Wenping Wang is a Professor in the Department of Computer Science & Engineering at Texas A&M University, part of the College of Engineering. His research focuses on computer graphics, computer vision, geometric modeling, and visualization. He holds Fellowships from ACM and IEEE, and has received notable awards including the 2021 AsiaGraphics Outstanding Technical Contributions Award and the 2017 John Gregory Memorial Award. Wang's educational background includes a Ph.D. from the University of Alberta and M.Eng. and B.Sc. degrees from Shandong University. His work spans advancements in neural implicit surfaces, 3D reconstruction, and medical imaging applications such as orthodontic treatment prediction. He has authored numerous influential papers in top-tier conferences like SIGGRAPH and journals like ACM Transactions on Graphics. His research interests emphasize bridging geometric modeling with machine learning, particularly in neural rendering, surface parameterization, and medical visualization. Recent projects include developing frameworks for automatic tooth alignment and high-fidelity 3D geometry generation. Wang's contributions have significantly impacted both theoretical foundations and practical applications in computer graphics.
Jiang Hu is a Professor in the Department of Electrical and Computer Engineering at Texas A&M University, holding the Eric D. Rubin '06 Endowed Professorship. He also serves as Co-Director of Graduate Programs and is affiliated with the Computer Science & Engineering department. His research focuses on VLSI design automation, machine learning applications, and hardware security. He has held roles as editor for IEEE Transactions on CAD and ACM Transactions on Design Automation, and chaired the 2012 ACM International Symposium on Physical Design. Education: B.S. in Optical Engineering (Zhejiang University, 1990), M.S. in Physics (1997), and Ph.D. in Electrical Engineering (University of Minnesota, 2001). He worked at IBM Microelectronics before joining Texas A&M in 2002. Research interests include energy-efficient VLSI circuits, on-chip communication fabrics, analog layout automation, and AI-driven EDA. Recent work emphasizes machine learning for design closure, privacy-preserving frameworks, and systolic array-based architectures. Awards: IEEE Fellow (2016) Humboldt Research Fellowship (2012) Multiple best paper awards at DAC, ICCAD, and ASPDAC Advising and grants: Leads initiatives like the SLICE project, NSF workshops on ML-EDA infrastructure, and serves as Editor-in-Chief of ACM TODAES since 2024. His work bridges academic research and industry applications in EDA and semiconductor design. Labs/Teams: Active contributor to open-source tools like ALIGN for analog layout generation and collaborations on machine learning for EDA commons.
Dr. Jing Li is an Associate Professor and Eduardo D. Glandt Faculty Fellow at the University of Pennsylvania , holding dual appointments in the Electrical and Systems Engineering and Computer and Information Science departments. As co-director of the CyberSavvy nationwide security research center and director of the Penn Computational Intelligence Lab (PennCIL) , she pioneers innovations in non-von Neumann computing paradigms. Her research spans post-CMOS technologies, in-memory computing, and hardware-software co-design for security and AI applications. PhD in Computer Engineering, Purdue University (2009) BSc in Electrical Engineering, Shanghai Jiaotong University (2004) Research Focus: Dr. Li's work addresses fundamental challenges in computer systems across the stack. Key areas include: In-Memory Computing: Liquid Silicon architecture combining RRAM with silicon CMOS through monolithic 3D integration Security Engineering: Transforming computer security from "Art" to formal "Engineering" discipline within CyberSavvy Virtualization: Cloud FPGA abstraction layers decoupling compilation from runtime resource management Graph Analytics: Degree-aware optimization techniques for massive-scale graph processing Deep Learning Systems: Roofline model extensions for FPGA-based CNN acceleration Scientific Impact: Awarded DARPA Young Faculty Award , NSF CAREER Award , and IBM CEO Milestone Award , her team has achieved world records in energy-efficient computing (ENIAD supercomputer). With 46 U.S. patents and over 80 publications, she leads ecosystem development for emerging computing architectures through initiatives like the open-source MEG simulation platform . Community Leadership: Dr. Li serves on program committees for flagship conferences ( ISCA , FPGA Symposium ), chairs the International Memory Workshop , and contributes to the MLsys conference's inaugural committee. She actively mentors through multiple PhD openings and industry collaborations.
Celeste Sagui is a Professor in the Department of Physics at North Carolina State University (NC State), affiliated with the College of Sciences. She holds additional roles as a faculty affiliate in Genomics Sciences at NC State and is a member of the Center for High Performance Simulation. Her research focuses on computational biophysics, biomolecular simulations, and free energy methods applied to nucleic acid structures, protein dynamics, and nanotechnology systems. She has contributed to the AMBER simulation package development, co-authoring versions from 10 to 14. Education: Doctorate in Physics, University of Toronto (1995) Licentiate degree, National University of San Luis, Argentina Research Interests: Sagui’s work explores DNA/RNA structure and phase transitions, electrostatic interactions, and methodologies for large-scale molecular simulations. Recent studies include nucleic acid hairpin instabilities linked to neurodegenerative diseases, polyglutamine aggregation mechanisms, and novel DNA motifs like the eGZ structure in Z-DNA. She employs quantum chemistry, density functional theory, and phase-field models to investigate systems ranging from biomolecules to nanomaterials. Publications: Her recent work emphasizes nucleic acid dynamics, free energy landscapes, and computational methods for studying diseases such as Friedreich’s ataxia and polyglutamine disorders. Key contributions include advancements in laser-driven simulations and infrared spectroscopy analysis of protein structures. Labs/Teams: Active in the Center for High Performance Simulation, focusing on high-throughput computational modeling and collaborative software development for biomolecular research.
Shuran Song is an Assistant Professor of Electrical Engineering at Stanford University, with a courtesy appointment in Computer Science. Previously, she was faculty at Columbia University. She holds a Ph.D. in Computer Science from Princeton University and a BEng from HKUST. Her research focuses on the intersection of computer vision and robotics, particularly in embodied AI, robot manipulation, and sensorimotor learning. Song's work emphasizes learning from physical interactions to enable robots to perform complex tasks autonomously. She leads the Robotics and Embodied AI Lab (REAL@Stanford) and has received prestigious awards, including the NSF Career Award, Sloan Fellowship, and Microsoft Faculty Fellowship. Education: Ph.D., Computer Science, Princeton University; BEng, HKUST Affiliations: Stanford School of Engineering, Department of Electrical Engineering Research interests include deformable object manipulation, visuomotor policy learning, and generalizable robot skills. Her lab develops algorithms for robots to learn through interaction, with applications in household assistance (e.g., TidyBot) and industrial automation. Notable contributions include the TossingBot and Diffusion Policy frameworks. Publications span robotics, computer vision, and AI conferences (RSS, ICRA, CVPR), focusing on policy learning, deformable object handling, and embodied intelligence. Awards highlight her impact in advancing robot learning and perception. Advises doctoral and master's students in robotics and AI, and collaborates on grants from NSF, DoD, and industry partners. Teaches courses on robot perception and embodied AI at Stanford.
Edward H. Kaplan is the William N. and Marie A. Beach Professor of Management Sciences at the Yale School of Management, Professor of Public Health at the Yale School of Medicine, and Professor of Engineering at the Yale School of Engineering and Applied Sciences. He holds secondary appointments in Chemical and Environmental Engineering, Health Policy & Management, the Institution for Social and Policy Studies, and Statistics. Education: PhD in Urban Studies, Massachusetts Institute of Technology (1984) SM in Mathematics, Massachusetts Institute of Technology (1982) SM in Operations Research and City Planning, Massachusetts Institute of Technology (1979) BA in Urban/Economic Geography, McGill University (1977) Kaplan is an expert in operations research, mathematical modeling, and statistics, focusing on public policy and management. His research spans counterterrorism, HIV prevention, bioterrorism, and public health modeling. He has developed models for suicide bomber detection, smallpox response logistics, needle exchange program effectiveness, and wastewater-based disease surveillance. His work has been recognized with numerous awards, including the Koopman Prize (2003, 2005), INFORMS President’s Award (2002), Charles C. Shepard Science Award (2009), and INFORMS Fellow (2005). He has also served as President of INFORMS (2016) and co-directs the Daniel Rose Technion-Yale Initiative in Homeland Security.
Dr. Alain Bonneville is a Lab Fellow and Geophysicist at Pacific Northwest National Laboratory (PNNL) and holds a Courtesy Professor appointment at Oregon State University's College of Earth, Ocean, and Atmospheric Sciences. With extensive experience in geological storage of CO2, geothermal energy, and geophysical monitoring techniques, Dr. Bonneville leads diverse research projects that bridge fundamental science and practical applications for energy and environmental challenges. Dr. Bonneville's educational background includes: PhD in Geophysics from the University of Montpellier, France MS in Petroleum Geophysics from IFP-School, Paris, France BS in Geology from the University of Lyon, France Dr. Bonneville's research spans several critical areas in Earth sciences and energy systems. His work on geothermal energy focuses on super-hot enhanced geothermal systems (EGS), site characterization, monitoring, and stimulation fluids. In geological CO2 storage, he investigates project management, site characterization, numerical modeling, and monitoring methods using potential fields and remote sensing. His expertise in geophysical methods includes heat flow measurements, gravity surveys, muon tomography development for borehole deployment, and remote sensing applications. Additional research areas encompass marine heat flow instrumentation development, thermal monitoring of active volcanoes, and intraplate volcanism studies in the Indian and Pacific Oceans. Dr. Bonneville has received significant recognition for his contributions to science, including: Membership in the Washington State Academy of Sciences Lab Fellow position at Pacific Northwest National Laboratory Executive Committee membership on the U.S. National Risk Assessment Partnership Scientific Committee membership at IFP-Energies Nouvelles, France He also holds two U.S. patents related to electrophilic acid gas-reactive fluids for enhanced fracturing and recovery of energy producing materials. Throughout his career, Dr. Bonneville has led significant research initiatives, including the PNNL Carbon Sequestration Initiative (2009-2013) and the European Marie Curie Research Training Network on Greenhouse Gas Removal (GRASP), which involved 14 academic and industrial institutions across 7 countries and supported 35 PhD students and post-docs. His work on the FutureGen 2.0 project demonstrates his leadership in large-scale carbon storage site characterization and monitoring program design. Dr. Bonneville maintains active collaborations with research teams at PNNL's Environmental Molecular Sciences Laboratory and works closely with Oregon State University's geoscience researchers. His laboratory work focuses on developing novel instrumentation for geophysical monitoring, particularly in the areas of muon tomography for subsurface characterization and thermal monitoring systems for geothermal and carbon storage applications.
Hank Childs is a Professor in the School of Computer and Data Sciences at the University of Oregon, specializing in scientific visualization and high-performance computing. He leads the Research Group on Computing and Data Understanding at eXtreme Scale (CDUX) and has held leadership roles including Interim Executive Director of the School of Computer and Data Sciences. His educational background includes a Ph.D. (2006) and B.S. (1999) in Computer Science from the University of California at Davis. Prior to academia, he worked for 14 years at Lawrence Livermore and Lawrence Berkeley National Laboratories, where he served as architect of the VisIt open-source visualization tool. Research interests center on visualizing extreme-scale scientific datasets from supercomputers, with a focus on in situ visualization for cosmology, seismology, and fluid dynamics. He has pioneered projects like VTK-m and Ascent, and his work explores power-performance tradeoffs and data-parallel algorithms for GPUs. Recent publications emphasize scalable visualization techniques for exascale computing, with 15 notable works from 2021-2020 covering particle advection, in situ triggering, and power-aware frameworks. His research has been honored with multiple best paper awards at IEEE LDAV, EGPGV, and SC conferences. DOE Early Career Award (2012) University of Oregon Faculty Excellence Award (2018) 4+ million dollars in research funding since 2013 5 Best Paper awards in 2021 alone As an educator, he received four consecutive CIS Best Teacher Awards (2014-2019). He has served as Associate Editor for IEEE Transactions journals and organized numerous visualization workshops including Dagstuhl seminars and Shonan workshops.
Susan Powers is a Professor of Civil & Environmental Engineering at Clarkson University, where she also serves as the Spence Professor of Sustainable Environmental Systems, Director of the Institute for a Sustainable Environment, and Associate Director of Sustainability. She earned her Ph.D. in Environmental Engineering from the University of Michigan in 1992. Research Interests: Dr. Powers specializes in lifecycle assessment and environmental impact metrics for energy systems, with recent interdisciplinary work on anaerobic digestion for food waste management and smart housing to encourage energy conservation. Her educational research integrates project-based learning to enhance energy and climate change literacy across K-12 and college levels. Scientific Awards: AEESP Distinguished Service Award (2019) AEESP Fellow (2019) Spirit of Entrepreneurship Award (2012) Premier Curriculum Award for K-12 Engineering (2009) NSF Directors Award – Distinguished Teaching Scholar (2004) Grants & Educational Leadership: Dr. Powers has led multiple education-oriented grants, including the NSF Distinguished Teaching Scholars award, to develop project-based modules for sustainability education. She actively incorporates campus sustainability initiatives into student projects to teach real-world environmental problem-solving.
Fabian Fritz holds an M.Sc. degree and works at the Technical University of Munich (TUM) within the Chair of Aerodynamics and Fluid Mechanics . His research focuses on computational fluid dynamics (CFD) and numerical simulation of multiphase flows, particularly using Smoothed Particle Hydrodynamics (SPH) . He collaborates on projects like PBF-LB/M (additive manufacturing) and contributes to Lagrangian fluid mechanics benchmarking frameworks. Research Trends: His publications emphasize numerical methods (SPH, level-set, finite-volume), multiphase flow modeling , heat transfer , and thermoacoustic stability . Recent work includes hardware-agnostic code optimization and adaptive mesh refinement techniques. Education: Completed a master’s thesis on Diffusive-Interface Modeling of Multiphase Flows with Surface-Tension Effects , supervised by P.D. Dr.-Ing. habil. Stefan Adami.
Dr. Michael Kleeberger is a Researcher at the Chair of Materials Handling, Material Flow, Logistics (FML) at the Technical University of Munich, based at Boltzmannstr. 15 in Garching. He collaborates closely with Prof. Johannes Fottner and maintains an active research profile in crane dynamics and mechanical systems simulation. His research specializes in Materials Handling and Logistics with emphasis on Crane Dynamics, Flexible Multibody Systems, and Control Systems. He develops advanced models for hydraulic actuated cranes, focusing on dynamic behavior during hoisting, slewing, and trajectory operations using port-Hamiltonian formulations and geometrically exact beam theory. His work bridges theoretical mechanics with industrial applications in heavy machinery. Analysis of his 15 most recent publications reveals consistent focus on numerical methods for flexible crane structures, with growing emphasis on optimal control strategies (2020-2025). Key trends include port-Hamiltonian system applications, lunar crane feasibility studies, and vibration mitigation techniques for lattice boom and knuckle boom configurations across diverse operational scenarios. As part of FML, Dr. Kleeberger contributes to TUM's leadership in logistics engineering through industry-collaborative projects and fundamental research in material flow systems, maintaining the chair's reputation for excellence in mechanical dynamics and practical engineering solutions.
Samia Khan is a Professor in the Department of Curriculum & Pedagogy at the University of British Columbia's Faculty of Education, where she also serves as Associate Dean of Research. Her academic work bridges educational technology, science education, and teacher preparation across K-16 contexts. Dr. Khan earned her PhD from the University of Massachusetts. Her educational background informs her interdisciplinary approach to learning sciences and technology integration. Her research centers on how digital technologies transform science learning , with emphases on model-based teaching , visualization tools , and equitable participation in STEM . She investigates simulation technologies, future-state modeling, and strategies to broaden science engagement through interpretive and mixed-methods research. Her work particularly examines teacher epistemologies, scientific reasoning development, and socio-cultural factors in technology-mediated learning environments. Analysis of her recent publications reveals three dominant trends: (1) International comparative studies of science curricula across Southeast Asia, (2) Efficacy of digital tools (PhET, GeoGebra, Symbolab) in conceptual understanding, and (3) Pre-service teacher development in model-based science instruction. Her research spans diverse contexts from Canadian classrooms to Rwandan and Vietnamese educational settings. Dr. Khan's contributions have been recognized through: New Scholar Award from the Canadian Society for Study in Education Prime Minister’s Award of Canada for Teaching Excellence in Science, Technology, and Mathematics As former MET Director (2021-2022) and author of foundational courses ETEC 530/533, she has significantly shaped UBC's educational technology programs. Her Faculty Associate role at the Institute of Resources, Environment, and Sustainability demonstrates cross-disciplinary engagement with sustainability education. Her research appears in leading journals including Journal of Technology and Teacher Education, Computers and Education, and Educational Technology Research and Development, with consistent citation as field-shaping work in educational technology.
Arthur Gervais is a Professor of Information Security at University College London's Department of Computer Science. His work focuses on blockchain systems, smart contract security, and decentralized finance (DeFi) risk analysis. He has published extensively on topics ranging from privacy technologies to systemic vulnerabilities in financial cryptography. Research Interests: Gervais investigates security challenges in blockchain ecosystems, including censorship mechanisms, zero-knowledge proofs, and DeFi liquidation risks. His interdisciplinary approach bridges computer science, cryptography, and financial systems. Publications Trends: Recent articles emphasize empirical studies of DeFi attacks, hybrid fuzzing for smart contract verification, and privacy trade-offs in blockchain mixers. His work spans conferences like ACM SIGMETRICS, IEEE Security & Privacy, and World Wide Web Conference.
Marcia C. Linn is the Evelyn Lois Corey Professor of Instructional Science in the Berkeley School of Education at the University of California, Berkeley. She serves as Chair of the Graduate Group in Science and Mathematics Education (SESAME) and has made significant contributions to the field of science education for over five decades. Dr. Linn is a member of the National Academy of Education and a Fellow of multiple prestigious organizations including the American Association for the Advancement of Science (AAAS), the American Psychological Association (APA), the Association for Psychological Science (APS), the American Educational Research Association (AERA), and the International Society of the Learning Sciences (ISLS). Dr. Linn earned her B.A. in Psychology and Statistics (1965), M.A. in Educational Psychology (1967), and Ph.D. in Educational Psychology (1970) from Stanford University, where she worked under Lee Cronbach. Her early career included working with Jean Piaget at the Institute Jean Jacques Rousseau in Geneva, Switzerland (1967-68), serving as a Fulbright Professor at the Weizmann Institute of Science in Israel (1983), and conducting research at University College in London. She has been a fellow at the Center for Advanced Study in Behavioral Sciences three times and a Writing Resident at the Rockefeller Foundation Bellagio Center twice. Dr. Linn's research focuses on how students learn science and how technology can be used to improve science education. She developed the Knowledge Integration framework, which has become widely used in science education. Her work explores the intersection of cognitive science and educational practice, with particular attention to how students develop understanding of complex scientific concepts. She has pioneered the use of technology in science education, developing the Web-based Inquiry Science Environment (WISE) and directing the NSF-funded Technology-Enhanced Learning in Science (TELS) center. Dr. Linn's recent publications demonstrate a clear trajectory toward integrating artificial intelligence with science education. Her work increasingly focuses on how AI can support knowledge integration, facilitate science learning opportunities, and promote equitable educational experiences. She examines how technology can help students develop deeper understandings of scientific concepts through inquiry-based learning while addressing issues of social justice in science education. Scientific Awards and Honors National Association for Research in Science Teaching Award for Lifelong Distinguished Contributions to Science Education American Educational Research Association Willystine Goodsell Award Council of Scientific Society Presidents first award for Excellence in Educational Research Fulbright Professor (1983) Apple Wheels for the Mind grant (1985) National Institute of Education grant (1983) Throughout her career, Dr. Linn has secured significant funding for educational research, including multiple National Science Foundation grants. She directed the NSF-funded Technology-Enhanced Learning in Science (TELS) center and has led numerous projects investigating the cognitive consequences of computer environments for learning. She has advised countless students and researchers in the field of science education, shaping the next generation of educational researchers and practitioners. Dr. Linn directs the Web-based Inquiry Science Environment (WISE) project and has been instrumental in developing technology-enhanced learning environments for science education. Her laboratory has been at the forefront of creating and testing innovative learning technologies that support students in developing deep understanding of scientific concepts through inquiry-based approaches.
Laurent Tapie is a Senior Lecturer at Paris Descartes University with a focus on Biomedical Engineering, Mechanical Engineering, and CAD/CAM . As Deputy Director of the URB2i research unit and manager of the PlatiNum platform , he coordinates the 3d4care.org consortium . His academic background includes a Doctorate in Mechanical Engineering from École Normale Supérieure de Cachan and authorization to direct research (HDR) from Université Paris 13. Research Interests: Mechanical Engineering, Biomedical Engineering, Medical Devices, CAD/CAM, Shaping of Biomaterials Theses Supervised: 3D evaluation of dento-prosthetic joints, impact of CAD/CAM on dental prosthesis integrity, and metrological evaluations of prostheses. Publications: His work spans dental CAD/CAM systems, surface integrity of prostheses, additive manufacturing, and 3D printing applications during the COVID-19 pandemic . Recent articles focus on data dispersion in CAD/CAM chains, tool-material influence on roughness, and numerical workflow standardization . Scientific Award: Prix du comité scientifique de la session recherche (2019). Projects: Currently leads initiatives like ProGéoMéca (Labex LaSIPS), Bio-Dents (CNRS Biomimicry), and additive process development for multi-material dental aligners .
