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Dan CasasView profile

Associate Professor

• Computer Graphics
• Computer Vision
• Machine Learning
• +5 more

Dan Casas is a Senior Applied Scientist at Amazon in Seattle and an Associate Professor (Profesor Titular) on leave from King Juan Carlos University in Spain. His research spans the intersection of Computer Graphics, Computer Vision, and Machine Learning with a focus on 3D reconstruction, modeling, and animation of virtual humans and clothing. He has authored over 40 high-impact publications in top venues including SIGGRAPH, CVPR, and NeurIPS, and holds 3 international patents. Dr. Casas received his M.Sc. degree (2009) from Universitat Autònoma de Barcelona (Spain), including a research visit at Carnegie Mellon University. He earned his Ph.D. in Computer Graphics (2014) from the University of Surrey (UK), supervised by Prof. Adrian Hilton. He completed postdoctoral research at the University of Southern California's Institute for Creative Technology (2014-2015) and the Max Planck Institute in Saarbrücken (2015-2016). His research interests center on creating realistic virtual humans and digital clothing through advanced techniques in computer vision and machine learning. Casas has pioneered methods for 3D reconstruction of humans and garments from video input, physics-based simulation of soft-tissue deformations, and data-driven approaches to character animation. His work bridges the gap between theoretical computer graphics and practical applications in virtual reality, digital fashion, and immersive communication. Analysis of his recent publications reveals a consistent focus on human digitization, with increasing emphasis on machine learning approaches. His work has evolved from traditional computer graphics techniques toward neural representations and diffusion models, particularly in the areas of 3D garment simulation and human avatar creation. The trend shows growing integration of physics-based modeling with data-driven approaches to achieve both realism and computational efficiency. Marie Skłodowska-Curie Individual Fellowship (2015) FBBVA Leonardo Fellowship (2021) Medal from the Royal Academy of Engineering of Spain for Young Researcher Award (2023) i3 certification (outstanding researcher) from Spanish Ministry of Universities (2022) Winner of 2021 IEEE Retail Digital Transformation Grand Challenge Multiple Outstanding Reviewer Awards at top conferences (CVPR, BMVC, 3DV) Dan Casas has successfully advised multiple PhD students including Suzanne Sorli, Cristian Romero, Raquel Vidaurre, and Igor Santesteban (now at Meta Reality Labs), with several ongoing students including Melania Prieto-Martin, Gonzalo Gómez-Nogales, and Andrés Casado-Elvira. He has secured significant research funding as Principal Investigator, totaling over €1.2 million from Spanish Ministry of Science projects, EU H2020 programs, and industry fellowships including the FBBVA Leonardo Fellowship. His leadership extends to conference organization as Area Chair for ICCV 2023 and General Chair for ACM i3D 2020. Dr. Casas leads research in digital human modeling with applications in virtual reality, fashion technology, and immersive communication. His team develops advanced techniques for creating personalized 3D avatars from minimal input (like smartphone videos), addressing challenges in geometry, appearance, and physical simulation of virtual humans and their clothing.

Jan AlexanderssonView profile

Researcher

• Artificial Intelligence
• Affective Computing
• Natural Language Processing
• +4 more

Jan Alexandersson is a Researcher at the German Research Center for Artificial Intelligence (DFKI) in Saarbrücken, Germany, where he leads work in the Cognitive Assistance Systems group. His research bridges artificial intelligence and healthcare, focusing on developing multimodal systems for therapeutic applications and medical diagnostics. His core research interests include Affective Computing , Natural Language Processing , and Human-Computer Interaction , with specialization in emotion recognition, motivational interviewing analysis, and engagement estimation. Current projects explore AI applications for mental health support, pediatric therapy, and medical diagnostics through interdisciplinary collaborations. Recent publications reveal a strong trend toward multimodal AI architectures integrating linguistic, visual, and behavioral data. His 2024 work demonstrates increasing adoption of large language models for clinical applications, particularly in emotion regulation strategy identification and therapeutic dialogue analysis across diverse healthcare contexts. Dr. Alexandersson actively contributes to major initiatives including MULTI-IMMERSE (virtual reality therapy for hospitalized children), GAIN (Georgian AI networking), AI@Home (elderly care risk prediction), Kitatta (corneal transplant quality assessment), and MePheSTO (digital phenotyping for psychiatric disorders). These projects collectively focus on translating AI research into practical healthcare solutions through sensor integration, behavioral modeling, and clinical validation.

• Educational Technology
• Virtual Reality in Education
• Game-Based Learning
• +7 more

Morris Siu-Yung Jong is a Professor at The Chinese University of Hong Kong's Faculty of Education, Department of Educational Administration and Policy. With over 100 publications from 2006 to 2025, he has established himself as a leading researcher in educational technology, particularly focusing on innovative applications of virtual reality, artificial intelligence, and game-based learning in formal education settings. His work bridges theoretical frameworks with practical classroom implementations, making significant contributions to how technology enhances teaching and learning experiences. Professor Jong's research interests span multiple cutting-edge domains in educational technology. He has pioneered work in spherical video-based virtual reality (SVVR) applications for education, developing frameworks for immersive learning experiences that transform traditional classroom instruction. His research on AI in education, particularly examining ChatGPT's impact on student engagement and language learning, positions him at the forefront of understanding how emerging technologies reshape educational practices. Additionally, his extensive work on game-based learning, flipped classroom methodologies, and STEM education integration demonstrates a comprehensive approach to technology-enhanced learning across multiple educational contexts and age groups. Analysis of Professor Jong's recent publications reveals a clear progression from foundational work in game-based learning to more sophisticated applications of immersive technologies. His research increasingly focuses on AI integration in educational settings, with a significant portion of his 2023-2025 publications examining ChatGPT's educational applications. The methodological approaches in his work have evolved from basic implementation studies to more sophisticated designs incorporating hierarchical linear modeling, longitudinal analyses, and systematic reviews, demonstrating growing methodological rigor and impact across the educational technology field. Professor Jong has made substantial contributions to educational research through his extensive publication record in top-tier journals including Computers & Education, British Journal of Educational Technology, and Educational Technology & Society. His work has significantly influenced how educators implement virtual reality, AI, and game-based approaches in classrooms worldwide. His research on teacher concerns, implementation challenges, and pedagogical frameworks provides crucial insights for successful technology integration in formal educational settings. Through his supervision of numerous research projects and collaborations with scholars worldwide, Professor Jong has fostered a robust research ecosystem focused on advancing educational technology. His work on design-based research approaches has provided valuable methodological frameworks for studying technology implementation in authentic educational contexts. His recent focus on AI in education positions him as a key contributor to understanding how emerging technologies can be effectively integrated into teaching and learning processes. Professor Jong's research laboratory focuses on immersive learning environments, with particular emphasis on spherical video-based virtual reality applications. His team has developed several innovative frameworks for implementing VR in educational settings, addressing both technical and pedagogical challenges. Current projects appear to focus on AI integration with immersive technologies, exploring how generative AI can enhance virtual learning experiences and support student engagement across various subject areas.

• Computer Science
• Machine Learning
• Image Processing
• +4 more

Yang Cao is a Professor at the University of Science and Technology of China , Department of Automation, Hefei, China. He holds a PhD from Northeastern University (2004, Shenyang, China) and has active affiliations with institutions like Virginia Tech and Huazhong University of Science and Technology. Research Focus: Spatiotemporal modeling, event-based vision, 3D human-object interaction, and industrial defect detection. Publications: 15 recent articles highlight his work in diffusion models, transformers, and state-space networks for tasks like traffic emission imputation, eye tracking, and PCB defect detection. Collaborative Work: Co-authored with Zheng-Jun Zha, Wei Zhai, Yu Kang, and others in journals like IEEE Transactions on Neural Networks and CVPR Workshops. Scientific Contributions: His research bridges computer vision, machine learning, and industrial applications, emphasizing real-world challenges such as low-light enhancement and sensor fusion.

• Developmental Psychology
• Cognitive Psychology
• Educational Psychology
• +4 more

Prof. Azzurra Ruggeri is a Professor in the Professorship for Cognitive and Developmental Psychology at Technical University of Munich (TUM). Her research focuses on understanding how children and adults strategically gather information, make decisions, and learn through embodied and active processes. She leads the iSearch Lab (https://isearchlab.org) and explores topics such as active learning, embodied cognition, and social-cognitive development. Her academic background includes a Dr. rer. nat. (PhD) in Psychology. She is based in Munich, coordinating research on developmental trajectories of learning, exploration strategies, and the interplay between motor skills and cognitive planning. Key areas of investigation include children's decision-making in uncertain environments, the impact of active learning on memory, and the role of embodiment in cognitive development. Recent work emphasizes adaptive information search behaviors, the effectiveness of question-asking strategies, and the application of embodied cognition principles to training interventions. Her studies often employ experimental paradigms involving climbing and spatial navigation to explore motor-cognitive interactions.

Julija ZavadlavView profile

Assistant Professor

• Multiscale Modeling
• Machine Learning
• Computational Chemistry
• +3 more

Prof. Julija Zavadlav is an Assistant Professor of Multiscale Modeling of Liquid Materials at the Technische Universität München (TUM), affiliated with the TUM School of Engineering and Design. Her research integrates physical modeling with machine learning and Bayesian techniques to develop multi-scale simulation frameworks for diverse applications in bioinformatics and engineering. Education: She earned her PhD in Physics from the University of Ljubljana (2015) and conducted postdoctoral research at ETH Zurich (2016–2019), where she received an ETH Postdoctoral Fellowship. Since 2019, she has held her current position at TUM. Research Interests: Her work focuses on advancing machine learning potentials, Bayesian uncertainty quantification, and multi-scale modeling for complex systems like ionic liquids, metal-organic frameworks, and biomolecules. Her ERC Starting Grant (2022) supports the SupraModel project, emphasizing scalable and interpretable models. Awards: Golden Teaching Award 2022 (Best Lecture), ERC Starting Grant 2022, and ETH Postdoctoral Fellowship. Her recent publications emphasize neural network potentials, transfer learning, and computational tools like JaxSGMC for Bayesian analysis. Grants and Labs: While specific lab names are not mentioned, her ERC grant underscores active funding. No formal student advisee list is provided, but her collaborative work suggests involvement in training next-generation computational scientists.

Santiago BerrezuetaView profile

Lecturer

• Virtual Reality
• Augmented Reality
• eXtended Reality
• +7 more

Santiago Berrezueta is a Lecturer at the School of Computation, Information and Technology within Technical University of Munich (TUM). He actively contributes to research in Virtual Reality , Artificial Intelligence , and Robotic Assistance , with a focus on applications in Education and Healthcare . 2025 : 10 publications in VR, robotics, and AI ethics. 2024 : 4 co-authored studies on ChatGPT in ADHD therapy and collaborative learning. His research intersects Interactive Learning and Computer Vision , particularly in ADHD treatment , Language disorder detection , and VR-based education . He supervises Bachelor’s/Master’s theses on topics like gaze-based interaction, collaborative VR workflows, and AI-driven therapeutic tools. 2025: Excellent Paper Award at International Symposium on Educational Technology (ISET 2025) 2024: Best Presentation Award at International Conference in Intelligent Environments (IE 2024) He leads courses on Software Engineering and Programming Fundamentals at TUM’s Heilbronn campus. His work also explores IoT applications and ethical AI in vulnerable populations.

• Computer Science Education
• Logic Programming
• Ontology Engineering
• +2 more

Laura Andrea Cecchi is an active researcher specializing in computer science education, logic programming, and ontology engineering. Her work focuses on developing innovative pedagogical approaches for teaching computational thinking at early educational stages, with significant contributions to gender-inclusive STEM education. Research Interests: Her primary research explores: Logic programming curriculum development for primary education Visual tools for ontology engineering and knowledge representation Eye-tracking and HCI applications in programming pedagogy Gender-inclusive strategies for computer science education Automated reasoning in ontology-based systems Publication Trends: Her recent work (2020-2024) shows a strong focus on educational technology and democratizing programming education, while earlier publications (2015-2019) centered on formal methods and ontology engineering. She consistently publishes at premier computer science conferences including ICLP, CLEI, and IJCAI. Collaborations: Frequent collaborations with Jorge Pablo Rodríguez and Verónica Dahl appear across her publication record, particularly in education-focused research.

Peter SollichView profile

Professor

• Non-equilibrium statistical physics
• Soft matter rheology
• Active matter
• +7 more

Prof. Dr. Peter Sollich is a Professor of Theoretical Physics at Georg-August-Universität Göttingen, affiliated with the Institute for Theoretical Physics. His research spans non-equilibrium statistical physics with applications to soft matter, active systems, and complex networks. He maintains a small part-time appointment at King's College London. His primary research interests focus on non-equilibrium statistical physics , particularly soft and active matter rheology, jamming transitions, glassy dynamics, dynamical phase transitions, and inference from dynamical data. His work bridges theoretical physics with applications in materials science and network theory, emphasizing both fundamental mechanisms and quantitative modeling approaches. Analysis of his recent publications reveals strong thematic consistency in studying glassy dynamics and active matter systems , with increasing integration of machine learning techniques for network analysis. Key methodological threads include coarse-grained modeling, spectral analysis of complex systems, and non-equilibrium thermodynamics frameworks. His 2023-2025 work shows growing emphasis on nonreciprocal interactions in active mixtures and physics-inspired machine learning applications. Prof. Sollich actively supervises Bachelor's, Master's, and PhD students, welcoming thesis inquiries in theoretical physics. His group develops analytical and computational approaches to complex dynamical systems, with recent grants likely supporting work on network dynamics and active matter modeling (specific grants not detailed in source text). His research group operates within the Institute for Theoretical Physics at Göttingen, focusing on computational and analytical modeling of disordered systems. Current projects involve elastoplastic modeling of amorphous solids, spectral analysis of heterogeneous networks, and theoretical frameworks for active matter phase separation.

• Human-Computer Interaction
• Machine Learning
• Affective Computing
• +4 more

Ilhan Aslan is a researcher affiliated with the University of Augsburg and DFKI GmbH , focusing on Human-Computer Interaction , Machine Learning , and Affective Computing . His work explores emotional speech interfaces , tangible interaction , and playful multimodal systems . Research Themes : Speech emotion recognition, proxemic-aware interaction, AI ethics, and user experience design. Recent Contributions : 2025 studies on proactive AI agents , voice emotion conversion , and solo role-playing AI ; 2024 work on audio enhancement and emotional trajectory modeling . His publications with Elisabeth André , Timothy Merritt , and Niels van Berkel demonstrate cross-institutional collaboration. Current projects emphasize sustainable creativity support , haptic feedback systems , and bias detection in speech AI .

Olga SteinbergView profile

Researcher

• Educational Psychology
• Adolescent Development
• School Motivation
• +3 more

Dr. Olga Steinberg serves as a Researcher in the Department of Developmental and Educational Psychology at the University of Greifswald's Faculty of Psychology since January 2023. Previously, she worked as a research assistant at University of Rostock (2021-2023) and Greifswald University's School Education Department (2015-2022), with additional research experience at Freie Universität Berlin where she earned her doctorate magna cum laude in 2015. Her educational background includes a Master in Intercultural Education from Freie Universität Berlin (2010-2011) and teacher training at Moscow State Pedagogical University (2005-2010) with distinction. She completed an ERASMUS research term at University of Jyväskylä, Finland in 2017. Steinberg's research focuses on educational psychology with emphasis on adolescent development, specifically examining academic self-concepts, social and motivational relationships with peers and teachers, and the emergence of interest in school contexts. Her methodological expertise spans both qualitative and quantitative approaches in educational research, with recent work exploring digitalization and innovations in higher education teaching. Her publication record reveals a consistent trajectory examining the interplay between school self-concept, social relationships, and academic motivation throughout adolescence. Recent work has expanded to include cross-cultural comparisons (Germany/Russia, Finland, Philippines), teacher-student relationship dynamics, and interventions addressing student stress during the pandemic. Her research demonstrates strong methodological diversity, utilizing both qualitative approaches and sophisticated statistical modeling techniques. Quality assurance in teacher education (2022 book) Cross-cultural studies of adolescent motivation Teacher-student relationship dynamics Pandemic-related educational interventions Steinberg actively contributes to educational practice through seminars for teacher students at Greifswald University since 2015 and has practical teaching experience in both Germany and Russia. Her international collaborations reflect a strong European research network with colleagues from Finland, Canada, and various international contexts.

Martim BrandãoView profile

Assistant Professor

• Robotics
• Artificial Intelligence
• Responsible AI
• +12 more

Martim Brandão is a Lecturer (Assistant Professor) in Robotics and Autonomous Systems at King’s College London, where he leads the Responsible Robotics and AI (RRAI) Lab and serves as Co-Director of the UKRI Centre for Doctoral Training in Safe and Trusted AI. His research focuses on ethical, explainable, and safe AI and robotics, with applications in human-robot interaction, motion planning, fairness, and societal impact. His research interests include: Explainable AI and Motion Planning Fairness and Bias in AI Systems Human-Robot Interaction and Social Robotics Adversarial Robustness in Robotics Value Alignment and Ethical AI Inclusive and Participatory Robotics Design His recent publications (2023–2025) reflect a strong trend toward socially responsible robotics, focusing on fairness in navigation, explainability of planning failures, worker-centered agricultural robotics, environmental justice in drone delivery, and the dangers of bias in drowsiness detection and LLM-driven robots. His work emphasizes user understanding, societal impact, and ethical safeguards in autonomous systems. He has advised and collaborated with numerous students and researchers across diverse topics in robotics and AI. He is actively involved in shaping responsible robotics through: Leadership in the RRAI Lab Co-directing a national CDT in Safe and Trusted AI Developing fairness-aware algorithms Advocating for inclusive and ethical design practices His lab and research group focus on: Responsible Robotics and AI Explainability in Multi-Agent Planning Fairness in Coverage and Navigation Human-Centered Evaluation of AI Systems

Tobias BornemannView profile

Professor

• Production Management
• Lean Management
• Industrial Engineering
• +5 more

Prof. Dr.-Ing. Tobias Bornemann holds a Cooperative Professorship for Production & Lean Management at Frankfurt University of Applied Sciences , while also serving as Head of Product Management for Autonomous Mobile Robotics at Continental AG . His work bridges theory and practice in areas such as Lean Management , Industrial Engineering , and Smart Factory through teaching, research, and industry collaboration. His research focuses on Intralogistics , Autonomous Mobile Robotics , and Agile Product Development . He leads projects at the Research Lab for Urban Transport (ReLUT) and contributes to the House of Logistics and Mobility (HOLM) , where he co-developed a learning factory. His publications emphasize Industry 4.0 , AMR fleet management , and digital education tools . Cooperative Professorship at Frankfurt UAS Head of Industrial Engineering (M.Sc.) program Research Lab for Urban Transport (ReLUT) House of Logistics and Mobility (HOLM) collaboration Continental AG Tech Matinees and Ambassadorship His academic efforts include mentorship for students, interdisciplinary teaching , and industry-academia partnerships under the PROFfm initiative.

Gyunam ParkView profile

Researcher

• Process Mining
• Action-Oriented Process Mining
• Object-Centric Process Analysis
• +12 more

Gyunam Park is a Research Group Lead and Process and Data Scientist at Fraunhofer FIT and a Scientific Assistant at the Chair of Process and Data Science at RWTH Aachen University, a leading institution in computer science and engineering. He is actively involved in both research and teaching, contributing to the advancement of process mining, data science, and artificial intelligence. His work bridges academic research and industrial applications, particularly in SAP ERP systems and digital twins of organizations. Research Interests: Gyunam Park’s research focuses on Action-Oriented Process Mining (AOPM) , Object-Centric Process Analysis , and Responsible Machine Learning . He aims to transform process mining insights into actionable management decisions, ensuring transparency, fairness, and compliance. His work enables organizations to monitor operational constraints, generate corrective actions, and assess their impact using data-driven methods. Publication Trends: His recent publications emphasize object-centric approaches to process mining, predictive monitoring, constraint checking, and integration with AI planning. There is a strong trend toward preserving structural information in event logs, improving machine learning performance, and applying these techniques to real-world systems like SAP ERP and after-sales service processes. Scientific Awards: No awards are explicitly mentioned in the provided text. Advising and Grants: While no formal students are listed, Gyunam Park leads research projects and collaborates with industry partners such as Samsung Electronics and SAP. His projects involve root cause analysis, resource optimization, and educational data mining. He has developed open-source tools like ProAct and OCPA , indicating active grant or institutional support for software development and research dissemination. Labs and Teams: He is a core member of the Process and Data Science (PADS) group led by Prof. Wil van der Aalst at RWTH Aachen University and leads a research group at Fraunhofer FIT. These teams focus on cutting-edge research in process mining, data science, and AI, with strong industry collaborations and regular contributions to top conferences and journals.

Mario KupnikView profile

Professor

• Micromachined Sensors
• Ultrasonic Technology
• Non-Destructive Testing
• +4 more

Prof. Mario Kupnik is a Full Professor at the Technische Universität Darmstadt , leading the Measurement and Sensor Technology Group within the Department of Electrical Engineering and Information Technology. His academic career includes roles at Stanford University (2005–2011) and Brandenburgische Technische Universität Cottbus (2011–2014). He holds a doctorate from Montanuniversität Leoben (2000–2004) and a master's in Telematics from Graz University of Technology. His research focuses on micromachined sensors and actuators , ultrasonic and electroacoustic systems , and non-destructive testing . He pioneers innovations in wearable sensors, biomedical applications, and additive manufacturing for sensor integration. Notable contributions include air-coupled ultrasonic transducers, 3D-printed ferroelectret sensors, and robotics for STEM education. Recent work emphasizes biodegradable sensors , acousto-optic modulation , and multi-parameter medical measurement systems . His projects span from fundamental material science to applied engineering solutions, often leveraging open-source hardware. Kupnik’s labs integrate interdisciplinary approaches, combining electrical engineering, materials science, and biomedical engineering.