Find Academic Supervisors

• Psychology
• Educational Psychology
• Human-AI Interaction
• +9 more

Gerrit Anders is a scientific researcher and doctoral student at the Leibniz-Institut für Wissensmedien (IWM) in Tübingen, Germany, affiliated with the Perception and Action lab. He has been a member of the institute since January 1, 2023, and contributes to multiple interdisciplinary research projects at the intersection of psychology, education, and artificial intelligence. Education: B.Sc. in Physics, Ruprecht-Karls-Universität Heidelberg (2016) B.Sc. in Psychology, Ruprecht-Karls-Universität Heidelberg M.Sc. in Physics, Ruprecht-Karls-Universität Heidelberg (2021) His research focuses on video perception, mind wandering, event segmentation, emotional narration, and metacognition. He explores how the structure of videos influences learning and perception, and how artificial intelligence can support self-regulated learning. His work integrates machine learning methodologies to analyze behavioral and psychological data, particularly in educational contexts. The recent publications of Gerrit Anders reflect a strong trend in applying AI and computational methods to psychological and educational research. Key themes include human-AI interaction, misinformation, self-regulation in video learning, and large-scale analysis of online behavior. His software tools (e.g., mGPT, whisperTranscriber, SponScraper) demonstrate a commitment to open science and technical innovation. Scientific Awards: No awards explicitly mentioned. Gerrit Anders actively supervises and collaborates on research, though no formal advisees are listed. He has contributed to projects funded by institutional and collaborative grants, including longitudinal studies on human-AI interaction and educational AI applications. He has developed software and shared datasets, indicating active involvement in grant-supported open research initiatives. Labs and Teams: He is a core member of the Perception and Action lab and collaborates across multiple IWM labs, including Multimodal Interaction , Everyday Media , and Knowledge Construction . He co-organizes the Video-SRS workshop series, fostering collaboration in video-based learning research.

Bart KempenaersView profile

Professor

• Behavioural Ecology
• Ornithology
• Sexual Selection
• +3 more

Professor Bart Kempenaers serves as Director of the Department of Ornithology at the Max Planck Institute for Biological Intelligence in Seewiesen, Germany. Formerly known as the Department of Behavioural Ecology and Evolutionary Genetics (renamed in July 2023), this department conducts cutting-edge research in avian behavioural ecology. Kempenaers also holds the position of Honorary Professor in Behavioural Ecology at Ludwig-Maximilian University of Munich, a title he has held since 2004. His leadership guides a highly international research team focused on understanding how sexual selection drives biodiversity in bird species worldwide. Professor Kempenaers' academic foundation was established in Belgium where he was born in 1967. His formal education includes: Biology studies at the Universities of Limburg and Antwerp (completed 1989) PhD at the University of Antwerp (completed 1994) As a leading behavioural ecologist and ornithologist, Kempenaers' research centers on sexual selection and the evolution of avian mating behavior. His work investigates mate selection mechanisms, divorce patterns in bird pairs, and the prevalence and implications of extra-pair copulations. He coordinates field studies on arctic-breeding shorebirds with diverse mating systems, employing technology-assisted behavioral observations on large populations of free-living birds. His department maintains a comprehensive database linking life-history traits of approximately 10,000 bird species with environmental and ecological data, enabling large-scale comparative analyses of evolutionary patterns. Their research combines field observations, captive studies, and advanced statistical methods to understand how sexual selection shapes behavioral and life-history traits. Analysis of Kempenaers' recent publications reveals a strong interdisciplinary focus spanning behavioural ecology, evolutionary biology, and conservation science. His research consistently examines breeding site fidelity, sexual selection mechanisms, extra-pair paternity, and the ecological factors influencing avian coloration and behavior. Methodologically, his work incorporates innovative approaches including accelerometry, machine learning, and comparative analyses across diverse bird species. A significant portion of his research investigates shorebirds, particularly species with alternative reproductive tactics, while other studies explore urban adaptation, vocal communication, and host-microbiome interactions. Professor Kempenaers leads a highly international department that investigates how sexual selection acts as a driver of biodiversity. The department's work is characterized by technology-assisted behavioral observations on large numbers of individuals in free-living populations, complemented by captive studies and advanced statistical approaches. They maintain an extensive database linking life-history traits of the world's bird species with environmental data, enabling large-scale comparative analyses of evolutionary patterns. The department's research spans multiple continents, with field studies conducted in Europe, North America, and other regions where their study species breed.

Alyson van RaalteView profile

Researcher

• Mortality Disparities
• Lifespan Inequality
• Population Health
• +7 more

Alyson van Raalte is a Research Scientist at the Max Planck Institute for Demographic Research (MPIDR) in Rostock, Germany, where she is affiliated with the Laboratory of Population Health and the MaxHel Center. Her research focuses on mortality disparities, lifespan inequality, and socioeconomic determinants of population health. She earned her PhD in public health from Erasmus Medical Center, Rotterdam, and is an alumnus of the European Doctoral School of Demography. Her work integrates advanced demographic methods with public health applications to understand how inequalities in mortality evolve across populations and over time. Her research interests include lifespan inequality, healthy longevity, cohort analysis, demographic methods for measuring mortality variation, and the social determinants of health. She investigates how factors such as education, region, and childhood adversity contribute to disparities in life expectancy and lifespan variation. Her methodological contributions include perturbation analysis of lifespan variability and decomposition techniques for mortality inequalities. The trends in her recent publications reveal a strong focus on lifespan inequality, cohort mortality profiling, educational and regional disparities in mortality, and the impact of structural factors like smoking and reunification on population health. She frequently employs decomposition methods, multistate models, and innovative visualization techniques to analyze complex demographic data. European Demographer Award (mid-career) from Population Europe European Research Council (ERC) Starting Grant (LIFEINEQ) Max Planck Society STG Extension Award Alyson van Raalte has been supported by significant grants, including an ERC Starting Grant and Max Planck Society funding. She collaborates extensively with international researchers and contributes to large-scale projects such as COVerAGE-DB, a global database on age-structured COVID-19 cases and deaths. While no formal students are listed, she likely mentors junior researchers through her collaborative work and leadership in methodological innovation. She is actively involved in research groups focused on population health and contributes to software development, including R and Stata packages for Markov chain analysis. Her work bridges demographic theory, statistical methodology, and real-world public health challenges.

• History of Chinese Medicine
• Daoism and Religion
• Digital Humanities
• +5 more

Michael Stanley-Baker (徐源) is an Associate Professor in the History Department at Nanyang Technological University, specializing in the history of Chinese medicine and religion, particularly Daoism. He also serves as an associate of the UCL China Centre for Health and Humanities and as treasurer on the council of the International Association for the Study of Traditional Asian Medicines. Stanley-Baker received his PhD from University College London and holds a clinical degree in Chinese medicine from Ruseto College, Boulder CO. His academic journey includes work and study at Indiana University, Bloomington; Academia Sinica, Taipei; and the Needham Institute, Cambridge. He has conducted extensive fieldwork with healers in Taiwan and China, bridging scholarly and clinical perspectives. His research focuses on the intersection of religion, medicine, and science in Chinese history, with particular emphasis on Daoist healing practices and the circulation of medical knowledge. He employs innovative digital humanities methods to analyze historical medical texts, creating tools like the Polyglot Asian Medicine Knowledge Graph System and the DAOBUDMED6D corpus for studying Six Dynasties medical, Daoist, and Buddhist texts. His recent publications reveal a growing emphasis on transnational medical exchanges, the historical geography of drugs, and the political dimensions of medical knowledge. Stanley-Baker's work challenges conventional boundaries between 'medicine' and 'religion,' arguing for more nuanced understandings of how healing knowledge was organized and transmitted in pre-modern contexts. International Association for the Study of Traditional Asian Medicines Council Member Lead developer of Polyglot Asian Medicine digital project Co-editor of the Routledge Handbook of Chinese Medicine Stanley-Baker directs the 'Drugs across Asia' digital humanities project, which creates new tools for analyzing pharmacological knowledge in Asian historical texts. His work combines traditional philological approaches with cutting-edge digital methods, facilitating large-scale analysis of drug references across religious and medical canons. His research has important implications for understanding how medical knowledge circulated across cultural and political boundaries in pre-modern Asia.

David NgugiView profile

Researcher

• Microbial Ecology
• Host-Microbe Interactions
• Climate Change Microbiology
• +6 more

Dr. David Ngugi is a Researcher at GEOMAR - Helmholtz Centre for Ocean Research Kiel, where he leads the Aquatic Microbial Ecogenomics Group (AquaMEG) within the Research Division 3: Marine Ecology, specifically in the Marine Symbioses department. His work bridges microbial ecology, climate change impacts, and host-microbe interactions in aquatic systems. Dr. Ngugi's research focuses on two primary areas: (1) host-microbe interactions in non-model organisms, particularly examining microbial roles in sponge meta-organisms, invertebrate adaptation at marine/land boundaries, and the evolutionary history of herbivorous surgeonfish microbiomes; and (2) climate change microbiology in aquatic systems, investigating eco-evolutionary responses of microorganisms to environmental change and the relationship between microbial biodiversity and ecosystem services. His approach combines large-scale metagenomic and multi-omics data with computational biology techniques, including machine learning. His publication record shows a strong emphasis on microbial adaptation to environmental changes, with recent work spanning from European lakes to Red Sea ecosystems. His research demonstrates how microbial communities respond to and shape aquatic environments under climate change pressure, with particular attention to nitrogen cycling processes and host-microbe symbioses. ISME12 travel grant from the International Max Planck Research School DAAD Doctoral fellowship DFG research grants totaling over 500,000 € as PI and Co-PI (2020-2024) Dr. Ngugi serves as Managing Board Member for Peer Community in Microbiology and as Associate Editor for Marine Biology and Frontiers Microbiology. His research group includes MSc students, PhD candidates, and visiting scientists, reflecting his active role in mentoring the next generation of marine microbial ecologists. The group maintains extensive international collaborations across Germany, Saudi Arabia, South Africa, Tanzania, Switzerland, and Kenya.

Tronje KemenaView profile

Researcher

• Ocean Modeling
• Climate Dynamics
• Earth System Modeling
• +5 more

Tronje Kemena is a researcher at GEOMAR Helmholtz Centre for Ocean Research Kiel, focusing on ocean modeling, data analytics, and machine learning applications in climate science. His work primarily involves support and operation of the FOCI model for the BM Group, with emphasis on understanding Earth system dynamics and climate change impacts. Dr. Kemena completed his PhD in 2017 and Master's degree in 2014, both at Christian-Albrechts-Universität Kiel. His doctoral research focused on 'Assessment of Feedbacks in the Earth System under Anthropogenic Forcing: Two Case Studies,' while his Master's thesis examined 'Impact of the carbon cycle on the development of a Snowball Earth.' His research interests span ocean modeling, climate dynamics, Earth system science, and data-intensive approaches to understanding biogeochemical cycles. Kemena specializes in analyzing carbon cycle feedbacks, ocean deoxygenation processes, and the application of machine learning to climate and oceanographic data. His work often bridges theoretical modeling with practical data analysis techniques. Kemena's publication record shows a strong focus on Earth system modeling, with particular expertise in ocean biogeochemistry and climate dynamics. His recent work has increasingly incorporated data analytics and machine learning approaches, reflecting the growing importance of these techniques in climate science. He has made significant contributions to understanding carbon cycle feedbacks, ocean deoxygenation, and the development of ocean alkalinity enhancement techniques as potential climate intervention strategies. Dr. Kemena is actively involved in several research projects related to ocean-based climate solutions, including the OceanNETs project which focuses on developing and assessing ocean-based negative emission technologies. His work combines theoretical modeling with practical applications to address pressing climate challenges.

• Natural Language Processing
• Computational Linguistics
• Speech Processing
• +5 more

Philippe Blache is a senior researcher at the Laboratoire Parole & Langage (LPL), a joint research unit of the French National Centre for Scientific Research (CNRS) and Aix-Marseille University. With a career spanning several decades, he has established himself as a leading figure in computational linguistics, natural language processing, and conversation analysis. His work bridges theoretical linguistics with practical applications in language technology. His research focuses on several key areas: Natural language processing and computational linguistics Speech and conversational systems Multimodal interaction analysis Linguistic annotation and corpus development Cognitive modeling of language processing Blache's recent work (2020-2025) has centered on developing methods to evaluate spoken language features in conversational models, studying common ground instantiation through multimodal signals (including brain activity), and investigating how language models process complex linguistic phenomena. His research often involves creating and analyzing large multimodal corpora that capture not just verbal content but also nonverbal cues, prosody, gestures, and neuro-physiological signals. His publication record shows consistent high-quality output in top-tier venues, with numerous papers in LREC, COLING, and SIGDIAL proceedings. His work demonstrates a strong interdisciplinary approach, combining insights from linguistics, computer science, cognitive science, and neuroscience to advance our understanding of language processing. Among his notable contributions is the BrainKT corpus project, which represents an innovative approach to studying the cognitive and neural underpinnings of conversation through simultaneous recording of audio, video, and EEG signals during natural interactions. Blache has received recognition through his sustained research productivity and leadership in major projects. His collaborations span multiple institutions and disciplines, reflecting the interdisciplinary nature of his work and its broad relevance across fields studying human communication.

Jan-Bennet VoltmerView profile

Lecturer

• Computer-Supported Collaborative Learning (CSCL)
• Diversity in Education
• Distance Learning
• +3 more

Jan-Bennet Voltmer is a Lecturer in Social Psychology at the Distance University's Faculty of Psychology. His academic career includes a Dr. phil. in Business Psychology from Leuphana University (2020), M.Sc. in Psychology from TU Braunschweig (2013), and B.Sc. in Business Psychology from Leuphana (2011). Research focuses on computer-supported collaborative learning (CSCL) , student diversity effects , and gendered mental labor . Notable projects include the DivAdapt study analyzing diversity in CSCL groups and a systematic review on unpaid cognitive labor disparities. He teaches empirical research methods (R workshops) and coordinates the M6b internship program. Recent work explores social identity threat in online learning environments, the role of communication tools in group cohesion, and the cognitive dimensions of household labor division. His articles consistently address intersectional diversity impacts and methodological innovations like topic modeling for educational data analysis. Award-winning TRUST teaching intervention (2024) demonstrates his commitment to intrinsic motivation in medical education. Presentations span interdisciplinary venues including FGSP conferences and CATALPA research clusters.

Jürgen AdamyView profile

Professor

• Control and Systems Theory
• Mobile Robotics
• Autonomous Systems
• +3 more

Prof. Jürgen Adamy is a Professor at Technische Universität Darmstadt, leading the Control Methods and Robotics lab since 1998. His research focuses on Control and Systems Theory, Mobile Robotics, Autonomous Systems, and Artificial Intelligence. He previously held engineering roles at Siemens AG (1992–1998) and was a Scientific Assistant at Universität Dortmund (1987–1991). His work integrates theoretical advancements with practical applications in robotics, energy systems, and autonomous vehicles. Research interests span nonlinear control, autonomous navigation, and multi-objective optimization. Notable contributions include PRORETA systems for urban driving safety and UAV path-planning algorithms. Recent articles emphasize remote driving performance, energy management, and bioengineering's role in societal progress. His lab develops technologies for smart grids, human-robot cooperation, and advanced control systems. Collaborations include Siemens and interdisciplinary projects like bio-engineering applications. He has advised numerous projects in robotics and control theory, though specific student names are not listed here.

• Public Economics
• Labor Supply
• Inequality and Redistribution
• +5 more

Max Löffler is an Assistant Professor in the Department of Microeconomics and Public Economics at Maastricht University and a Research Fellow at the same institution since December 2023. He is also affiliated with the ERC-funded HIPPO project at the University of Cologne, reflecting his strong ties to leading European economic research centers. His research focuses on public economics and labor economics, with particular emphasis on taxation, inequality, labor supply modeling, and urban/regional economic issues. His work combines structural econometric methods with empirical analysis to evaluate the welfare and distributional effects of public policies. The recent publications reveal a consistent trend in studying the real-world impacts of taxation, labor market policies, and inequality, while expanding into newer domains such as climate change exposure and the long-term economic consequences of surveillance. His research often involves collaboration with prominent economists and utilizes large-scale microdata and policy simulation models. IZA Discussion Papers and Standpunkte Published in journals: American Economic Journal: Economic Policy , Journal of the European Economic Association , Journal of Economic Inequality , Labour Economics , German Economic Review , Perspektiven der Wirtschaftspolitik Löffler has advised or contributed to policy-relevant research through institutions such as IZA, ifo Institute, and ZEW. He has not received any explicitly mentioned scientific awards in the provided text. He does not appear to lead a named lab or research team, but is deeply embedded in collaborative research networks focused on public policy evaluation.

Nils NappView profile

Assistant Professor

• Robotics
• Multi-Robot Systems
• Bio-Inspired Design
• +7 more

Nils Napp is an Assistant Professor in the School of Electrical and Computer Engineering at Cornell University , where he directs the Napp-lab. He also holds a Visiting Professor appointment at the Cluster of Excellence IntCDC at the University of Stuttgart. His research bridges algorithm design and robotics, focusing on systems that operate in uncertain environments, with applications in autonomous construction, digital agriculture, and design synthesis. His work emphasizes mapping mathematical insights to physical systems, enabling novel robotic behaviors through self-organization, noise management, and distributed reactive strategies. Collaborations with biologists inform his bio-inspired approaches to multi-robot systems. Recent projects include self-assembly of agents and large-scale robotic structures. NSF CAREER award (2019) Best paper awards finalist: RSS10, DARS12, DARS18, RSS18 Students in his lab, such as Andrew and Aaron, have presented at major robotics conferences (ICRA, IROS). His lab recently launched a new website to showcase ongoing work in robotic construction and algorithm robustness.

Pascal MindermannView profile

Researcher

• Coreless filament winding
• Additive manufacturing
• Fiber-reinforced composites
• +3 more

Pascal Mindermann is a postdoctoral researcher at the Institute for Textile and Fiber Technologies (ITFT) within the Cluster of Excellence IntCDC at the University of Stuttgart, Germany. His work focuses on coreless filament winding (CFW) for lightweight structures made of fiber composites, bridging computational design, engineering, and fabrication. Role: Postdoctoral Researcher Department: Institute for Textile and Fiber Technologies University: University of Stuttgart His research explores advanced manufacturing techniques like CFW, with applications in architecture, aerospace, and sustainable construction. Key areas include multi-scalar design frameworks , reciprocal feedback systems , and cross-industry technology transfer . Recent projects address planetary sunshade satellites , hybrid composite/metal structures , and material gradient innovations for space and lunar utilization. Publications highlight CFW advancements in structural performance , sensor integration , and digital workflows . Collaborations span institutions like the Fraunhofer IST and TU Braunschweig, with emphasis on robotic fabrication , cyber-physical systems , and bio-inspired design principles . No explicit scientific awards or student advising details are provided in the texts.

• Computer Science
• Distributed Systems
• Blockchain Technology
• +3 more

Hans-Arno Jacobsen is a Professor at the Faculty of Computer Science (Technische Universität München, TU Munich) and affiliated with the Department of Electrical and Computer Engineering at the University of Toronto. His work spans Computer Science , Distributed Systems , and Artificial Intelligence . Research interests include Blockchain Technology , Consensus Algorithms , Graph Neural Networks , and Quantum Computing . Recent projects focus on decentralized consensus , energy-efficient databases , and federated learning in edge environments. His 15 most recent articles (2024–2025) explore topics such as dynamic resource orchestration , CRDT-based blockchains , and multimodal depression recognition . Collaborates with researchers like Ruben Mayer , Gengrui Zhang , and Shiqiang Wang on systems for federated computing , blockchain benchmarking , and distributed GNN training .

• Machine Learning
• Computer Vision
• Medical Imaging
• +5 more

Yi Cheng is an active academic researcher with a prolific publication record spanning multiple disciplines in computer science and engineering. Their work demonstrates strong affiliations with research institutions in China, frequently collaborating with researchers from Chinese Academy of Sciences and other Chinese universities. The publication pattern shows consistent high-quality output across top venues in machine learning, computer vision, and control systems. Yi Cheng's research interests center around artificial intelligence applications across diverse domains. Their work bridges theoretical advances in machine learning with practical applications in medical imaging, robotics, control systems, and natural language processing. Recent publications show increasing focus on multimodal learning approaches, domain adaptation techniques, and interpretable AI systems. The research demonstrates both theoretical depth in algorithm development and practical implementation in real-world scenarios. Analysis of recent publications reveals a strong trend toward developing robust, efficient AI systems that can operate across different domains with minimal adaptation. The work spans from fundamental control theory to applied medical imaging, showing versatility across the AI spectrum. Key methodological contributions include novel transformer architectures, multi-view learning approaches, and stability analysis for complex systems. Yi Cheng has received recognition through publications in top-tier venues including IEEE transactions, ACL, AAAI, and other prestigious conferences and journals. While specific awards aren't detailed in the publication record, the consistent acceptance in high-impact venues indicates peer recognition of research quality. The research trajectory shows increasing collaboration with interdisciplinary teams, particularly in medical applications where AI techniques are applied to healthcare challenges. Current work emphasizes practical deployment considerations including computational efficiency, robustness to domain shifts, and interpretability of models - addressing key challenges in real-world AI deployment.

• Information Theory
• Machine Learning
• Fairness in AI
• +6 more

Changho Suh is a Professor in the Department of Electrical Engineering at Korea Advanced Institute of Science and Technology (KAIST), College of Engineering. His research spans information theory, machine learning, and data science with significant contributions to matrix completion, fairness in AI, and network communications. Dr. Suh's research interests focus on the theoretical foundations of information processing and machine learning. He has pioneered work in matrix completion with graph side information, developing efficient algorithms that leverage hierarchical structures and similarity graphs. His recent work emphasizes fairness in machine learning systems, addressing correlation shifts and developing methods for fair training and generative modeling. He has also made significant contributions to information theory, particularly in interference channels, network coding, and quantum key distribution. Analysis of his recent publications reveals a strong trend toward addressing fairness challenges in AI systems while maintaining theoretical rigor. His work bridges information theory with practical machine learning applications, particularly in recommender systems and community detection. Suh's research demonstrates how graph structures can enhance data recovery and how theoretical insights from information theory can improve modern machine learning systems. Dr. Suh has received recognition for his scholarly contributions through numerous publications in top-tier venues including IEEE Transactions on Information Theory, NeurIPS, ICML, and AAAI. His work has influenced both theoretical understanding and practical implementations in data science. As an academic advisor, Suh has mentored numerous graduate students who have gone on to publish significant research in their own right. His collaborative approach is evident in the diverse range of co-authors across his publications, indicating strong research partnerships both within KAIST and internationally. His laboratory work appears to focus on information-theoretic approaches to machine learning problems, with particular emphasis on structured data analysis, fairness considerations, and efficient algorithm design for large-scale data processing tasks.