Mohamed Sarwat is an Associate Professor at Arizona State University specializing in databases , spatial data management , and recommender systems . His research focuses on GeoSpark —a cluster computing framework for spatial data—and its extensions like GeoSparkViz for visualization and GeoSparkSim for traffic simulation. Key Contributions: LARS* (Location-Aware Recommender System), Horton* (Graph Reachability), Sindbad (GeoSocial Platform), and Riso-Tree (Graph Database Indexing) Research Themes: Integration of spatial/temporal data with machine learning, efficient indexing for big geospatial datasets, and scalable frameworks for mobility data science His work spans collaborations with 23+ co-authors across institutions like University of Minnesota, University of Melbourne, and University of Salzburg. Current projects emphasize GeoTorchAI —a spatiotemporal deep learning system—and mobility data science infrastructure.
Yan Zhang is a scientific leader at Meshcapade and a guest lecturer at ETH Zurich's Computer Vision and Learning Group (VLG). He previously served as a postdoctoral researcher at ETH Zurich (2020-2023) and research intern at Max Planck Institute for Intelligent Systems (2018-2020). His research focuses on generative human foundation models, human motion and behavior synthesis, 3D human perception, and applications in AR/VR, embodied AI, and interactive avatars. He has pioneered methods for scene-conditioned motion generation, contact-aware reconstruction, and egocentric interaction modeling. His recent publications (2025-2020) span Real-time motor models for avatars (PRIMAL, ICCV'25) Diffusion architectures for motion (RoHM, CVPR'24) Scene-population algorithms (Odysseus, CVPR'22) Physics-aware reconstruction (EgoHMR, ICCV'23) Whole-body grasping models (SAGA, ECCV'22) Multi-modal datasets (EgoBody, ECCV'22) Scientific recognition includes the Qualcomm Innovative Fellowship Europe 2023 . He organized workshops at CVPR'25, ECCV'24, and ECCV'22, and served on senior program committees (AAAI'26) and area chairs (CVPR'25). As co-supervisor, he mentored student projects on diffusion-based hand motion capture, 3D pose estimation, body-scene interaction, and mixed reality navigation at ETH Zurich (2020-2023). His work bridges computer vision, machine learning, and computer graphics to advance human-centric AI systems.
Oleg Lashinin is an active researcher in the field of Recommender Systems , with a focus on Machine Learning , Temporal Modeling , and User Behavior Analysis . He has contributed to 15 recent publications spanning 2021–2025, including conference papers at ECIR, SIGIR, RecSys, and workshops like KaRS@RecSys and ORSUM@RecSys. His work explores advanced techniques such as Self-Attention Models , Time-Aware Item Weighting , and Cost-Constrained Recommendations . Key research trends in his publications include Deep Learning for sequential recommendation tasks, Crowdsourcing for explanation evaluation, and Temporal Dynamics in user behavior. Notable projects include the GPT3RecBot Telegram chatbot and the RecBaselines2023 dataset for benchmarking recommender systems.
Ulrich Schroeders is a Professor of Psychological Diagnostics at the University of Kassel, where he has been employed since October 2017. His work focuses on developing and validating psychological assessment tools, with particular expertise in cognitive diagnostics and educational measurement. He teaches various programs for approximately 500 students annually and serves as a supervisor for teacher training students preparing for their oral state examinations in Pedagogy/Psychology. Dr. Schroeders earned his PhD from Humboldt University of Berlin in 2010 with a dissertation titled "Measurement of Cognitive Abilities Using Modern Technologies: Artifacts, Equivalence, and New Constructs." Prior to that, he completed his Diploma in Psychology at Julius-Maximilians-University Würzburg in 2004 with a thesis on diagnosing dyscalculia in first-grade students. His research spans several key areas in psychological assessment. He specializes in technology-based competency diagnostics, developing innovative methods for measuring cognitive abilities and school competencies. A significant portion of his work involves applying Machine Learning and metaheuristics to psychometric problems, particularly in structural equation modeling. His methodological expertise includes advancing techniques in Local Structural Equation Modeling (LSEM) and Meta-Analytic Structural Equation Modeling (MASEM), with applications across educational and clinical psychology contexts. Analysis of Dr. Schroeders' recent publications reveals a strong focus on computational approaches to psychological assessment. His work frequently employs optimization algorithms like Ant Colony Optimization and Bee Swarm Optimization to address challenges in test construction and validation. There's a clear trajectory toward game-based and technology-enhanced assessment methods, as seen in studies using Mastermind and Wordle as assessment tools. His research also demonstrates growing interest in applying machine learning to predict behavioral outcomes, including juvenile delinquency, suicide risk, and psychotherapy outcomes. Dr. Schroeders has secured significant research funding, including projects funded by the German Research Foundation (DFG) and the Hector Foundation. His current projects include "Facing the Replication Crisis in Machine Learning Modeling" (2025-2027) and "PINGUIN: Potenzialidentifikation IN der GrUndschule" (2024-2027), which focuses on identifying elementary students' initial competencies. He leads the development of the BEFKI assessment system (Berliner Test zur Erfassung fluider und kristalliner Intelligenz), which includes versions for different age groups (5-7, 8-10, and 11+). His methodological toolbox includes specialized approaches for test construction and validation, particularly focusing on optimization algorithms applied to psychological measurement problems.
Dr. Pascal Reuss is a Researcher at the Intelligent Information Systems (IIS) Division within the Institute of Computer Science , University of Hildesheim . His work focuses on Case-Based Reasoning (CBR) systems, Multi-Agent Systems , and Knowledge Management applications. Active in CBR framework development and game-based AI research Teaching Computer Science III (Databases) for winter 2025/26 Participating in university sustainability initiatives like Stadtradeln 2024/25 Reuss contributes to AI education through practical implementations in gaming environments and has developed visualization tools for CBR agent behavior. His research spans multi-agent collaboration , dynamic case bases , and domain-specific language implementations for knowledge maintenance. Notable contributions include: Co-developing the FEATURE-TAK framework for knowledge extraction Designing case factories for distributed CBR systems Implementing finite state machines for tactical game agents Creating CBR-based fitness planning systems His work appears in various CBR and Game Development publications from 2011-2024. The research demonstrates practical applications of CBR in aircraft maintenance diagnostics , training plan generation , and educational technology contexts.
Barbara Namer is an Adjunct Professor at Friedrich-Alexander University Erlangen-Nuremberg and leads the IZKF-funded "Neuroscience: translational pain research" group at RWTH Aachen University Hospital. Her career spans 20+ years in neurophysiological pain research with clinical translations. Doctor of Medicine (2000-2004), Erlangen-Nuremberg Venia Legendi (Habilitation) in Physiology (2010) Adjunct Professor title (2018) Her research focuses on nociceptor mechanisms in diabetic neuropathy, migraine pathophysiology, and TRPA1 channel dynamics . Computational modeling and human microneurography techniques are central to her work. Key publication trends show expertise in peripheral nerve sensitization , diabetic pain mechanisms , translational pain modeling , and ion channel pharmacology . She has received multiple DGSS and German Neurology Society awards for her pain research. 2019 - DGSS Poster Award 2015 - DGSS Poster Award 2010 - EFIC Grünenthal Grant 2003 & 2018 - German Neurology Society Awards National and international collaborations include research stays in Norway and Sweden, with extensive grant funding from DFG and IZKF projects.
Stefan Decker is a full University Professor (Universitätsprofessor) at RWTH Aachen University, Germany, where he heads the Chair of Information Systems and Databases (Informatik 5) within the Faculty of Mathematics, Computer Science and Natural Sciences. He is actively involved in teaching, research, and the supervision of numerous ongoing and completed doctoral, master’s, and bachelor theses. Education & Academic Background Doctorate (Dr. rer. pol.) – field of Information Systems or related (exact institution/year not stated in text). Appointed as University Professor and Chair of Information Systems and Databases at RWTH Aachen University. Research Interests Prof. Decker’s work lies at the intersection of databases, knowledge graphs, semantic web technologies, data science, and cybersecurity . He investigates architectures and algorithms for large-scale, privacy-preserving, decentralized data analytics , develops ontology-driven information systems , and explores the use of large language models (LLMs) for educational technology, anomaly detection, and incident-response playbooks. Additional focal areas include smart energy systems, mixed-reality learning environments, FAIR data principles, and federated machine learning . Scientific Contributions & Trends His recent publications (2022-2025) demonstrate a clear trend toward explainable AI, LLM-enhanced systems, secure data spaces, and semantic interoperability . Key contributions include novel anomaly-detection frameworks for encrypted power-grid communications, knowledge-graph-driven chatbots for higher-education support, and methodological advances in decentralized analytics and FAIR data sharing. These works are disseminated in top-tier venues such as AAAI, IEEE ISGT Europe, ESWC, IDEAL, and various Springer LNCS and IEEE Transactions. Supervision & Grants Doctoral Theses Advised: A. T. Neumann – “Chatbots as professional companions in large-scale community information systems” (2024) S. M. Welten – “Methods for practical data sharing and decentralized analytics” (2025) Master’s Theses Co-Advised: A. R. Küsters – “Object-centric process constraints using variable bindings” (2025) Additionally supervising more than 30 ongoing bachelor, master, and doctoral projects covering topics such as LLM-driven cybersecurity playbooks, knowledge-graph construction for German law, privacy-preserving analytics in smart grids, and mixed-reality learning agents. Principal investigator or senior researcher in large collaborative projects including NFDI4DS, WestAI, champI4.0ns and several EU/national initiatives on sovereign data spaces and AI services. Labs & Teams Prof. Decker leads the Information Systems & Databases (DBIS) Research Group . The group operates well-equipped laboratories for knowledge-graph engineering, mixed-reality applications, privacy-enhancing technologies, and secure distributed analytics . Current team size exceeds 30 researchers including PhD candidates, postdocs, and scientific programmers, supported by national and EU funding streams.
Despina Kontos, PhD is the Herbert and Florence Irving Professor of Radiological Sciences at Columbia University Irving Medical Center (CUIMC), with appointments in the Department of Radiology and the Herbert Irving Comprehensive Cancer Center. She serves as the Chief Research Information Officer for CUIMC, Vice Chair of Artificial Intelligence and Data Science Research in the Department of Radiology, and Director of Biomarker Imaging at NewYork-Presbyterian Hospital. Additionally, she holds appointments in the Departments of Biomedical Informatics and Biomedical Engineering. Dr. Kontos received her educational training from prestigious institutions: BS in Engineering from the University of Patras, Greece MSc and PhD in Computer and Information Sciences from Temple University Postdoctoral training in Radiology at the University of Pennsylvania Certificates in Biostatistics and Epidemiology from UPenn, Cancer Biology from Harvard, and AI for Decision Making from Wharton As a computer scientist with expertise in artificial intelligence and machine learning, Dr. Kontos focuses on developing computational methodologies to leverage imaging as quantitative biomarkers for personalized disease prediction, particularly in cancer. Her research program investigates how imaging data can be mined to extract sophisticated phenotypic signatures with diagnostic, prognostic, and predictive value. While her primary focus has been on breast cancer, her lab also pursues related research in lung cancers, evaluating the integration of CT radiomic features with liquid biopsy data to characterize tumor heterogeneity. Dr. Kontos founded and directs Columbia University's Center for Innovation in Imaging Biomarkers and Integrated Diagnostics (CIMBID), a multidisciplinary center dedicated to developing and integrating quantitative imaging and non-imaging biomarkers for personalized disease prediction. Through CIMBID, she has built a vibrant scientific ecosystem that brings together expertise across Columbia's campuses, linking basic science, engineering, clinical medicine, public health, and health services research. Analysis of Dr. Kontos's publication record reveals a strong focus on applying AI and machine learning to biomedical imaging, particularly for cancer risk prediction and personalized treatment. Her work demonstrates a progression from foundational methodological development to clinical translation, with increasing emphasis on multi-modal biomarker integration. Recent publications show expansion into new disease areas including Alzheimer's disease prediction, while maintaining her strong focus on breast and lung cancer applications. Dr. Kontos has received significant recognition for her contributions to the field: Academy for Radiology and Biomedical Imaging Research Distinguished Investigator Award (2020) Eastern Cooperative Oncology Group - American College of Radiology Imaging Network ECOG-ACRIN Young Investigator Award of Distinction for Translational Research (2014) Dr. Kontos has been highly successful in securing research funding, with numerous grants from federal agencies including the National Institutes of Health (NIH) and the Department of Defense (DOD), as well as private foundations such as the American Cancer Society (ACS) and the Radiological Society of North America (RSNA). Her leadership extends to mentoring students and postdoctoral researchers through her roles at CIMBID and the Department of Radiology. As the founding director of CIMBID, Dr. Kontos leads a multidisciplinary team that includes the Computational Imaging Biomarker Group (CBIG), the Laboratory of AI and Biomedical Science (LABS), and several other affiliated research labs. The center leverages Columbia's institutional strengths in engineering, data science, and clinical medicine to advance personalized healthcare through AI and imaging technologies.
Enrico Rukzio is a Full Professor of Human-Computer Interaction at the University of Ulm, leading the Human-Computer Interaction group and directing the Institute for Media Research and Media Development. His research spans interactive systems design, focusing on automotive UIs, extended reality, accessibility, and sustainable interaction. He holds a Ph.D. in Computer Science from the University of Munich and has held prior roles at Lancaster University and the Ruhr Institute for Software Technology. Roles: Faculty Dean (2017–2020), Admissions Committee Chairman, Doctoral Committee Chairman Educations: Ph.D., Munich; Lecturer qualifications from Lancaster and Duisburg-Essen Research emphasizes enabling efficient, inclusive, and sustainable interactions through novel interfaces. His work addresses automated vehicles, health-supporting tech, and accessibility for visually impaired users. Recent articles explore Bayesian optimization for UI design, automated vehicle communication, and urban air mobility visualization. Notable awards include best paper recognitions at CHI, EuroVR, and IEEE VR. His grants come from BMBF, DFG, and industry partners like Mercedes-Benz Group. Advises on over 20 funded projects and has mentored award-winning students.
Prof. Dr. Carolin Wienrich is a Professor of Psychology of Intelligent Interactive Systems at Julius-Maximilians-University Würzburg, Faculty of Human Sciences, and Co-director of XR HUB Würzburg since 2020. Her work bridges psychology, virtual reality, and human-computer interaction to understand human experiences in digital environments. Her educational background includes: 2010: Psychology Degree from Martin Luther University Halle/Wittenberg 2015: Interdisciplinary PhD from TU Berlin | Faculty of Traffic and Machine Systems Prof. Wienrich's research explores psychological aspects of presence, embodiment, and social interaction in XR systems. She investigates how device characteristics affect user experience, with applications ranging from workplace collaboration to therapeutic interventions. Her systematic review on psychological ownership of virtual objects has provided foundational insights into how users form emotional connections with digital assets. She has made significant contributions to understanding avatar embodiment effects on body image and self-esteem, as well as the impact of immersion levels on social presence and task performance. Analysis of her recent publications reveals several key research trends: Investigating cross-device collaboration and asymmetric interaction in virtual environments Exploring psychological ownership of virtual objects and environments Developing VR applications for therapeutic interventions in mental health Studying human-AI interaction dynamics in spatial computing environments Examining privacy, safety, and harassment issues in social VR Developing training approaches to improve user competence with intelligent systems Her notable awards include: 2020 Research Prize of the Faculty of Human Sciences (JMU Würzburg) 2019 Prize for Good Teaching Bavaria (Free State of Bavaria) 2018 Best Impact German Institute for Virtual Reality Best Poster award at IEEE VRW 2025 IDEATExR Best Paper award at IEEE VRW 2025 Prof. Wienrich actively engages with policy makers and the public, having presented to the Federal Commissioner for Data Protection and Information Security, participated in discussions at the German Ethics Council, and demonstrated her research to members of the German parliament. Her presentations cover critical topics such as the psychological consequences of the metaverse and human-centered AI interaction in virtual environments. As Co-director of XR HUB Würzburg, she leads an interdisciplinary initiative that connects researchers across psychology, computer science, and medicine to advance XR technologies and applications. The hub serves as a central platform for academic research, industry collaboration, and public engagement with extended reality technologies.
Prof. Dr. Reinhold Decker is a full professor of business administration with a focus on marketing and market research at Bielefeld University's Faculty of Economics, where he has been affiliated since 1997. He currently serves as the Rector's Representative for Cooperation with Business, BRIC, and Research Transfer (since October 2023), following previous roles as Vice Rector for Information Infrastructure and Business (2019-2023), Vice Rector for Information Management (2015-2019), and Vice Rector for Financial Affairs and Resources (2012-2015). He is also the Scientific Director of BI2000plus - Research Projects on the Region since 2005 and a member of the Bielefeld Graduate School of Economics and Management (BiGSEM). Decker earned his degree in industrial engineering with a focus on OR/Computer Science in 1988, received his doctorate in 1993, and completed his habilitation in 1997, all from the University of Karlsruhe (KIT). His academic career includes visiting professorships at the University of Vienna, Moscow Academy of Economics, University of Veliko Turnovo, Universidade NOVA de Lisboa, Université Paris III – Sorbonne Nouvelle, and the University of Maryland. His research focuses on the development and empirical testing of methods and models for acquiring and analyzing consumer data, particularly from social media, and data-driven, consumer-centric development of intelligent products and services. His work spans social media analysis, buyer behavior modeling, brand image analysis, web mining in marketing, and internet-based preference measurement. Decker's interdisciplinary approach bridges marketing, data science, and consumer behavior, with increasing emphasis on intelligent systems and digital transformation in marketing contexts. Analysis of his 15 most recent publications reveals a strong focus on emerging technologies in marketing, including augmented reality, voice assistants, and social media analytics. His research increasingly examines privacy concerns in the digital age, sustainability communication, and the integration of AI in consumer decision-making processes. The interdisciplinary nature of his work is evident in publications spanning marketing journals, data science publications, and technology-focused outlets. Member of the Scientific Council of the journal Argumenta Oeconomica Cracoviensia (since 2013) Associate Editor of the journal Behaviormetrika (since 2012) Member of the Editorial Board of the Springer series Studies in Classification, Data Analysis, and Knowledge Organization (since 2004) Vice President of the European Association for Data Science – EuADS (2018-2022) Decker has served on numerous editorial boards and scientific program committees, including for the International Federation of Classification Societies. His extensive reviewing work spans prestigious journals such as Journal of Business Research, Journal of Product Innovation Management, and Review of Managerial Science. His leadership extends to project management, including BiLinked (2025), Bielefeld 2000plus (2024), and Bielefelder DatenNarrative (2022). Decker has also edited multiple volumes and special issues on data analysis and marketing, demonstrating his commitment to advancing methodological approaches in business research. As Scientific Director of BI2000plus, Decker leads interdisciplinary research projects focused on regional development. His work bridges academia and industry through initiatives like the Bielefeld Center for Data Science (BiCDaS) and the Bielefeld Graduate School in Theoretical Sciences. His recent projects emphasize data narrative techniques, linking data analysis with effective communication strategies for diverse audiences.
Zhang Yang is an Associate Professor at the School of Medical Engineering, Harbin Institute of Technology (Shenzhen), with a joint appointment as Visiting Professor at the University of Tokyo starting in July 2024. He holds a PhD from the University of Cambridge's Department of Pathology and an M.Phil. from the University of Hong Kong's HKU-Pasteur Research Center. Previously, he served as an Assistant Professor at Harbin Institute of Technology (Shenzhen) from September 2015 to December 2020. His research integrates computational and experimental approaches to address challenges in pathogen and cancer research. On the computational side, his work focuses on developing AI-powered microscopic imaging systems, applying deep learning to analyze multi-omics data (including proteins, DNA, miRNAs, LncRNAs, and mRNAs), and utilizing deep learning in cheminformatics for drug discovery. On the experimental side, his laboratory combines imaging, high-throughput sequencing, mass spectrometry, and chemical biology to understand disease mechanisms at the molecular level. His publication record demonstrates significant impact, with over 50 SCI-indexed papers in high-impact journals including Nature Communications, Briefings in Bioinformatics, Bioinformatics, Analytical Chemistry, and Trends in Biotechnology. His work has been cited by prestigious journals such as Nature Reviews Methods Primers and Nature Communications, with three ESI highly cited papers. His research spans multiple interdisciplinary fields, combining artificial intelligence with biomedical applications to advance diagnostic and therapeutic approaches. World's Top 2% Scientists 2021 Fellow of the Royal Society of Biology Three ESI Highly Cited Papers Five authorized national invention patents As an academic leader, he serves as Associate Editor for BMC Biology and Frontiers in Microbiology, Academic Editor for PLOS Genetics, Editorial Board Member for Communications Biology, and Guest Editor for a Special Issue on AI in analytical chemistry in Trends in Analytical Chemistry. His laboratory actively collaborates with international institutions, with graduates pursuing further studies at Hong Kong Chinese University, Hong Kong University of Science and Technology, Hong Kong Polytechnic University, Macau University, and the University of New South Wales. He teaches Introduction to Modern Biology for undergraduates and Bioanalytical Chemistry for graduate students.
Prof. Verena Hafner is a Professor at the Institute of Computer Science within the Faculty of Mathematics and Natural Sciences at Humboldt University of Berlin. She leads the Adaptive Systems group, focusing on interdisciplinary research at the intersection of robotics, AI, and cognitive science. Her work emphasizes human-robot interaction, adaptive learning mechanisms, and embodied cognition. Her research explores: Design and impact of socially interactive robots in educational and cognitive contexts Trust dynamics and transparency in human-robot relationships Development of bio-inspired AI models and sensorimotor learning systems Philosophical and ethical dimensions of artificial consciousness and agency Analysis of her recent publications (2023-2025) reveals strong emphasis on educational robotics, cognitive modeling, and humanoid robot design. Key trends include multimodal learning architectures, trust calibration in HRI, and biologically-inspired AI frameworks. Her work consistently bridges theoretical AI with applied human-centered experimentation. She supervises graduate students including Michael Piechotta (doctoral candidate) and leads the Adaptive Systems laboratory investigating lifelong learning in artificial agents.
Dr. Lisa Merten is a Senior Researcher at the Leibniz Institute for Media Research | Hans Bredow Institute (HBI), where she leads computational social science projects like POLTRACK, examining the relationship between information repertoires and political polarization. She also coordinates the DFG-funded 'Public Connection' project, exploring how users engage with diverse publics through media practices. Her research focuses on digital media environments, algorithm-driven personalization, and the impact of social media on news consumption and public opinion. Education : - Studied Communication Science at Universität Leipzig, TU Dresden, Universiteit van Amsterdam, and Boston University - German Academic Scholarship Foundation recipient Research Interests : - Algorithmic publics and personalization - Political information seeking and polarization - Digital methods for media analysis - Journalism and online commentary dynamics Key Projects : - Co-led the POLTRACK project with GESIS and universities of Bremen/Konstanz - Investigated Instagram influencers' reach in 'Media Use and Social Cohesion' with Hannah Immler - Pioneered browser data donation methods in journalism studies Awards : - 2021 Digital Journalism 'Article of the Year' for work on social media news curation Teaching : - Held part-time junior professorship in Computational Social Science at University of Konstanz (2021) - Taught at University of Hamburg, Augsburg, and Kiel University of Applied Sciences Labs/Teams : - Core member of HBI's Computational Social Science team - Collaborates with GESIS, Bremen/Konstanz universities, and international partners
Thorsten Joachims is a Professor at Cornell University with a focus on machine learning, recommendation systems, and algorithmic fairness. His work spans conferences like ICML, NeurIPS, KDD, and SIGIR, emphasizing counterfactual learning, contextual bandits, and ethical AI. Key Research Themes: Fairness in rankings, reinforcement learning, position bias estimation, and NLP applications to recommendation systems. Recent Publications: 2025 work on policy decomposition for contextual bandits, 2024 studies on fairness under uncertainty, and 2023 papers on LLM steerability and bias mitigation. Awards: Recipient of the ACM SIGKDD 2020 Innovation Award . Collaborators: Regularly works with Adith Swaminathan, Tobias Schnabel, Yuta Saito, Ashudeep Singh, and Yi Su.
