RWTH Aachen University (2014) and completed postdoctoral research in Stockholm, Berlin, and Berkeley. Prior to his current role, he served as a Junior Professor of Logic and Complexity at Humboldt-Universität, leading the DFG-funded Emmy Noether Junior Research Group on Representation Complexity of Enumeration and Counting Algorithms. His research focuses on theoretical computer science, particularly algorithmic methods and their fundamental limits. Core questions driving his work include conditions for efficient algorithms, structural differences between 'light' and 'heavy' input instances, and the capabilities/limitations of algorithmic strategies. Research applications span query optimization in databases, SAT solving, and constraint solving. He has published ~30 papers and actively participates in program committees for AI, theoretical computer science, and database systems conferences. Prof. Berkholz's group investigates methods ranging from classical decision problems (e.g., propositional logic satisfiability) to dynamic algorithms supporting efficient updates. Key application areas include probabilistic databases and SAT/constraint-solving data structures. No specific awards are listed, though his Emmy Noether Research Group indicates significant grant recognition.
Subramanian Ramanathan is a researcher at the School of Computing, National College of Ireland , specializing in affective computing, multimodal behavior analysis, and human-computer interaction. His work spans machine learning, computer vision, and neuro-signal processing. Key research areas: Affective Computing, Deep Learning, Stress Detection, Depression Biomarkers Notable collaborations: Roland Göcke, Abhinav Dhall, Nicu Sebe His publications focus on: EEG-based cognitive load estimation Head motion pattern analysis for mental health Deepfake detection systems Audio-visual saliency prediction Transformers in behavioral modeling Stress detection via multimodal fusion Recent work includes medical imaging applications for autism detection and computational advertising systems using emotion recognition. He contributes to open science through dataset creation (SALSA, DECAF) and collaborative research in affective computing.
Prof. Ralf Möller is a Professor of Artificial Intelligence in Humanities at the University of Hamburg's Faculty of Humanities, Department of Philosophy. He leads the Institute of Humanities-Centered Artificial Intelligence (CHAI) and serves as spokesperson for the 'Data Linking' research field within the Cluster of Excellence ‘Understanding Written Artefacts’ (UWA, 2019–2025). His research focuses on intellectics, causal and probabilistic-relational models, and AI applications in humanities, emphasizing sustainable data management and multimodal foundation models. He heads the Data Linking Lab and the CHAI Institute, overseeing projects like the TAMAR initiative for manuscript research. His work bridges technical AI advancements with humanities needs, addressing challenges in data curation, federated information systems, and ethical AI integration. He contributes to interdisciplinary collaborations, including ethics committees and cultural heritage preservation initiatives. Key research themes include lifted inference in probabilistic graphical models, temporal data prediction, and synergistic OCR-LLM systems for damaged documents. His projects emphasize scalability, sustainability, and human-centered design principles. Supervising doctoral candidates in AI and humanities intersections, he advocates for AI systems grounded in cultural and ethical considerations. Labs/Teams: CHAI Institute, Data Linking Lab. Current Projects: UWA Cluster (2019–2025), Data Linking Infrastructure development, Humanities-Centered AI applications. Grants include leadership roles in institutional and collaborative research funding.
Yann Strozecki is an Associate Professor (Maître de Conférences HDR) at the University of Versailles Saint-Quentin, where he is based in the DAVID Laboratory and leads the ALMOST research team focused on algorithms and stochastic models. He is currently on a part-time assignment at LIGM, Gustave Eiffel University, and has previously held positions at LIP6 (RO team), Paris-Sud University (ALGO team), and completed a postdoctoral fellowship at the University of Toronto's Theory Group. He earned his PhD from Paris Diderot (Paris 7) under Arnaud Durand. His research lies at the intersection of theoretical computer science and discrete mathematics, with core interests in: Enumeration complexity, especially delay and space constraints Algorithmic game theory, particularly simple stochastic games (SSGs) Graph and matroid algorithms Cheminformatics and molecular structure generation Sparse polynomials and algebraic complexity Analysis of his recent publications reveals a strong trend in developing efficient enumeration algorithms with provable delay and space bounds, advancing the theoretical foundations of output-sensitive computation. He also contributes to practical algorithms for Cloud RAN scheduling and cheminformatics, often combining theoretical rigor with real-world applications. His work on geometric amortization and strategy improvement in SSGs demonstrates innovation in algorithm design. Notable scientific contributions include: Generic strategy improvement methods for SSGs Polynomial-delay enumeration via closure operations Efficient deterministic scheduling for low-latency networks Tools for molecular cage generation in chemistry Yann Strozecki actively supervises PhD and master’s students, including Noé Demange, Maël Guiraud, and Xavier Badin de Montjoye. He co-organizes the ALMOST team seminar and has advised numerous interns in algorithmics and game theory. His research has been supported through collaborations with Nokia Bell Labs (CIFRE thesis) and interdisciplinary projects in cheminformatics and networking.
Stefan Borgwardt is a Researcher and Teaching Associate at the Chair of Automata Theory, Faculty of Computer Science, Technische Universität Dresden, working under Prof. Franz Baader. He leads key research projects such as Practical Planning with Ontologies (PPO) and contributes to the Center for Perspicuous Computing (CPEC), funded by DFG and SNF. His work bridges theoretical logic and practical AI systems. Research Interests: Knowledge Representation and Reasoning Description Logics and Ontology-Mediated Query Answering Explainable AI and Proof Visualization (Evee/ Evonne systems) Temporal, Fuzzy, and Probabilistic Logics Automated Planning with Ontologies Logic-Guided Natural Language Generation His recent publications (2021–2025) show a strong trend toward explainability in logical reasoning, with a focus on modular, interactive, and user-friendly proof systems. He investigates reasoning complexity in expressive description logics and develops practical tools for ontology explanation and planning. His work often combines symbolic AI with real-world applications such as sensor data interpretation and autonomous systems. Scientific Awards: Harold Boley Distinguished Paper Award (RuleML+RR'23) Best Paper Award (RuleML+RR'22, JELIA'19, AI'15) Quality Champion for ECAI'23 reviewing Commerzbank Award for PhD thesis (2014) Distinguished Student Paper Awards (DL'14, DL'13) N. J. Lehmann Award (2011) Stefan Borgwardt actively advises and collaborates with students and early-career researchers on projects involving explanation, planning, and reasoning. He has secured competitive funding through DFG and SNF grants and plays a leadership role in the KR community as PC co-chair, senior PC member, and steering committee member for major conferences like DL, KR, and JELIA. He is also a reviewer for top-tier journals including Artificial Intelligence and Journal of the ACM . Labs and Teams: He is a core member of the Chair of Automata Theory at TU Dresden and contributes to the Center for Perspicuous Computing (CPEC), where he works on projects A3 (Description Logic Explications) and E2 (Safe Handover in Mixed-Initiative Control) in collaboration with Vera Demberg and Antonio Krüger.
Ido Guy is a researcher at Yahoo Labs, specializing in recommender systems, e-commerce, and social media analytics. He has extensive publications in top-tier venues like WWW, SIGIR, WSDM, and NeurIPS, often collaborating with researchers such as Sharon Hirsch, Kira Radinsky, and Slava Novgorodov. Key research areas: Recommender systems, temporal modeling, graph neural networks, and e-commerce search. Notable projects: Automated category tree construction, calibration error measurement, and event-driven consumer demand prediction. His work integrates natural language processing and machine learning to solve practical challenges in product recommendation, review analysis, and enterprise social media. While no awards or students are listed here, his contributions to algorithm design and data-driven system development are significant.
Raul Castro Fernandez is a prominent researcher in data management and database systems, with a focus on data discovery, integration, and marketplaces. He has collaborated extensively with leading institutions and researchers, contributing to projects like Data Station and Nexus for secure data sharing. His work bridges theoretical innovation with practical implementations in cloud optimization, differential privacy, and LLM-driven data tools. Key Contributions : Data market frameworks, LLM applications in databases, differential privacy platforms Collaborators : Yue Gong, Samuel Madden, Michael Stonebraker, Eugene Wu, Kyle Chard Research Themes Fernandez explores automated metadata management for data catalogs, spatiotemporal data sharing with privacy guarantees, and LLM-based data discovery . His work on stateful stream processing (e.g., SABER system) and cost optimization in cloud analytics shows technical depth. Recent Trends 2023-2025 publications highlight his pivot toward LLM applications in data management, including tabular data representation and hypothesis assessment tools. He also investigates sustainability in HPC through carbon credit systems.
Sihem Amer-Yahia is a distinguished Research Professor at the University of Grenoble Alpes (affiliated with Grenoble Informatics Laboratory ), with significant contributions to database systems , data exploration , and fairness in AI . Her work bridges human-computer interaction and machine learning to create systems that enhance data-driven decision-making. Research Pillars : Algorithmic fairness, interactive data mining, recommender systems, and human-AI collaboration Recent Advances : 2023-2025 publications focus on statistically sound hypothesis testing , multi-objective recommendation , and conversational analytics Leadership : Co-organized major conferences (DASFAA 2024) and led DEI initiatives in database communities Her 15 most recent articles (2020-2025) span topics like producer fairness in recommendation , statistical hypothesis frameworks , and AI-powered education systems , with keywords covering database optimization , reinforcement learning , and ethical data mining . She actively contributes to ACM/IEEE journals and VLDB/SIGMOD conferences.
Professor Dr. Tom Hanika is affiliated with the University of Hildesheim , working in the Intelligent Information Systems (IIS) division within the Institute of Computer Science. His research bridges formal concept analysis , machine learning , and knowledge representation , focusing on geometric interpretations of data and explainable AI systems. Research Themes: Intrinsic dimensionality, lattice structures, and hybrid human-AI collaboration Teaching: Offers courses in databases, C++ programming, and semantic technologies Contact: Office (SC.C. 2.03), Phone +49 5121 883-40312, Email via contact form Recent publications highlight his work on geometric data analysis and formal context manipulation , including applications in graph neural networks, ordinal pattern recognition, and conceptual lattice visualization. His Collaborative Hybrid Human AI Learning framework demonstrates practical implementations of these theories. Current projects explore dimensionality resilience in machine learning models and topic flow visualization in academic networks, reflecting his dual focus on theoretical foundations and applied knowledge systems.
David Bani-Harouni is a researcher at the Chair of Computer Aided Medical Procedures at Technische Universität München (TUM). His work focuses on Medical Informatics , Artificial Intelligence , and Deep Learning , with an emphasis on Clinical Decision Support and Medical Image Analysis . Research Interests : Large Language Models (LLMs), Vision Language Models (VLMs), interpretability in deep learning, multimodal clinical decision support, and medical image analysis. Teaching : He contributes to lectures and practical courses such as Computer Aided Medical Procedures I , Medical Augmented Reality , and Deep Learning for Medical Applications . Publications : His research spans reinforcement learning for clinical decision-making, multimodal operating room datasets, toxin prediction systems (e.g., ToxNet), and graph convolutional networks for intoxication prediction. Contact : david.bani-harouni@tum.de
Radu Calinescu is Professor of Computer Science at the University of York, UK, where he serves as Principal Investigator for the UKRI Trustworthy Autonomous Systems Node in Resilience and leads the Trustworthy Adaptive and Autonomous Systems and Processes (TASP) Research Team. His academic career includes previous positions as Lecturer in Computer Science at Aston University (2009-2012), Senior Researcher at the University of Oxford (2008-2009), and part-time Lecturer at Oxford (2005-2009). Professor Calinescu's research focuses on formal modelling, analysis, verification and controller synthesis for autonomous and self-adaptive systems, with particular emphasis on parametric and probabilistic model checking, automated and model-driven software engineering. His work applies these approaches to robotic, cyber-physical, embedded and service-based systems, with a strong commitment to using formal methods at runtime to enhance the resilience and safety of critical autonomous systems. His extensive publication record spans top-tier journals including IEEE Transactions on Software Engineering, Journal of Systems and Software, and Automated Software Engineering. His research demonstrates consistent focus on verification techniques for adaptive systems, with increasing attention to safety-critical applications in recent years. His work bridges theoretical formal methods with practical applications in robotics and autonomous systems. British Computer Society Distinguished Dissertation Award for his DPhil thesis on Autonomic-Independent Loop Parallelisation Principal Investigator for multiple major projects including Continual Verification and Assurance of Robotic Systems under Uncertainty (ORCA Hub/EPSRC), Safety of AI Techniques (AAIP/Lloyd's Register Foundation), and CSI:Cobot Program Committee Co-Chair for major conferences including SEFM 2021, SEAMS 2020, and SERENE 2019 Professor Calinescu actively supervises numerous PhD students and postdoctoral researchers, with current team members including Faisal Alhwikem, Xinwei Fang, Mario Gleirscher, James Harbin, and Colin Paterson. His research group is based at the Ron Cooke Hub in York, a purpose-built facility housing world-class research groups and startups. His former students have gone on to academic positions at institutions worldwide and industry roles at major technology companies.
Xin Zhang serves as an Assistant Professor in the Department of Computer Science and Technology within the School of Electronics Engineering and Computer Science at Peking University. His academic profile demonstrates deep engagement with programming languages and software engineering research communities through active participation in major conferences including ASE, SPLASH/OOPSLA, PLDI, and ICSE. Dr. Zhang's research focuses on the synergistic relationship between program analysis and machine learning. He investigates how ML/AI techniques can enhance traditional program analysis methods while simultaneously developing program analysis approaches to improve the interpretability, fairness, robustness, and safety of AI systems. His work spans probabilistic program analysis, abstraction refinement techniques, Bayesian modeling for program semantics, and applications of graph neural networks to static analysis problems. His publication record shows consistent contributions to top venues from 2016 through 2025, with recent work emphasizing Bayesian program analysis, abstraction refinement methods, and the intersection of formal methods with machine learning. The trajectory of his research demonstrates increasing sophistication in combining traditional program analysis techniques with modern AI approaches. Dr. Zhang actively contributes to the academic community as a program committee member for major conferences including ASE, SAS, PLDI, and SPLASH. His service includes reviewing, session chairing, and committee participation across multiple venues, reflecting his standing in the programming languages and software engineering communities.
