جست‌وجوی استاد و پژوهشگر

Michael Grabeمشاهده پروفایل

استاد

• Ion Channel Function
• Membrane Biology
• Computational Biology
• +۳ مورد دیگر

Michael Grabe is a Professor in the Cardiovascular Research Institute (CVRI) at the University of California San Francisco (UCSF). He holds a joint appointment in the Department of Pharmaceutical Chemistry. His work focuses on computational methods to study biological phenomena, particularly ion transport across membranes and the molecular mechanisms of ion channels/transporters. He has pioneered theoretical approaches to understand membrane protein function and organelle acidity regulation. Education: PhD in Physics, University of California, Berkeley (2002) ScB in Mathematics-Physics, Brown University (1996) Research Interests: Dr. Grabe’s lab investigates ion channel function, membrane remodeling by TMEM16 proteins, lysosomal pH regulation, and computational modeling of membrane-associated processes. Key themes include: Mechanics of ion transport and lipid flipping Protein-induced membrane deformations Simulations of organelle microphysiology Development of computational tools for membrane protein analysis Recent Research Trends: Recent work emphasizes dynamic protein design using AI (e.g., Science 2025), structural studies of K2P channels, and functional insights into TMEM16 scramblases. His team also explores SARS-CoV-2 protein interactions and mitochondrial uncoupling mechanisms. Awards: NSF CAREER Award (2009-2014) Alfred P. Sloan Research Fellowship (2009-2011) Shining Star Community Service Award (2012) Grants & Advising: Principal Investigator of NIH grants studying TMEM16 proteins (R01GM137109) and lysosomal physiology (R21GM100224). His lab trains graduate students and postdocs in computational biophysics and membrane biology. Labs/Teams: Leads the Grabe Lab at UCSF, which collaborates with experimental groups to bridge theory and experiment in membrane systems. Active in developing open-source tools like APBSmem for electrostatic calculations.

• Ecological Epidemiology
• Wildlife Disease Dynamics
• Veterinary Epidemiology
• +۴ مورد دیگر

Dr. Martin Lange is part of the Project Group Ecological Epidemiology within the Department of Ecological Modelling at the Helmholtz Centre for Environmental Research (UFZ). His research focuses on computational epidemiology, particularly in wildlife and livestock systems. Key areas include disease transmission dynamics, surveillance strategies, and the development of predictive models for infectious diseases like African Swine Fever (ASF) and Bovine Viral Diarrhea Virus (BVDV). Affiliations: UFZ, Department of Ecological Modelling; EcoEpi research group. Education: PhD in Veterinary Epidemiology (2013, TU Bergakademie Freiberg). Research interests span ecological epidemiology, wildlife disease control, and the application of agent-based and individual-based models to understand pathogen spread. Notable contributions include modeling ASF in wild boar populations and BVDV eradication strategies in Ireland. Awards: Received the 2018 UFZ-Wissenstransferpreis (shared with H.H. Thulke) and the Konrad-Bögel-Nachwuchsförderpreis for his doctoral work on eco-epidemiology of wild boar diseases. Also recognized at the DACh Epidemiologietagung 2011. Publications: Over 50 peer-reviewed articles since 2006, emphasizing interdisciplinary approaches to disease modeling and environmental systems. Recent work includes digital twin frameworks for biodiversity monitoring and Python-based model coupling tools like FINAM. Labs/Teams: Active in the EcoEpi group and contributes to projects like BioDT (Biodiversity Digital Twin) and CauSES (Causation in Social-Ecological Systems).

Michael Scottمشاهده پروفایل

استاد

• Structural Engineering
• Nonlinear Structural Analysis
• Finite Element Analysis
• +۶ مورد دیگر

Michael Scott is a Professor in the Department of Civil and Construction Engineering at Oregon State University, part of the College of Engineering. He holds a Ph.D. in Structural Engineering from the University of California, Berkeley (2004), an M.S. from the same institution (1999), and a B.S. in Civil Engineering from North Carolina State University (1998). His research focuses on nonlinear structural analysis, dynamics, numerical methods, and software design, particularly in the context of bridges and fluid-structure interactions. He has developed widely used tools like OpenSees and contributed to multi-hazard resilience frameworks for infrastructure. Key research areas include tsunami impacts on coastal bridges, GPU-accelerated analysis, and machine learning applications for structural simulation. His work emphasizes sensitivity analysis and software reliability, with notable contributions to OpenSees and PFEM methodologies. He has been recognized with the 2019 Celebrate Excellence Awards for Online Teaching. His research also addresses bridge loading from truck platooning, seismic sensitivity, and ponding effects on structural steel roofs. Educations: Ph.D., Structural Engineering, UC Berkeley (2004) M.S., Structural Engineering, UC Berkeley (1999) B.S., Civil Engineering, NC State University (1998) Awards: 2019 Celebrate Excellence Awards, Online Teaching Award Key Contributions: Development of OpenSees software framework Pioneering work in PFEM for fluid-structure interaction Multi-hazard engineering for tsunami resilience

Hazem U. Abdelhadyمشاهده پروفایل

پژوهشگر ارشد

• Coastal Processes
• Hydrodynamic Modeling
• Remote Sensing
• +۴ مورد دیگر

Hazem U. Abdelhady is a Postdoctoral Research Fellow at the University of Michigan's School for Environment and Sustainability (SEAS), jointly appointed with the Cooperative Institute for Great Lakes Research (CIGLR). He will join Texas A&M University's Department of Geography as an Assistant Professor in Fall 2025. His research focuses on coastal and hydrodynamic processes, leveraging machine learning, remote sensing, and physics-based modeling to address climate-driven challenges in lakes and coastal systems. Dr. Abdelhady holds a PhD from Purdue University in Hydraulics and Hydrology Engineering and Computational Engineering (2024), an MS in Irrigation and Hydraulics Engineering from Cairo University (2020), and a BS in Civil Engineering (2018). His postgraduate certificate in Geospatial Information Sciences underscores his expertise in spatial data analysis. His research interests include coastal hydrodynamics, physical limnology, and AI-driven environmental modeling. Key projects involve understanding shoreline changes in Lake Michigan, predicting ice cover dynamics, and developing tools for climate adaptation. The Aggie CIS Lab , launching at Texas A&M in Fall 2025, will further advance these efforts, bridging disciplines to enhance coastal resilience. Recent work highlights include studies on climate impacts on lake temperatures, machine learning applications for wave modeling, and automated shoreline detection algorithms. He actively recruits PhD students for Fall/Spring 2026 to investigate Great Lakes dynamics and resilience strategies. Collaborations span academic and governmental institutions, emphasizing interdisciplinary approaches to environmental challenges. His publications span high-impact journals and conferences, reflecting contributions to both theoretical and applied aspects of water resources engineering.

• Computer-aided drug discovery
• Docking
• Virtual screening
• +۸ مورد دیگر

Garrett M. Morris is an Associate Professor in Systems Approaches to Biomedicine at the University of Oxford, affiliated with the Department of Statistics and Green Templeton College. He holds roles as Deputy Director of Graduate Studies, Co-Director of the SABS R³ Centre for Doctoral Training, and Research Fellow at Green Templeton College. His research focuses on computational chemistry, drug discovery, and AI integration in biomedicine. He earned his DPhil from Oxford under Prof. W. Graham Richards, with subsequent work at The Scripps Research Institute and Oxford spinouts like InhibOx and Crysalin. Research interests include protein-ligand docking, virtual screening, and machine learning applications in cheminformatics. Notable contributions include the AutoDock software and the FightAIDS@Home project. He co-organizes conferences like the Royal Society of Chemistry’s 'AI in Chemistry' and founded Comp Chem Kitchen. His lab, Oxford Protein Informatics Group (OPIG), develops novel methods for drug discovery and evaluates AI-based docking methods' validity (e.g., PoseBusters). Recent work critiques AI docking methods' physical plausibility and generalizability. He advises numerous graduate students in statistics and drug discovery, with alumni in academia, pharma, and venture capital. Publications span molecular generation, scoring functions, and computational tools for drug design. Collaborations emphasize reproducibility, responsible research, and cloud computing in biomedicine.

• Networking and Operating Systems
• Information Security and Assurance
• Network Architecture and Protocols
• +۳ مورد دیگر

Sonia A. Fahmy is a Professor of Computer Science and Associate Department Head at Purdue University's Department of Computer Science (College of Science). She holds a PhD from The Ohio State University (1999). Her research focuses on network architectures, protocols, and security, with over 100 refereed publications. Key areas include virtual reality networking, cellular network optimization, and network experimentation tools like NFV-VITAL and ENVI. Her work is supported by NSF, DHS, industry partners, and she leads Purdue's CERIAS cybersecurity initiatives. Education: PhD in Computer and Information Science from The Ohio State University (1999). Research Interests: Network security, distributed systems, wireless sensor networks, and network function virtualization. Notable contributions include the HEED clustering algorithm and Contain-ed latency management system. Awards: NSF CAREER Award (2003), IEEE Fellow. Grants: NSF, DHS, AT&T, Cisco, Juniper, and Meta-funded projects. Professional service includes leadership roles in IEEE ICNP, INFOCOM, and editorial roles in top journals. Advising: Mentored over 20 PhD students and postdocs. Current advisees include Umakant Kulkarni and Yufeng Chen. Research teams collaborate with industry partners like Hewlett-Packard and Sandia National Labs. Labs/Teams: Active in Purdue's CERIAS, leading projects on secure network protocols and experimentation frameworks. Tools developed include EMIST, Testbed Mapping, and iHEED for sensor networks.

Carlos Argüelles-Delgadoمشاهده پروفایل

استادیار

• Neutrino Physics
• Astroparticle Physics
• High-Energy Astrophysics
• +۳ مورد دیگر

Carlos Argüelles-Delgado is an Assistant Professor of Physics at Harvard University's Department of Physics within the Faculty of Arts and Sciences. His research focuses on neutrino physics and astroparticle physics, particularly using data from the IceCube Neutrino Observatory. He explores properties of neutrinos, including potential Beyond Standard Model effects, and contributes to the development of the IceCube-Gen2 upgrade. His work includes analyzing high-energy neutrinos, studying cosmic origins, and advancing detector capabilities. Education: Ph.D. in Physics from the University of Wisconsin at Madison (2015), M.Sc. in Physics from Pontificia Universidad Católica del Perú (2012), and B.Sc. in Physics from the same institution (2008). Research Interests: Neutrino oscillations, dark matter annihilation signatures, sterile neutrinos, and astrophysical neutrino flavor measurements. He develops novel analysis techniques, such as Bayesian methods and Monte Carlo simulations, and collaborates on global neutrino data projects. Awards: 2021 Sloan Research Fellow, 2021 IUPAP Young Scientist Prize, and 2020 IceCube Collaboration Impact Award. His contributions include advancing diversity initiatives and software tools for neutrino data analysis. Teaching: Taught Electricity and Magnetism at Harvard, and previously taught physics at the undergraduate and high school levels. Active in outreach, including mentoring students and organizing workshops like the IceDUNE Workshop (2021). Labs/Teams: Leads analyses in the IceCube Collaboration’s Beyond the Standard Model Working Group and participates in the IceCube-Gen2 project. Collaborates with Janet Conrad’s group at MIT and the Wisconsin IceCube Particle Astrophysics Center.

Jackie Chaمشاهده پروفایل

استادیار

• Human Factors and Ergonomics
• Healthcare Systems
• Surgical Robotics
• +۴ مورد دیگر

Dr. Jackie Cha serves as an Assistant Professor in the Department of Industrial Engineering within Clemson University's College of Engineering, Computing and Applied Sciences. Her research bridges human factors engineering with healthcare innovation, focusing on surgical robotics, physiological signal analysis, and wearable medical technologies to enhance clinical performance and safety. Her academic foundation includes advanced degrees from leading institutions: Ph.D. in Industrial Engineering from Purdue University M.S.E. in Biomedical Engineering from the University of Michigan B.S.E. in Biomedical Engineering from the University of Michigan Cha's research program centers on quantifying human performance in high-stakes medical environments through sensor-based metrics. She investigates nontechnical skills in surgical teams, mental workload during robotic procedures, and ergonomics of exoskeleton implementation in operating rooms. Her work integrates physiological signals, eye-tracking, and proximity sensors to develop objective assessment tools for surgical proficiency and team dynamics. Analysis of her 2023-2025 publications reveals consistent thematic focus on human-robot collaboration in surgery, with emerging trends in AI-driven workload detection (s-DResNet), neural correlates of surgical expertise, and environmental factors affecting robotic surgery outcomes. Key methodological approaches include scoping reviews of human-robot interaction metrics, mixed-methods evaluations of exoskeleton efficacy, and extended reality applications for nontechnical skills training. She leads the ECHO Lab (Engineering for Clinical and Human Outcomes) at Clemson, which develops translational solutions for healthcare human factors challenges. Her lab's work spans from fundamental physiological signal analysis to applied interventions in operating rooms and emergency medical settings.

Sudeep Hegdeمشاهده پروفایل

استادیار

• Proactive Organizational Learning
• Human-AI Teaming
• Remote Physiological Monitoring
• +۵ مورد دیگر

Dr. Sudeep Hegde is an Assistant Professor in the Department of Industrial Engineering at Clemson University, where he also serves as a Faculty Scholar in the Clemson University School of Health Sciences Research (CUSHR). His research focuses on proactive organizational learning, human-AI collaboration, and remote physiological monitoring, with applications in healthcare, education, and transportation. He holds a Ph.D. in Industrial and Systems Engineering from the University of Buffalo (2015), an M.S. from the University at Buffalo (2010), and a B.S./M.S. from Ramaiah Institute of Technology in India (2007). His professional experience includes roles at Texas A&M University, the State University of New York at Buffalo, and Harvard Medical School. Hegde’s work emphasizes resilience engineering, cognitive systems, and applied ergonomics. Notable research contributions include studies on organizational adaptive capacity during crises, healthcare workflow optimization, and leveraging AI for healthcare decision-making. He teaches courses such as Human Factors Engineering and Cognitive Systems and Resilience Engineering. Key research thrusts include: Proactive organizational learning frameworks Human-AI teaming for large-scale learning Remote physiological monitoring in high-stress environments Resilience in healthcare systems and emergency response Past grants and collaborations include studies on pandemic response strategies, ED physician workloads, and biofeedback applications for mental health. His interdisciplinary approach bridges systems engineering, human factors, and healthcare innovation.

Pamela Murray-Tuiteمشاهده پروفایل

استاد

• Transportation Systems
• Disasters and Hazards
• Resilience
• +۱۲ مورد دیگر

Dr. Pamela Murray-Tuite is a Professor of Civil Engineering at Clemson University's College of Engineering, Computing and Applied Sciences. Her work bridges transportation systems, disaster resilience, and emergency response modeling. Education: B.S. (1998), M.S. (1999), and Ph.D. (2003) in Civil Engineering from the University of Texas at Austin and Duke University Teaching: Courses include CE 3110 Transportation Engineering and CE 8930 Mathematical Modeling for Civil Infrastructure Systems Analysis Her research focuses on transportation resilience , with emphasis on evacuation modeling, emergency response, and infrastructure interdependence. She explores the impact of disasters on mobility patterns, utilizing mathematical modeling and simulation tools to enhance urban systems' robustness. The 15 most recent articles span hurricane evacuation behavior, agent-based modeling, and infrastructure resilience under climate change. Keywords include transportation systems, disaster management, and behavioral modeling, while subfields cover evacuation routing, network analysis, and risk perception dynamics. Dr. Murray-Tuite's work integrates statistical methods with transportation policy, addressing challenges in both natural disasters and public sector disruptions. Her contact details include office location (210 Lowry Hall) and phone (864-656-3802).

Sudip Mittalمشاهده پروفایل

استادیار

• Cybersecurity
• Artificial Intelligence
• Cyber-Physical Systems

Dr Sudip Mittal is an Assistant Professor in Computer Science & Engineering at Mississippi State University and Associate Research Director of the PATENT Lab. His research spans cybersecurity, artificial intelligence, and cyber-physical systems, with a focus on building self-protecting systems and predictive security for unmanned vehicles. He leads the SECRETS Lab and has published over 70 papers in top venues, with work featured in The LA Times and WIRED. Research interests include: Autonomous intrusion response systems AI-driven threat detection in IoT/CPS Adversarial machine learning His publications (2019-2025) show a strong emphasis on AI security applications, particularly in malware detection, healthcare compliance, and anomaly detection using large language models. Recent articles explore MLOps security, adaptive cyber defense, and synthetic data generation for critical systems.

• Social Physics
• Graph Convolutional Neural Networks
• Large Language Models (LLMs)
• +۴ مورد دیگر

Dr. Alexander Mantzaris is an Associate Professor in the Department of Statistics & Data Science at the University of Central Florida, College of Sciences. His research bridges physics and sociology through Social Physics frameworks, focusing on statistical mechanics and thermodynamic analogies to model social phenomena. Current research explores criticality points in social systems Developing computational tools for NLP and big data Former work on Graph Convolutional Networks in social analysis Specializes in entropy-based modeling of polarization and segregation His publications emphasize interdisciplinary approaches combining network science, computational modeling, and sociological dynamics. Recent articles address thermodynamic formulations of political cycles, energy states in Schelling models, and memory-efficient data processing algorithms. Dr. Mantzaris teaches graduate courses in big data analytics and statistical learning theory. He maintains active research in computational social science with applications to political dynamics, media influence, and complex systems analysis.

Mojgan A. Jadidiمشاهده پروفایل

دانشیار

• Geospatial Visual Analytics
• Building Information Modeling (BIM)
• 3D GIS Integration
• +۵ مورد دیگر

Dr. Mojgan A. Jadidi serves as Associate Professor in the Teaching Stream and Director of Common Engineering & BSc Science within the Department of Civil Engineering at York University's Lassonde School of Engineering. A Professional Engineer (P.Eng) and founder of the GeoVA Lab, she leads research at the intersection of geospatial analytics, digital infrastructure, and innovative engineering education aligned with UN Sustainable Development Goals. Education: PhD in Geomatics, Université Laval (2014) MSc in Earthquake and Seismology Engineering, ROSE Center (Italy) & Université Joseph Fourier (France) BSc in Civil-Survey Engineering, Iranian University of Science and Technology Research Focus: Her pioneering work in Geospatial Visual Analytics spans 2D/3D environments, Building Information Modeling (BIM) and 3D GIS integration, and Spatial Quantum Computing applications for smart cities. She develops Infrastructure Digital Twins using sensor data fusion while revolutionizing engineering education through gamification and augmented/virtual reality pedagogies that transform complex spatial concepts into immersive learning experiences. Research Trends: Recent publications (2021-2023) demonstrate convergent innovation across three domains: (1) Building energy optimization through BIM-graph analytics, (2) Transportation safety via AI-driven situational awareness, and (3) Educational technology using VR sandboxes and visual-verbal comics. These works consistently integrate quantum computing principles and UN SDG frameworks to solve urban sustainability challenges. Scientific Recognition: ASEE Zone III Best Paper Award (2023) ASEE Saint Lawrence Best Research Paper & Poster (2022) 3D GeoInfo Conference Best Paper (2018) NSERC Postdoctoral Fellowship (2016) ESRI Student Award (2011) Erasmus Mundus Scholarship (2006) Research Leadership: As Associate Director of York's ESRI Center of Excellence, she manages multi-source funding from NSERC, Mitacs, and York University internal grants. Her professional service spans global organizations including ISPRS Commission IV (Secretary), IEEE Women in Engineering (Member), PEO Etobicoke (Chair), and buildingSMART Canada (Committee Member), driving standards for BIM and digital twin implementation in urban infrastructure. Lab Innovation: The GeoVA Lab develops cutting-edge tools including the TopoSurvey Game for immersive surveying education, PAN-Lassonde XR Sandbox for virtual lab experiences, and quantum computing frameworks for bike-sharing optimization, establishing new paradigms in spatial data interaction and engineering pedagogy.

Olivier FARGESمشاهده پروفایل

مدرس ارشد

• Thermal Engineering
• Energy Systems
• Fluid Dynamics
• +۳ مورد دیگر

Olivier FARGES is a Senior Lecturer and HDR (Habilitation à Diriger des Recherches) holder at the University of Lorraine, affiliated with ENSGSI (École Nationale Supérieure de Géologie et Sciences Industrielles) within the Groupe INP. He serves as Director of Industrial Partnerships at ENSGSI and is part of the LEMTA Laboratory (CNRS-University of Lorraine), focusing on multiphysics and multiscale modeling of heat transfer in complex environments. His academic roles include teaching courses such as Heat and Mass Transfer, Fluid Mechanics, Scientific Computing Modeling, and Renewable Energy. Dr. FARGES holds a Ph.D. in Energy and New R&D (2014) and an Engineering degree in Energy Engineering (2010), both from the École de Mines Albi. His research emphasizes coupled conductive-radiative heat transfer in porous media, thermal property characterization of heterogeneous materials, and Monte Carlo-based computational methods for energy systems. He has contributed to advancements in photovoltaic system modeling, solar thermal power optimization, and urban climate studies. His work bridges theoretical and applied thermal engineering, with applications in sustainable energy systems, material science, and industrial partnerships. Key research themes include radiative transfer modeling, multiphysics simulation frameworks, and the development of innovative tools for thermal property measurement and energy performance assessment.

• Maritime safety management and leadership
• Maritime accident and casualty analysis
• Maritime Autonomous Surface Ships (MASS)
• +۳ مورد دیگر

Tae Eun Kim is an Associate Professor in Maritime Safety Management at UiT The Arctic University of Norway, working within the Department of Technology and Security. Her research, teaching, and industrial collaboration focus on maritime safety and human factors, with particular expertise in maritime safety management, accident analysis, Maritime Autonomous Surface Ships (MASS), and human factors in maritime operations. Dr. Kim's research spans four interconnected domains: maritime safety management and leadership, maritime accident and casualty analysis, Maritime Autonomous Surface Ships (MASS), and human factors in maritime operations. She has developed assessment instruments like the Safety Leadership Self-Efficacy Scale (SLSES) and conducted STAMP-based causal analyses of maritime accidents. Her work on MASS addresses safety challenges in mixed navigational environments and examines leadership competencies for autonomous shipping operations. Her human factors research explores how technological advancements impact navigators' performance, crew dynamics, and safety outcomes, including gender parity issues in the maritime industry. Dr. Kim's publication record reveals a strong focus on the intersection of maritime safety, technology, and human performance. Her recent work increasingly addresses autonomous shipping technologies, with numerous publications on AI decision transparency, learning analytics in maritime simulator training, and multi-modal data analysis for nautical skill development. She has conducted systematic reviews on simulator training approaches and scenario design, contributing significantly to methodology development in maritime education and training. Her research demonstrates a clear trajectory toward integrating emerging technologies with traditional maritime safety practices as the industry transitions toward greater automation. Dr. Kim is actively involved in several significant research projects, including the i-MASTER EU Horizon Europe Research and Innovation Project, the REFRAME project, and the SPRICE project (Multidisciplinary approach for spray icing modelling). She is a member of both the Advanced Maritime Ship Operations research group and the Maritime Safety Science (MARSCI) Research Group, demonstrating her commitment to collaborative research in maritime safety science. Dr. Kim teaches several specialized courses at UiT, including SVF-3206 Safety Management and Accident Investigation, TEK-3014 Navigation Technology, MFA-2100 Maritime Digitalization, MFA-8010 Maritime HTO (Human-Technology-Organisation) and Innovation, and MFA-2018 Maritime Administration and Leadership. Her teaching portfolio reflects the interdisciplinary nature of her expertise, bridging engineering, safety science, and organizational behavior in maritime contexts.