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Yun Seong SongView profile

Associate Professor

• Physical Human-Robot Interaction
• Rehabilitation Robotics
• Wearable Devices
• +3 more

Dr. Yun Seong Song is an Associate Professor in the Department of Mechanical and Aerospace Engineering at Missouri University of Science and Technology (Missouri S&T), directing the Physical Human-Robot Interaction Laboratory. He holds a Ph.D. from MIT (2012), M.S. from Carnegie Mellon University (2006), and dual B.S. degrees from Seoul National University (2004). Prior to joining Missouri S&T, he conducted postdoctoral research at EPFL (2012-13) and served as a postdoc/lecturer at Georgia Tech (2014-16). Recognized for both research and teaching excellence, he has received the NSF CAREER Award (2021) and Faculty Teaching Award (2019). His research focuses on the intersection of robotics and biomechanics, emphasizing physical human-robot interaction (pHRI), rehabilitation robotics, wearable devices, energy harvesting from human motion, and medical device design. Key projects include developing robots for overground interaction experiments and assistive technologies for mobility support. His lab explores human motor communication through stiffness modulation, haptic feedback systems, and energy-efficient human-assistance mechanisms. Notable achievements include pioneering interactive stairs for energy-efficient mobility and a light-touch based virtual cane for walking assistance. His work integrates mechanical engineering, control systems, and biomedical applications to advance assistive technologies and human-robot collaboration. Education: Ph.D. Mechanical Engineering, MIT (2012) M.S. Mechanical Engineering, CMU (2006) B.S. Mechanical Engineering & B.S.E. Computer Science, Seoul National University (2004) Awards: NSF CAREER Award (2021) Missouri S&T Faculty Teaching Award (2019) Lab Focus: Physical Human-Robot Interaction, Wearable Robotics, Biomechanical Energy Harvesting

David E. BreenView profile

Professor

• Biomedical Image Informatics
• Geometric Modeling
• Textile Modeling and Design
• +12 more

David E. Breen is a Professor in the Department of Computer Science within the College of Computing & Informatics (CCI) at Drexel University. He leads the Geometric Biomedical Computing Group and is affiliated with the Metadata Research Center and the Center for Biological Discovery from Big Data. His research spans interdisciplinary domains including biomedical image informatics, geometric modeling, textile modeling, and bio-inspired self-organization algorithms. Education: PhD, Computer and Systems Engineering, Rensselaer Polytechnic Institute MS, Computer and Systems Engineering, Rensselaer Polytechnic Institute BA, Physics, Colgate University His research interests focus on computational methods for biomedical applications, including shape and image analysis for cancer diagnosis, 3D reconstruction of biological tissues, and video analysis of animal behavior. He also investigates geometric modeling techniques for textiles and self-organizing systems. His work integrates computer science with biology, medicine, and engineering to solve complex problems in biomedical computing. The recent publications highlight a strong trend in computational modeling of textiles, biomedical image informatics, and AI-driven data analysis. Key themes include geometric modeling of knitted fabrics, deep learning for medical image classification, agent-based modeling of cancer metastasis, and metadata generation for biological image collections. His work bridges fundamental geometric algorithms with practical applications in healthcare and digital archives. Scientific Awards: No specific awards mentioned in the provided text. Breen has advised numerous students and collaborators across multiple domains, particularly in biomedical computing and textile modeling. His research has been supported through affiliations with major centers and collaborations with institutions such as Johns Hopkins University and the Max Planck Institute. He has been involved in projects related to NSF Center for Visual & Decision Informatics and has contributed to over 100 technical publications. He leads the Geometric Biomedical Computing Group , which conducts research at the intersection of biology, medicine, engineering, and computer science. The group develops algorithms and software for geometry-related computing problems in biomedical applications. Collaborations include the Drexel Integrated Laboratory for Cellular Tissue Engineering, Dr. Dan Marenda's Lab, and Dr. Aleister Saunder's Lab in Drexel's Biology Department.

Richard SilberglittView profile

Professor

• Emerging Technologies
• Science, Technology, and Innovation Policy
• Energy Security
• +17 more

Richard Silberglitt is a Senior Physical Scientist at RAND and Professor of Policy Analysis at the RAND School of Public Policy, where he conducts interdisciplinary research at the nexus of science, technology, and public policy. With over 50 years of experience across academia, government, and industry, he is a leading expert in technology foresight, energy systems, and R&D portfolio management. His research focuses on emerging technologies , science and innovation policy , energy security , and nanotechnology . He has developed influential methodologies such as an energy scenario analysis framework and the PortMan portfolio management system, both widely applied in U.S. and international contexts. His work supports strategic planning in defense, public safety, and economic development. His recent publications reflect a strong trend toward technology foresight , critical materials supply chains , quantum technology assessment , and public sector innovation . These works often employ scenario planning, Delphi methods, and data-driven analytics to inform high-stakes policy decisions. Member, American Physical Society Member, Materials Research Society Member, American Ceramic Society As a subject matter expert, Silberglitt has advised the United Nations, U.S. Department of Defense, National Security Agency, and multiple federal agencies. He has chaired the International Advisory Board of the APEC Center for Technology Foresight and delivered testimony to U.S. Congressional committees on critical materials and technology policy. His research has been supported by grants and contracts from the U.S. Army, Navy, CDC, and National Institute of Justice. He has led major initiatives on law enforcement technology, transportation safety ( The Road to Zero ), and international technology foresight. His work often involves collaborative teams and cross-sector partnerships to address complex technological and policy challenges.

Márton KarsaiView profile

Associate Professor

• Network Science
• Human Dynamics
• Temporal Networks
• +11 more

Márton Karsai is an Associate Professor and Head of the Department of Network and Data Science at the Central European University in Vienna, and a Research Professor at the HUN-REN Alfréd Rényi Institute of Mathematics in Budapest. He leads the Computational Human Dynamics Lab, focusing on data-driven modeling of social and biological systems. He is also the Editor-in-Chief of the journal Advances in Complex Systems . His research interests lie at the intersection of network science, human dynamics, and socioeconomic systems. He specializes in temporal and spatial networks, modeling contagion processes (both social and biological), and analyzing large-scale human behavioral datasets. His work integrates computational methods with real-world data to understand complex social phenomena such as mobility patterns, migration, segregation, and epidemic spread. He is particularly known for using remote sensing and digital trace data to infer poverty and socioeconomic conditions in urban areas. The recent publications highlight a strong trend in applying network science and machine learning to societal challenges. His work spans high-impact journals in complex systems, data science, and computational social science, with recurring themes in epidemic modeling, urban analytics, socioeconomic inference, and the structure of temporal and spatial networks. The research is highly interdisciplinary, combining physics, computer science, and social science methodologies. He has been invited to speak at major events such as the Conference on Complex Systems, the Lake Como School on Complex Networks, and workshops on data for vulnerability assessment. He served as general co-chair of CCS 2021 in Lyon, demonstrating leadership in the complexity science community. General Co-Chair, Conference on Complex Systems (CCS) 2021, Lyon Invited speaker, 4th Workshop on Data for the Wellbeing of the Most Vulnerable @ ICWSM'23 Invited speaker, Complexity72h Workshop Invited lecturer, Lake Como School on Complex Networks Invited talk, Hungarian Academy of Sciences on COVID-19 modeling While specific grant details are not listed, his coordination of projects on segregation, migration, and poverty inference—often in collaboration with the Complexity Science Hub—suggests active involvement in externally funded interdisciplinary research. He advises students through the Department of Network and Data Science at CEU, though specific advisees are not named. His lab, the Computational Human Dynamics Lab, serves as a hub for data-driven research on social systems.

• Sustainable Development
• Low-Carbon Technologies
• Energy and Building Technology
• +13 more

Dr. Behrang Vand Alimohammadisagvand is a Lecturer at the School of Computing, Engineering and the Built Environment, Edinburgh Napier University. His research focuses on sustainable energy systems, building performance, and smart energy management, with strong international collaboration across Iran, China, Finland, and the UK. B.Sc. and M.Sc. in Mechanical and Energy Engineering Ph.D. in Energy and Building Technology His research interests include sustainable development, low-carbon technologies, energy policy, thermal comfort in buildings, and energy management systems at multiple scales. He actively investigates model predictive control (MPC), demand response, energy sharing in communities, and low-temperature heating networks. His work bridges theoretical modeling and practical implementation in real-world buildings. Dr. Vand's recent publications highlight trends in integrated energy management , smart grid optimization , and decarbonization of building heating systems . His articles emphasize mathematical modeling, simulation, and control strategies to improve energy efficiency and reduce carbon emissions in the built environment. He supervises doctoral students working on smart IoT systems and low-temperature heat networks. His research is supported by funders such as the Academy of Finland and Energy Technology Partnership. Active supervisor for PhD project on smart IoT systems with real-time deep learning (since 2022) Director of Studies for PhD on heating Scottish public buildings with low-temperature networks (2018–2024) He is affiliated with the Institute for Sustainable Construction and contributes to the research theme Culture and Communities . His work aligns with global efforts toward net-zero emissions and sustainable urban development.

John Di BartoloView profile

Professor

• Type-II superconductors
• Educational software development
• Mechanics of articulated figures
• +3 more

John Di Bartolo is an Industry Professor and Department Chair of Applied Physics at New York University Tandon School of Engineering , with a career spanning computational physics, sports biomechanics, and educational software development. His work bridges theoretical physics with practical applications in technology and pedagogy. Bachelor of Arts in Mathematics, Boston College (1991) Master of Science in Physics, Boston College (1993) Doctor of Philosophy in Physics, University of Virginia (1997) Research interests include Type-II superconductors , physics of sports , and innovative educational software like Physics of Sports 3.0 and Quantum Sandbox . He has pioneered computational methods in teaching and developed interactive tools for optics and quantum mechanics. His publications span from superconductivity studies in Physical Review B to cutting-edge educational software. Notable awards include the 2013 Distinguished Teacher Award . He has presented at conferences including AAPT Summer Meetings and NYU-Poly Research Expo, and featured in media outlets like WIRED Magazine and Science Channel.

• Aeolian geomorphology
• Wind erosion processes
• Dust emission dynamics
• +5 more

Jeffrey A. Lee is a Professor in the Department of Geosciences at Texas Tech University, specializing in aeolian geomorphology and environmental processes. With academic appointments since 1988, his work focuses on wind-driven sediment transport and dune dynamics across multiple continents. Education: B.A. in Geography (1979), University of California, Los Angeles M.A. in Geography (1984), University of California, Los Angeles Ph.D. in Geography (1990), Arizona State University Research interests center on aeolian processes, particularly dust emission dynamics and wind erosion patterns. His work examines temporal trends in blowing dust across Texas and the American Southwest, while also investigating long-term shifts in wind climate patterns. Professor Lee teaches foundational physical geography courses including Global Environmental Science and advanced topics in arid land geomorphology. Recent publications demonstrate interdisciplinary focus across aeolian geomorphology, environmental monitoring, and climate science. Key themes include dust source characterization using satellite data, drought legacy effects on erosion, and historical analysis of environmental disasters like the Dust Bowl. Contact: jeff.lee@ttu.edu | 213 Holden Hall, Texas Tech University | (806) 834-8228

• Medical Physics
• Radiation Therapy
• Medical Imaging
• +7 more

Andrew Fielding is an Associate Professor in the School of Chemistry & Physics at Queensland University of Technology (QUT), Faculty of Science. His research and teaching focus on medical physics, particularly in radiation therapy, medical imaging, and Monte Carlo dosimetry techniques. He is the Course Coordinator for the Graduate Diploma and Master of Applied Science in Medical Physics programs at QUT. He holds a PhD in Physics from the University of Portsmouth and a B.Sc. (Hons) from the University of Surrey. He completed postdoctoral research at the Institute of Cancer Research / Royal Marsden Hospital and the University of Liverpool before joining QUT in 2004. His academic progression includes Lecturer (2004–2008), Senior Lecturer (2008–2022), and Associate Professor (2023–present). His research interests lie in medical imaging, radiation therapy, image-guided radiotherapy, Monte Carlo techniques for dosimetry, and radiation oncology physics. He emphasizes translating research into clinical practice to improve cancer care. His recent publications reflect a strong focus on Monte Carlo simulations, small-field dosimetry, preclinical irradiation, and the integration of AI and simulation in radiotherapy education and treatment verification. His scientific achievements are recognized through professional memberships including Fellow of the Institute of Physics (FInstP), Chartered Physicist (CPhys), and Member of the Australasian College of Physical Scientists and Engineers in Medicine (MACPSEM). Fellow of the Institute of Physics (FInstP) Chartered Physicist (CPhys) Member of the Australasian College of Physical Scientists and Engineers in Medicine (MACPSEM) Andrew Fielding actively supervises PhD and research master’s students in areas such as Monte Carlo dosimetry, tumor motion tracking, and radiotherapy optimization. He has secured competitive research grants, including Australian Competitive Grants for projects on tumor motion monitoring and in-vivo dosimetry verification. His teaching philosophy emphasizes authentic, clinically aligned learning using simulation, virtual reality, and real-world applications. He leads or teaches several core medical physics units, including Radiation Physics, Radiotherapy, Medical Imaging Science, and Research Methodology. He is involved in developing and evaluating innovative tools such as 3D volumetric outlining systems and immersive simulation environments for radiotherapy training. His work bridges physics, clinical application, and education, contributing significantly to the advancement of medical physics both in research and pedagogy.

• Smart Energy Systems
• Multi-agent Systems
• Computational Intelligence
• +7 more

Han La Poutré is a Professor at Delft University of Technology and a Senior Researcher/Manager at CWI's Intelligent and Autonomous Systems group. His research focuses on multi-agent systems, computational intelligence, and optimization techniques for Smart Energy Systems (SES), integrating electrical engineering and economics frameworks. M.Sc. in Mathematics (1986, TU Eindhoven) Ph.D. in Computer Science (1991, Utrecht University) His work spans game theory , reinforcement learning , and market mechanism design for energy grid optimization, addressing challenges in congestion management, demand-side bidding, and AI-driven media sector innovation. Recent projects include AI, Media & Democracy Lab and RESCUE (Cyber-Physical Energy Security). Key trends in his 2020-2025 publications include: Hybrid fairness/welfare frameworks for grid congestion Double-sided auction mechanisms for heat/power markets Deep reinforcement learning in discrete action spaces Game-theoretical cybersecurity modeling Multi-temporal demand response coordination Scientific Awards 2015: Best paper award at GECCO-2015 He has led NWO-funded projects like Computational Capacity Planning in Electricity Networks and served as Vice President of ERCIM. Current affiliations include ACM Transactions on Intelligent Systems editorial board.

Corrado MauriniView profile

Professor

• Fracture Mechanics
• Damage Mechanics
• Computational Mechanics
• +5 more

Corrado Maurini is a Professor in Mechanics at Sorbonne University , Paris, France. He leads two international master programs: Mécanique des Solides (Solid Mechanics) and Computational Mechanics .

• Computer Science
• Electrical Engineering
• Machine Learning
• +7 more

Andrew Ho is an active academic researcher with publications spanning computer science, electrical engineering, and interdisciplinary applications. His recent work focuses on hybridizable discontinuous Galerkin methods for plasma simulations (2024) and AI/LLM applications in scholarly knowledge organization. 2025: Project Alexandria (LLM for copyright-free knowledge) 2024: Hybridizable DG plasma methods, GPU-accelerated kinetic simulations 2023: Low-resource translation techniques 2022: Multimodal VR interfaces 2003-2006: High-speed serial link transceivers and radiography artifact detection His research interests include: Computer science applications in plasma physics and medical imaging LLM-based scholarly knowledge graphs and literature reviews High-speed communication systems Educational technology implementations Co-authors include Vladimir Stojanovic (Stanford, 2003-2005), Carl W. Werner (2003-2005), and Genia Vogman (GPU plasma simulations, 2024).

Hailong ChenView profile

Associate Professor

• Computational Mechanics
• Computational Materials
• Meshfree Methods
• +6 more

Hailong Chen is an Associate Professor in the Department of Mechanical and Aerospace Engineering within the Stanley and Karen Pigman College of Engineering at the University of Kentucky. His academic journey includes a Ph.D. in Mechanical Engineering from Arizona State University (2015) and an M.S. in Mechanical Engineering from the University of Florida (2012). Dr. Chen's research focuses on Computational Mechanics & Materials, with expertise spanning meshfree methods, multi-scale multi-physics modeling, mechanics of stochastic heterogeneous microstructures, and pervasive fracture and impact modeling. His work bridges theoretical developments with practical engineering applications through the CM 3 (Computational Mechanics and Methods) research group, which develops advanced computational techniques for real-world mechanics problems. The CM 3 group specializes in multi-scale multi-physics modeling of solid materials, damage and failure analysis under extreme conditions, mechanics of stochastic heterogeneous microstructures (composites, polycrystals), and computational materials engineering. Recent publications demonstrate strong activity in peridynamics, lattice particle methods, and computational homogenization techniques for fibrous and porous materials. Dr. Chen's research has resulted in numerous publications in top journals including Computer Methods in Applied Mechanics and Engineering and Mechanics Research Communications, with recent work focusing on generalized peridynamic formulations, micro-CT-based property computation, and fluid-structure interaction frameworks for hypersonic applications. His academic progression shows steady advancement from Postdoctoral Computational Scientist at Idaho National Laboratory (2015-2018) to Assistant Professor (2018-2024) and currently Associate Professor (2024-present) at the University of Kentucky.

• Computational Materials Science
• 2D Materials
• High-Entropy Ceramics
• +5 more

Dr. Rico Friedrich is a computational materials scientist leading the "Autonomous Materials Thermodynamics - AutoMaT" research group, jointly operated by the Chair of Theoretical Chemistry at Technische Universität Dresden and the Helmholtz-Zentrum Dresden-Rossendorf (HZDR). His work focuses on data-driven computational design of advanced materials for information technology and energy applications through the DRESDEN-concept research alliance. His research spans several cutting-edge areas: Discovery and design of 2D non-van der Waals materials with novel electronic and magnetic properties Data-driven modeling of high-entropy ceramics based on entropy maximization principles Development of computational methods for accurate thermodynamic stability prediction, particularly the coordination corrected enthalpies (CCE) method Applications of artificial intelligence in materials design Dr. Friedrich's publication record shows a strong trend toward computational materials discovery, with significant contributions to understanding non-van der Waals 2D materials and high-entropy ceramics. His work bridges theoretical developments with practical applications, resulting in publications in high-impact journals including Nature, Nano Letters, and Advanced Electronic Materials. His key scientific contributions include: Development of the coordination corrected enthalpies (CCE) method for accurate formation enthalpy calculations Creation of the AFLOW-CCE module implemented in the AFLOW software ecosystem Discovery of novel 2D non-van der Waals materials with ultra-low exfoliation energies Formulation of the disordered enthalpy-entropy descriptor (DEED) for high-entropy ceramics Dr. Friedrich actively mentors the next generation of materials scientists, currently supervising PhD students and postdoctoral researchers in his AutoMaT lab. His research group collaborates extensively within the DRESDEN-concept research alliance, leveraging computational resources and expertise across multiple institutions to advance materials science and engineering.

Franz PfeifferView profile

Professor

• X-ray Physics
• Biomedical Imaging
• Computed Tomography
• +6 more

Prof. Dr. Franz Pfeiffer is a full professor at the Chair of Biomedical Physics within the Department of Physics at the Technical University of Munich (TUM) . He has served as director of the Munich School of BioEngineering since 2016. His research focuses on translating advanced X-ray physics concepts to biomedical imaging and clinical applications, particularly for early cancer and osteoporosis diagnostics. Research Interests: X-ray phase-contrast and dark-field imaging, synchrotron instrumentation, CT reconstruction algorithms, and medical imaging technology. Awards: Alfred Breit Prize (2017) ERC Advanced Grant (2016) Leibniz Prize (2011) National Latsis Prize (2010) ERC Starting Grant (2009) His work bridges fundamental X-ray physics with clinical translation, involving collaborations with radiologists, engineers, and medical researchers. Recent publications emphasize AI integration in CT, dark-field chest radiography, and spectral imaging applications.

Bradley SchmerlView profile

Researcher

• Software Engineering
• Software Architecture
• Self-Adaptive Systems
• +5 more

Bradley Schmerl serves as a Principal Systems Scientist in the Software and Societal Systems Department (S3D) within Carnegie Mellon University's School of Computer Science. His research advances software engineering practices for modern challenges in distributed heterogeneous systems, self-adaptation, and cyber-physical integration. He leads the ABLE research group and actively mentors students in the Masters in Software Engineering program while teaching core courses like Software Architecture and Software Engineering Practicum. Dr. Schmerl's work addresses critical challenges in composing continuously evolving software systems, including components from untrusted third parties and on-the-fly recomposition for environmental changes. His research develops reusable, analyzable tools for software composition with emphasis on model-based adaptation, uncertainty management, and cross-language integration. Key projects include Rainbow for runtime architecture reflection, Acme for formal architectural foundations, and Cyber-physical Systems research linking software models with physical dynamics. Analysis of his 2023-2025 publications reveals intensifying focus on robotics software architecture (particularly ROS-based systems), explainable AI for architectural tradeoff analysis, and configuration management in adaptive systems. Trends show growing integration of machine learning for auto-tuning, empirical studies of misconfigurations, and dimensionality reduction techniques for visualizing design spaces—consistently bridging theoretical rigor with practical tool development for real-world applications. Scientific Awards: No specific awards were documented in the source materials. Dr. Schmerl serves as Practice Area Lead and mentor in CMU's Masters in Software Engineering program, guiding client projects including Rainbow UI for self-adaptive framework interfaces, CoBot UI for telepresence robots, and Educational Telepresence Tasking Language development. His research receives support through ABLE group projects funded by grants targeting software architecture foundations, adaptation mechanisms, and cyber-physical system validation. As a core member of the ABLE research group, he directs investigations into architecture-based self-adaptation with active projects spanning Rainbow (runtime architecture models for dynamic adaptation), Acme (formal architectural styles and tools), and Cyber-physical Systems (software-physical model integration). The group also maintains legacy work in End-User Architecting, Architecture Evolution, and service-oriented platforms for intelligence analysis through SORASCS.