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Frank PetzoldView profile

Professor

• Architectural Informatics
• Building Information Modeling (BIM)
• Computer-Aided Design
• +3 more

Prof. Frank Petzold is a Full Professor at the Chair of Architectural Informatics within the Department of Architecture at the Technical University of Munich (TUM). His academic career includes roles as Vice Dean of the Department of Architecture (2010–2012) and Dean of the Faculty of Architecture at Bauhaus-University Weimar (2008–2009). He holds a Doctorate in Computer Science from Bauhaus-University Weimar (2001) and has been a core member of the Munich Data Science Institute (MDSI) since 2021. His research focuses on digital methods in architectural design, BIM integration, AI-driven generative design, and sustainable urban planning. Key affiliations include the Leonhard Obermeyer Center for Digital Methods (co-founder, 2014) and leadership roles in DARL (Deans' Association for Architecture, 2015–present). Research interests span computational design tools, digital twin technologies, and AI applications in construction. Notable contributions include the TUM2TWIN benchmark dataset and frameworks for automated design decision support. He serves on editorial boards for journals like the International Journal of Architectural Computing and chairs Bavaria’s 'Digital Construction and Planning' platform. His work bridges academic research with industry applications, emphasizing interdisciplinary collaboration and digital innovation in built environment disciplines. Education: Doctorate in Computer Science, Bauhaus-University Weimar (2001) Affiliations: Munich Data Science Institute, TUM Georg Nemetschek Institute, German Association of Computing in Civil Engineering Committees: DARL, Task Group AKIM (Construction Industry), Bavarian Digitalization Center (Z.DB) Publications emphasize BIM interoperability, generative design systems, and urban sustainability. Current projects explore AI for building lifecycle assessments, digital twin applications, and crowd-safety simulations in early design stages.

Peter SchüfflerView profile

Professor

• Digital Pathology
• Computational Pathology
• Machine Learning
• +5 more

Prof. Peter Schüffler is a Professor of Computational Pathology at the Technical University of Munich (TUM), holding joint appointments in the TUM School of Medicine and Health and the TUM School of Computation, Information and Technology. His research focuses on applying machine learning to digital pathology, including cancer detection, prognostic marker identification, and AI-driven diagnostic tools. He earned an MSc in Bioinformatics from Saarland University and a PhD in Machine Learning for Medical Image Analysis from ETH Zurich. As a postdoc and senior ML scientist at Memorial Sloan Kettering Cancer Center, he co-founded Paige, a digital pathology company. He joined TUM in 2021. Education: MSc in Bioinformatics and Computational Biology, Saarland University and Max Planck Institute (MPI Saarbrücken) PhD in Machine Learning for Medical Image Analysis, ETH Zurich (2015) Research Interests: His work spans machine learning applications in pathology, including AI-driven diagnosis, automated quality assurance, and ergonomic systems for pathologists. Key areas include: Cancer detection and segmentation in pathology images Prognostic marker discovery for treatment success Efficient visualization of digital histopathology slides Integration of AI into clinical and educational workflows Awards: Best Paper Award (2014): Computer Aided Crohn's Disease Severity Assessment in MRI Outstanding Paper Award (2013): Crohn's Disease Severity Assessment Model Advising & Grants: No specific student advisees or grants are listed in the provided text. Labs/Teams: Co-founder of Paige, a leader in AI-driven digital pathology systems. Active in TUM's computational pathology research groups.

Bernhard KainzView profile

Professor

• Artificial Intelligence in Healthcare
• Medical Imaging
• Machine Learning
• +6 more

Prof. Bernhard Kainz is a Professor at Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), leading the Department Artificial Intelligence in Biomedical Engineering (AIBE). He holds a W3-Professorship for Image Data Exploration and Analysis. His research focuses on developing intelligent algorithms for healthcare applications, particularly in medical imaging and real-time diagnostics. Key interests include self-driving medical image acquisition systems, normative learning through population data integration, and interpretable AI decision-making in clinical settings. Teaching: Real-time computing, Machine Learning, Image Analysis, Computer Graphics, and Visualization His work bridges technical innovation with clinical needs, addressing challenges like democratizing rare medical expertise through AI and enhancing patient care through explainable AI systems. Active in research collaborations, he contributes to FAU's interdisciplinary environment, leveraging partnerships with institutions like Fraunhofer and Helmholtz.

Dario ZancaView profile

Researcher

• Visual Perception
• Machine Learning
• Neural Networks
• +4 more

Dario Zanca is a Postdoctoral Researcher in the Department of Artificial Intelligence in Biomedical Engineering (AIBE) at Friedrich-Alexander-Universität Erlangen-Nürnberg. He is affiliated with the Machine Learning and Data Analytics (MaD) Lab and focuses on visual perception, eye movement analysis, and machine learning applications. His work bridges computational models of human attention with neural network behavior, contributing datasets like FixaTons and CapMIT1003. Research areas include: Biologically-inspired visual attention models Eye tracking and gaze analysis in clinical and computational contexts Deep learning interpretability and explainable AI Time series analysis and predictive process monitoring Key contributions include tools like the SaccadeAnalysisMatlabApp for gaze data processing and participation in initiatives like the PolarBearVidID dataset for wildlife behavior analysis. His interdisciplinary projects span healthcare, robotics, and environmental science, emphasizing real-world applications of AI.

Barbara HammerView profile

Professor

• Machine Learning
• Explainable Artificial Intelligence
• Trustworthy AI
• +13 more

Prof. Dr. Barbara Hammer is a full professor of Machine Learning at Bielefeld University, Faculty of Engineering, and leads the Machine Learning Group at the Center for Cognitive Interaction Technology (CITEC). She is actively involved in multiple interdisciplinary research centers including the Bielefeld Center for Data Science (BiCDaS), the Research Institute for Cognition and Robotics, and the Institute for Bioinformatics Infrastructure (BIBI). She holds leadership roles in major research initiatives such as the TRR 318 'Constructing Explainability', the graduate school Data-NinJA on Trustworthy AI, and the research network SAIL on sustainable AI systems. Her research focuses on intelligent data analysis , explainable and trustworthy AI , and machine learning in dynamic environments . She investigates foundational algorithms for learning from complex, non-Euclidean, and evolving data streams, with applications in urban infrastructure (especially water systems), life sciences, and socio-technical systems. Her work bridges algorithmic innovation with societal impact, particularly in fairness, ethics, and human-AI interaction. The recent publications highlight a strong trend towards explainability in dynamic environments , concept drift detection and explanation , fairness in streaming data , and real-world applications in critical infrastructure . Her team develops both theoretical frameworks and practical tools, such as EPyT-Flow for water network simulation, and contributes to high-impact AI challenges in health, environment, and industry. ERC Synergy Grant – Smart Water Futures LAMARR Fellow She advises several PhD and Master’s students and leads numerous funded projects from the European Union, DFG, and national agencies. Her leadership extends to editorial roles, including on the IEEE TPAMI editorial board. She is also deeply involved in academic governance, serving on examination boards, habilitation committees, and interdisciplinary research centers, reflecting her central role in shaping AI research and education at Bielefeld and beyond.

• Dependable, reliable, and secure computing
• Stochastic behavioural modelling and verification
• Concurrency theory
• +12 more

Holger Hermanns is a Full Professor of Computer Science at Saarland University, Germany, where he leads the Dependable Systems and Software group. He is co-spokesperson of the Center for Perspicuous Computing (SFB/TRR 248) and has been a scientific director at Schloss Dagstuhl – Leibniz Center for Informatics since 2025. He is a member of Academia Europaea and holds an ERC Advanced Grant (POWVER) and a Proof-of-Concept Grant (LEOpowver) . His leadership roles include former Dean of the Faculty of Mathematics and Computer Science and active service in major academic organizations such as the ETAPS Association (Vice President) and Friends of Dagstuhl (President, 2013–2023). Education: Researcher and academic positions at Universität Erlangen-Nürnberg (1993–1998), Universiteit Twente (1998–2006), and INRIA Grenoble Rhône-Alpes (2007–2010). His research focuses on modeling and verification of concurrent systems , stochastic behavioral modeling , energy informatics , and proactive algorithmic accountability . He is a leading figure in probabilistic model checking, formal methods for cyber-physical systems, and software dependability. His work bridges theory and practical applications in satellite systems, AI safety, and environmental monitoring. The recent publications reflect a strong trend toward AI accountability , software doping detection , formal verification of machine learning systems , and dependable satellite network design . His team develops tools like RTLola and Momba, integrating formal methods into real-world systems. There is a growing emphasis on interdisciplinary work, particularly in human oversight of AI and ethical algorithmic design. Scientific Awards and Recognition: ERC Advanced Grant POWVER ERC Proof-of-Concept Grant LEOpowver Member, Academia Europaea (2013) Dutch Vernieuwingsimpuls (2001) Preis des Fakultätentages Informatik (2013) ERC Advanced Investigators Grant (2016) Otto Mønsted-Guest Professorship (2013) Hermanns has advised numerous students and researchers, though specific names are not listed. He leads major research grants, including ERC projects and collaborative efforts within SFB/TRR 248. His leadership extends to program committees of top conferences such as CAV, CONCUR, TACAS, and QEST, and he has chaired LICS and ICALP. He is affiliated with the Dependable Systems and Software lab at Saarland Informatics Campus and contributes to the Schloss Dagstuhl ecosystem as scientific director and former president of its support association. His recent work is increasingly centered on the AI Act , algorithmic transparency, and the formal foundations of trustworthy AI.

• Theoretical Physics
• Quantum Optics
• Cavity Optomechanics
• +12 more

Florian Marquardt is the Scientific Director and Head of the Theory Division at the Max Planck Institute for the Science of Light in Erlangen, Germany, and holds a part-time professorship at Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU). He leads a research group focused on the intersection of quantum physics, optics, and machine learning, contributing to foundational and applied advances in quantum technologies. His educational background includes a PhD summa cum laude from the University of Basel (2002), followed by postdoctoral research at Yale University and in the Swiss National Center for Competence in Research. He served as a Junior Professor at LMU Munich (2005–2010) and Full Professor at FAU (2010–2016) before joining MPL. Marquardt's research spans cavity optomechanics , quantum many-body physics , neuromorphic computing , and machine learning for physics . His group develops minimalist theoretical models to explain experimental phenomena and explores how AI can automate scientific discovery. He is particularly interested in energy-efficient analog computing devices inspired by neural networks and in leveraging physics to improve machine learning. The recent publications reflect a strong trend toward integrating artificial intelligence with quantum science, including automated experiment design, quantum error correction via neural networks, and neuromorphic photonics. There is also sustained work in quantum measurement, topological photonics, and nonequilibrium dynamics, showing both breadth and depth in theoretical physics. Emmy-Noether Fellowship (DFG, 2007) Walter Schottky Prize (DPG, 2009) ERC Starting Grant (2011) Marquardt actively supervises research and teaches advanced courses such as 'Machine Learning for Physicists' and 'Foundations of Quantum Mechanics' at FAU. His group participates in major research networks including the Max Planck School of Photonics, ELLIS, and the European Laboratory for Learning and Intelligent Systems. The Theory Division fosters collaborations on quantum technologies and neuromorphic systems, with strong emphasis on interpretability, generalization, and hardware implementation.

• History of Science
• Digital Humanities
• Knowledge Diffusion and Transmission
• +5 more

Prof. Matteo Valleriani holds an Honorary Professorship at Technische Universität Berlin's Department of History of Science (Faculty I: Humanities and Educational Sciences) and serves as Principal Investigator (W2) at the Max Planck Institute for the History of Science (MPIWG) in Berlin. He also leads research at the Berlin Institute for the Foundations of Learning and Data (BIFOLD) and holds a Professorship by Special Appointment at the University of Tel Aviv. Research Focus: Transformation of scientific, practical, and technological knowledge across historical periods (Hellenistic, late medieval, early modern), with emphasis on mechanisms of knowledge diffusion and cultural/political entanglements. Digital Humanities Integration: Pioneering use of AI, machine learning, and complex systems theory to analyze historical sources and reconstruct scientific networks. Key Projects: Leads The Sphere initiative on European scientific identity formation (13th–17th centuries) and investigates visual epistemology in knowledge transmission. Publications span historical methodologies, cosmological treatises, and AI-driven analysis of early modern astronomical data. His work bridges traditional historiography with computational tools, advocating for open access to digitized sources. Labs/Teams: Coordinates MPIWG research groups, BIFOLD collaborations, and the PhD Candidate Group in Department 1.

• Explainable AI
• Machine Learning
• Software Verification
• +6 more

Alnis Murtovi is an Assistant Professor at TU Dortmund University's Faculty of Computer Science, Department of Software Engineering. They are a prominent researcher in the fields of explainable AI, machine learning, and software verification, collaborating extensively with Professor Bernhard Steffen and other colleagues at the university. Dr. Murtovi's research focuses on making machine learning models more interpretable and verifiable, particularly through work on decision trees, random forests, and neural networks. Their recent publications demonstrate a strong emphasis on developing practical tools and compilation-based approaches to explain complex models while maintaining performance. The research spans theoretical foundations in formal methods to practical implementations like the Forest GUMP tool for verification and explanation. The publication trend shows a consistent output with increasing focus on explainable AI in recent years, with significant contributions in 2023-2025. Their work bridges the gap between theoretical computer science and practical machine learning applications, particularly in verification techniques for AI models. The research has strong connections to software engineering principles and formal methods. Dr. Murtovi has made substantial contributions to the field through numerous publications in top venues including TACAS, STTT, and various conference proceedings. Their work on Forest GUMP represents a significant practical contribution to the community for model verification and explanation. As part of the research group led by Bernhard Steffen at TU Dortmund, Dr. Murtovi contributes to interdisciplinary projects that combine language-driven engineering approaches with practical software development challenges. Their research has implications for both academic and industrial applications where model interpretability and verification are critical requirements.

Igor VatolkinView profile

Researcher

• Music Information Retrieval
• Machine Learning
• Computational Intelligence
• +3 more

Dr. Igor Vatolkin is a researcher at TU Dortmund University's Faculty of Computer Science, specifically within the Algorithm Engineering department (Chair 11). As of August 2023, he holds the position of Akademischer Rat at RWTH Aachen University's Chair for AI Methodology while maintaining an affiliation with TU Dortmund. His research focuses on Computational Intelligence applied to Music Information Retrieval (MIR) and Music Data Analysis, emphasizing multi-objective optimization, feature selection, and interpretable machine learning models. He teaches courses such as 'Music Data Analysis' and 'Music Informatics' at TU Dortmund, blending computer science with musicological applications. Vatolkin is a co-editor of the textbook Music Data Analysis: Foundations and Applications (CRC Press, 2016), which serves as a foundational resource in the field. His work also involves developing the AMUSE framework, a comprehensive tool for music data analysis. Key research interests include audio feature engineering, evolutionary algorithms for feature selection, and cross-modal music classification. He leads the Special Interest Group on Music Analysis (SIGMA), organizing regular seminars and fostering interdisciplinary collaboration. Vatolkin’s projects often bridge computational methods with creative applications, such as reactive accompaniment systems and automated music composition. Education: Ph.D. in Computer Science (university unspecified) Labs/Teams: SIGMA Group, AMUSE Framework Development Grants: DFG-funded projects on evolutionary optimization in music segmentation

Ali SunyaevView profile

Professor

• Health Information Technology
• Cloud Computing
• Distributed Ledger Technologies
• +3 more

Prof. Dr. Ali Sunyaev is a Professor of Computer Science at the Technical University of Munich (TUM), serving as Vice President of TUM Campus Heilbronn. He holds dual affiliations with the TUM School of Computation, Information and Technology and the TUM School of Management. His academic career includes roles as Director of the Institute for Applied Informatics and Formal Description Methods at the Karlsruhe Institute of Technology (KIT) and professorships at the Universities of Cologne and Kassel. Education: Studied Computer Science at TUM (2000–2005), earned a PhD in Computer Science and Information Systems focused on healthcare telematics security analysis. Research Interests: Interdisciplinary work on digital systems' societal impacts, health IT, cloud computing, blockchain, trustworthy AI, and information security. Notable Projects: Funded by DFG, Helmholtz Association, and Russian Science Foundation, exploring topics like federated learning, AI accountability, and decentralized systems. Awards: AIS Distinguished Member (Cum Laude), KIT Faculty Teaching Award (2020) Labs/Teams: Leader of the Karlsruhe Decision & Design Lab (KD²Lab), part of DFG Review Board and German Informatics Society (GI) Board. Grants: Multiple projects on health AI, blockchain in mobility, and cloud security certifications. Future Focus: Developing AI accountability frameworks, advancing federated learning applications in healthcare, and addressing ethical challenges in decentralized technologies.

• Schizophrenia
• Psychopharmacology
• Meta-Analysis Methods
• +3 more

Prof. Stefan Leucht is a Professor of Psychiatry and Psychotherapy at the TUM School of Medicine and Health, Technische Universität München. His work focuses on schizophrenia pharmacotherapy, meta-analysis methodologies, and treatment-resistant mental disorders. He leads international studies on antipsychotic efficacy, diagnostic scale validation (e.g., PANSS), and clinical guideline development (e.g., INTEGRATE guidelines). His research bridges clinical practice and evidence synthesis, addressing issues like side effects of antipsychotics and renaming schizophrenia to better reflect symptomatology. Research interests include optimizing treatment algorithms, understanding drug mechanisms (e.g., clozapine), and improving quality-of-life measurements for psychiatric patients. He collaborates globally on projects such as the PRoGO framework for multidimensional mental healthcare and the COSMIN reviews of diagnostic tools. His work emphasizes patient-centered approaches and reducing stigma through terminology reform. Recent publications (2023-2025) highlight contributions to psychedelic psychosis reviews, machine learning for mortality risk assessment in bipolar disorder, and digital health interventions for schizophrenia. He advises on guideline implementation strategies and has developed tools like the Shared Decision Making Assistant (SDMA) for antipsychotic selection. Prof. Leucht’s expertise spans clinical trials, systematic reviews, and meta-analyses, with a focus on translating research into actionable clinical practices. His team addresses gaps in understanding treatment-resistant cases and promotes personalized medicine approaches in psychiatry.

Gihanna ST GalindezView profile

Researcher

• Bioinformatics
• Systems Biology
• Computational Biology
• +5 more

Gihanna ST Galindez is a Researcher at the Chair of Computational Molecular Medicine, Technische Universität München (TUM), Germany (since 2024). She also serves as a Data Steward at the Munich Data Science Institute. Her academic journey includes a PhD in Data Science in Biomedicine from TU Braunschweig (2021–2024) and a prior PhD at TUM's Chair of Experimental Bioinformatics (2019–2021). She holds an M.Sc. in Life Science Informatics from the University of Bonn (2016–2019) and a B.Sc. in Molecular Biology and Biotechnology from the University of the Philippines–Diliman (2007–2011). Her research focuses on computational biology, systems biology, and network medicine. Key areas include gene regulatory network inference, drug repurposing via platforms like NeDRex, and bioinformatic analysis of genomic and transcriptomic data. Her work integrates machine learning, network analysis, and computational tools to address challenges in disease modeling, infectious diseases, and marine organism growth biology. Her recent publications highlight advancements in DNA language models for functional genomics, network-based drug discovery strategies for SARS-CoV-2 and infectious diseases, and transcriptome analysis in marine species like Holothuria scabra. She actively contributes to open-source tools and platforms for biomedical research. Galindez has held roles ranging from bioinformatician to patent examiner, reflecting her interdisciplinary expertise. Her work bridges computational methods with translational applications in medicine and marine biology.

Julien GagneurView profile

Professor

• Genomics
• Computational Biology
• RNA Sequencing Analysis
• +5 more

Julien Gagneur is a Full Professor at the Technische Universität München (TUM) and a Group Leader at Helmholtz Munich. He holds a PhD in Applied Mathematics from École Centrale Paris and a Master's in Machine Learning from École Normale Supérieure de Cachan. His research focuses on computational methods for genomics, with an emphasis on developing tools like FRASER (for splicing outlier detection) and OUTRIDER (RNA sequencing outlier analysis) to advance rare disease diagnostics. His work integrates multi-omics data, including genomics, transcriptomics, and proteomics, to uncover genetic and molecular mechanisms underlying diseases. Key research areas include rare disease genomics, aberrant splicing detection, and machine learning applications in biology. He has contributed to clinical implementation of RNA sequencing for Mendelian disorder diagnosis and pioneered methods for analyzing single-cell epigenomics and chromatin accessibility. His academic journey includes roles as an Assistant Professor at TUM (2016–2019) and a Group Leader at the Gene Center, LMU Munich (2012–2015). He has advised numerous students, including Ziga Avsec, Daniel Bader, and others, many of whom have contributed to impactful projects in computational biology and genomics. Dr. Gagneur's lab collaborates globally, particularly through initiatives like the Solve-RD consortium, enhancing rare disease diagnostics through harmonized RNA-seq frameworks. His work bridges computational innovation with clinical applicability, addressing pressing challenges in genomic medicine.

• Learning Analytics
• Artificial Intelligence in Education
• Educational Technology
• +3 more

Hendrik Drachsler is a Professor for Educational Technologies at Goethe University Frankfurt am Main and the DIPF | Leibniz Institute for Educational Research and Information. He also serves as Head of Unit for Educational Technologies and Director of studiumdigitale, a central innovation unit for Technology-Enhanced Learning at Goethe University. His primary affiliation is with the Information Centre for Education department. Drachsler's professional journey includes roles such as Guest Professor at the Open University Netherlands (since 2023) and Associate/Assistant Professor positions at the Open University of the Netherlands. He holds a PhD in Technology-Enhanced Learning and has extensive experience in research, IT consulting, and educational technology development. Research Interests : His work focuses on Learning Analytics, AI in Education, Assessment and Feedback mechanisms, and Learning Design. He leads projects like ALICE, AR4STE(A)M, and DELTA, which explore innovative educational technologies, gamification, and formative assessment systems. Grants & Projects : Involved in major initiatives such as DiFA (Digital Formative Assessment), ML2MT (Machine Learning to Teaching), and Hyperchalk (online educational tools). His work emphasizes integrating AI and data analytics to enhance learning outcomes. Labs/Teams : Leads studiumdigitale and collaborates with institutions like the Welten Institute and CELSTEC. His research groups focus on developing dashboards, feedback systems, and adaptive learning technologies.