Katy Börner is a Professor affiliated with Indiana University, Bloomington, USA. Her research focuses on data visualization, scientometrics, and the science of science, with contributions to tools like Network Workbench (NWB) and Sci2. She leads interdisciplinary projects such as the Human Reference Atlas and explores visualization literacy in education and public health. Her work spans virtual reality applications, biomedical knowledge networks, and AI-driven scientific discovery. Key research interests include mapping scientific collaboration networks, analyzing scholarly publications, and developing visualization frameworks for big data. She collaborates widely with institutions like NIH and VIVO, advancing open science and interdisciplinary research. Her projects often bridge computational methods with human-centric design, enhancing understanding of complex systems in health, technology, and social sciences. Publications highlight innovations in interactive visualization tools (e.g., opioid crisis research networks), data integration (Human Reference Atlas), and AI applications in biomedical research. She emphasizes translating data into actionable insights through visual analytics, impacting policy, education, and healthcare.
Dr. Anett Hoppe is a research staff member at the Leibniz Information Centre for Science and Technology (TIB) in Hannover, Germany, where she works in the Visual Analytics research group. Her research focuses on the intersection of artificial intelligence, education technology, and information science, with particular emphasis on how people learn through search processes and educational video consumption. Dr. Hoppe completed her academic journey with: Ph.D. in Semantic Web technologies for online user profiles from the University of Burgundy, Dijon, France Her primary research interests span Search as Learning, software-based support for scientific reproducibility, and ethical considerations in computer-based decision making. She investigates how visual elements, reading sequences, and AI technologies impact knowledge acquisition during web search and educational video consumption. Her work bridges human-computer interaction, educational psychology, and information retrieval to create more effective learning experiences, with recent publications examining the role of large language models, vision-language models, and visual complexity in educational contexts. Analysis of her recent publications (2024-2025) reveals a strong interdisciplinary focus combining computer science, educational psychology, and information science. Her research examines video-based learning effectiveness, knowledge gain prediction, educational resource discovery, and the impact of visual elements on learning outcomes. She consistently explores how AI technologies can be leveraged to enhance educational experiences while maintaining attention to ethical considerations and scientific reproducibility. Dr. Hoppe maintains active collaborations with researchers across multiple institutions, with frequent co-authorship patterns indicating strong research partnerships, particularly with Ralph Ewerth and other members of the Visual Analytics group at TIB. Her work supports TIB's mission to advance knowledge infrastructure and scholarly communication through innovative technological solutions while directly addressing practical challenges in educational technology and information retrieval.
Libby Gerard is an Associate Adjunct Research Professor at the University of California, Berkeley School of Education and a Research Director for the Technology-Enhanced Learning in Science (TELS) Center. Her work focuses on leveraging innovative technologies to enhance science education through student idea capture, automated assessment, and teacher professional development. Doctorate in Educational Leadership (EdD), Mills College (2008) Bachelor’s in English Literature and Philosophy, Emory University (2000) Her research emphasizes: Automated scoring of student essays using NLP to improve science explanations Real-time instructional customization using embedded assessment data Technology-driven professional development for teachers and principals Social justice integration in science pedagogy Collaborative revision frameworks for inquiry-based learning K-12 education adaptation during the pandemic Recent publications highlight trends in educational technology for science learning, with a focus on NLP applications, interactive inquiry modules, and equitable teaching practices. She has authored studies in journals like Science , Review of Educational Research , and Computers & Education , often exploring how automated systems can enhance teacher-student dynamics. Scientific Awards : Best Paper Award at the AI4EDU Workshop (AAAI Conference, 2020) Libby leads funded projects such as: TIPS (NSF, 2021-2025): NLP for science education ARISE (Hewlett Foundation, 2020-2023): Anti-racism in science education STRIDES (NSF, 2018-2022): Responsive instruction for science teachers PLANS (NSF, 2015-2020): Automated learning support systems She contributes to teacher training through courses like Research Methods for Science Teachers and Apprentice Teaching in Science , emphasizing data-driven pedagogy and inquiry-based instruction.
Mihaela van der Schaar is the John Humphrey Plummer Professor of Machine Learning, Artificial Intelligence, and Medicine at the University of Cambridge, leading the van der Schaar Lab. She holds dual affiliations with the Department of Applied Mathematics and Theoretical Physics (DAMTP) and the Centre for Mathematical Imaging in Healthcare. Her research focuses on healthcare AI, machine learning, and operations research. She has authored over 250 journal articles and 275 conference papers, with notable contributions to synthetic data for privacy, causal inference, and clinical decision-making. Her work has led to 35 U.S. patents, including foundational innovations in streaming video compression (MPEG-4 standards). Awards include the Oon Prize (2018), IEEE Fellow (2009), and recognition as the UK's most-cited female AI researcher (2019). Leadership roles include Director of the Cambridge Centre for AI in Medicine and Co-Director of the European Laboratory for Learning and Intelligent Systems. She has mentored global academic leaders and pioneered initiatives like the Inspiration Exchange for early-career researchers. Key projects include predictive models for hospital resource allocation during pandemics and AI tools for personalized medicine. Publications span machine learning theory, healthcare applications, and interdisciplinary fields like network science. Her lab's impact includes tools like AutoPrognosis (automated ML for clinical prediction) and SynthCity (synthetic healthcare data generation).
Laura Toni is an Associate Professor in the Department of Electronic and Electrical Engineering at University College London's Faculty of Engineering Sciences. She serves as the leader of a research team focused on advanced signal processing and machine learning applications, documented at https://lasp-ucl.github.io . Additionally, she holds prestigious affiliations as an ELLIS (European Laboratory for Learning and Intelligent Systems) Member and Turing Fellow Alumni. PhD in Electrical Engineering, University of Bologna (2009) MS in Electrical Engineering, University of Bologna (2005) Professor Toni's research spans theoretical and applied aspects of machine learning with particular emphasis on graph-based approaches. Her work integrates signal processing techniques with modern AI methodologies to address complex problems in communication systems, multimedia processing, and scientific discovery. She has made significant contributions to reinforcement learning theory, graph signal processing, and their applications across diverse domains including drug discovery and immersive technologies. Analysis of her recent publications reveals a strong focus on graph-based machine learning approaches, with increasing emphasis on reinforcement learning applications. Her work demonstrates a progression from theoretical foundations to practical implementations, particularly in multimedia processing, network science, and drug discovery applications. Many of her recent papers combine graph neural networks with diffusion models and reinforcement learning for complex prediction and generation tasks. Professor Toni has received notable recognition through her ELLIS membership and Turing Fellow Alumni status, which represent significant achievements in the European AI research community. ELLIS (European Laboratory for Learning and Intelligent Systems) Member Turing Fellow Alumni As an academic leader, Professor Toni supervises postgraduate students and leads a research team at UCL, focusing on cutting-edge projects at the intersection of signal processing and machine learning. Her team has secured research funding through various channels including European initiatives and industry partnerships, enabling them to pursue ambitious projects in graph learning, reinforcement learning, and multimedia processing. The team actively collaborates with institutions worldwide, including previous connections with UCSD and EPFL. Professor Toni leads the LASP research group at UCL (https://lasp-ucl.github.io), which focuses on Large-scale Adaptive Signal Processing for intelligent systems. The team comprises researchers working on graph signal processing, reinforcement learning, and multimedia applications, with strong connections to both theoretical foundations and practical implementations across various domains including healthcare, communications, and immersive technologies.
Ruben Verborgh is a Professor of Decentralized Web Technology at the Ghent University – imec and a Visiting Fellow at the Oxford Martin School (University of Oxford). He leads the Internet Technology and Data Science Lab (IDLab) and co-founded the Solid platform with Tim Berners-Lee to re-decentralize the Web. His research focuses on Linked Data Fragments , a paradigm for Web-scale query execution, and explores decentralized data governance , user-controlled data ownership , and rule-based Web agents for policy enforcement. He has co-authored two books on Linked Data and contributed to over 250 publications. Recent articles highlight trends in decentralized data ecosystems , including ODRL policy interoperability , event notification systems , and personal data vaults . His work bridges Linked Data , hypermedia APIs , and privacy-preserving technologies . Verborgh collaborates with institutions like MIT, Oxford, and the European Commission, and advises companies through Inrupt . His labs ( IDLab , Solid Ecosystem ) focus on sustainable data-driven societies.
Aldo Gangemi is a Full Professor in the Department of Philosophy at the University of Bologna, specializing in Informatics (INFO-01/A). His research integrates Semantic Technologies, Natural Language Processing, Data Science, and Cognitive Science to address challenges in knowledge representation, ontology engineering, and cultural heritage informatics. He co-founded the STLab at ISTC-CNR and DHARC at the University of Bologna, and is on leave from Sorbonne Paris Nord University (Computer Science Lab - LIPN). Scientific roles include serving as area chair for Web Semantics , editorial board member for Semantic Web and Applied Ontology , and conference chairs for major events like WWW2015 and ESWC2018. He has led European projects such as GALEN, WonderWeb, NeOn, and MARIO, and developed software tools like FRED, Aemoo, and Framester. Research interests focus on semantic technologies, knowledge patterns, and applications in humanities, medicine, law, and fisheries. Over 250 peer-reviewed publications span these areas, with emphasis on ontology-based knowledge integration, multimodal reasoning, and ethical AI. His work bridges cognitive science and technological innovation, particularly in virtual reality's societal applications and cultural heritage preservation. Notable contributions include the PRIVAFRAME knowledge graph for sensitive data, the ImageSchemaNet ontology for embodied cognition, and the Sandra neuro-symbolic reasoner. His interdisciplinary approach addresses challenges in AI ethics, creative systems, and citizen-driven data curation.
Norman Sadeh is a Professor in the School of Computer Science at Carnegie Mellon University (CMU), where he has made significant contributions to cybersecurity, privacy, and AI research. He has co-founded and co-directed several groundbreaking graduate programs at CMU, including the Privacy Engineering Program (2012-present), the Ph.D. Program in Societal Computing (2003-2013), and the MBA track in Technology Strategy and Product Management (2005-2017). Carnegie Mellon University, School of Computer Science Software and Societal Systems Department CyLab Security and Privacy Institute Manufacturing Futures Institute Dr. Sadeh received his Ph.D. in Computer Science at CMU with a major in Artificial Intelligence and a minor in Operations Research. He holds an M.Sc. in computer science from the University of Southern California and a BS/MS degree in electrical engineering and applied physics from the Free University of Brussels (Belgium) as 'Ingénieur Civil Physicien.' Professor Sadeh's research spans cybersecurity, online privacy, Human-AI Interaction, AI governance, mobile computing, the Internet of Things, user-oriented machine learning, and language technologies. He is particularly known for his pioneering work on AI-based privacy enhancing technologies, including privacy assistants, automated privacy compliance tools, and NLP-based privacy solutions. His work has influenced the design of privacy features at major technology companies including Apple, Google, and Facebook/Meta, as well as privacy policies at regulatory agencies like the Federal Trade Commission and the California Office of the Attorney General. Analysis of his recent publications shows a strong focus on practical privacy solutions, particularly in mobile and IoT contexts, with an emphasis on making privacy more usable and understandable for end users. His work bridges technical innovation with policy implications, addressing both the technological and human aspects of privacy protection. 2018 Outstanding Entrepreneur of the Year award from the Pittsburgh Venture Capital Association Test of time award by the AAAI Conference on Web and Social Media (ICWSM) Gartner Group's Magic Quadrant leader in Security Awareness Computer-Based Training for 4 consecutive years Deloitte's Technology Fast 500 recognition for 3 consecutive years Professor Sadeh has advised numerous students, including PhD candidates like Aerin (Shikhun) Zhang, whose dissertation focused on understanding diverse privacy attitudes. His research has been funded through various grants, including NSF SaTC projects, and has resulted in technologies that protect tens of millions of users worldwide. He also founded Wombat Security Technologies, which was acquired by Proofpoint in 2018 and whose technologies are used by over 75% of Fortune 100 companies. Professor Sadeh leads several research initiatives including the Privacy Engineering Program, the Usable Privacy Policy Project, the Personalized Privacy Assistant Project, and CMU's Privacy Infrastructure for the Internet of Things. His Mobile Commerce Lab and E-Supply Chain Management Lab have produced influential research that has been commercialized by major organizations including IBM, Raytheon, Boeing, and the U.S. Army.
Karthik Menon serves as an Assistant Professor with a joint appointment in the Woodruff School at Georgia Institute of Technology and the Coulter Department of Biomedical Engineering. His research integrates fluid mechanics, computational modeling, and data-driven methodologies to address critical challenges in healthcare, renewable energy, and bio-inspired engineering systems. His academic credentials include: Ph.D. in Mechanical Engineering, Johns Hopkins University (2021) M.S. in Mechanical Engineering, Johns Hopkins University (2019) B.E. in Mechanical Engineering, Birla Institute of Technology and Science, Pilani, India (2015) Menon's research program centers on three interconnected domains: cardiovascular flows for personalized treatment of heart disease, fluid-structure interactions in biological systems like heart valves and bio-mimetic robots, and vortex-dominated flows for renewable energy applications. His approach combines high-fidelity computational modeling with machine learning to uncover fundamental physics and develop clinical solutions, such as cardiovascular digital twins for non-invasive risk assessment. Current projects focus on patient-specific hemodynamics using CT imaging and uncertainty quantification to improve surgical planning. Analysis of his 15 most recent publications (2023-2025) reveals a dominant focus on advancing multi-fidelity computational frameworks for cardiovascular applications. Key trends include Bayesian uncertainty quantification, zero-dimensional solver development, and integration of clinical imaging data to create predictive digital twins. His work bridges fluid dynamics with clinical cardiology, targeting improved outcomes in coronary artery disease and Kawasaki-related complications through physics-informed machine learning. Menon's scholarly contributions have been recognized through competitive awards: WCCM-PANACM 2024 Travel Award, U.S. Association for Computational Mechanics (2024) Future Faculty Symposium Travel Award, Society of Engineering Science Conference (2023) Mark O. Robbins Prize in High-performance Computing, Johns Hopkins University (2021) Corrsin-Kovasznay Outstanding Paper Award, Johns Hopkins University (2020) Prosperetti Travel Award, Johns Hopkins University (2017) Mechanical Engineering Departmental Fellowship, Johns Hopkins University (2016) As principal investigator of the ComBiNE Fluid Dynamics Lab, Menon mentors graduate students in developing computational tools for fluid-structure interaction problems. His collaborative projects with cardiologists at Stanford and Emory hospitals translate engineering principles into clinical applications for cardiovascular disease management. Current grant activities focus on NSF and NIH-funded initiatives for uncertainty-aware cardiovascular modeling and bio-inspired flow energy harvesting. The ComBiNE Fluid Dynamics Lab operates as an interdisciplinary hub where engineers, clinicians, and data scientists collaborate on fluid mechanics challenges. Current lab initiatives include developing real-time hemodynamic simulators for surgical planning, creating reduced-order models for cardiac device optimization, and investigating vortex dynamics in fish schooling for underwater vehicle design. The lab maintains strong partnerships with Children's Healthcare of Atlanta and the Parker H. Petit Institute for Bioengineering and Bioscience.
Jan Borchers is a full professor of computer science and head of the Media Computing Group, an endowed Chair in the Computer Science Department at RWTH Aachen University. He serves as deputy member of the Faculty Council for Mathematics, Computer Science and Natural Sciences (2024-2026) and previously headed the Computer Science Department's Examination Board from 2015 until February 2025. Borchers established his research group in 2003, pioneering modern HCI academic research and teaching in Germany, and opened Germany's first Fab Lab in 2009. Borchers received his PhD 'summa cum laude' in computer science from Darmstadt University of Technology in 2000. Before joining RWTH Aachen, he held faculty positions at Stanford University and ETH Zurich. His PhD thesis, 'A Pattern Approach to Interaction Design,' became the first book to bring design patterns to HCI. His research focuses on Human-Computer Interaction with particular interest in new user interfaces for soft robotics, textile user interfaces, 3D printing and personal fabrication, augmented reality, wearable and tangible computing, interfaces for software development, deceptive patterns, and interactive guides and exhibits. He is a member of ACM, SIGCHI, SIGCHI Germany, and GI, and introduced the Interactivity format to the CHI conference in 2005. Recent publications reveal a strong emphasis on deceptive patterns/dark patterns in UI design, textile interfaces, and the impact of generative AI on creative teamwork. His work spans from fundamental research in interaction techniques to practical applications in smart homes, accessibility, and children's interfaces, with a consistent focus on usability and user-centered design. IDC 2025 Best Work in Progress: 'If They Have No Choice, They'll Accept!' CHI'25 Student Games Competition Winner: 'The Deceptive Dungeon' RWTH-Wissenschaftsnacht 2024 Best Science Slam: 'Usability: 4 Prinzipien guter User Interfaces' Multiple CHI/UIST Honorable Mentions and Best Paper awards spanning two decades Author of influential books including 'A Pattern Approach to Interaction Design' and 'Arduino In A Nutshell' Borchers has provided extensive consulting, training, and user interface design services to major clients including AirBus, Apple, ARD, Bayer, Children's Museum Boston, Daimler, Handelsblatt, OTIS, Scout24, TEDx, and Deutsche Telekom. He actively organizes the Computer Science Department's weekly Faculty Lunch since 2003 and coordinates the department's public relations and web presence since 2010. He leads the Media Computing Group, which has become a leading German lab in terms of archival publications at CHI, the top international conference in HCI. Since March 2025, he serves as faculty patron for TechLabs Aachen, a student initiative providing practical digital skills training.
Andrew Lan is an Associate Professor in the College of Information and Computer Sciences at the University of Massachusetts Amherst, where he also serves as the CS Undergraduate Program Director. He was granted tenure by the UMass Board of Trustees in June 2025 and is currently on leave through Spring 2026. His research focuses on developing human-in-the-loop machine learning methods to enable scalable, effective, and personalized learning experiences in education. Dr. Lan received his BS in Physics and Mathematics from the Hong Kong University of Science and Technology, followed by his MS (2014) and PhD (2016) in Electrical and Computer Engineering from Rice University. He completed postdoctoral research at Rice University (2016) and Princeton University's EDGE Lab (2017-2018). His research spans artificial intelligence for education, with particular expertise in educational data mining, knowledge tracing, personalized learning systems, and human-AI collaboration in educational contexts. Dr. Lan's work leverages massive and multimodal learner and content data collected from both traditional classrooms and online learning platforms to develop systems that deliver high-quality, affordable, and personalized learning experiences. He has made significant contributions to areas including computerized adaptive testing, math word problem generation, student affect detection, and automated grading systems. His recent work increasingly focuses on leveraging large language models for educational applications while maintaining rigorous scientific validation of these approaches. Best Student Paper Award at the 2024 AIED Conference (with Alexander Scarlatos) Best Paper Nominee at LAK 2021 Best Student Paper Award at IEEE Big Data 2020 NAEP Math Automated Scoring Challenge Grand Prize Winner Dr. Lan actively mentors graduate students and postdoctoral researchers, with several of his advisees receiving recognition for their work. He has secured substantial funding from the National Science Foundation, including a $90M grant for the SafeInsights project, a secure cyberinfrastructure for educational research. His research group collaborates with institutions including Worcester Polytechnic Institute, University of Pennsylvania, and Rice University. He teaches undergraduate and graduate courses including COMPSCI 240 (Reasoning under Uncertainty) and COMPSCI 590OP (Applied Numerical Optimization), with a focus on the practical application of theoretical concepts in machine learning and artificial intelligence. His educational philosophy emphasizes bridging the gap between theoretical foundations and real-world implementation in educational technology.
Mark d'Inverno is a Professor in the Department of Computing at Goldsmiths, University of London, where he has established himself as a leading researcher at the intersection of artificial intelligence, multi-agent systems, and creative applications. His academic journey began with foundational work in formal methods and agent-based systems, culminating in his 1998 PhD thesis 'Agents, Agency and Autonomy: A Formal Computational Model' from University College London, and has evolved toward practical applications in music technology and ethical AI systems. Professor d'Inverno's research interests span multiple interconnected domains, with a particular focus on computational creativity, multi-agent systems, and the application of AI in musical contexts. His work explores how artificial intelligence can enhance creative processes, particularly in music composition and performance, while maintaining ethical considerations in social AI systems. He has made significant contributions to understanding how agents can interact meaningfully in social contexts, how ethical frameworks can be embedded in online systems, and how technology can support creative learning experiences. His recent scholarly output demonstrates a clear trajectory toward applied research with social impact, as evidenced by his 2021-2024 publications which increasingly address ethical considerations in AI, human-AI collaboration in creative domains, and educational applications of technology. These works reveal a researcher deeply engaged with both theoretical foundations and practical implementations, bridging the gap between abstract computational models and real-world creative and educational applications. Professor d'Inverno maintains an extensive collaborative network, frequently working with Matthew Yee-King on music technology applications, with Pablo Noriega on ethical AI frameworks, and with Jon McCormack on computational creativity. His research has been supported through various projects that connect theoretical computer science with practical creative applications, particularly in the development of systems that facilitate human-AI creative collaboration.
Maria Chikina is an Assistant Professor at the University of Pittsburgh School of Medicine's Department of Computational and Systems Biology. She holds a PhD in Molecular Biology from Princeton University. Her research focuses on developing computational methods to analyze large-scale genomic datasets, bridging statistical rigor with biological insights to overcome experimental biases. Key research areas include latent variable modeling (e.g., PLIER, CellCODE), interpretable neural networks for sequence-to-function modeling, evolutionary rate analysis (RERconverge), and applications in tumor immunology, exercise genomics, and infectious disease (e.g., SARS-CoV-2). Her lab has developed tools like InstaPrism, NIFA, and L0 segmentation for data-driven biological discovery. Her work spans collaborations with institutions like UPMC (on tumor microenvironment) and the Molecular Transducers of Physical Activity Consortium (MoTraPAC). Notable projects include analyzing convergent evolution in marine mammals and subterranean species, and developing epigenetic biomarkers for disease states through the ECHO program. Lab members include PhD students (Rezwan Hosseini, Tugrul Balci) and postdocs (Tina Subic, Anish Sevekari). Past students Wynn Meyer now leads a group at Lehigh University. Her group emphasizes open-source tools (GitHub repository ChikinaLab) and interdisciplinary approaches to systems biology challenges.
Steve Collins is an Associate Professor of Mechanical Engineering at Stanford University, with a courtesy appointment in the Department of Bioengineering. His research focuses on wearable robotics, biomechanics, and human-machine interaction. He leads projects on exoskeleton optimization, prosthetic design, and energy-efficient robotic actuators. His work aims to improve mobility for older adults and individuals with mobility impairments through innovative assistive technologies. Research Interests: Collins explores biomechanical principles underlying human movement, exoskeleton torque control strategies, and the design of devices that reduce metabolic costs during walking. His lab develops both hardware (e.g., exoskeleton emulators) and software (e.g., AddBiomechanics modeling tools) to advance assistive technologies. Key Contributions: He pioneered human-in-the-loop optimization methods for exoskeleton control, demonstrated energy-saving designs for ankle exoskeletons, and investigated how exoskeletons can enhance balance and reduce fall risks. His team also developed the 'Tripod' prosthesis emulator and electrostatic clutch systems for energy-efficient actuators. Grants & Collaborations: His work is supported by NSF grants (e.g., NRI: Small grant for exoskeleton control) and industry partnerships. He collaborates with clinicians to translate robotic innovations into clinical applications for amputees and aging populations. Labs & Teams: His research is conducted in Stanford's robotics and biomechanics facilities, focusing on interdisciplinary projects at the intersection of mechanical engineering, bioengineering, and computer science.
Ming Lin is a Distinguished University Professor at the University of Maryland, College Park, holding joint appointments in Computer Science (Department of Computer Science), the Institute for Advanced Computer Studies (UMIACS), Electrical and Computer Engineering (ECE), and the Maryland Robotics Center. She holds the Dr. Barry Mersky and Capital One E-Nnovate Endowed Professorships. Her research focuses on physically-based modeling, virtual environments, haptics, robotics, and AI applications in healthcare and urban computing. Education: Ph.D., M.S., and B.S. in Electrical Engineering & Computer Sciences from UC Berkeley. She previously spent 20 years at UNC Chapel Hill before joining UMD in 2018. Research interests include collision detection algorithms (e.g., Lin-Canny algorithm), real-time physics simulation, virtual/augmented reality systems, and medical imaging applications. Her work has led to over 2 million downloads of her group's software tools and licenses with 60+ companies. Notable contributions include the Oculus Rift-related VR technologies and Amazon's virtual try-on system. Awards: IEEE Fellow (2012), ACM Fellow (2011), NAI Fellow (2022), and Washington Academy of Sciences Distinguished Career Award (2020). Active in professional service, she serves on the CRA Board and chairs the Committee on Widening Participation in Computing Research. Advising: Supervises 12+ PhD/Master's students. Her lab (GAMMA Group) focuses on AI-driven robotics, autonomous systems, and physically-based simulations. Key projects include traffic simulation frameworks, medical VR applications, and 3D garment modeling.
