جست‌وجوی استاد و پژوهشگر

• High-energy physics
• Particle physics
• Nuclear physics
• +۳ مورد دیگر

Else Lytken is a Professor and Head of the Department of Physics at Lund University, part of the Faculty of Science. She is also a Senior Lecturer and Project Manager for the Particle and Nuclear Physics group. Her research focuses on testing the Standard Model and searching for new physics beyond it using leptons, particularly through collaborations with the ATLAS experiment at CERN and the upcoming νESS experiment at ESS (European Spallation Source). Her work includes high-energy physics studies at the LHC (e.g., Higgs boson properties, top quark physics, dark matter searches) and high-intensity experiments exploring neutrino properties and coherent elastic neutrino-nucleus scattering. She has contributed to over 1,175 publications, with recent work emphasizing precision measurements in pp collisions and advanced analysis techniques for ATLAS data. Notable projects include: ATLAS Run-2 results on Higgs boson decays, top quark production, and dark matter mediator searches Planning for νESS to study neutrino-nucleus interactions Development of jet substructure algorithms and detector calibration methods Her research combines experimental particle physics with cutting-edge computational tools, contributing to both LHC physics and future neutrino experiments.

• Data-Intensive Technologies
• Software Engineering
• Machine Learning
• +۵ مورد دیگر

Welf Löwe is a资深 researcher and faculty member at Linnaeus University's Faculty of Technology, Department of Computer Science and Media Technology, and also teaches at Linköping University's Department of Computer and Information Science. His research focuses on data-intensive technologies, software metrics, design pattern detection, and context-aware systems. He leads the Data Intensive Software Technologies and Applications (DISTA) group and contributes to the Linnaeus University Centre for Data Intensive Sciences and Applications (DISA). He actively collaborates on projects like the Data Intensive Applications (DIA) graduate school and the High-Performance Computing Center (HPCC). His work spans machine learning applications in healthcare, forestry, and industrial automation. Recent research includes feature engineering in medical data, skeleton avatar technology for aging studies, and AI-driven diagnostics. He has authored over 150 peer-reviewed publications and participates in interdisciplinary initiatives such as the iSchool project.

• Pediatric Intensive Care
• Critical Care Medicine
• Nutrition in Critical Illness
• +۸ مورد دیگر

Sascha C.A.T. Verbruggen is a researcher at Erasmus MC in the Neonatal and Pediatric Intensive Care department, contributing significantly to pediatric critical care and clinical nutrition research. Their work aligns with UN Sustainable Development Goals, particularly in health and sustainable food systems. The research interests of Dr. Verbruggen span a wide range of topics in pediatric intensive care, including: Nutritional support for critically ill children Metabolic responses during critical illness Parenteral and enteral feeding strategies Early mobilization in pediatric ICU Application of machine learning in pediatric sleep and critical care Sustainable medical nutrition and food systems in hospitals The recent publications (2024–2025) show a strong trend toward interdisciplinary research combining clinical pediatrics, nutrition science, and data science. Articles focus on lipid metabolism in critical illness, evolving feeding paradigms, early mobilization outcomes, and innovative use of ECG data with machine learning for sleep classification. These works appear in high-impact journals such as Critical Care , Clinical Nutrition , and Journal of Clinical Sleep Medicine . While no specific scientific awards are listed in the provided text, the extensive research output (111 publications) and active collaborations suggest recognition within the field. Dr. Verbruggen has supervised at least five research works, indicating a role in mentoring junior researchers, though specific student names are not provided. There is no information available regarding formal education, grants, labs, or teams led by Dr. Verbruggen in the current text.

Robin Toft Klarمشاهده پروفایل

استاد بالینی

• Global Health
• Population Health
• Nursing Workforce Development
• +۹ مورد دیگر

Robin Toft Klar is a Clinical Professor at NYU Rory Meyers College of Nursing, where she has dedicated her career to advancing nursing through environmental and systems-level research. Her work focuses on the built environment’s impact on population health, nursing workforce capacity building in low- and middle-income countries, and interprofessional education. She is a Fellow of the American Academy of Nursing and actively mentors PhD students. Education: Post-Doc, Case Western University (2012) DNSc, Yale University (2002) MS, Boston College (1980) BSN, Fitchburg State College (1979) Diploma, Worcester City Hospital School of Nursing (1975) Her research interests include global health, nursing workforce development, infection prevention and control, continuing professional development, and environmental design in healthcare. She has led major initiatives in Liberia and Rwanda to strengthen nursing and midwifery education and practice. Her recent publications show a strong trend in global health systems strengthening, particularly in post-crisis settings (e.g., post-Ebola Liberia), with a focus on curriculum reform, preceptor training, PPE access, and pandemic adaptation. She integrates geospatial tools, simulation, and quality improvement frameworks into her work. Scientific Awards: Fellow, American Academy of Nursing (2019) Nurses with Global Impact Award, United Nations (2019) Recognition, Rwanda Ministry of Health (2016) Fellow, Comprehensive Geriatric Education Program, UMass (2013) Educational Achievement Award, UMass (2009) Fellow, Career Development Institute, UMass (2008) Leadership Award, Fitchburg State College (2003) Dr. Klar has been instrumental in advising and leading multiple federally funded and international projects, including HRSA/PEPFAR-supported programs in Liberia and Rwanda. She collaborates extensively with academic and governmental partners to strengthen health systems. Her work in developing continuing professional development programs, curricular reforms, and resilient health workforce models demonstrates a sustained commitment to equity and sustainability. She is actively involved in media and public engagement, with recent coverage of her work in Ghana involving health screenings and student training. Her lab and team efforts focus on global nursing innovation, particularly through the Resilient and Responsive Health Systems (RRHS) initiative.

Xibin Zhangمشاهده پروفایل

استاد

• Bayesian inference
• Financial econometrics
• Nonparametric estimation and testing
• +۷ مورد دیگر

Xibin Zhang is a Professor in the Department of Econometrics and Business Statistics at Monash Business School, Monash University, Australia. He has been a faculty member since 2004, progressing from Lecturer to full Professor in 2021, and has established a strong research profile in econometrics and applied statistics. Educational Background: PhD in Econometrics, Monash University, Australia Doctoral Degree in Systems Engineering, Tianjin University, China MSc in Probability and Statistics, Nankai University, China BSc in Mathematical Statistics, Nankai University, China Research Interests: Professor Zhang’s research focuses on advanced econometric methodologies, including Bayesian inference, semiparametric and nonparametric estimation, panel data models, and hypothesis testing. His work extends into financial econometrics, actuarial studies, and energy economics, with a strong emphasis on empirical applications related to climate policy, energy transition, and economic sustainability. He applies sophisticated statistical techniques to real-world data, contributing to both methodological development and policy-relevant insights. Publication Trends: His recent publications (2019–2025) reveal a consistent focus on nonparametric and Bayesian methods in econometrics, with applications in energy economics, stochastic frontier analysis, and financial modeling. He frequently publishes in top-tier journals such as Journal of Econometrics , Energy Economics , and Economic Modelling , often in collaboration with leading researchers. A notable trend is the integration of kernel-based methods and Bayesian bandwidth selection across various modeling frameworks. Scientific Awards: Dean's Award for Excellence in Research (2009) Dean's Commendation for Excellence in Research Publication (2007) Advising and Grants: He has supervised over 15 PhD and Master’s students to completion and currently supervises multiple PhD candidates. His research is supported by competitive grants, including four Australian Research Council (ARC) Discovery Projects (2006–2016) and a DFAT-funded project (2017–2018) on Chinese tourism dispersal in regional Australia. Labs and Research Teams: While no formal lab is mentioned, Professor Zhang collaborates extensively within econometrics and business statistics research groups at Monash, particularly with colleagues like M.L. King, Jiti Gao, and Russell Smyth. He also engages in interdisciplinary work involving energy policy, finance, and actuarial science.

Wendy Pollockمشاهده پروفایل

استاد

• severe maternal morbidity
• Maternal health
• workforce retention
• +۴ مورد دیگر

Wendy Pollock is a Professor at the School of Nursing and Midwifery, Monash University, where she contributes significantly to research, education, and policy in maternal and critical care. She joined Monash in 2021 after a distinguished career spanning clinical practice, research, and academic leadership. Education: PhD in Nursing, The University of Melbourne (awarded 2008) Graduate Diploma in Education (1994) Graduate Diploma in Critical Care Nursing (1992) Graduate Certificate in Advanced Learning & Leadership (2009) Her research focuses on severe maternal morbidity, maternal health, critical care, midwifery, workforce retention, and implementation science . She employs both quantitative and qualitative methodologies to improve health systems for critically ill pregnant and postnatal women. Her work aligns with the UN Sustainable Development Goals, particularly in health and education. The recent publications highlight a strong trend in maternal health system improvements, early warning systems, workforce studies in ICU, and innovative applications of AI and NLP in maternity care. Her research bridges clinical practice, education, and policy, with increasing emphasis on climate change impacts and digital health solutions. Scientific Awards: Life Membership of the Australian College of Critical Care Nurses (2023) Monash Nursing and Midwifery 2023 Excellence Award The Peter Hicks Fellowship Award (2023) Vice Chancellor's Education Excellence Award for Programs that Enhance Learning (2024) Wendy Pollock actively supervises Master’s and PhD students and leads major research projects, including national workforce surveys and educational innovations. She is a key contributor to the Australasian Maternity Outcomes Surveillance System (AMOSS) and frequently engages in public lectures and conference organization. Her work is supported by institutional and collaborative grants focused on improving ICU and maternity care systems. She is involved in research teams and networks focused on maternal health, critical care nursing, and healthcare education, contributing to both national and international collaborations.

Kalil Erazoمشاهده پروفایل

استاد آموزشی

• Structural Health Monitoring
• Civil Infrastructure Resilience
• Stochastic Methods
• +۴ مورد دیگر

Kalil Erazo is an Assistant Teaching Professor in the Department of Civil and Environmental Engineering at Rice University. His research focuses on structural health monitoring (SHM) for resilient civil infrastructure, particularly in regions prone to natural hazards. Erazo emphasizes integrating stochastic methods, Bayesian estimation, and advanced sensor technologies to assess structural integrity and predict performance under extreme events. Education: Postdoctoral Scholar, Rice University (2015-2016) Ph.D. in Civil and Environmental Engineering, University of Vermont (2015) M.S. in Civil and Environmental Engineering (Fulbright Fellow), Georgia Tech (2012) B.S. in Civil Engineering, Instituto Tecnológico de Santo Domingo (2009) Research Interests: Erazo’s work bridges theory and practice in resilient infrastructure design. Key areas include SHM for historic structures (e.g., UNESCO’s Colonial City of Santo Domingo), stochastic modeling for uncertainty quantification, Bayesian methods for nonlinear systems, and post-disaster decision-making frameworks. He advocates for integrating computational tools with physical infrastructure to enhance safety and sustainability. Research Group: The Structural Monitoring for Resilient and Sustainable Infrastructure group addresses National Academy of Engineering Grand Challenges by developing cyber-physical systems that monitor infrastructure health and predict performance under hazards like hurricanes and earthquakes. Outputs include sensor-based frameworks, digital twin technologies, and risk-assessment protocols.

Seyed Ziae Mousavi Mojabمشاهده پروفایل

استادیار

• Data Mining
• Machine Learning
• Evolutionary Algorithms
• +۶ مورد دیگر

Seyed Ziae Mousavi Mojab is an Assistant Professor (Teaching) in Computer Science at Wayne State University's James and Patricia Anderson College of Engineering. He holds a B.S.E. in Computer Science from the University of Michigan Ann Arbor, and both M.S. and Ph.D. degrees in Computer Science from Wayne State University. His research focuses on evolutionary computation, optimization methods, workflow scheduling, medical image processing, data analytics, and business intelligence. Key investigations include cultural algorithms for big data workflows, deep learning ensembles for medical diagnostics, and neural architecture synthesis techniques. Recent publications demonstrate applications in robotic surgical segmentation (2023), COVID-19 detection via chest X-ray analysis (2020-2021), and optimization frameworks for cloud-based big data systems (2019-2022). Research consistently integrates evolutionary computation with deep learning architectures across medical and data-intensive domains.

Tung-Lung Wuمشاهده پروفایل

دانشیار

• Boundary crossing probability
• Sequential analysis
• Pattern distributions
• +۳ مورد دیگر

Dr. Tung-Lung Wu is Associate Professor of Statistics at Mississippi State University, specializing in applied probability and statistical methodology. His research develops analytical frameworks for boundary crossing problems, pattern distributions, and high-dimensional data. Research interests include first passage time calculations for stochastic processes, sequential analysis techniques in clinical trials, quality control applications of pattern distributions, and hypothesis testing for high-dimensional covariance structures. His work has applications in finance, manufacturing quality, and spatial epidemiology. Recent publications advance methods for scan statistics of Poisson processes, distribution-free control charts, and projected tests for covariance matrices. Analytical approaches include Markov chain imbedding techniques, random matrix projections, and adaptive scanning procedures. No awards, student advising, or laboratory affiliations are detailed in the source materials.

Jelena Schmalzمشاهده پروفایل

مدرس

• Applied Mathematics
• X-ray Imaging
• Tomography
• +۲ مورد دیگر

Jelena Schmalz is a Lecturer in Mathematics at the School of Science and Technology, University of New England. She holds an M.Sc. in Mathematics from Moscow State Lomonossov University and a Ph.D. in Physics from UNE. Her background includes extensive teaching experience in first-year mathematics, number theory, and cryptography courses. Dr. Schmalz's research focuses on applied mathematics with applications to X-ray imaging, tomography, sport science, cryptography, and tertiary education. Her doctoral work investigated direct and inverse problems in X-ray phase-contrast imaging. Her publications demonstrate consistent focus on mathematical applications and educational innovation, with recent work exploring mathematical education during COVID-19 and security protocols in banking systems. Earlier research established foundations in X-ray physics and tomography techniques.

• Computer Networks
• Edge Computing
• Quantum Computing
• +۵ مورد دیگر

Xiaojun Shang is an Assistant Professor in the Department of Computer Science and Engineering at the University of Texas at Arlington. His research focuses on distributed computing environments, including edge computing, quantum computing, and network optimization for AI applications. PhD in Computer Engineering from Stony Brook University (2023) MS in Electrical Engineering from Columbia University (2016) BS in Information and Communication Engineering from Zhejiang University (2014) Research interests span computer networks , edge computing , edge AI , and quantum computing , with emphasis on algorithm development for distributed environments. Recent work includes quantum interconnection networks and edge AI co-design , with applications in connected vehicles and 6G systems. Publications demonstrate trends in service function chain deployment , quantum entanglement routing , and mobility-aware edge computing . His team's work on blockchain-based O-RAN optimization and distributed quantum computing topology has won Best Paper Awards at ICDCS 2024 and INFOCOM 2020. Best Paper Award, IEEE ICDCS 2024 UT System STARs Award 2023 Best Paper Award, IEEE INFOCOM 2020 As a Faculty Mentor for OurCS Workshop and committee member for CSE PhD Admissions, he advises PhD students including Wei Lin and Wentao Dexter Gao . Reviewer roles include IEEE TPDS, ACM Computing Surveys, and IEEE IoT Journal.

Kerry Wardمشاهده پروفایل

دانشیار

• Enterprise Resource Planning
• Information Technology in Law Enforcement
• Information Warfare
• +۳ مورد دیگر

Kerry Ward is an Associate Professor in the Department of Information Systems and Quantitative Analysis at the University of Nebraska Omaha's College of Information Science & Technology. He holds a Ph.D. in Management Information Systems from Indiana University, an MBA from the University of Notre Dame, and dual bachelor's degrees in Accounting (Indiana University-Indianapolis) and Psychology/Religion (Wabash College). His professional background includes seven years in public accounting with firms like Deloitte and PriceWaterhouseCoopers, followed by seven years as an officer with the Indianapolis Metropolitan Police Department. Dr. Ward teaches courses on Enterprise Resource Planning (using Epicor's Kinetic), Risk Management in IT, Information Warfare, and Research Methods. His research focuses on ERP systems, IT in law enforcement, information warfare, and research methodologies. His work has appeared in top journals such as the Journal of the AIS, European Journal of Information Systems, and IEEE IT Professional. He has served as co-chair for research methods tracks at AMCIS and HICSS, and as an ad hoc reviewer for multiple journals. Dr. Ward has received the UNO Alumni Outstanding Teaching Award (2010) and has contributed to academic service through roles on committees like the Doctoral Program Committee and Judicial Committee. His research emphasizes organizational adaptation, IT governance, and knowledge management. He has explored topics ranging from SaaS adoption to regulatory impacts on IS strategy, reflecting a blend of theoretical rigor and practical relevance.

Panos Trahaniasمشاهده پروفایل

استاد

• Robotics
• Computer Vision
• Humanoid Robots
• +۱ مورد دیگر

Panos Trahanias is Professor and Vice Chair in the Department of Computer Science at the University of Crete, and heads the Computational Vision and Robotics Laboratory (CVRL) at Foundation for Research and Technology - Hellas (FORTH). His research bridges computational vision, robotics, and embedded systems, with specific focus on humanoid robot navigation, real-time SLAM implementations, and adaptive control systems. Recent investigations include developing climbing quadruped robots, robust grip-lifting mechanisms, and probabilistic contact estimation methods for dynamically challenging terrains. Medical applications feature prominently through neural network approaches for ventilator waveform analysis in critical care settings. Hardware innovation is demonstrated through FPGA-accelerated visual SLAM architectures and reconfigurable embedded systems designed for resource-constrained robotic platforms. His work consistently advances the integration of probabilistic methods, deep learning, and adaptive control in autonomous systems.

Mark S. Shephardمشاهده پروفایل

استاد

• Scientific Computing
• High-Performance Computing
• Numerical Simulation
• +۵ مورد دیگر

Mark S. Shephard is the Samuel A. Johnson '37 and Elizabeth C. Johnson Professor of Engineering and Director of the Scientific Computation Research Center (SCOREC) at Rensselaer Polytechnic Institute, with joint appointments in Mechanical, Aerospace and Nuclear Engineering and Computer Science departments. His research pioneers Scientific Computing and High-Performance Simulation , driving innovations in automatic mesh generation , adaptive analysis methods , and parallel adaptive simulation technologies . SCOREC's work spans five core areas: High-Performance Simulation Methods - advanced mathematical models and discretization Simulation Reliability – uncertainty quantification and adaptive techniques Massively Parallel Computations – scalable solutions for real-world engineering problems Multiscale Computations - cross-scale modeling frameworks Construction of Simulation Systems – collaborative workflow development Applications include fusion plasma, soft tissue modeling, additive manufacturing, and microelectronics. Recent publications (2022-2025) reveal intense focus on GPU-accelerated unstructured mesh methods for fusion energy research and multiscale material science, with growing emphasis on exascale computing and cyberinfrastructure for plasma physics. His work increasingly bridges computational theory with industrial applications in CAE and medical device evaluation. Professor Shephard has graduated 24 Ph.D. students and secured over 65 research grants from 13 government agencies including DOE (SciDAC Institutes, Exascale Computing Program), NSF, NIH, DoD, and NASA, plus funding from 44 industry partners. His leadership extends to SCOREC's collaborations with ten+ universities and commercial impact through medical simulation software used in arterial stent evaluation. As SCOREC's founder and director for 32 years, he integrates faculty from seven departments across Rensselaer to advance simulation technologies. His Simmetrix Inc. co-founding role demonstrates commitment to translating research into engineering solutions, with current work targeting fusion plasma systems and heterogeneous supercomputing environments.

• Coastal Engineering
• Coastal Hazards
• Numerical Modeling
• +۳ مورد دیگر

Matthew Janssen is a Research Assistant Professor at the Department of Civil, Environmental, and Ocean Engineering, Stevens Institute of Technology. His research focuses on coastal hazards, littoral processes, and developing computationally efficient models to assess risks to coastal infrastructure using field observations, numerical modeling, and data-driven techniques. He holds a PhD (2022), MS (2016), and BS (2011) in Ocean Engineering from Stevens Institute of Technology and the University of Rhode Island, respectively. His work emphasizes understanding storm erosion potential, dune performance under climate change scenarios, and the impact of coastal structures. Notable contributions include methodologies for quantifying storm erosion considering sea level rise and probabilistic forecasting of coastal storm impacts. He currently serves as Assistant Director of the NJ Coastal Protection Technical Assistance Service and has prior industry experience with firms like Rising Tide Waterfront Solutions and McLaren Engineering Group. Key Research Areas: Coastal resilience, numerical modeling, climate adaptation, dune dynamics, sediment transport. Recent Focus: Long-term dune performance under extreme and nuisance erosion events; integration of machine learning (CART models) for erosion prediction. Publications highlight his work on hurricane impacts, breakwater effectiveness, and navigation channel management. He received the John P. Breslin Award (2022) and is active in professional societies like ASBPA and COPRI. His technical reports include analyses of New Jersey beach sediment characteristics and shoreline impacts at North Wildwood. He collaborates on projects balancing engineering solutions with ecological and economic considerations.