Find Academic Supervisors

• Machine Learning
• Explainable Artificial Intelligence (XAI)
• Safety in Learning Processes
• +3 more

Antonia Holzapfel is a doctoral researcher at the Institute for Data Science in Mechanical Engineering (DSME) of RWTH Aachen University. Supervised by Prof. Sebastian Trimpe, her work focuses on safe and explainable machine learning (XAI) for robotics and time-series modeling, with applications in mechanical engineering and medical technology. Bachelor's in Mechanical Engineering (Design Engineering specialization) Master's in General Mechanical Engineering (Simulation Technology and Medical Technology specializations) Her research addresses safety in learning processes , including Bayesian optimization for quadcopters and concept extraction for model interpretability. Her Friedrich-Wilhelm Award -winning thesis (2024) explored safe online learning in time-varying environments, presented at the L4DC conference. She contributes to DFG-funded projects on data-driven process modeling in forming technology. Key scientific contributions include: Advancing safe Bayesian optimization for autonomous systems Developing explainable AI methods for time-series analysis Improving model robustness through internal representation patterns Scientific awards: Friedrich-Wilhelm Award for outstanding Master's thesis (2024) Antonia's work bridges machine learning safety with practical applications in robotics and industrial processes.

Stephen MeisenbacherView profile

Researcher

• Privacy-preserving Natural Language Processing (NLP)
• Differential Privacy
• Hybrid, Expert-Driven Classification Systems
• +5 more

Stephen Meisenbacher is a Research Associate at the Technical University of Munich (TUM) , affiliated with the School of Computation, Information and Technology and the Department of Computer Science, I19 . He has been part of the Software Engineering for Business Information Systems (SEBIS) chair since March 2022. His research focuses on Privacy-Preserving Natural Language Processing (NLP) , Differential Privacy , and Privacy-Enhancing Technologies (PETs) , with a particular interest in their integration into software development and business applications. His work also explores Hybrid, Expert-Driven Classification Systems and Usable Privacy solutions. Stephen’s recent publications address trends in AI Privacy Risks , Text Rewriting with DP , Legal AI Use Cases , and Data Protection Compliance . He has contributed to GDPR-related research and PETs adoption in small enterprises. His teaching includes Natural Language Processing seminars and Software Engineering lecture courses for Master’s and Bachelor’s students at TUM. He also organizes Entrepreneurship for Small Software-Oriented Enterprises seminars. Stephen holds a Master’s in Informatics from TUM (DAAD Graduate Scholarship) and a Bachelor’s in Computer Science from the University of Notre Dame, with additional studies in German Language and Literature. Contact: stephen.meisenbacher@tum.de | LinkedIn

Yolanda GilView profile

Research Professor

• Knowledge capture
• Semantic Workflows
• Ontology Tools
• +7 more

Dr. Yolanda Gil is a Research Professor in Computer Science and Spatial Sciences at the University of Southern California, where she serves as Principal Scientist and Senior Director for Strategic Initiatives in Artificial Intelligence and Data Science at the Information Sciences Institute (ISI). She is also the Director of AI and Data Science Initiatives in the Viterbi School of Engineering and leads the USC Center for Knowledge-Guided Interdisciplinary Data Science (CKIDS). Dr. Gil received her Licenciatura in Computer Science from the Polytechnic University of Madrid and her M.S. and Ph.D. in Computer Science from Carnegie Mellon University, with a focus on artificial intelligence and cognitive science. Her research focuses on developing AI approaches that use knowledge to accelerate scientific discovery processes. Her key research interests include knowledge capture and representation, semantic workflows, ontology tools, scientific discovery methods, task-based collaboration, provenance tracking, knowledge networks, reproducibility in science, and machine learning for data analysis. She collaborates with scientists across multiple domains to improve how scientific knowledge is created, shared, and used. Dr. Gil's work has significant impact across multiple scientific domains including climate science, neuroscience, and omics research. Her projects demonstrate her commitment to building knowledge-guided systems that transform how scientists conduct research. She has pioneered approaches to capture the provenance of scientific experiments and to automate the analysis of complex scientific data. Fellow of the Association for Computing Machinery (ACM) Fellow of the Association for the Advancement of Science (AAAS) Fellow of the Institute of Electrical and Electronics Engineers (IEEE) Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) 24th President of the Association for the Advancement of Artificial Intelligence Co-chair of the CRA/AAAI 20-Year Artificial Intelligence Research Roadmap for the US Initiator and leader of the W3C Provenance Group that resulted in a widely-used standard for web trust As an educator and leader, Dr. Gil directs the Data Science Program in Computer Science and serves as Co-Director of multiple joint MSc programs including Communication Data Science, Spatial Data Science, Environmental Data Science, Public Policy Data Science, and Healthcare Data Science. She also leads the new dual degree USC-Tsinghua University on Communication Data Science. Her leadership extends to mentoring numerous students and researchers in AI and data science. Through the USC Center for Knowledge-Guided Interdisciplinary Data Science (CKIDS), Dr. Gil organizes DataFest events each semester, fostering collaboration and innovation in data science across disciplines.

• Medical Informatics
• Artificial Intelligence in Healthcare
• Healthcare Data Integration
• +7 more

Prof. Martin Boeker is a Professor of Medical Informatics at the Technical University of Munich (TUM), affiliated with the TUM School of Medicine and Health. His work focuses on advancing healthcare through AI-driven solutions, interoperability frameworks, and precision medicine initiatives. Key projects include the German Medical Text Corpus (GeMTeX) and the MIRACUM DIFUTURE Alignment Hub. Expertise: Medical Informatics, AI in Healthcare, Federated Learning, Health Data Integration Key Contributions: FHIR-based systems, clinical decision support, patient-centered outcomes research Leadership: Director of the Institute for AI and Informatics in Medicine at TUM Hospital Right of the Isar Research emphasizes bridging clinical practice and data science through projects like modular health crawlers, automated guideline adherence monitoring, and cross-institutional medical NLP solutions. His work spans oncology informatics, rare disease management, and pandemic response data ecosystems. Recent articles highlight innovations in digital twins for precision oncology, federated analysis in oncology, and German-language medical NLP challenges. He collaborates internationally on EHR standardization and healthcare interoperability, contributing to the Medical Informatics Initiative (MII) and pandemic evidence ecosystems. Grants and collaborations involve the German Federal Ministry of Education and Research, European initiatives, and industry partnerships. Educational efforts focus on training future medical informatics professionals through MII competency programs.

Feng ChengView profile

Researcher

• Data-Driven Security Engineering
• Security Analytics
• Network Security
• +19 more

Dr. Feng Cheng is a Senior Researcher and head of the IT Security Engineering (Sec-Eng) Team at the Hasso Plattner Institute (HPI), Germany, and serves as the Representative of the Chair "Internet Technologies and Systems" at the Digital Engineering Faculty, University of Potsdam. He holds a PhD from the University of Potsdam, an MEng from Beijing University of Technology, and a BEng from Beijing University of Aeronautics and Astronautics. His research is centered on data-driven security engineering, security analytics, network security, cloud security, firewall, IDS/IPS, attack modeling, and penetration testing. Dr. Cheng's research interests span a broad range of cybersecurity domains, with a strong emphasis on security analytics , data-driven threat detection , cloud and network security , and authentication . His work integrates advanced data engineering and machine learning techniques to enhance security operations, including intrusion detection, threat intelligence, and security information and event management (SIEM). Key application areas include IoT security, mobile security, and web security. The recent publications of Dr. Cheng and his team reflect a significant trend towards leveraging big data analytics , machine learning , and graph-based methods for cybersecurity. Their work focuses on developing frameworks for blockchain-based public key infrastructures, advanced SIEM analytics, malware detection using NLP, and chaos engineering for cloud security. This demonstrates a strong commitment to creating practical, scalable, and intelligent solutions for modern security challenges in complex, distributed environments. Dr. Cheng is actively involved in the academic community as a member of ACM and IEEE, a program committee member for numerous international conferences, and an organizer of workshops on cloud computing and cybersecurity. He has also served on editorial boards for international journals. He supervises a large group of PhD, master's, and bachelor's students, guiding research on topics such as security analytics, cyber threat intelligence, digital credentials, and behavioral authentication. He leads the Sec-Eng team, which conducts research on a diverse portfolio including the Security Analytics Lab, HPI-VDB (vulnerability database), Lock-Keeper (physical separation technology), and the Security Lab Generator for scenario-driven security training.

• Materials Science
• Corrosion Engineering
• Alloy Design
• +3 more

Dr. KN Sasidhar is a Researcher in the Department of Microstructure Physics and Alloy Design at Heinrich Heine University Düsseldorf. His work focuses on advanced materials science, particularly corrosion mechanisms, alloy design, and nanoscale structural analysis. He employs cutting-edge techniques like in situ synchrotron investigations and deep learning frameworks to study material behavior under extreme conditions. Current research emphasizes corrosion resistance in stainless steels, phase transformations during nitriding, and radiation effects on coatings. Key achievements include pioneering studies on nanoscale amorphization in metallic systems, data-centric approaches for materials discovery, and the development of predictive models for alloy performance. His work bridges experimental materials characterization with computational methods, addressing challenges in energy and aerospace applications. Publications span corrosion analysis, microstructural evolution under irradiation, and phase separation phenomena. Collaborative projects involve synchrotron facilities and interdisciplinary teams focusing on materials informatics. No formal awards or grants are explicitly listed in the provided texts, though his prolific publication record indicates active academic engagement.

Elena SimperlView profile

Professor

• Human-centric AI
• Knowledge Graphs
• Knowledge Engineering
• +7 more

Elena Simperl is a Professor of Computer Science and Deputy Head of Department for Enterprise and Engagement at King's College London's Department of Informatics. She co-directs the King's Institute for Artificial Intelligence and serves as Director of Research for the Open Data Institute. As a Hans Fischer Senior Fellow at the Technical University of Munich's Institute for Advanced Study, she leads the Trustworthy Knowledge Graphs focus group and contributes to advancing human-centric AI research across European institutions. Professor Simperl obtained her doctoral degree in Computer Science from the Free University of Berlin and her diploma from the Technical University of Munich. Prior to joining King's, she held academic positions in Germany, Austria, and at the University of Southampton, and was a Turing Fellow. Her career trajectory demonstrates consistent leadership in bridging academic research with practical applications in data ecosystems. Her research sits at the critical intersection of AI and social computing, focusing on human-centric approaches to building sociotechnical systems that integrate data, algorithms, and human capabilities. She investigates how to make knowledge engineering more accessible, how to leverage collective intelligence for data quality improvement, and how to design participatory AI systems that address societal challenges like misinformation. Her work spans knowledge graphs, semantic technologies, crowdsourcing, and open data, with particular emphasis on the social dimensions of data-intensive systems and the governance frameworks needed for trustworthy AI deployment. Analysis of her recent publications reveals a strong evolution toward integrating large language models with traditional knowledge engineering practices while maintaining human oversight. There's a clear trajectory from foundational work on knowledge representation toward increasingly applied research addressing real-world challenges in media ecosystems, citizen science, and data governance, with growing attention to policy implications of AI technologies. Fellow of the British Computer Society Fellow of the Royal Society of Arts Hans Fischer Senior Fellow at TUM-IAS (2023) Ranked among top 100 most influential scholars in knowledge engineering of the last decade Included in Women in AI 2000 ranking Professor Simperl has led 14 major European and national research projects totaling millions in funding, including MediaFutures (a Horizon 2020 program tackling online misinformation), QROWD, ODINE, Data Pitch, and ACTION. She currently co-chairs the Croissant working group in ML Commons developing data standards for AI, and serves as president of the Semantic Web Science Association. Her research has directly influenced the development of data ecosystems supporting startups and citizen science initiatives across Europe, demonstrating exceptional ability to translate theoretical advances into practical impact. As Director of Research at the Open Data Institute, she oversees initiatives connecting data entrepreneurs with artists and civic organizations. Her leadership in the MediaFutures project established a data-driven innovation hub that supported 51 startups/SMEs and 43 artists through three open calls, creating a sustainable model for arts-technology collaborations addressing media challenges. Her work with the ODINE project helped create a European ecosystem for data-driven startups, demonstrating her commitment to building practical applications of open data principles.

• Machine Learning
• Reinforcement Learning
• Robotics
• +3 more

Georg Martius is a Full Professor in the Department of Computer Science at the University of Tübingen's Faculty of Science and a Max Planck Research Group Leader at the MPI for Intelligent Systems. Since April 2023, he has been a core member of the DFG-funded Cluster of Excellence 'Machine Learning: New Perspectives for Science,' which received extended funding through 2032 for its mission to integrate machine learning into fundamental scientific discovery processes. His academic foundation includes a PhD from the University of Göttingen and Bernstein Center for Computational Neuroscience (2005), a Diploma in Computer Science from the University of Leipzig (2003), and a visiting research period at the University of Edinburgh's Division of Informatics. Postdoctoral positions followed at the Max Planck Institutes for Dynamics and Self-Organization (Göttingen, 2009), Mathematics in the Sciences (Leipzig, 2010), and IST Austria (2015). Professor Martius's research pioneers the intersection of reinforcement learning, robotics, and tactile sensing, with emphasis on developing autonomous systems capable of natural locomotion, dexterous manipulation, and physical-world understanding. His work bridges theoretical machine learning with practical hardware applications, particularly in creating differentiable simulators, superresolution tactile sensors, and biologically plausible learning frameworks for robotic control. Analysis of his 2024-2025 publications reveals dominant trends in offline reinforcement learning (especially goal-conditioned and diversity-maximization techniques), object-centric representation learning for video understanding, and tactile sensing innovations. A strong thread connects foundation models to world model construction, while his work on differentiable physics engines enables precise collision handling and contact dynamics for real-world robotic control. His leadership roles include directing the Distributed Intelligence research team at Tübingen and contributing to major collaborative initiatives like the Real Robot Challenge and Myochallenge 2022. The Cluster of Excellence appointment represents recognition of his contributions to transforming scientific methodology through machine learning, particularly in automating hypothesis generation and experimental design. Current projects focus on integrating large-scale machine learning with embodied intelligence, advancing tactile perception systems like the Minsight vision-based sensor, and developing neuroplasticity-inspired approaches for robust out-of-distribution detection. His work directly impacts fields requiring physical interaction intelligence, from autonomous navigation to medical robotics, with emphasis on sample-efficient learning from limited real-world data.

• Self-organized criticality
• Neuronal network dynamics
• Excitation/Inhibition balance
• +5 more

Anna Levina is an Assistant Professor for Computational Neuroscience at the University of Tübingen , affiliated with the Department of Computer Science under the Faculty of Science. Her research focuses on the self-organization of neuronal activity, critical dynamics in neural networks, and the excitation/inhibition balance in cortical circuits. Current positions: Assistant Professor (since 2018), Group Leader (2017-2018), Equality Officer (Computer Science) Previous roles: IST Fellow (2015-2017), Associated Researcher (2011-2015), Postdoc/PI (2011-2015), Postdoc (2008-2011) Her research integrates mathematical modeling , statistical physics , and computational neuroscience to study criticality phenomena, neural avalanches, and adaptive network dynamics. Key interests include: Self-organized criticality in neural systems Excitation/Inhibition balance mechanisms Network topology and dynamics Timescale analysis in neural processing Stochastic modeling of neural activity Recent publications reveal trends in understanding critical dynamics across biological and artificial networks, with applications to memory systems, sensorimotor integration, and disease modeling. She has received recognition as an IST Fellow .

Massimo FornasierView profile

Professor

• Mathematical Modeling
• Data Analysis
• Image/Signal Processing
• +5 more

Prof. Massimo Fornasier holds the Chair of Applied Numerical Analysis at the Technical University of Munich (TUM), within the School of Computation, Information and Technology and the Department of Mathematics. His research focuses on mathematical modeling, numerical analysis, and data-driven methods, particularly in areas like compression, sparse recovery, and optimization. He has made significant contributions to consensus-based optimization, control of multiagent systems, and applications in image/signal processing. Education: PhD in Computational Mathematics, University of Padua (2003) Postdoctoral fellowships at University of Vienna, Sapienza University of Rome, and Princeton University Awards: ERC Starting Grant (2012) START Prize (2011) Prix de Boelpaepe (2009) His work bridges theoretical analysis and computational methods, with applications ranging from compressive sensing to machine learning. Recent research emphasizes consensus-based optimization frameworks and their global convergence properties. Editorial roles include journals like Networks and Heterogeneous Media and Calcolo . He leads research groups in areas such as Data Science and Numerical Analysis at TUM.

• Computational Pathology
• Medical Image Analysis
• Deep Learning
• +4 more

Nasir M. Rajpoot is a Professor in the Department of Computer Science at the University of Warwick, UK. His research focuses on computational pathology, medical image analysis, and deep learning applications in histology. He leads interdisciplinary projects integrating artificial intelligence with healthcare, particularly in cancer diagnostics and pathology workflows. Rajpoot’s work emphasizes developing robust algorithms for histology image analysis, including nuclear segmentation, tumor classification, and domain generalization in computational pathology. His contributions include the TIAToolbox, an open-source framework for tissue image analytics, and the CoNIC Challenge to advance nuclear detection and counting in histology images. He collaborates with clinicians and biologists to translate AI models into clinical practice, addressing challenges like tumor heterogeneity and staining variability. Rajpoot’s research spans colorectal, lung, and oral cancers, with a focus on predicting clinical outcomes via histological features and genomic data integration. Notable projects include the development of Handcrafted Histological Transformer (H2T) for unsupervised representations of whole slide images and the SAFRON framework for histology image synthesis. His work addresses domain adaptation, robustness evaluation, and explainability in AI-driven pathology systems.

• Data Visualization
• Scientometrics
• Science of Science
• +4 more

Katy Börner is a Professor affiliated with Indiana University, Bloomington, USA. Her research focuses on data visualization, scientometrics, and the science of science, with contributions to tools like Network Workbench (NWB) and Sci2. She leads interdisciplinary projects such as the Human Reference Atlas and explores visualization literacy in education and public health. Her work spans virtual reality applications, biomedical knowledge networks, and AI-driven scientific discovery. Key research interests include mapping scientific collaboration networks, analyzing scholarly publications, and developing visualization frameworks for big data. She collaborates widely with institutions like NIH and VIVO, advancing open science and interdisciplinary research. Her projects often bridge computational methods with human-centric design, enhancing understanding of complex systems in health, technology, and social sciences. Publications highlight innovations in interactive visualization tools (e.g., opioid crisis research networks), data integration (Human Reference Atlas), and AI applications in biomedical research. She emphasizes translating data into actionable insights through visual analytics, impacting policy, education, and healthcare.

Marc HodesView profile

Professor

• Heat transfer
• Thermal management
• Supercritical fluids
• +3 more

Marc Hodes is Professor in Mechanical Engineering and Mathematics at Tufts University. With a PhD from MIT, his research focuses on heat transfer phenomena with applications in electronics cooling, supercritical fluids, and thermoelectric systems. He directs the graduate program in Mechanical Engineering. Education: BS, University of Pittsburgh (1990) MS, University of Minnesota (1994) PhD, Massachusetts Institute of Technology (1998) Research Areas: Thermal management of electronics through microchannel cooling and liquid metal technologies; Apparent slip phenomena in microstructured surfaces; Mass transfer in supercritical CO 2 systems for aerogel processing; Thermoelectric module optimization for precision temperature control. Awards & Honors: NSF REU Fellowship (1989) E.T.S. Walton Visitorship Award Best Associate Editor, ASME Journal of Heat Transfer (2023) Research Leadership: Principal investigator on multiple NSF grants including projects on aerogel manufacturing, dropwise condensation, and analysis of convection in slip flows. Industry collaborations include Google, DARPA, and Bell Labs.

• Climate Policy
• Long-Term Policy
• International Institutions
• +3 more

Detlef F. Sprinz is a Senior Scientist at the Potsdam Institute for Climate Impact Research (PIK) and a Professor at the Faculty of Economics and Social Sciences of the University of Potsdam, Germany. During 2024-2025, he leads the project "AMaReNa - A Market for the Restoration of Nature" , focusing on carbon removals. His academic background includes a Ph.D. and M.A. in Political Science from the University of Michigan and an M.A. in Economics from the University of the Saarland. Research Interests: Climate policy at multiple levels, long-term policy design, evaluation of international/national institutions, European/international environmental policy, and modeling political decisions for sustainability. Notable Projects: Tandem Fellowship (2020-2022) for interdisciplinary course development, Visiting Researcher at University of Lund (2022-2023), and coordination of research at the Käte Hamburger Kolleg (2017). Publications emphasize climate cooperation under the Paris Agreement, effectiveness of environmental regimes, and predictive modeling for climate negotiations. His work spans empirical analyses of acid rain regulation, Kyoto Protocol implementation challenges, and bioenergy accounting errors. Scientific Awards & Memberships: Tandem Fellowship (German Stifterverband) Member, European Academy Co-founder, Ecologic Institute Teaching includes courses on global climate governance, political decision modeling, and environmental policy at the University of Michigan, Yale, University of Oslo, and University of Potsdam. He has contributed to IPCC’s Sixth Assessment Report as a Lead Author. Advisory Roles: Chairmanship of the European Environment Agency’s Scientific Committee (2009-2012), Senior Research Fellow at CICERO (2011-2013), and consultancy for governments, corporations, and think tanks.

Sarah PinkView profile

Professor

• Design Anthropology
• Emerging Technologies
• Automated Decision Making
• +5 more

Sarah Pink is a leading Professor at Monash University , holding a joint appointment across the Faculty of Art, Design and Architecture and the Faculty of Information Technology . She serves as Director of the FUTURES Hub and Emerging Technologies Research Lab , focusing on human futures in digital and net zero transitions. PhD in Social Anthropology, University of Kent Honorary Doctorates from Malmö and Halmstad Universities Fellow, Academy of the Social Sciences in Australia Her research spans design anthropology, emerging technologies, automated decision making, and future mobilities . Recent work examines: AI ethics in clean energy Smart homes for seniors Automated mobility futures Selected 2025 article trends show focus on technological ethics, urban data systems, and human-machine interaction across 6 subfields including automated decision making and sensory design. Major awards: CIOB Award for Best International Paper (2016) Asia Pacific Eldercare Innovation Gold (2021) Good Design Awards (2023) As research director for Monash Grid Innovation Hub and Human-Centred Computing , she leads projects funded by Australian Research Council, EU, and national research councils across five countries. Her lab engages 20 active projects and 20+ industry partners globally.