جست‌وجوی استاد و پژوهشگر

• Neuroimaging
• Neuroinformatics
• MRI-based morphometric analysis
• +۷ مورد دیگر

David N. Kennedy is a Professor at UMass Chan Medical School, affiliated with the T.H. Chan School of Medicine. He holds appointments in the Departments of Psychiatry and Behavioral Sciences, Radiology, NeuroNexus Institute, and Eunice Kennedy Shriver Center. He also contributes to the MD/PhD Program and Neuroscience Department at the Morningside Graduate School of Biomedical Sciences. PhD in Nuclear Engineering, Massachusetts Institute of Technology, Cambridge, MA, United States Dr. Kennedy is a leading expert in neuroimaging and neuroinformatics, with foundational contributions to MRI-based morphometric analysis, functional MRI, and diffusion tensor imaging. His research focuses on developing neuroinformatics tools and resources, including the Internet Brain Volumetric Database and the Neuroimaging Informatics Tools and Resources Clearinghouse (NITRC). He co-founded the Center for Morphometric Analysis at Massachusetts General Hospital and has been instrumental in advancing data sharing, reproducibility, and standardization in neuroscience. His recent publications emphasize FAIR data principles, neuroimaging data models, open-source tools like DataLad and HeuDiConv, and collaborative frameworks for neuroscience. These works reflect a strong trajectory toward enhancing data interoperability, transparency, and community-driven standards in neuroimaging research. Community liaison for the Neuroimaging Informatics Tools and Resources Clearinghouse (NITRC) Co-PI of the morphometry Biomedical Informatics Research Network (mBIRN) Founding editor of the journal Neuroinformatics Dr. Kennedy has not listed specific grants or advising roles in the provided text but plays a central role in major neuroinformatics initiatives. He has no listed scientific awards in the provided materials. He is a key figure in the development and dissemination of neuroimaging informatics resources, contributing to national and international efforts to standardize and share neuroscience data through platforms like NITRC and mBIRN.

Patrick Doncasterمشاهده پروفایل

استاد

• Population ecology
• Conservation biology
• Forest ecosystems
• +۷ مورد دیگر

Professor Patrick Doncaster is an academic faculty member at the School of Biological Sciences, University of Southampton, within the Faculty of Environmental and Life Sciences. His research spans population ecology, conservation management of forest and freshwater lake ecosystems, and development of conservation technology and data analysis tools. BSc in Environmental Sciences (1st-class) from University of East Anglia D.Phil. in Zoology from University of Oxford Research interests include: Population ecology Forest biodiversity Human-predator-shared prey interactions Acoustic ecology Open-source conservation technology Early warning signals for ecosystem tipping points Climate change mitigation mechanisms Robust evidence-based analysis methods His publications from 2023-2025 demonstrate expertise in statistical ecology, conservation technology, and ecosystem dynamics across terrestrial, freshwater, and marine environments. He develops tools for ANOVA analysis, radio-tracking data, and evidence-based conservation. Academic contributions include: Active projects on lake ecosystem recovery pathways Completed NERC and PTES-funded field studies Methods development for statistical power and ecological modeling Interdisciplinary collaborations across environmental science and technology Teaching materials on statistics and evolutionary genetics Leadership in the Ecology and Evolution Centre and Institute for Life Sciences He supervises PhD students and maintains active research groups focused on ecological theory and conservation practice.

• Mobile Software Engineering
• Intelligent Software Engineering
• Software Security
• +۶ مورد دیگر

Xiaoyu Sun is a Lecturer in the School of Computing at Australian National University (ANU), specializing in Software Engineering with a focus on Mobile Software Engineering and Intelligent Software Engineering. She holds a PhD from Monash University (2023) and a Bachelor's degree in Computer Science from Beijing Normal University (2016). Her research emphasizes applying static code analysis, dynamic testing, and NLP techniques to enhance software security and reliability, particularly in Android systems. Current projects include tools for detecting compatibility issues and privacy leaks in mobile apps. Her research interests span code generation frameworks (e.g., A^3-CodGen), security management in open-source projects, and AI-enhanced software development practices. Collaborations with tech giants like Bytedance and Alibaba highlight her industry engagement. Xiaoyu is Co-Investigator in the Tech4HSE project (2025-2027), developing AI-based monitoring systems for workspace safety. She has published in top venues including ICSE, ASE, and IEEE Transactions on Software Engineering. Her work bridges academia and industry, addressing challenges in mobile app security, code reuse efficiency, and developer toolchain innovation. Ongoing efforts focus on AI-driven solutions for software engineering tasks and fostering transparent privacy practices in open-source AI applications.

Oliver Watsonمشاهده پروفایل

استادیار

• Infectious Disease Modelling
• Malaria Drug Resistance Modelling
• Pandemic Preparedness
• +۳ مورد دیگر

Oliver Watson is an Assistant Professor and Imperial College Research Fellow at the School of Public Health, Imperial College London. He holds an Eric and Wendy Schmidt AI in Science Fellowship and is part of the MRC Centre for Global Infectious Disease Analysis and Imperial's AI Initiative (I-X). His research focuses on infectious disease modelling, particularly malaria drug resistance, pandemic preparedness, and mortality estimation in humanitarian settings. Education includes a PhD in Infectious Disease Modelling from Imperial College (2016–2019), an MRes in Epidemiology from Imperial (2015–2016), and degrees in Natural Sciences and Systems Biology from the University of Cambridge (2011–2015). Key research areas involve integrating genetic data into malaria models, developing mortality estimation methods using novel data sources like social media, and advancing open-source tools for public health data analysis (e.g., the rdhs R package). Scientific awards include the Schmidt Science Fellowship and Wellcome Trust PhD funding. His work has informed WHO malaria control strategies and global pandemic response policies.

• Atmospheric Science
• Air Pollution
• Atmospheric Chemistry
• +۶ مورد دیگر

Professor William Bloss is Pro-Vice Chancellor and Head of College for Life & Environmental Sciences at the University of Birmingham. A leading atmospheric scientist specializing in air pollution and atmospheric chemistry, he combines field measurements, laboratory studies, and numerical modeling to address urban air quality challenges in the UK and developing megacities like Delhi and Beijing. Education: PhD in Atmospheric Chemistry and BA in Natural Sciences (Chemistry) from the University of Cambridge Career: Postdoctoral work at NASA Jet Propulsion Laboratory, Research Fellow/Lecturer at the University of Leeds, and faculty member at the University of Birmingham since 2007 His research spans three key areas: Urban Pollutant Sources: Investigating air pollution origins in the UK, Delhi, and Beijing through field studies and receptor modeling Atmospheric Oxidants: Quantifying sources/sinks for reactive nitrogen emissions and particulate-mediated renoxification Policy Application: Translating air pollution science into regional air quality strategies via projects like WM-Air and ASAP-Delhi Key projects include: WM-Air: A £5M NERC initiative applying environmental science to improve air quality for 2.8 million West Midlands residents ASAP-Delhi: Authoritative assessment of Delhi's particulate matter sources using receptor modeling ARNA: Studying photoenhanced nitrous acid formation in marine environments Scientific contributions include: Over 180 research outputs Fellow of the Royal Society of Chemistry Leadership in UK and international atmospheric research programs His work directly informs decision-making through datasets on PM2.5 sources, climate risk mapping tools, and collaborations with 20+ regional stakeholders.

José Zofioمشاهده پروفایل

استاد

• Microeconomics
• Economic Measurement
• Productivity and Efficiency Analysis
• +۷ مورد دیگر

José Zofio is a Professor of Economics at the Universidad Autónoma de Madrid and a visiting scholar at the Erasmus University Rotterdam. He is affiliated with the Rotterdam School of Management (RSM) through the Department of Technology and Operations Management and the Erasmus Research Institute of Management (ERIM), collaborating with scholars on productivity and efficiency analysis. Role: Professor, Universidad Autónoma de Madrid Visiting Affiliation: Erasmus University Rotterdam, RSM Research Focus: Microeconomics, Productivity, Efficiency Analysis, Transport Economics Research Interests: His work spans Economic Measurement (Index Numbers, Duality Theory), Productivity and Efficiency Analysis (Data Envelopment Analysis, Stochastic Frontier Analysis), and Spatial Economics (New Economic Geography, Transport Networks). He applies these to Environmental Economics , Regional Development , and Industrial Organization , using Operations Research and Econometrics as empirical tools. Recent Publications highlight trends in economic inefficiency decomposition , transport infrastructure impacts , and machine learning integration into productivity models. His collaborations extend to European Commission and World Intellectual Property Organization, though no specific scientific awards are listed here. Advising and grant activity are not explicitly detailed in available texts, but his leadership in national research projects and consultancy roles indicate significant contributions. He has also developed open-source DEA toolboxes for mathematical optimization in economic analysis.

• Process Systems Engineering
• Nonlinear Optimization
• Parallel Scientific Computing
• +۹ مورد دیگر

Carl D. Laird is the John E. Swearingen Professor and Department Head of Chemical Engineering at Carnegie Mellon University's College of Engineering. He holds affiliations with the Center for Advanced Process Decision-making (CAPD) and the Center for Materials Data Science for Reliability and Degradation (MDS-Rely). His academic journey includes a B.S. from the University of Alberta (2000), a Ph.D. from Carnegie Mellon (2006), and postdoctoral training in Epidemiology at the University of Pittsburgh. Laird's research integrates optimization, data science, and systems engineering. Key focus areas include high-performance computing for large-scale nonlinear problems, parallel scientific computing, and open-source software development, applied to domains like public health, homeland security, critical infrastructure, and energy systems. His work also spans machine learning, operations research, and advanced process decision-making. Awards and Honors: 2024 Steven J. Fenves Award for Systems Research INFORMS Computing Society Prize Wilkinson Prize for Numerical Software (for IPOPT development) NSF CAREER Award Best Oral Presentation at ESCAPE 33 John E. Swearingen Endowed Professorship He advises graduate students including Carolina Colombo Tedesco and leads the Laird Research Group, part of the Center for Advanced Process Decision Making (CAPD). His collaborations include projects with Imperial College and Sandia National Labs, such as the OMLT Python package for machine learning optimization.

• Seismic Hazard Assessment
• Earthquake Risk Modeling
• Natural Disaster Risk Analysis
• +۵ مورد دیگر

L. Oostwegel is a Researcher at GFZ German Research Centre for Geosciences, working within the Seismic Hazard and Risk Dynamics department. The researcher specializes in earthquake exposure modeling, building stock characterization, and multi-hazard risk assessment using geospatial data and open-source information. Oostwegel's research focuses on seismic hazard and risk assessment, with particular emphasis on building exposure modeling using OpenStreetMap and remote sensing data. Key research areas include earthquake risk modeling, flood risk assessment, landslide risk management, and the development of tools for multi-hazard risk assessment. The researcher has made significant contributions to understanding building stock characteristics at various scales, from individual buildings to global assessments. Analysis of recent publications reveals a strong focus on utilizing volunteered geographic information and Earth observation datasets for risk assessment. Oostwegel has developed methods for assessing the completeness of OpenStreetMap building data globally and has created tools like 'risk-calculator' for multi-hazard risk assessments. The research shows an interdisciplinary approach combining geophysics, geospatial analysis, and disaster risk reduction. Top-down or bottom-up in earthquake exposure modeling (2025) Safe Haven – Landslides: A Serious Game for Enhancing Risk Awareness (2025) A model of European buildings (2024) From Shelters to Skyscrapers: Worldwide Building Exploration (2024) Seismic loss assessment sensitivity study (2023) Oostwegel has collaborated extensively with researchers including Evaz Zadeh, T., Schorlemmer, D., and others across multiple projects focused on natural disaster risk assessment. The research has practical applications in urban planning, disaster risk reduction, and climate adaptation strategies, particularly for coastal megacities and areas prone to seismic activity.

• Optimization
• Unit Commitment
• Generation Expansion Planning
• +۴ مورد دیگر

Dr. Diego Alejandro Tejada Arango serves as a Visiting Professor in the Energy Systems Modeling area at the Institute for Research in Technology (IIT) of Comillas Pontifical University's School of Engineering. His academic journey includes a BSc in Electrical Engineering from Universidad Nacional (Colombia, 2006), an MSc from Universidad de Antioquia (Colombia, 2013), and ongoing MSc studies in Research in Engineering Systems Modeling at Universidad Pontificia Comillas. BSc Electrical Engineering, Universidad Nacional, Medellin (2006) MSc Electrical Engineering, Universidad de Antioquia (2013) MSc Research in Engineering Systems Modeling, Universidad Pontificia Comillas (in progress) His research focuses on developing advanced modeling tools for energy sector analysis and planning, with particular expertise in optimization techniques for generation capacity expansion. Dr. Tejada's work bridges theoretical advancements with practical applications in power system operations and planning. His research interests span optimization methodologies, unit commitment formulations, generation and transmission expansion planning, and energy storage integration within modern power systems. Analysis of his publication record reveals a strong focus on improving computational efficiency in energy system modeling while maintaining accuracy, with recent work addressing temporal resolution flexibility, hydrogen infrastructure integration, and interpretable machine learning applications for power system operations. His research demonstrates consistent evolution from traditional power system planning toward addressing contemporary challenges of renewable integration and decarbonization. 4th EASE Student Award for PhD thesis on energy storage co-optimization Dr. Tejada has contributed extensively to collaborative research projects with industry partners including Endesa and Enel Iberoamerica, focusing on generation expansion, renewable integration, and strategic network planning. His technical reports for the Spanish Ministry of Economy demonstrate engagement with policy-relevant research. As a reviewer for leading power systems journals including IEEE Transactions on Power Systems and International Journal of Electrical Power & Energy Systems, he contributes to scholarly discourse in the field.

Turaj Ashuriمشاهده پروفایل

استاد

• Multiphysics Modeling
• Design Optimization
• Renewable Energy Systems
• +۵ مورد دیگر

Dr. Turaj Ashuri is an Assistant Dean of Research Initiatives and Graduate Programs (interim) at the College of Engineering and Engineering Technology, Kennesaw State University. He previously held faculty positions at the University of Texas at Dallas and Arkansas Tech University, where he served in leadership roles such as Director of the Interdisciplinary Research Center and Graduate Program Director. Before academia, he accumulated over a decade of industry experience as a Structural Design Engineer, Research Engineer, and R&D Project Manager. PhD, Aerospace Engineering, Delft University of Technology MSc, Aerospace Engineering, Sharif University of Technology BSc, Mechanical Engineering, Tehran Azad University Postdoctoral Research Fellowship, University of Michigan - Ann Arbor Dr. Ashuri's research focuses on Multiphysics Modeling and Design Optimization , spanning theoretical and applied domains. His work develops numerical methods for unsteady flow fields, nonlinear composite structures, and machine learning algorithms, while applying these techniques to soft robots, unmanned aerial vehicles, and renewable energy systems. He actively explores Uncertainty Quantification and Optimal Control in energy systems, with particular emphasis on wind turbine technology and biomedical soft robotics. His recent publications demonstrate expertise in Wind Farm Optimization (2016-2018), Soft Robotics (2020), and Hydrogen Fuel Cell Vehicles (2019). These works intersect Computational Design , Control Theory , and Renewable Energy Systems , reflecting his multidisciplinary approach. Scientific recognitions include: Associate Fellow, American Institute of Aeronautics and Astronautics (AIAA) Chair, 2023 AIAA Aviation Forum Discipline Committee Dr. Ashuri's lab at KSU conducts research in Multidisciplinary Design Optimization and Machine Learning applications for intelligent mechanical systems. The lab receives funding from NASA and NIH , supporting work on soft robots, wind turbine engineering, and energy systems optimization.

Bryan Palmintierمشاهده پروفایل

پژوهشگر

• Power Systems
• Energy Systems Integration
• Distributed Energy Resources
• +۵ مورد دیگر

Bryan Palmintier is the Group Manager of the Transmission & Distribution Interactions Group at the National Renewable Energy Laboratory (NREL), with 25 years of experience in multi-domain energy systems integration. His work spans electric power systems, multi-energy systems (transportation, buildings, water), and aerospace, focusing on advanced tools for distributed energy integration. He emphasizes holistic modeling that incorporates technical, social, economic, and regulatory factors. Bachelor, Aerospace Engineering, Georgia Institute of Technology Master, Aeronautics & Astronautics Engineering, Stanford University Master, Mechanical Engineering (Design Division), Stanford University PhD, Engineering Systems, Massachusetts Institute of Technology His research drives grid modernization through synthetic electricity systems, co-simulation frameworks like HELICS, and equity-focused energy modeling. He leads teams in technical projects and outdoor expeditions, blending engineering rigor with interdisciplinary collaboration. Notable trends in his publications include scalable co-simulation for multi-sector systems, distributed resource integration, and techno-economic pathways for 100% renewable grids. Tools like HELICS (2019 R&D 100 Award) highlight his technical contributions. NREL Distinguished Member of Research Staff (2021) NREL President’s Award (2016) NREL Tech Transfer Rising Star Award (2017) R&D 100 Award for HELICS (2019) Palmintier's career bridges academia (Visiting Lecturer, University of Washington; Adjunct Faculty, Santa Clara University) and industry, with affiliations in IEEE Industrial Electronics Society and Power & Energy Society. His work underpins U.S. energy system modernization efforts.

Kurt Schmollerمشاهده پروفایل

پژوهشگر

• Cell Size Control
• Organelle Size Regulation
• Histone Homeostasis
• +۵ مورد دیگر

Dr. Kurt Schmoller is a Group Leader at Helmholtz Munich, heading the Schmoller Lab within the Institute of Functional Epigenetics (IFE). His research focuses on understanding how cells regulate their size, maintain organelle homeostasis during growth, and adjust protein composition according to cell size. The lab employs interdisciplinary approaches combining quantitative biology, live-cell microscopy, AI-based image analysis, and mathematical modeling. Dr. Schmoller received his Diploma in Biophysics from TU München (2004-2008), followed by PhD studies at TU München in Andreas Bausch's lab (2008-2012), where he studied the mechanics of in vitro reconstituted cytoskeletal networks. He then pursued postdoctoral research at Stanford University with Jan Skotheim (2012-2017), developing his interest in cell size regulation. Since 2017, he has led the 'Cell Size and Organelle Control' research group at Helmholtz Munich. His research spans four main areas: Maintenance and Adaptation of Cell Size, Histone Homeostasis, Mitochondrial DNA Maintenance, and AI for live-cell imaging data analysis. Using model organisms including budding yeast (S. cerevisiae) and green algae (C. reinhardtii), his lab investigates fundamental cellular processes that are broadly conserved across eukaryotes. His work has significant implications for understanding diseases like cancer where cell size regulation is often disrupted. Analysis of his recent publications reveals a strong focus on the relationship between cell size and various cellular processes, with particular attention to histone regulation, mitochondrial DNA homeostasis, and the development of AI tools for image analysis. His work bridges molecular biology, biophysics, and computational approaches to address fundamental questions in cell biology. Dr. Schmoller leads a diverse research team including postdocs, doctoral researchers, and technicians working across multiple projects. His lab has developed notable tools such as Cell-ACDC, an open-source software for bioimage analysis that has been adopted by multiple laboratories. The lab maintains strong collaborations across institutions and participates in the broader 'Epigenetics at Helmholtz Munich' initiative. The Schmoller Lab operates from the Neuherberg Campus (Building 35.26 / Room 014) and maintains an active presence through their lab website, BioRxiv, and GitHub repositories. Their work receives support from Helmholtz Munich's infrastructure and resources, including access to advanced microscopy facilities and computational resources for AI-based image analysis.

Franco Pestilliمشاهده پروفایل

دانشیار

• Computational Behavior
• Cognition/Sensory Systems
• Computational/Theoretical
• +۵ مورد دیگر

Franco Pestilli is an Associate Professor in the Department of Psychology at the University of Texas at Austin, where he has been since 2020. Previously, he served as an Associate Professor at Indiana University. His research focuses on brain networks, white matter, and the development of open-science technologies, including the cloud platform brainlife.io , which supports neuroscience education, transparency, and data standardization. He earned a Ph.D. in Psychology from New York University, a B.A. from the University of Rome La Sapienza, and completed postdoctoral training at Stanford University and Columbia University. His work integrates computational neuroscience, neuroimaging, and data science to address challenges in brain research and promote FAIR (Findable, Accessible, Interoperable, Reusable) data principles. Recent publications highlight his leadership in advancing decentralized data management, brain imaging standards (BIDS), and reproducible neuroimaging analysis. His lab develops tools like ezBIDS and Neurodesk , emphasizing open-source platforms and collaborative frameworks for global neuroscience communities. Despite his focus on technological innovation, no scientific awards are explicitly mentioned in the text. Dr. Pestilli collaborates with international researchers and accepts graduate students for the 2025–2026 academic year. He drives initiatives to democratize access to neuroimaging infrastructure, aiming to accelerate brain health discovery through technology and data science integration.

Jessica Bloisمشاهده پروفایل

استاد

• Paleoecology
• Niche Modeling
• Community Modeling
• +۵ مورد دیگر

Jessica Blois is a Professor in the School of Natural Sciences at the University of California, Merced, where she focuses on ecological and evolutionary responses to environmental change. Her research spans paleoecology , niche modeling , and phylogenetics , integrating data from fossil records and ancient DNA to understand biodiversity dynamics across millennia. Ph.D., Stanford University (2009) M.A., Humboldt State University (2005) B.S., University of California, San Diego (2000) Her lab develops cyberinfrastructure for paleoecological databases like Neotoma , emphasizing ethical open science and FAIR data principles. Recent work explores climate velocities, ecological novelty, and no-analog communities to inform conservation strategies. Key scientific contributions include: Developing frameworks for paleoecological data integration Advancing user-centered design in open-source scientific software Investigating millennial-scale drivers of mammal community dynamics Linking phylogeography to climate refugia identification Training initiatives for equitable academic workplaces Notable recognition includes the National Science Foundation CAREER Award . Her lab (Science and Engineering 1, Rooms 235/279/281) provides resources for students and researchers via https://jessicablois.com/ .

• Geographic Information Systems (GIS)
• Spatial Data Management
• Maritime Transportation Networks
• +۷ مورد دیگر

Christophe Claramunt is a distinguished researcher affiliated with the Naval Academy Research Institute in Brest, France. His work spans interdisciplinary domains at the intersection of Geographic Information Systems (GIS) , spatial data science , and human mobility analysis . He has pioneered hierarchical graph-based models for maritime and urban systems, semantic trajectory frameworks, and spatiotemporal approaches for urban soundscapes and shipping networks. Research Themes: GIS modeling, maritime transportation analytics, urban conflict analysis, semantic trajectories, geospatial data infrastructures Methodologies: Graph theory, machine learning, knowledge graphs, spatiotemporal databases, remote sensing Applications: Port network resilience, demining systems, tourist behavior analysis, cadastral digitization His recent publications focus on graph neural networks for spatial analysis, knowledge-driven competitive intelligence , and maritime network cascading failures . Collaborative work includes contributions to global container shipping modeling and urban acoustic environments. While no formal academic rank is listed in the source data, his extensive publication record and leadership in interdisciplinary projects confirm his status as a full faculty researcher.