Edward Lank was a Professor at the Cheriton School of Computer Science, University of Waterloo, and held an Inria International Research Chair at Inria Lille-Nord Europe (2019–2023). His research focused on Human-Computer Interaction (HCI), including intelligent user interfaces, mobile/multi-touch interaction, gesture recognition, and mathematical software design. He earned a BSc from the University of Prince Edward Island and a PhD from Queen's University. Research Highlights: Pioneered work on gesture-based interaction, including MathBrush for mathematical expression recognition. Explored large-display interaction, powerwall design, and mass user engagement with public displays. Investigated kinematics of user input, endpoint prediction, and mode inference in interfaces. Collaborated on persuasive technology for energy demand management and health-related serious games. Awards & Recognition: National Science Foundation Career Award (2004) Best of CHI Nominee (2008) Inria International Research Chair (2019–2023) Grants & Funding: Supported by Google, NSERC, GRAND NCE, and the ORF Program. His research addressed challenges in wearable tech, VR/AR, and sustainable HCI practices. Legacy: Edward Lank passed away on March 21, 2022. His contributions to HCI, including foundational work on gesture recognition and user-centered design, continue to influence the field. His courses, such as CS 889 (HCI Seminar) and CS 449 (HCI Fundamentals), emphasized user-centered design and empirical methods.
Andrea Stevenson Won is a researcher at Cornell University in the Department of Communication , focusing on virtual reality (VR), human-computer interaction, and social dynamics in immersive environments. Her work explores avatar embodiment , nonverbal behavior , and accessibility in VR for users with disabilities. Research Themes : Virtual embodiment and its psychological effects Accessibility solutions for blind and low-vision users in social VR Nonverbal communication analysis in immersive environments Pro-social behavior through VR interventions Collaborative VR systems and AI integration Recent Article Trends : 2024: Investigated avatar behavior transformation in mixed reality ( MRTransformer ), AI-guided accessibility tools, and nonverbal cue adaptations 2023-2022: Focused on educational VR applications, 360° video narratives, and longitudinal team dynamics 2021-2014: Pioneered avatar embodiment studies, anxiety detection via movement tracking, and homuncular flexibility in VR
Jürgen Gauss is a Professor of Theoretical Chemistry at Johannes Gutenberg-Universität Mainz, Germany. With over 350 publications and an h-index of 85 (ISI WebOfScience)/96 (Google Scholar), his work focuses on high-accuracy quantum-chemical methods for energy and property calculations. Education: PhD in Theoretical Chemistry (1988), Universität zu Köln Positions: Full Professor (2001-present), Associate Professor (1995-2001), Research Associate (1991-1995), Postdoctoral Researcher (1990-1991) His research revolutionized NMR chemical shift calculations through the GIAO-MP2 scheme, extended to Cholesky decomposition techniques. He pioneered the first CCSD(T)-level analytic second derivatives for magnetic properties and developed the HEAT protocol for sub-kJ/mol thermochemical accuracy. Scientific Awards: Carl-Duisberg Gedächtnispreis (1996) Medal of International Academy of Quantum Molecular Science (1997) Akademiepreis (2003) Gottfried-Wilhelm Leibniz-Prize (2005) Foreign Member, Norwegian Academy of Science and Letters (2018) Advisees: Current PhD students include Sophia Burger, Florian Mast, Max Erichsen, and Malte Hellmann. His group develops the widely-used CFOUR quantum chemistry software package (over 1,000 licenses).
Dr. Joshua T. Weinhandl is an Associate Professor in the Department of Kinesiology at the University of Tennessee, part of the College of Education, Health, and Human Sciences. His research focuses on lower extremity injury biomechanics, movement coordination, and neuromuscular control deficits. He teaches courses such as KNS 332 Applied Anatomy and KNS 575 Matlab for Biomechanics. Education: PhD in Biomechanics, University of Wisconsin – Milwaukee MS in Biomechanics, Ball State University BS in Kinesiology, Grenville College Research Interests: Dr. Weinhandl investigates ACL injury mechanisms, knee osteoarthritis, chronic ankle instability, and musculoskeletal injury prevention using kinetic, kinematic, and electromyographic analyses. He emphasizes computational modeling and neuromuscular control to improve intervention strategies. Professional Activities: He serves as a reviewer for journals like Journal of Biomechanics and Medicine & Science in Sports & Exercise , and is a member of the American Society of Biomechanics and the American College of Sports Medicine. Labs: His work aligns with the Biomechanics/Sports Medicine Laboratory and other research groups within the department.
Professor Celso Grebogi, Sixth Century Chair in Nonlinear & Complex Systems at the University of Aberdeen, is a globally recognized leader in nonlinear dynamics , chaos theory , and systems biology . He founded the Institute for Complex Systems and Mathematical Biology and co-founded the Aberdeen-Lanzhou-Tempe Research Centre. His career spans institutions including University of Maryland, University of São Paulo, and Max-Planck-Society (External Scientific Member since 1998).
Miklós Koren is a Professor of Economics at Central European University and Senior Research Fellow at the HUN-REN Centre for Economic and Regional Studies. His work bridges international trade , economic development , and managerial economics , focusing on trade policy, productivity spillovers, and the role of managers in development. Ph.D., Harvard University (2005) M.A., Central European University (2000) M.Sc., Budapest University of Economics (1999) His research explores trade facilitation , managerial impact on firm performance , and technological diversification . Recent work includes studies on expatriate managers, pandemic-related business disruptions, and the legacy of communist-era management practices. Key trends in his publications (2020–2024) emphasize managerial mobility and firm productivity (2024) machine learning vs. gravity models (2024) trade volatility and development (2023) data transparency standards (2022) Scientific awards include ERC Starting Grant (2012) Nicholas Káldor Prize (2014) Young Economist Award (2002, 2004) As Data Editor for Review of Economic Studies and Associate Editor for Journal of International Economics , he shapes methodological rigor in empirical research. His 2013 paper on technological diversification remains foundational for understanding volatility in developing economies.
Dr. Sheng Yang is an Assistant Professor in the School of Engineering at the University of Guelph. He leads the Design Innovation and Intelligent Manufacturing (DIIM) lab, focusing on advancing additive manufacturing, generative design, and smart manufacturing technologies. His research integrates IoT, big data analytics, and bio-inspired design to address challenges in aerospace, green energy, and healthcare. Key areas include computational design for additive manufacturing, data-driven mass customization, and digital twin-based optimization. Education: Ph.D. in Mechanical Engineering from McGill University (2019), followed by a Postdoctoral Fellowship at McGill (2019–2020). Joined University of Guelph in 2020. Research interests span energy efficiency, complex system optimization, and personalized healthcare products. Recent work emphasizes digital twin synchronization in robotics, machine learning for quality prediction, and sustainable additive manufacturing processes. Notable awards include the 2019 Association of Commonwealth Universities Blue Charter Fellowship and 2018 ASME Best Paper Award. His lab actively seeks partnerships in personalized healthcare, product design, and smart manufacturing. Grants and collaborations focus on advancing manufacturing technologies and sustainability. No formal advisees listed, but active in graduate training through lab projects. The DIIM lab explores cutting-edge solutions for industrial and societal challenges through interdisciplinary approaches.
Hassan Foroosh is a Professor in the Department of Electrical Engineering and Computer Science at the University of Central Florida (UCF), directing the Computational Imaging Laboratory (CIL). He holds a Ph.D. in Computer Science from INRIA-UNSA, France (1996). Prior to UCF, he worked as a Senior Research Scientist at UC Berkeley (2000–2002) and an Assistant Research Professor at the University of Maryland, College Park (1997–2000). Research Interests: His work focuses on Computer Vision, Image Processing, Machine Learning, and Signal Processing. Notable contributions include LiDAR-based perception, adversarial attacks on detectors, medical imaging analysis, and dataset design for action recognition. His research is supported by NASA, NSF, ONR, and industry partners. Publications & Impact: Over 130 peer-reviewed papers, including influential work on super-resolution techniques, transformer networks for 3D object detection, and adversarial machine learning. His recent work explores analytical reasoning in LLMs and multimodal fusion in sports analytics. Awards: Pierro Zamperoni Award (2004), Best ICPR Paper (2004), Sun Microsystems Academic Excellence Award (2004). Labs/Teams: Director of the Computational Imaging Lab (CIL), UCF. Grants: Active funding from NASA, NSF, and industry collaborators.
Ulrich Tallarek serves as Professor of Analytical Chemistry in the Faculty of Chemistry at Philipps University of Marburg, where he has held a W3 professorship since 2011. He also serves on the Board of Directors for the Materials Science Center at the university, a position he has held since 2007. His research group focuses on the fundamental understanding of transport phenomena in porous media with applications spanning chromatography, battery technology, and microfluidic systems. The group maintains strong collaborations with institutions worldwide and secures substantial research funding for advanced computational and experimental work. Professor Tallarek's research interests center on functional porous solids, with specific focus on morphology-transport-performance relationships. His work bridges multiple scales from molecular dynamics simulations of solute behavior in nanopores to macroscopic transport in chromatographic columns and battery electrodes. Key research areas include diffusion in hierarchical porous media, electrokinetic phenomena in microfluidic systems, molecular simulation of chromatographic processes, and advanced characterization of porous materials using tomography and other techniques. His group has pioneered multiscale simulation approaches that connect molecular-level surface chemistry to macroscopic transport properties. The research output demonstrates consistent focus on understanding fundamental transport mechanisms in porous systems, with recent publications emphasizing multiscale simulation techniques, molecular dynamics studies of solvent effects in chromatography, advanced characterization of mesoporous structures, and applications to separation science and energy storage. The work shows strong integration of computational modeling with experimental validation across multiple length scales. 2003: Desty Memorial Prize for Innovation in Separation Science, The Royal Institution of Great Britain, London 2006: Young Scientist Award from DECHEMA e.V. 2011: Named Discussion Leader at the 2011 Gordon Research Conference on Physics & Chemistry of Microfluidics 2011–2012: Chairman of the German Chemical Society (GDCh), Marburg 2013: Finalist, World Technology Awards, for category Environment 2013: Named as one of the 100 most influential analytical scientists in the world (The Analytical Scientist Power List) 2017: Recipient of the Silver Jubilee Medal 2017, The Chromatographic Society, UK Professor Tallarek's research has been supported by numerous grants enabling high-performance computing resources, advanced instrumentation, and international collaborations. His group maintains strong ties with industry partners in separation science and analytical instrumentation. The Tallarek Research Group includes postdoctoral researchers, PhD students, and technical staff working across experimental and computational domains. Current projects focus on molecular simulation of chromatographic processes, advanced characterization of porous battery electrodes, and development of novel separation methodologies. The Tallarek Research Group operates state-of-the-art facilities for computational modeling, including access to high-performance computing resources at Forschungszentrum Jülich. The group also maintains experimental capabilities for chromatographic analysis, materials characterization, and microfluidic device development. Their work on physically reconstructed porous media has established new standards for connecting microstructure to transport properties in complex materials systems.
Adjunct Professor Evgeny Osipov is affiliated with La Trobe University's Business Analytics department. His research spans artificial intelligence, hyperdimensional computing, and neural network architectures. Academic Rank: Adjunct Professor Department: Business Analytics Email: E.Osipov@latrobe.edu.au Research interests include: Hyperdimensional computing and vector symbolic architectures Spiking neural networks and reservoir computing Hardware-efficient AI implementations Causal reasoning in large language models Self-organizing maps and spatiotemporal sequence learning Applications in smart cities and robotic navigation Recent research outputs demonstrate expertise in: Developing unsupervised learning frameworks using hypervectors Optimizing reservoir computing with cellular automata Creating memory-efficient neural network models Advancing hyperdimensional classification techniques Exploring causal graph integration in language models
Chris Fuller, Ph.D., is the Samuel Langley Distinguished Professor of Engineering at the College of Engineering , Virginia Tech. He leads the Vibrations and Acoustics Laboratory (VAL) , focusing on active/passive noise control systems, metamaterials, and their application to aerospace, medical devices, and industrial machinery. Education: Ph.D. (1979) and B.E. (1974) from the University of Adelaide, Australia. Research Interests: Structural acoustics, adaptive materials, machine learning in noise prediction, and biomedical acoustics (e.g., neonatal incubators). Awards: ASME Rayleigh Award (2017), NASA Team Achievement Award (1996), and Fellow of the Acoustical Society of America. Recent Publications: Highlight advancements in drone noise reduction using neural networks, metamaterials for HVAC systems, and poro-elastic materials for low-frequency noise control.
Alberto Rodrigues da Silva is a Professor at the Institute Superior Técnico , part of the University of Lisbon . He teaches Fundamentals of Information Systems , primarily during the 1st Semester of the 2025/2026 academic year. His scientific interests revolve around Information Systems , Model-Driven Engineering (MDE), Requirements Engineering (RE), Social Computing , and Software Engineering . He has extensively contributed to the development of rigorous requirements specification languages like RSL (Requirements Specification Language) and its extensions (e.g., RSL-IL4Privacy for privacy policies). His collaborative work spans automated acceptance testing, GDPR compliance, and domain-specific languages (DSLs) for applications such as mobile development , digital twins , and legal contexts (e.g., LegalLanguage ). His research trends focus on integrating model-driven engineering with privacy policies , IoT applications , and low-code platforms . He has also explored tools like Maestro for data classification and usability testing, and RiverCure for flood simulation. Email: alberto.silva@tecnico.ulisboa.pt .
Barbara Drossel is a Full Professor at the Institute of Solid State Physics within the Faculty of Physics at the Technical University of Darmstadt, where she has been conducting research since February 2002. Her work bridges theoretical physics, complex systems theory, and theoretical ecology, focusing on interdisciplinary approaches to understanding emergent phenomena in natural systems. She leads the AG Drossel research group that investigates the theoretical foundations of complex networks, ecological communities, and quantum systems. Professor Drossel's research spans multiple domains with emphasis on complex systems theory, where she has made significant contributions to understanding random Boolean networks, food web modeling, and the physics of ecological communities. Her work demonstrates how simple rules can lead to complex emergent behavior across different scales, from quantum systems to ecological networks. She investigates how top-down causation operates in complex systems and explores the relationship between microscopic dynamics and macroscopic patterns in diverse contexts. Analysis of her recent publications reveals a consistent focus on theoretical frameworks that connect physics with ecology. Her work shows increasing integration of quantum mechanics with ecological modeling, particularly in understanding emergence and time evolution in complex systems. She frequently employs network theory to analyze ecological communities and has developed innovative approaches to studying species interactions, mutualistic networks, and spatial dynamics in meta-communities. Minerva Fellowship Heisenberg Fellowship DFG Fellowship for research at MIT Professor Drossel has supervised numerous doctoral students whose work spans theoretical ecology, complex systems, and statistical physics. Her research group has secured funding for projects examining the stability of ecological networks, quantum decoherence, and the mathematical foundations of complex systems. She maintains active collaborations with researchers across Europe and has contributed to major theoretical advances in understanding how complexity emerges from simple interactions in diverse systems. The AG Drossel research group operates at the intersection of physics and theoretical biology, maintaining strong connections with both the physics and biology departments at TU Darmstadt. The group combines mathematical rigor with biological relevance, developing models that capture essential features of complex natural systems while remaining analytically tractable. Their work has influenced both theoretical physics and ecological theory, demonstrating the power of interdisciplinary approaches to complex systems.
Dr. Xiaopeng Li is the Harvey D. Spangler Professor in the Department of Civil and Environmental Engineering at the University of Wisconsin-Madison, with an affiliation in the Department of Electrical and Computer Engineering. He leads the USDOT Rural Autonomous Vehicle Program and previously directed the National Institute for Congestion Reduction. He earned his B.S. in Civil Engineering from Tsinghua University (2006), M.S. in Civil Engineering (2007), M.S. in Applied Mathematics (2010), and Ph.D. in Civil Engineering (2011) from the University of Illinois at Urbana-Champaign. His research focuses on modeling and field experiments for connected, electric, and automated vehicles (CAVs), infrastructure systems analysis, and interdependent network modeling. He has pioneered physics-enhanced machine learning frameworks for vehicle control and developed simulation tools for CAV deployment. His 2025-2024 publications highlight advancements in Connected vehicle trajectory modeling Energy consumption optimization Edge computing for autonomous operations Residual learning control systems Equity analysis in AV deployment Communication technologies for V2X Awards include: TRB Best Paper Award (2025) NSF CAREER (2015) ASCE Fellow (2024) IEEE Senior Member (2022) Multiple institution-specific fellowships He has advised 15+ graduate students, secured $35M+ in grants from NSF, USDOT, and industry partners, and chairs the IEEE ITSS Emerging Transportation Technology Testing committee. His work addresses real-world AV implementation, safety validation, and sustainable transportation systems.
Maarten Renkema is an Assistant Professor at the University of Twente, specializing in Artificial Intelligence (AI) and its intersection with Human Resource Management (HRM) , Healthcare , and Organizational Innovation . His research explores how AI transforms work design, employee autonomy, and HRM practices in formalized contexts. Key Research Themes: Algorithmic HRM and Digitalization Human-AI Collaboration in Healthcare Self-Managing Teams and Organizational Structures AI Literacy in Education Scientific Awards: Best Dissertation Award (2019) from the Dutch HRM Network Research Trends: His recent work focuses on AI adoption in HRM, ethical implications of algorithmic management, and redefining professional roles in AI-integrated environments. Articles from 2024-2025 highlight practical applications of AI in healthcare workflows, employee-driven innovation, and the evolving responsibilities of HR professionals. Collaborations: Active in cross-sector partnerships, including projects on AI literacy frameworks and talent management in sports. Regularly contributes to professional discourse through invited talks and media commentary on AI's societal impact.
