جستجوی استاد راهنمای دانشگاهی

• History of Science
• Digital Humanities
• Knowledge Diffusion and Transmission
• +۵ مورد دیگر

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
• +۶ مورد دیگر

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 Vatolkinمشاهده پروفایل

پژوهشگر

• Music Information Retrieval
• Machine Learning
• Computational Intelligence
• +۳ مورد دیگر

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 Sunyaevمشاهده پروفایل

استاد

• Health Information Technology
• Cloud Computing
• Distributed Ledger Technologies
• +۳ مورد دیگر

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
• +۳ مورد دیگر

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 Galindezمشاهده پروفایل

پژوهشگر

• Bioinformatics
• Systems Biology
• Computational Biology
• +۵ مورد دیگر

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 Gagneurمشاهده پروفایل

استاد

• Genomics
• Computational Biology
• RNA Sequencing Analysis
• +۵ مورد دیگر

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
• +۳ مورد دیگر

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.

Moritz von Zahnمشاهده پروفایل

پژوهشگر

• Artificial Intelligence
• Machine Learning
• Causal Inference
• +۴ مورد دیگر

Dr. Moritz von Zahn is a Researcher at the Institute for Information Systems and Information Management, Goethe University Frankfurt, where he joined in February 2021. His research employs quantitative methods to investigate artificial intelligence applications, development, and societal impact, with current projects focusing on causal machine learning and explainable AI for economic problem-solving. He earned a doctorate in business informatics summa cum laude in 2024 under Prof. Oliver Hinz at Goethe University. Prior to this, he completed Bachelor's and Master's degrees in industrial engineering with distinction from Karlsruhe Institute of Technology (KIT), including visiting research periods at UNAM Mexico City, IST Lisbon, ETH Zurich, and Boston University. Von Zahn's work spans AI-driven solutions in e-commerce, mobile marketing, and sustainability. Key themes include causal targeting for mobile push notifications, fairness-explainability-performance trade-offs in AI systems, and 'smart green nudging' to reduce product returns. His methodology emphasizes causal inference to move beyond predictive correlations toward actionable insights. Analysis of his 15 most recent publications reveals a consistent trajectory toward integrating causal machine learning with explainable AI frameworks, particularly addressing ethical dimensions in recommendation systems and digital marketing. Recent work increasingly incorporates human factors, examining how AI explanations affect user trust and decision-making in commercial contexts. His research excellence is recognized through multiple awards: WISE Best Paper Award Dissertation Prize of the Faculty (Foit Foundation) INFORMS Best Paper Award in Information Systems Research AIS Best Information Systems Publication Award Finalist, INFORMS Gary Lilien Practice Prize Competition With industry experience at Robert Bosch, Swisscom, Payback, and McKinsey, von Zahn bridges academic rigor and practical implementation. He operates within Prof. Hinz's research team at Goethe University's Business Informatics Professorship, contributing to the institute's focus on information systems innovation while maintaining active industry collaborations.

Gero Szepannekمشاهده پروفایل

استاد

• Machine Learning
• Data Science
• Statistics
• +۴ مورد دیگر

Prof. Dr. rer. nat. Gero Szepannek is a Professor at the Faculty of Business at Stralsund University of Applied Sciences, specializing in Statistics, Business Mathematics, and Machine Learning. He teaches courses such as Analytical Statistics, Exploratory Data Analysis, and Data Science for Business. His research focuses on clustering algorithms for mixed-type data, machine learning interpretability, credit scoring models, and educational technology. Notable contributions include work on k-prototypes initialization strategies, explainable AI, and R package development for data science (e.g., clustMixType , mlr3shiny ). Prof. Szepannek organizes the European Conference on Data Analysis (ECDA2026) and participates in academic committees such as the GfKl. His work emphasizes practical applications in finance, healthcare, and education. He advises students on thesis topics in Machine Learning/Data Science/Statistics and engages in R User communities.

Oliver Burgertمشاهده پروفایل

استاد

• Medical Informatics
• Artificial Intelligence in Healthcare
• Surgical Robotics
• +۴ مورد دیگر

Oliver Burgert is a full Professor of Computer Science specializing in Medical Informatics at Reutlingen University's Faculty of Informatics. He holds leadership roles as the current Dean and spokesperson of the Computerassisted Medicine research group (CaMed). His research focuses on intelligent surgical assistance systems, medical image processing with AI, and optimizing operating room workflows through interoperable systems. Education & Career: - Since 2011: Professor at Reutlingen University - 2007-2011: Founded and managed SWAN - Scientific Workflow Analysis GmbH - 2005: PhD from Karlsruhe Institute of Technology on surgical planning and simulation systems using volumetric data. Research Interests: Burgert's work spans: - Intelligent Cognitive Operating Rooms : Developing context-aware systems for real-time situational awareness - Intraoperative Assistance Systems : Including robotic systems, surgical navigation, and automated documentation - Medical Image Analysis : Focusing on AI-driven tumor segmentation and image fusion techniques. His projects often integrate standards like DICOM, BPMN, and IEEE 11073 for medical device interoperability. Publications & Innovation: Recent work includes: - Explainable AI systems for neurosurgery - Prototypes like the OR-Pad sterile information system - Workflow management frameworks using OMG standards. Over 50 peer-reviewed articles since 2011, with focus on surgical informatics and medical device integration. Awards & Recognition: While no specific awards listed, his work has been presented at leading conferences like MICCAI, SPIE Medical Imaging, and CARS. Grants & Collaboration: - Active in EU-funded projects like HEIDI and TeleDerm - Partnerships with Siemens Healthineers and other medical tech companies. - Leads interdisciplinary teams involving clinicians, engineers, and data scientists. Labs & Infrastructure: Runs the Computerassisted Medicine Lab with capabilities in: - Surgical simulation environments - AR/VR prototyping - Medical device integration testing.

Sonja Lückمشاهده پروفایل

پژوهشگر

• Empirical Economics
• Patent Analysis
• Machine Learning
• +۴ مورد دیگر

Dr. Sonja Lück is an academic affiliated with the University of Paderborn's Faculty of Business Administration and Economics, specifically within the Department 4: Economics. She contributes to the Empirical Economics research group under Prof. Schmitz. Her work spans empirical economic studies, innovation policy, and higher education management. She holds an office at Q4.331 and maintains teaching responsibilities in statistical methods and econometrics. Her research focuses on patent analysis, machine learning applications in economics, and the dynamics between academic research and teaching. Notable projects include studies on knowledge spillovers through co-inventor networks, effects of early invention disclosure, and the integration of NLP techniques for patent corpus analysis. Teaching duties include courses such as Empirische Wirtschaftsforschung mit Stata , Applied Microeconometrics using STATA , and quantitative methods in statistics. She plays a role in university services as an Upper Studies Councillor, focusing on improving academic administration and student support systems.

Simon Hentschelمشاهده پروفایل

پژوهشگر

• Machine Learning
• Computer Vision
• Climate Modeling
• +۴ مورد دیگر

Simon Hentschel, M.Sc., is a researcher at the Chair of Data Science (Informatik X) within the Faculty of Mathematics and Computer Science at the University of Würzburg. He joined the institution in November 2024 after completing his Master's degree in Computer Science. His work focuses on interdisciplinary applications of machine learning, particularly bridging climate modeling with computer vision techniques. His research spans topics including: Climate modeling using deep learning architectures Computer vision for environmental data interpretation Transformer-based models for vegetation activity analysis Image aesthetic assessment using CLIP and vision-language frameworks Transfer learning from weather prediction models to ecological monitoring Recent work demonstrates his expertise in adapting foundational AI models like FourCastNet and CLIP to novel ecological and visual domains. His publications reveal a pattern of innovation in leveraging pre-trained architectures for specialized tasks while optimizing computational efficiency. Beyond technical contributions, he maintains active involvement in: Collaborative research projects like BigData@Geo 2.0 Laboratory infrastructure at CAIDAS (Center for Artificial Intelligence and Data Science) Development of open-source models and datasets Contact: Room 50.03.009, Emil-Fischer-Straße 50, 97074 Würzburg, Germany

Sebastian Wankerlمشاهده پروفایل

پژوهشگر

• Machine Learning
• Natural Language Processing
• Deep Learning
• +۳ مورد دیگر

Sebastian Wankerl is a researcher at the Chair of Data Science (Informatics X) within the Faculty of Mathematics and Computer Science at the University of Würzburg. His work focuses on machine learning applications in mathematics education, natural language processing, and structured sentiment analysis. He collaborates with Prof. Andreas Hotho and other colleagues on projects involving pointer networks, cross-lingual models, and imbalanced data challenges. Key research areas: Mathematical reasoning, NLP, and deep learning Recent publications explore tree-structured pointer networks for axiom detection and cross-lingual sentiment analysis Active in ML explainability and multilingual model adaptation His publications demonstrate expertise in adapting transformer architectures for specialized tasks while addressing overfitting challenges through novel regularization techniques.

Frank Heyenمشاهده پروفایل

پژوهشگر

• Visualization
• Human-Computer Interaction
• AI-Assisted Music Composition
• +۳ مورد دیگر

Frank Heyen is a Researcher at the Visualization Institute of the University of Stuttgart (VISUS) , focusing on interdisciplinary applications of visualization and interactive systems in music, education, and data analysis. His work bridges computer science, music technology, and human-computer interaction. Research Interests : Heyen specializes in creating visualization tools for AI-assisted music composition, augmented reality (AR) applications for instrument learning, and immersive analysis of musical performance data. His projects include interactive systems for classifier comparison (ClaVis), AR guitar tutorials (AR Hero), and haptic feedback devices (PropellerHand). Scientific Contributions : His publications highlight collaborations with Michael Sedlmair and others, emphasizing user-centered design and real-time data interaction. In 2025, his paper MAICO received the Replicability Stamp by the Graphics Replicability Stamp Initiative (GRSI), underscoring methodological rigor. His work spans conferences like IEEE Transactions on Visualization and Computer Graphics , ISMIR, IEEE VR, and ACM CHI workshops. Awards : Replicability Stamp by GRSI for MAICO (2025)