Find Academic Supervisors

• Business Process Management
• Process Mining
• Information Systems
• +3 more

Jan Mendling is a Professor at Humboldt-Universität zu Berlin, Germany, with a former affiliation at Vienna University of Economics and Business, Austria. His research focuses on Business Process Management (BPM), Process Mining, Decision Modeling, and Information Systems. Over 500+ publications highlight his contributions to advancing process-aware systems, including seminal work on process mining techniques, decision model comprehension, and systematizing BPM challenges. He actively participates in leading conferences like BPM, CAiSE, and EDOC, and collaborates with global researchers on topics ranging from sustainability compliance to AI-driven process automation. His work addresses both technical innovations (e.g., timeline-based process discovery, LLM integration) and organizational aspects (e.g., process redesign frameworks, audit risk perception). Recent trends in his publications emphasize interdisciplinary approaches combining AI, sustainability, and ethics with traditional BPM domains. He chairs sessions, delivers keynotes, and contributes to methodological advancements in visual analytics and process complexity measurement.

• Database Systems
• Semantic Heterogeneity
• Federated Databases
• +3 more

Dennis McLeod is a Professor at the University of Southern California , specializing in Database Systems , Ontology Engineering , and Semantic Heterogeneity resolution. His work bridges Federated Database Systems and Web Engineering , focusing on component-based architectures, adaptive ontologies, and geospatial data processing. Key Research Areas : Semantic heterogeneity, ontology-driven data mining, mobile web information management, and distributed systems. Collaborations : Extensive partnerships with researchers like Qing Li , Cyrus Shahabi , and Stefania Leone across institutions. Publications (2014-1995) span topics from component-based web engineering to distributed earthquake science , with recent work on geostreaming, social network tag-geotag analysis, and spam filtering using ontologies. His contributions include frameworks for multi-resolution document transmission , object-oriented database sharing , and adaptive query optimization in federated systems. Scientific Impact : Co-chairs and tutorials at major conferences (VLDB, CoopIS, ICDE). Co-edited proceedings for ICWL 2008 and contributed to Expert Database Systems (1989-1991). Pioneered INTERBASE for controlled sharing in federated databases (1990).

Sebastian BöckerView profile

Professor

• Metabolomics
• Bioinformatics
• Machine Learning
• +7 more

Prof. Sebastian Böcker leads the Chair of Bioinformatics at the Institute of Computer Science, Friedrich Schiller University Jena. His lab focuses on metabolomics data analysis, computational mass spectrometry, and algorithm development for biological applications. Affiliations: HUMAN doctoral network (EU Horizon), AS-MS project (TMWWDG/ERDF), International Max Planck Research Schools Research Focus: Metabolite identification, retention order prediction, computational modeling of molecular diversity Recent publications highlight advancements in retention order index prediction, molecular diversity estimation, and large-scale query processing. The lab collaborates with Max Planck Institutes for Chemical Ecology and Biogeochemistry. Scientific Awards ERC Advanced Grant recipient (2025) His team includes PhD student Nils Haupt and the independent Bio Systems Analysis group led by Prof. Peter Dittrich. The lab develops software tools like SIRIUS+CSI:FingerID and participates in EU-funded harmonization of metabolomic analysis methods.

• Sociology of Algorithms
• Artificial Intelligence
• Social Theory
• +3 more

Elena Esposito is Professor of Sociology at Bielefeld University and the University of Bologna, specializing in sociological systems theory. Her research examines sociological implications of algorithms, prediction, and AI through projects like the ERC Advanced Grant 'The Future of Prediction' and the Collaborative Research Center TRR 318. She co-leads interdisciplinary initiatives at the Center for Interdisciplinary Research and Center for Uncertainty Studies. Research interests include: Sociology of algorithmic prediction in insurance, medicine, and policing Social consequences of machine learning transparency Memory theory and media transformations Artificial communication systems Her publications analyze algorithmic governance mechanisms across domains including healthcare, criminal justice, and climate policy. Recent work explores how predictive technologies reshape institutional trust and decision-making under uncertainty. Awards include: ERC Advanced Grant (€2.08M) for prediction sociology research Volkswagen Foundation Grant (€1.5M) for AI discourse studies FAIR Project leadership for AI social impact analysis She directs projects examining explainable AI systems and advises doctoral researchers in sociology and technology studies. Future work focuses on generative AI's societal implications.

• Human-Computer Interaction
• Affective Computing
• Multimodal Interaction
• +4 more

Overview Gualtiero Volpe is a Professor affiliated with the Department of Informatics at the University of Genoa, Italy. His research focuses on Human-Computer Interaction (HCI), Affective Computing, and Multimodal Interaction, with particular emphasis on analyzing social interactions, embodied cognition, and expressive movement qualities in contexts such as music performance and education. He has collaborated extensively with researchers like Antonio Camurri, contributing to interdisciplinary projects that bridge computer science, cognitive science, and the arts. Research Interests Analysis of human movement and affect through computational methods Design of multimodal interactive systems for education and healthcare Investigation of laughter and social dynamics in human interactions Development of technologies for music performance and collaborative creativity Key Contributions Volpe's work spans over 140 publications in top-tier venues such as IEEE Transactions on Affective Computing, ACM Conference on Human Factors in Computing Systems (CHI), and International Conference on Multimodal Interaction (ICMI). His research has led to innovations in: Automated analysis of movement qualities using machine learning Sonification platforms for exergames and rehabilitation Embodied interaction frameworks for children's education Computational models of group cohesion and synchronization Recent Trends in Publications Recent studies emphasize the integration of cognitive science principles into movement analysis and the application of multimodal systems to address challenges in post-pandemic social interaction (e.g., digital commensality). His work on 'Embodied Multisensory Training' and 'Hybrid Co-Working in Industry 5.0' highlights a growing focus on real-world applications of HCI research. Awards & Grants No specific awards or grants are explicitly mentioned in the provided data, though his prolific publication record and leadership in collaborative projects suggest sustained academic recognition and funding support. Labs & Teams Volpe is part of the EyesWeb XMI research group at the University of Genoa, developing open-source platforms for real-time multimodal interaction analysis. He collaborates with institutions like the University of Trento and Goldsmiths, University of London on projects related to music technology and social robotics.

• Pattern Recognition
• Biomedical Signal Processing
• Human-Computer Interaction
• +5 more

Réjean Plamondon is a Professor at Polytechnique Montréal, specializing in handwriting analysis, neuromotor control, and biometric technologies. His research focuses on the Sigma-Lognormal model to analyze human movement dynamics, with applications in e-security, e-health, and e-learning. He has contributed extensively to the study of handwriting generation mechanisms, signature verification, and motor disorders analysis through kinematic modeling. Key research areas include: 1) Biometric authentication via handwriting dynamics, 2) Neuromuscular system analysis using kinematic theories, 3) Development of synthetic signature generation techniques, and 4) Applications in stroke rehabilitation and pediatric motor skill assessment. Over 175+ publications span journals like Pattern Recognition, IEEE Transactions, and conference proceedings of ICPR/ICFHR. His work bridges computer science and neuroscience, addressing challenges in 3D kinematics, CAPTCHA generation, and digital health monitoring systems like the RPM3D project. Collaborations include institutions like Universidad de Granada and Università degli Studi di Messina.

• Process Systems Engineering
• Optimization
• Chemical Engineering
• +6 more

Matthew J. Realff is a Professor in the School of Chemical and Biomolecular Engineering at the Georgia Institute of Technology. With an extensive publication record spanning over three decades, his work focuses on the intersection of chemical engineering, process systems engineering, and advanced computational methods. His research has made significant contributions to optimization techniques, supply chain management, and sustainable engineering practices. Dr. Realff's research interests span a broad range of topics in process systems engineering, with particular emphasis on optimization under uncertainty, supply chain modeling, and sustainable engineering practices. His work combines rigorous mathematical approaches with practical engineering applications, particularly in the areas of biorefinery systems, carbon capture technologies, and renewable energy integration. He has pioneered methodologies that bridge traditional chemical engineering with modern computational techniques including machine learning and Bayesian statistics. Analysis of his recent publications reveals a consistent focus on addressing complex engineering challenges through advanced computational methods. His work demonstrates a strong trajectory toward integrating data-driven approaches with traditional process engineering, particularly evident in his recent papers on Bayesian experimental design, uncertainty quantification, and machine learning applications in chemical processes. The interdisciplinary nature of his research connects chemical engineering with operations research, environmental science, and computer science. Throughout his career, Dr. Realff has maintained a productive research program with consistent publication output in top-tier chemical engineering and operations research journals. His collaborations span multiple institutions and disciplines, reflecting the interdisciplinary nature of modern engineering research. He has supervised numerous graduate students and contributed significantly to the education and training of future engineers through his academic appointments and research mentorship.

• Human-Computer Interaction
• Data Science Education
• Ethics in Computing
• +3 more

Ben Rydal Shapiro is an active researcher in Human-Computer Interaction (HCI), focusing on data science education, ethics in computing, and collaborative learning. His work bridges technical and social dimensions of technology design, particularly in educational contexts and workplace practices. He collaborates with institutions exploring topics such as data literacy, ethical training for teaching assistants, and the intersection of critical race theory in education. Shapiro's research often involves creating innovative tools for visual analytics and reflective practice, as seen in projects like the Interaction Geography Slicer and the Re-Shape method for data ethics education. His contributions span conferences like CHI and SIGCSE, emphasizing applied research with tangible impacts on teaching and workforce development.

Heidi WebberView profile

Professor

• Climate Risk Assessment
• Crop Modeling
• Plant Stress Physiology
• +3 more

Prof. Heidi Webber is a leading agricultural scientist at the Leibniz Centre for Agricultural Landscape Research (ZALF), serving as Co-Head of Program Area 3 'Agricultural Landscape Systems' and Head of the 'Integrated Crop Production Systems Analysis' working group. Her research focuses on climate risk assessment, crop stress modeling, and sustainable agronomic systems. She holds a Diplom-Agraringenieurin degree and has extensive experience in interdisciplinary studies combining biophysical and socio-economic analyses. Her expertise includes modeling plant growth under abiotic stresses and developing resource-efficient cultivation strategies. Notable contributions involve the AgMIP-Wheat initiative, high-yielding trait experiments, and climate-resilient crop systems in diverse regions like West Africa, South Korea, and Europe. She co-leads international projects addressing climate change impacts on agriculture and leads modeling efforts for crop diversification and adaptation strategies. Key research themes include: Climate risk assessment frameworks Multi-model ensemble approaches for crop yield prediction Abiotic stress tolerance mechanisms in cereals Climate-smart agricultural practices Agroecological transition in smallholder farming systems Her work bridges field experiments with advanced computational models, emphasizing data-driven solutions for sustainable intensification. Current projects explore index-based crop insurance, soil carbon dynamics, and the transformation of farming systems toward silicon-enhanced resilience.

• Machine Learning
• Control Systems
• Signal Processing
• +3 more

Shuo Huang is an academic researcher with a focus on interdisciplinary fields spanning Machine Learning, Control Systems, and Signal Processing. Their work integrates theoretical advancements with practical applications in areas like neural networks, robotics, and sensor technology. They have contributed significantly to methodologies in predictive modeling, privacy-preserving machine learning, and optimization algorithms. Key research areas include the development of distributed Kalman filters for robotics, semi-supervised learning frameworks, and deep learning applications in computer vision and autonomous systems. They also engage in statistical process control and quality assurance, reflecting a blend of theoretical and applied engineering expertise. Notable contributions include advancements in privacy-preserving neural networks, adaptive control systems for electromechanical devices, and sensor design innovations. Their interdisciplinary approach bridges computer science, electrical engineering, and industrial applications.

• High-Performance Computing
• Parallel Computing
• Power Management
• +3 more

Barry Rountree is a researcher affiliated with Lawrence Livermore National Laboratory (LLNL), specializing in High-Performance Computing (HPC), Power Management, and Parallel Computing. His work focuses on optimizing energy efficiency, fault tolerance, and performance in large-scale supercomputing environments. He has contributed extensively to the development of tools and frameworks for power-aware scheduling, hardware overprovisioning, and visualization performance optimization. Key research interests include HPC system design, processor manufacturing variability, and energy-efficient algorithms. Rountree has collaborated with institutions like NERSC and Intel on projects involving exascale computing challenges and resource management strategies. His publications span conferences such as SC, IPDPS, and ICS, emphasizing practical solutions for scalable and sustainable HPC systems. Notable contributions include the SpackNVD vulnerability audit tool, the msr-genie framework for processor register navigation, and adaptive power management strategies for visualization applications. His work highlights interdisciplinary approaches to addressing computational challenges in science and engineering domains.

• Medical Image Processing
• Remote Sensing
• Machine Learning
• +4 more

Jianwu Dang is a faculty member at Lanzhou Jiaotong University's School of Electronic and Information Engineering , with affiliations to the Gansu Provincial Engineering Research Center for Artificial Intelligence and Graphic and Image Processing . His academic career spans multiple institutions including a PhD from Southwest Jiaotong University in 1996. Research Interests : Focused on Medical Image Processing , Remote Sensing , and Wireless Sensor Networks , his work bridges Artificial Intelligence and Transportation Engineering , particularly in railway systems. Publications : Recent articles address Edge Computing coalition structures (2023), Extended Reality in education (2023), and Deep Learning for remote sensing (2022). Collaborations : Frequently co-authors with Yangping Wang and Zhanjun Hao , contributing to journals like IEEE Internet of Things Journal and conferences such as APSIPA .

• Neuroscience
• Neuroimaging
• Molecular Biology
• +3 more

Dr. Tina Kroll is a researcher at the Institute of Neuroscience and Medicine (INM-2) at the Research Center Jülich GmbH. Her work focuses on neuroimaging, particularly positron emission tomography (PET), to study neurodegenerative diseases like Alzheimer’s, adenosine receptor dynamics, and the effects of sleep deprivation on brain function. She investigates synaptic density, molecular pathology quantification, and the interplay between sleep architecture and neurodegenerative processes. Key projects include developing novel PET-derived biomarkers for Alzheimer’s (e.g., spatial extent analysis of pathology), optimizing awake PET methodologies in rodents, and exploring sex differences in synaptic density measurement. Her research integrates genetics, neurophysiology, and advanced imaging techniques to understand brain mechanisms in health and disease. Collaborations span neurology, psychiatry, and computational biology, with a focus on translating findings into clinical staging models (e.g., T-POT cohort for tau propagation). Technical contributions include enhancing PET/MRI image quality with deep learning, improving motion correction in animal PET studies, and validating radiotracer applications for adenosine receptors. Her work bridges preclinical models and clinical applications, addressing challenges in neuroimaging accuracy and translational research.

Tanja BurgardView profile

Researcher

• Meta-Analysis
• Research Synthesis
• Open Science
• +3 more

Dr. Tanja Burgard is a Senior Researcher in Psychological Metascience at the Leibniz Institute for Psychology Information (ZPID). She has been at ZPID since 2017 and served as acting head of the Research Synthesis department from 2021 to 2024. Burgard holds a PhD in Empirical Social Research (Dr. rer. pol.) from Trier University (2021). Her work focuses on meta-analytic methods, research synthesis, and open science practices, particularly through platforms like PsychOpen CAMA. She has contributed to developing guidelines for accessible meta-analysis summaries and tools to streamline systematic reviews using machine learning. Education: PhD in Empirical Social Research (Dr. rer. pol.), Trier University (2021) Key Roles: Senior Researcher in Psychological Metascience, Acting Head of Research Synthesis Department (2021–2024) Research Interests: Her research emphasizes improving the rigor, transparency, and accessibility of psychological research through meta-analyses, community-augmented methods, and open science initiatives. She explores topics like response rates in surveys, panel conditioning effects, and cumulative evidence synthesis. Publications: Burgard has authored/co-authored over 20 peer-reviewed articles on meta-analytic methods, open science infrastructure, and psychological methodology. Her work bridges theoretical advancements with practical tools for researchers. Labs/Teams: Member of the Psychological Metascience Lab and ForSynData project, advancing FAIR data standards for research synthesis.

Michael BussmannView profile

Research Fellow

• Laser Plasma Acceleration
• Plasma Physics
• Computational Physics
• +3 more

Dr. Michael Bussmann is the Founding Manager of the Center for Advanced Systems Understanding (CASUS) at the Helmholtz-Zentrum Dresden-Rossendorf (HZDR). He holds a Research Fellow position and has been a Group Leader at HZDR since 2016, focusing on computational radiation physics and laser-plasma interactions. His academic journey includes a PhD in Physics from Ludwig-Maximilians University Munich, with research encompassing laser-cooling of ion beams, plasma dynamics, and high-performance computing. Bussmann's work bridges experimental and computational physics, particularly in advancing particle acceleration technologies and plasma-based applications. Research Interests: His primary focus lies in laser plasma acceleration, computational physics, radiation physics, and the development of exascale simulation tools. Notably, he contributes to projects like PIConGPU and explores applications of AI in scientific workflows. His interdisciplinary approach integrates plasma physics with medical research, such as federated learning for oncology. Publications: Recent work spans topics from exascale plasma simulations to AI-driven medical research, reflecting his dual expertise in computational science and applied physics. Key contributions include optimizing laser-ion acceleration using machine learning and advancing federated learning frameworks in cancer studies. Awards: While no specific awards are listed, his sustained leadership in high-impact research and contributions to HZDR’s initiatives highlight his recognized expertise. Grants & Collaborations: Bussmann leads the CASUS initiative, fostering collaboration between HZDR and international partners. His projects often involve EU frameworks and industrial partnerships, emphasizing translational research in energy and healthcare. Labs/Teams: Directs the Computational Radiation Physics group at HZDR and oversees CASUS, a hub for data-driven systems research. His teams develop cutting-edge simulation tools and explore interdisciplinary applications of plasma physics.