جستجوی استاد راهنمای دانشگاهی

• Data Visualization
• Visual Data Analysis
• Data Science
• +۳ مورد دیگر

Univ.-Prof. Torsten Möller, PhD is a Professor at the University of Vienna and serves as Head of the Research Group Visualization and Data Analysis and Head of the Research Network Data Science. His work spans data visualization, visual analytics, and human-computer interaction, with a focus on biomedical, environmental, and societal data applications. Academic rank: Professor Research group: Visualization and Data Analysis Network: Data Science Email: torsten.moeller@univie.ac.at Research interests include: Visual data analysis for complex systems Interdisciplinary applications in climate science and medicine Human-computer interaction in data exploration Image processing and computer graphics Recent publication trends show expertise in: Visualizing climate change and pandemic data Multi-volumetric and network analysis Algorithmic transparency and user-centered design Interdisciplinary collaborations (e.g., astrophysics, medical imaging) Statistical and uncertainty visualization Design frameworks for visualization recommendation Teaching includes courses in: Computer graphics and visualization Image processing and analysis Human-computer interaction Data analysis projects Doctoral research seminars

• Solar Energy
• Thermochemical Conversion
• Carbon Capture
• +۳ مورد دیگر

LIN Meng is an Associate Professor at the Department of Mechanical and Energy Engineering , Southern University of Science and Technology (SUSTech) . He holds a Ph.D. in Mechanical Engineering from Swiss Federal Institute of Technology in Lausanne (EPFL) (2018) and has held postdoctoral positions at Caltech's Joint Center for Artificial Photosynthesis (2018-2019). Research Interests include solar thermal/thermochemical/(photo)electrochemical energy conversion devices , CO2 capture and utilization , and multi-scale modeling and simulation . His work focuses on optimizing energy systems through advanced computational models and cross-disciplinary integration of physics. Recent Publications highlight his contributions to solar fuel processing, CO2 conversion technologies, and hybrid electrochemical systems, with articles in Nature Communications , Joule , and Energy & Environmental Science . His research emphasizes scalable solutions for sustainable energy and carbon management. Scientific Awards ASME Graduate Student Award (2018) Outstanding Reviewer of Solar Energy (2018, 2015) Swiss National Science Foundation Postdoc Fellowship (2017) Shanghai Outstanding Master Thesis (2016) Shanghai Jiao Tong University Outstanding Graduate (2013)

Jennifer K. Ryanمشاهده پروفایل

استاد

• Numerical Analysis
• High-Order Numerical Methods
• Discontinuous Galerkin Methods
• +۴ مورد دیگر

Jennifer K. Ryan is a Professor and Division Head for Numerical Analysis, Optimization & Systems Theory at the Department of Mathematics, KTH Royal Institute of Technology, Stockholm. She is affiliated with the Digital Futures Faculty, a cross-disciplinary research center jointly established by KTH, Stockholm University, and RISE Research Institutes of Sweden. Her research focuses on developing numerical schemes for extracting enhanced accuracy from simulations, with applications in imaging, data analysis, and fluid dynamics. Ryan’s work emphasizes improving computational efficiency through theoretical insights and practical algorithms. Her academic roles include teaching courses like Numerical Methods for Differential Equations II and supervising student projects in numerical analysis. She has contributed to the SIAC MAGIC toolbox, a software package for accuracy-enhancing filtering techniques. Ryan’s research group actively explores discontinuous Galerkin methods, SIAC filtering, and multi-resolution analysis, addressing challenges in computational physics and engineering. Her publications span high-order numerical methods, mesh adaptivity, and applications in plasma physics and wave equations. Projects include error estimation for boundary integral methods and developing filters for noisy data. Ryan collaborates internationally, contributing to both theoretical advancements and practical implementations in computational science.

• Microelectronics
• Nanotechnology
• RF Engineering
• +۳ مورد دیگر

Prof. Dr. Amelie Hagelauer holds a professorship in Micro- and Nanosystem Technology at the TUM School of Computation, Information and Technology, Technical University of Munich. Her work focuses on advanced electronics and systems integration across quantum computing hardware, resistive memory technologies, and high-frequency RF systems. She has contributed to innovations in superconducting qubit readout architectures, multi-level RRAM designs, and 3D-integrated CMOS-compatible quantum devices. Research interests span quantum hardware design, nanoelectronic devices, RF front-end systems, and emerging memory technologies. Her work emphasizes practical implementation challenges such as low-power operation, high-voltage handling in RF switches, and wafer-scale fabrication processes. Recent projects include D-band radar systems, energy-efficient 60 GHz transceivers, and antenna tuning solutions for 5G applications. Publications from 2023-2025 showcase advancements in resistive switching device characterization, mitigation of TLS losses in superconducting qubits, and reconfigurable AI accelerators using RRAM-based digital twins. Her work bridges theoretical device physics with practical integrated circuit design, addressing scalability and reliability in next-gen electronics. Awards and grants: None explicitly listed in provided texts. Active collaborations include EU-funded projects on quantum computing platforms and TUM's Electronic Photonic Integration initiatives. Leads research teams in microsystem technology with emphasis on cross-disciplinary approaches combining CMOS processes, MEMS, and quantum engineering.

Ian Ewartمشاهده پروفایل

دانشیار

• Anthropology of Technology
• Built Environment
• Ethnographic Research
• +۷ مورد دیگر

Ian Ewart is an Associate Professor at the University of Reading's School of the Built Environment, serving as Head of Construction and Engineering Management and Research Group Lead for Organisation, People and Technology. He chairs the Research Ethics Committee since 2016 and supervises undergraduate/postgraduate dissertations. His academic journey spans engineering and anthropology: DPhil Social and Cultural Anthropology, University of Oxford, St Hugh's College (2007-2012) MSc Material Anthropology and Museum Ethnography, University of Oxford, St Hugh's College (2006-2007) BA (Hons) Archaeology and Anthropology, University of Oxford, Harris Manchester College (2003-2006) Diploma in Management Studies, University of the West of England (1990-1994) BEng (Hons) Mechanical Engineering, Staffordshire University (1983-1987) Ewart's research integrates ethnographic methods with digital technology studies, examining human-technology interactions in construction and domestic settings. His work bridges engineering practice and social anthropology, focusing on skill transmission, sustainable design, and multisensory experiences in virtual environments. Publications from 2025-2013 reveal a dominant trajectory in digital twins for socio-ecological sustainability, VR-based occupant behavior prediction, and HBIM for heritage conservation. The corpus demonstrates consistent cross-disciplinary innovation, merging archaeological reconstructions with healthcare applications while maintaining anthropological rigor. Key recognition: ESRC Future Research Leader fellowship (2013) for Designing Healthy Homes project He supervises PhD candidates like Afolabi Dania (Nigerian sustainable construction) and Joanna Hull (Heritage BIM), leveraging ESRC funding for ethnography-VR health studies. His grants emphasize participatory design and real-world impact assessment in built environments. Leaders the Organisation, People and Technology research group, developing multisensory Roman town reconstructions with sound/smell integration to advance archaeological and architectural experience modeling.

Zhibo Pangمشاهده پروفایل

استاد مدعو

• Embodied Intelligence
• Artificial Intelligence/ML
• Robotics
• +۷ مورد دیگر

Zhibo Pang is an Adjunct Professor at KTH Royal Institute of Technology's Department of Intelligent Systems (EECS) and Senior Principal Scientist at ABB Corporate Research Sweden. His work focuses on digital transformation in industry and healthcare, spanning robotics, AI, control systems, and wireless communication. He leads projects in embodied intelligence, Industry 4.0, and Healthcare 4.0, with 23 granted patents and over 120 journal papers. Education: PhD in Electronic and Computer Systems (KTH, 2013), MBA in Innovation & Growth (University of Turku, 2012). Key Roles: IEEE Technical Committee Chair, Editor of 6 IEEE journals, ABB Inventor of the Year (2016, 2018, 2021). Research Interests: Robotics safety, wireless automation, federated learning, digital twins, and IoT security. Recent Projects: Cloud-fog automation frameworks, robot skin systems for healthcare, and latency-aware industrial control. His work bridges academia and industry through cross-functional collaborations.

Simon Makمشاهده پروفایل

استادیار

• Statistical Modeling
• Bayesian Statistics
• Gaussian Process
• +۵ مورد دیگر

Simon Mak is an Assistant Professor of Statistical Science at Duke University and a Faculty Network Member of the Duke Institute for Brain Sciences. His educational background includes: Ph.D. in Statistics, Georgia Institute of Technology (2018) M.S. in Statistics, Georgia Institute of Technology (2018) B.S. in Statistics, Simon Fraser University (2013) Dr. Mak's research focuses on advanced statistical methodologies for complex scientific problems. His expertise spans statistical modeling , Bayesian inference , Gaussian process emulation , and uncertainty quantification . He applies these methods to nuclear physics (heavy-ion collisions), engineering (engine control systems), and music information retrieval, emphasizing scalability and interpretability in scientific computing. Analysis of his 2023-2025 publications reveals dominant trends in scalable Gaussian process methods for massive datasets and multi-fidelity simulations, particularly applied to high-energy physics and engineering systems. He has pioneered innovations in Bayesian optimization for expensive simulators and developed novel frameworks for online change-point detection in streaming data, demonstrating exceptional cross-disciplinary impact. Dr. Mak leads multiple significant research initiatives: Collaborative Research: Cost-Efficient and Confident Sampling for Modern Scientific Discovery (2023-2026) Science-Integrated Predictive modeLing (SCINPL) for scalable scientific computing (2022-2025) The X-SCAPE collaboration for statistically advanced nuclear collision modeling (2020-2025) These projects fund his development of statistical frameworks for scientific discovery in complex systems. He actively contributes to the JETSCAPE collaboration, developing multi-stage frameworks for studying jet quenching in heavy-ion collisions, and applies statistical methods through the Duke Institute for Brain Sciences to advance neuroscience research.

Majid Pahlevaniمشاهده پروفایل

استادیار

• Power Electronics
• Renewable Energy Systems
• Smart Power Grids
• +۷ مورد دیگر

Dr. Majid Pahlevani is an Assistant Professor at the Department of Electrical and Computer Engineering, Queen's University, affiliated with the Smith School of Engineering. He holds a Ph.D. from Queen's University (2012) and has prior roles as an Assistant Professor at the University of Calgary (2016–2019) and Chief R&D Engineer/VP of Technology at SPARQ Systems, Inc. (2011–2016). His research focuses on power electronics, renewable energy systems, smart grids, and energy storage, with a lab environment emphasizing interdisciplinary collaboration. He has authored over 130 publications, holds 50 U.S. patents, and serves as an Associate Editor for the IEEE Journal of Emerging and Selected Topics in Power Electronics. Education: Ph.D. (2012) – Queen's University; B.Sc./M.Sc. (2002) – Isfahan University of Technology. Research Interests: Power Electronics Technology, Renewable Energy Systems, Micro-Grids, Smart-Grids, Electric Vehicles, Energy Storage Systems, Solar Technology, LED Technology. His lab, ePOWER Lab, engages in industrial projects across these domains, fostering teamwork and cross-disciplinary innovation. Scientific Awards: Includes the Early Research Excellence Award (Alberta), Research Achievement Award (University of Calgary), Teaching Achievement Award, and IEEE Canada's Research Excellence Award. Current Supervision: Postdoctoral Fellows Laleh Saleh Ghadimi, Sergey Dayneko, and Pavel Linkov (2022). He leads the ePOWER Lab, collaborating with industry partners like Freescale Semiconductor and SPARQ Systems. Affiliations: Member of the IEEE Power Electronics Society and the Queen's Centre for Energy and Power Electronics Research.

• Innovation Management
• Entrepreneurship
• Healthcare Innovation
• +۴ مورد دیگر

Olga Kokshagina serves as an Associate Professor in Innovation & Entrepreneurship at The University of Sydney, with adjunct research appointments at Monash University's Emerging Technology Lab and the UNU Hub - Learning Planet Institute. She is also an active member of the French Digital Council. Her research program investigates technology-mediated collaboration in complex innovation systems, focusing on healthcare transformation, deep tech commercialization, and co-design methodologies. Kokshagina has led high-impact projects with global institutions including the World Health Organization, OECD, STMicroelectronics, Vall d’Hebron Hospital, and Roche, demonstrating strong translational research capabilities. Her scholarly work centers on value-based healthcare innovation, digital platform governance, and AI-enhanced collaborative systems. She examines how organizational capabilities evolve during technological transitions, particularly in healthcare ecosystems, and investigates regulatory frameworks for algorithmic control in digital markets. Kokshagina's research bridges theoretical innovation management with practical applications, evidenced by her co-founding of Ninti—an initiative advancing women's health in workplace environments—and her Open Covid-19 crowdsourcing campaign that mobilized global expertise during the pandemic. Analysis of her 2021-2025 publications reveals a cohesive trajectory examining innovation in socio-technical systems. Key themes include value digitalization in healthcare, mission-oriented interdisciplinary collaboration, and the impact of big data on technology management. Her work consistently addresses grand challenges through mixed-methods approaches, spanning conceptual frameworks in journals like Research Policy to applied studies in Technovation and R&D Management, with increasing focus on quantum readiness and AI-augmented learning systems. No scientific awards are documented in the provided materials. Kokshagina currently holds a 2025 research grant for "Co-designing societal readiness and scenario building for quantum" through the University of Sydney Nano Institute/Catalyst program. While no student supervision activities are mentioned, her collaborative projects involve multi-institutional teams across industry, government, and academic sectors. Kokshagina maintains active roles within the University of Sydney Nano Institute and contributes to international policy discourse through the French Digital Council. Her Ninti initiative exemplifies her commitment to human-centered innovation, while ongoing collaborations with healthcare providers like Roche and Vall d’Hebron Hospital demonstrate sustained engagement with real-world implementation challenges in value-based care systems.

• Maternal-Fetal Medicine
• Cardiovascular Disease in Pregnancy
• Hypertension in Pregnancy
• +۳ مورد دیگر

Dr. Annalies Denoble is an Assistant Professor of Obstetrics, Gynecology & Reproductive Sciences at Yale School of Medicine. She specializes in maternal-fetal medicine, focusing on high-risk pregnancies, particularly those complicated by cardiovascular disease. Her research addresses gaps in understanding maternal heart disease, hypertension, and vaccine safety during pregnancy. Education: MD and MSc in Clinical Research, University of Michigan Medical School (2014) Bachelor of Arts in French Studies, Duke University (2008) Research Interests: Her work centers on cardiovascular complications in pregnancy, including hypertension, heart failure, and congenital heart disease. She collaborates with Yale's multi-disciplinary Pregnancy Heart Team to optimize care for patients with cardiac conditions. Her studies also investigate vaccine safety, preeclampsia, and maternal mortality reduction strategies. Recent Research Trends: Her publications highlight late-onset postpartum hypertension, vaccine safety in pregnancy, and hypertension progression during and after pregnancy. She emphasizes extending postpartum care and improving risk prediction models for adverse cardiac events. Awards: Recognized as a 'Top Doctor' in Yale Ob/Gyn Physicians for 2025. Collaborations & Clinics: Dr. Denoble works with the Pregnancy Heart Team and Yale Medicine's Maternal-Fetal Medicine unit. She sees patients at locations in New Haven, New London, and Waterbury, CT.

John Shawمشاهده پروفایل

استاد

• Geology
• Geophysics
• Planetary Science
• +۴ مورد دیگر

John Shaw is the Harry C. Dudley Professor of Structural and Economic Geology and a Professor of Environmental Science and Engineering at Harvard University's School of Engineering and Applied Sciences. He also serves as Vice Provost for Research at Harvard, overseeing institutional research strategy and initiatives. His primary research focuses on structural geology, geophysics, and earthquake hazards, particularly in active fault systems, mountain belt tectonics, and subsurface energy development. Prof. Shaw leads the Structural Geology & Earth Resources Program, an industry-academic consortium that integrates geophysical data (3D seismic surveys, remote sensing) with advanced numerical modeling to address geological and environmental challenges. Education and professional experience: Joined Harvard Faculty in 1997. His research portfolio includes collaborations with the Southern California Earthquake Center (SCEC) and the development of critical infrastructure like the SCEC Unified Community Velocity Model (UCVM). His work emphasizes practical applications such as fault stability assessments and carbon sequestration impact studies. Research interests span: 1) active fault characterization for seismic hazard mitigation, 2) tectonic evolution of mountain belts, 3) numerical modeling of fault dynamics, and 4) geomechanical impacts of subsurface energy projects. His structural modeling innovations have advanced understanding of thrust fault systems and fault-bend folding mechanisms. Professional contributions include leadership roles in interdisciplinary consortia and development of open-source geophysical software frameworks. Administrative duties at Harvard's Office of the Vice Provost for Research (VPR) focus on advancing institutional research capacity and fostering cross-disciplinary collaborations.

Nazanin Tajikمشاهده پروفایل

استادیار

• Artificial Intelligence
• Machine Learning
• Infrastructure Resilience
• +۴ مورد دیگر

Nazanin Tajik is an Assistant Professor in the Department of Industrial and Systems Engineering at Mississippi State University (MSU). She holds a Ph.D. from the University of Oklahoma, an M.S. from the University of Tehran, and a B.S. from Sharif University of Technology. Her research focuses on integrating artificial intelligence, machine learning, and social science concepts to develop cross-disciplinary frameworks for infrastructure resilience, smart transportation systems, and disaster management. Her academic journey includes a Ph.D. at the University of Oklahoma where she contributed to the Risk-Based Systems Analytics Laboratory. At MSU, she established a research center bridging AI/ML tools with socio-technical systems. Key research domains include cyber-physical-social infrastructure resilience, search-and-rescue planning, and game-theoretic robotic designs. Tajik's work emphasizes optimization algorithms for network vulnerability assessment, resource allocation in disaster scenarios, and adaptive recovery strategies. She is actively involved with professional organizations such as INFORMS, ISE, and POMS, reflecting her commitment to advancing operations research and systems engineering methodologies.

Lisa Wangمشاهده پروفایل

استادیار

• Community Resilience Modeling
• Structural Engineering
• Disaster Risk Reduction
• +۴ مورد دیگر

Dr. Lisa Wang is a tenure-track Assistant Professor in the Department of Civil & Environmental Engineering at Old Dominion University (ODU). She holds a Ph.D. and Postdoctoral Fellowship in Structural Engineering from Colorado State University (CSU), with additional research at NIST’s Center for Risk-Based Community Resilience Planning. She is a licensed California Professional Engineer (PE) and has expertise in multidisciplinary community resilience assessment, mitigation strategies, and policy analysis. Her research focuses on integrating physical, socio-economic, and infrastructural systems to enhance disaster resilience in coastal and hazard-prone communities. Education: Ph.D. and Postdoc (CSU, 2022-2024), M.S. in Structural Engineering (University of Colorado Denver & Jilin University, 2015), B.S. in Civil Engineering (Jilin University, 2012). Professional licenses include CA PE #94251 and CO EIT #007558. Research Interests: Community resilience under multi-hazards (tornadoes, floods, climate change), resilience-based design of structural systems, data fusion across disciplines, and equitable decision-making for disaster resilience. Recent grants include $31k for AI-driven coastal resilience strategies and $10k for minimum building portfolio development. Grants & Awards: Principal Investigator (PI): Trustworthy AI in Coastal Resilience (ICAR, $31k), AI-Driven Building Portfolios (ODU PURS, $10k) Awards: O. H. Ammann Fellowship (ASCE), ICAR Travel Grant, AGU NSF Travel Grant, Jack E. Cermak Fellowship Labs & Teams: Wang Research Group and Structural Engineering Research Laboratory at ODU.

Paul G. O'Brienمشاهده پروفایل

دانشیار

• Clean Energy Transition
• Energy Storage Systems
• Thermophotovoltaic Technologies
• +۵ مورد دیگر

Dr. Paul G. O'Brien is an Associate Professor in the Department of Mechanical Engineering at York University, Canada, affiliated with the Lassonde School of Engineering. His research focuses on interdisciplinary clean energy solutions, including energy storage, thermophotovoltaic systems, decarbonization of buildings, and life cycle assessments. He leads the Advanced Materials for Sustainable Energy Technologies (AM-SET-Lab) and has authored over 50 journal articles. His work spans materials science, photonic crystal engineering, and radiative cooling technologies. Research interests include optimizing thermal energy storage systems, photonic crystal-based filters for solar applications, and CO2 capture via direct air capture systems. His lab develops materials for passive cooling, solar-thermal integration, and advanced photocatalytic CO2 reduction. Dr. O’Brien collaborates across engineering disciplines to address global energy challenges. Recent publications highlight innovations in ellipsoidal optical cavities for thermophotovoltaics, radiative cooling materials, and techno-economic assessments of carbon capture technologies. His work bridges fundamental material science with applied engineering solutions for sustainable energy systems.

Jennifer Dyمشاهده پروفایل

استاد

• Machine Learning
• Computer Vision
• Explainable AI
• +۲ مورد دیگر

Jennifer Dy is a Distinguished Professor at Northeastern University with joint appointments in Electrical and Computer Engineering and Khoury College of Computer Sciences. As Director of AI Faculty at the Institute for Experiential AI, she leads research in machine learning, computer vision, and explainable AI. Her work spans biomedical applications (COPD phenotyping, neuroimaging) and fundamental algorithms (active learning, continual learning). She holds a PhD from Purdue University and is an AAAI Fellow. Research Focus: Dy develops methodologies for robust and interpretable machine learning, including techniques for model stability in continual learning, dependency-aware active learning, and axiomatic explanation frameworks. Her applied research advances diagnostic tools using Raman spectroscopy, CT imaging, and multi-omics biomarker discovery. Awards: Recognized with the NSF CAREER Award, Faculty Research Team Award, and AAAI Fellowship for contributions to unsupervised learning and medical AI. Publication Trends: Recent articles demonstrate strong cross-disciplinary integration, combining theoretical advances in explainability/robustness with applications in healthcare, wireless systems, and particle physics. Methodological themes include optimal transport theory, probabilistic modeling, and transformer architectures.