جست‌وجوی استاد و پژوهشگر

Tamara L. F. De Beufمشاهده پروفایل

پژوهشگر ارشد

• Forensic Risk Assessment
• Wrongful Convictions
• Cognitive Biases
• +۵ مورد دیگر

Tamara L. F. De Beuf, PhD is a postdoctoral researcher at the Netherlands Institute for the Study of Crime and Law Enforcement (NSCR) in Amsterdam and a research fellow at the Leuven Institute of Criminology at KU Leuven and the Amsterdam Laboratory for Legal Psychology at VU Amsterdam. Her work focuses on enhancing fairness and accuracy in the criminal justice system through examining human judgment and decision-making processes, with particular attention to forensic risk assessment and wrongful convictions. Dr. De Beuf completed her PhD in Forensic Psychology at Universiteit Maastricht in 2022 with a dissertation titled "Risk assessment with the START:AV in Dutch secure youth care. From implementation to field evaluation." Her doctoral research examined the reliability and validity of youth risk assessment instruments in practice, establishing her expertise in implementation science within forensic contexts. Her research interests center on forensic risk assessment, wrongful convictions, cognitive biases, and community-based sanctions. She investigates how vulnerable individuals can be disproportionately affected by the legal system, particularly in cases of wrongful convictions, and how human factors such as stereotyping, confirmation bias, and irrelevant contextual information shape decisions throughout the criminal justice process. Her work extends beyond judicial reasoning to examine how cognitive biases affect expert conclusions by forensic psychologists and technical experts. Through her extensive publication record spanning from 2018-2025, Dr. De Beuf has contributed significantly to understanding the implementation and quality of forensic psychological evaluations. Her research demonstrates consistent focus on how professional judgments in the justice system shape legal outcomes, with particular attention to risk assessment tools like the START:AV in youth care settings. Her work bridges forensic psychology, criminology, and legal decision-making with strong emphasis on practical application. Dr. De Beuf values the applicability of research findings for practitioners and emphasizes accessible science communication. She serves as editor-in-chief of the online peer-reviewed magazine In-Mind, which aims to make psychological science accessible to a broad audience. She actively participates in educational initiatives like the "Voordeel van de Twijfel" project where students analyze dubious convictions, demonstrating her commitment to translating research into educational practice. As an active researcher, Dr. De Beuf is involved with several research teams including the Criminological and Experimental Legal Psychology Lab (CELL) at KU Leuven and the Amsterdam Laboratory for Legal Psychology at VU Amsterdam. She participates in large-scale longitudinal research programs evaluating the implementation and effectiveness of alternative sentencing in the Netherlands, contributing to evidence-based criminal justice reform.

• Medical Image Analysis
• Deep Learning
• Machine Learning
• +۷ مورد دیگر

İlkay Öksüz is an Associate Professor in the Department of Computer Engineering at Istanbul Technical University (ITU), where he has been serving since 2021, following his appointment as a Doctoral Academic Staff member in 2020. He previously held research positions at King's College London (2017–2020), The University of Edinburgh (2016), and Yale University (2015–2016). He earned his Ph.D. in Computer, Decision and Systems Science from IMT School for Advanced Studies Lucca, Italy, in 2017, under the supervision of Prof. Sotirios Tsaftaris. B.Sc. in Electronics Engineering, Istanbul Technical University (2010) M.Sc. in Electrical-Electronics Engineering, Bahçeşehir University (2013) Ph.D. in Computer, Decision and Systems Science, IMT School for Advanced Studies Lucca (2017) His research focuses on medical image analysis, particularly in cardiac MRI, with core interests in image segmentation, registration, quality assessment, and reconstruction using deep learning. He also explores electricity price forecasting and explainable AI. His work bridges machine learning with clinical applications, aiming to improve diagnostic accuracy and automation in radiology. The 15 most recent publications highlight a strong trend in applying deep learning to medical imaging, especially cardiac and prostate MRI, mammography, and ECG analysis. A significant emphasis is placed on explainability , optimization , and clinical applicability , with frequent participation in MICCAI challenges and collaborations with radiology departments. Projects also extend into energy forecasting, showing interdisciplinary versatility. Scientific awards include: 2024 ITU Young Scientist Award in Engineering 2024 Parlar Research Incentive Award He leads the Predictive Intelligence and Medical Imaging (PIMI) Lab at ITU, mentoring graduate students and managing multiple funded projects, including the TUBITAK International Fellowship. His lab has achieved top placements in national and international AI competitions such as Teknofest and MICCAI challenges. He serves as principal investigator (PI) on several active grants focused on interpretable deep learning for medical imaging and electricity forecasting. The PIMI Lab, under his leadership, actively participates in high-impact medical AI challenges and has consistently achieved top rankings in competitions related to prostate cancer detection, breast imaging, and cardiac MRI reconstruction, demonstrating strong translational research impact.

Haley McAvayمشاهده پروفایل

دانشیار

• Migration
• Social Stratification
• Urban Studies
• +۹ مورد دیگر

Haley McAvay is an Associate Professor of Sociology at the London School of Economics and Political Science (LSE) and is affiliated with the Sciences Po Center for Research on Social Inequalities. She is also a Research Officer at the Institut national d’études démographiques (INED), where she contributes to research on migration and ethno-racial inequalities. Her research centers on migration, social stratification, urban studies, race/ethnicity, discrimination, and political behavior, using quantitative methods. Key areas include ethno-racial categorization, residential segregation, socio-spatial mobility, and intergenerational inequalities, with a focus on France. Her publications in journals such as Demography , Urban Studies , and Population, Space and Place reveal a consistent focus on how structural and spatial factors reproduce inequality across generations. She examines long-term neighborhood exposure, immigrant integration, and desegregation trends, contributing significantly to the understanding of urban inequality in Europe. No scientific awards mentioned. She mentors students through research supervision and is actively involved in collaborative academic projects. Her work is supported by affiliations with leading demographic and sociological research institutions, including INED and Sciences Po.

Jonathan I. Maleticمشاهده پروفایل

استاد

• Software Engineering
• Software Evolution and Maintenance
• Program Comprehension
• +۶ مورد دیگر

Dr. Jonathan I. Maletic is a Professor in the Department of Computer Science at Kent State University , where he has been since 2001. He served as Interim Chair in 2010 and has a secondary role as Principal at srcML LLC , providing software solutions for code analysis. Education: Ph.D. in Computer Science from Wayne State University (1995) M.S. in Computer Science from Wayne State University (1989) B.S. in Computer Science from University of Michigan-Flint (1985) His research spans software evolution , program comprehension , software visualization , and eye tracking studies . He is a leading figure in the srcML and iTrace projects. Recent publications focus on quantum programming analysis , code naming semantics , and eye-tracking data correction . Notable trends include the integration of NLP in code analysis and quantum software engineering . Scientific Awards: Kent State President’s Faculty Excellence Award (2025) Most Influential Paper at SCAM (2021) Kent State Outstanding Research Award (2021) IEEE TCSE Distinguished Paper (2020) Mining Software Repositories Foundational Contribution (2020) Dr. Maletic has advised 40+ graduate students , including Ph.D. and M.S. candidates, with notable alumni in academia and industry. He has secured multiple NSF grants for projects like srcDiff.org (2023, $750K) and expanding srcML language support (2020, $652K). He leads the Software Development Laboratory (SDML) and iTrace teams, fostering collaborative research in code analysis and eye-tracking infrastructure.

Hunter N. Moseleyمشاهده پروفایل

استاد

• Bioinformatics
• Computational Biology
• Metabolomics
• +۱۲ مورد دیگر

Hunter N. Moseley is a Professor in the Department of Molecular and Cellular Biochemistry at the University of Kentucky. He maintains an active research laboratory focused on bioinformatics and computational biology, with a strong emphasis on metabolomics and systems biochemistry. His work bridges computational methodology development with biological applications, particularly in data integration and analysis. Education: B.A. in Chemistry, Computer Science, and Mathematics, Huntingdon College (1992) Ph.D. in Biochemistry, University of Alabama at Birmingham (1998) His research interests center on developing computational methods for analyzing biological and biophysical data, particularly through integration of omics-level datasets and leveraging public scientific databases. Key areas include metabolomics, systems biochemistry, structural biology, and data harmonization. His laboratory has produced numerous software tools for data analysis and database integration. The recent publications highlight a strong trend in developing computational frameworks for metabolite pathway prediction, database harmonization (particularly KEGG, Reactome, MetaCyc), and FAIR data principles. There is a consistent focus on methodological rigor, data quality, and software development for metabolomics data analysis, including tools for GPU utilization tracking, academic publication tracking, and metabolic database access. Dr. Moseley has mentored numerous students and collaborators who appear as co-authors on his publications. His lab has secured research support that enables collaborative work across disciplines, including environmental health, cancer research, and cardiovascular disease. He leads the Moseley Bioinformatics Lab, which develops and maintains several open-source software packages for the scientific community.

Junpei Komiyamaمشاهده پروفایل

استادیار

• Machine Learning Methodology
• Decision-making Models
• Algorithmic Bias
• +۷ مورد دیگر

Junpei Komiyama is an Assistant Professor in the Department of Technology, Operations, and Statistics at the Leonard N. Stern School of Business, New York University, where he has been a faculty member since September 2019. His research bridges machine learning methodology and business applications, focusing on decision-making, fairness, and reproducibility in data-driven systems. His educational background includes a PhD in Information Science, an MS in Applied Physics, and a BTech, all from the University of Tokyo. Professor Komiyama's research interests center on machine learning with a strong emphasis on ethical and robust methodologies. He investigates decision-making models such as multi-armed bandits, algorithmic fairness , experimental design , and the reproducibility of data science findings. His work aims to redefine how we validate and trust results generated by machine learning systems. The recent articles reflect a strong trend in fairness-aware machine learning , online decision-making , and reproducible research practices . They span applications in business analytics, recommender systems, pricing, and cloud computing, demonstrating both theoretical depth and practical relevance. Presented work at top venues including NeurIPS and KDD Focus on ethical AI and robust scientific methodology He has advised students in machine learning and data science, though specific names are not listed. His research has been supported through academic grants related to AI ethics and data science innovation, though specific grant details are not provided. Professor Komiyama teaches courses in programming and machine learning, contributing to the technological education of business students at Stern. He is actively involved in advancing the integration of rigorous machine learning methods into business decision-making frameworks.

• Historical Archaeology
• Industrial Archaeology
• Heritage and Energy Systems
• +۱۲ مورد دیگر

Timothy J. Scarlett is Associate Professor of Archaeology and Anthropology in the Industrial Heritage and Archaeology Program within the Department of Social Sciences at Michigan Technological University. He has been a faculty member since 2001 and is actively involved in interdisciplinary research, teaching, and public engagement. His work bridges archaeology, materials science, environmental policy, and community collaboration. PhD, University of Nevada, 2002 MA, Boston University, 1994 BA, University of Arizona, 1991 Dr. Scarlett’s research centers on historical and industrial archaeology, with a focus on creativity in work processes, heritage and energy systems, archaeometry, and conservation science. He employs diverse methodologies ranging from chemical and physical analyses to ethnographic and historical inquiry. His work emphasizes public archaeology, community-based scholarship, and participatory engagement in heritage and energy justice. His recent publications span topics including industrial heritage conservation using supercritical CO₂, rehydroxylation dating of ceramics, archaeological assessments at Pullman National Monument, and risk analysis of pipeline infrastructure. These works reflect a strong trend toward interdisciplinary collaboration, integrating archaeology with engineering, environmental science, and public policy. His research often involves partnerships with federal agencies such as the National Park Service and NSF. Scientific awards and honors are not explicitly mentioned in the provided texts. Principal investigator and co-PI on multiple funded projects including those from the National Science Foundation, Alfred P. Sloan Foundation, National Park Service, and USDA Forest Service. Has advised numerous students through team-based, project-based learning in conservation and industrial archaeology. Secured crowdfunding for research via SuperiorIdeas.org. Dr. Scarlett leads or co-leads several major initiatives, including the Keweenaw Energy Transitions Lab and the Keweenaw Communities Heritage Survey. He has also founded the Historical Archaeology interest group on LinkedIn, which has over 12,500 members, making it one of the largest online academic communities in the field.

Robert Rahmمشاهده پروفایل

دانشیار

• Functional Analysis
• Harmonic Analysis
• Operator Theory
• +۳ مورد دیگر

Robert Rahm is an Instructional Associate Professor of Mathematics at Texas A&M University, serving as a Faculty Advisor for APMT. His research focuses on functional analysis, harmonic analysis, and operator theory with applications to mathematical physics and partial differential equations. He holds an office at BLOC 332C and can be contacted via math-advising@tamu.edu. Rahm's work emphasizes weighted norm inequalities, sparse operators, and spectral theory. His recent studies explore connections between perturbed potentials and the Riemann zeta function, as well as inverse problems in Schrödinger equations. He has contributed to advancements in Bergman space projections, entropy conditions for operators, and wavelet theory applications. No scientific awards are explicitly listed in the provided materials. His advising role suggests active engagement with student mentorship, though specific advisee names are not documented here. Research collaborations likely involve advanced mathematical modeling and analytical techniques in both pure and applied contexts.

• Digital Photogrammetry
• Computer Vision
• Geospatial Imaging
• +۱۰ مورد دیگر

Dr. Jackson David Cothren is a Professor in the Department of Geosciences at the University of Arkansas, where he also serves as the Leica Geosystems Chair in Geospatial Imaging. He holds dual leadership roles as Director of the Center for Advanced Spatial Technologies (CAST) and the Arkansas High Performance Computing Center (HPCC). His academic affiliations are deeply rooted in geospatial science, computer vision, and high-performance computing, bridging engineering and environmental applications. Ph.D. in Geodetic Science and Surveying, The Ohio State University M.S. in Geodetic Science and Surveying, The Ohio State University B.S. in Applied Mathematics, United States Air Force Academy Dr. Cothren's research spans digital photogrammetry, computer vision, UAV-based geospatial monitoring, and spatial archaeometry. He investigates non-traditional sensor modeling, feature extraction, surface generation, and integration with enterprise geospatial systems. His work increasingly incorporates deep learning, transformer models, and AI-driven analytics for applications in renewable energy, autonomous systems, and environmental sustainability. His recent publications highlight innovations in solar PV profiling, aerial image segmentation, and fairness-aware domain adaptation. The trends in his recent scholarly output reflect a strong shift toward machine learning and AI in geospatial analysis, particularly using transformer architectures for high-resolution imaging and cross-domain adaptation. His work integrates Lidar, GPS, and InSAR for deformation monitoring and leverages HPC for large-scale data processing. Applications span archaeology, agriculture, transportation, and energy infrastructure. Dr. Cothren has received numerous competitive grants from NSF, NEH, and USDA, supporting interdisciplinary research in geospatial analytics, smart transportation, and cultural heritage. His projects emphasize data-driven decision-making, community engagement, and workforce development in geospatial technologies. Principal Investigator, NSF E-RISE Rll: Arkansas Smart Transportation Research Incubator (2025–2029) Lead, RII Track-1: DART – Data Analytics that are Robust and Trusted (NSF, 2020–2025) Director, OPEN-GATE: Expanding Geospatial Education (NSF, 2016–2020) He mentors a broad interdisciplinary team and leads collaborative research initiatives involving computer vision, environmental science, and archaeology. His labs and research centers—CAST and HPCC—serve as hubs for innovation in spatial technologies, high-performance computing, and data-intensive research across the university and beyond. These centers support large-scale projects in archaeo-geophysics, UAV monitoring, and enterprise GIS integration.

Shohei Moriمشاهده پروفایل

پژوهشگر

• Augmented Reality
• Diminished Reality
• Human-Centered Visual Computing
• +۳ مورد دیگر

Dr. Shohei Mori is a Junior Research Group Leader at the Institute for Visualization and Interactive Systems (VISUS) within the University of Stuttgart. His work centers on computational Mediated Reality , integrating Augmented Reality (AR) and Diminished Reality (DR) to advance human-centered visual computing applications. Key Research Areas: Mediated Reality, Visual Computing, and Computational Photography Applications: Education, Entertainment Technology, and Cinematography Recent publications highlight his focus on neural rendering techniques, RGB-D inpainting methodologies, and addressing reproducibility challenges in immersive technologies. Collaborations with researchers like Dieter Schmalstieg and Denis Kalkofen underscore interdisciplinary efforts in both technical innovation and educational applications. Dr. Mori’s contributions to AR/DR include: Developing Neural Bokeh for cinematographic mixed reality effects Advancing DeepDR for structure-aware scene editing Proposing frameworks to resolve replication issues in DR research Creative educational tools like the AR Hero guitar tutorial system

• Computational Science
• Climate Modeling
• High-Performance Computing
• +۶ مورد دیگر

Mario Cesar Acosta Cobos is a Researcher at the Department of Computer Architecture of the Barcelona School of Informatics (FIB) at the Universitat Politècnica de Catalunya (UPC). His work focuses on optimizing computational performance and energy efficiency in Earth system models, particularly within the EC-Earth3 framework for the Coupled Model Intercomparison Project (CMIP6). He collaborates extensively with the Barcelona Supercomputing Center (BSC) and other international institutions. Key research interests include high-performance computing , climate modeling , parallel computing , and scientific data compression . His contributions address challenges like workflow optimization for HPC platforms, GPU acceleration for atmospheric chemistry models, and minimizing computational costs in Earth system simulations. Recent work highlights include analyzing the computational and energy costs of CMIP6 simulations, improving EC-Earth3 coupling efficiency, and developing optimized GPU implementations for atmospheric chemistry solvers. His publications span journals like Geoscientific Model Development , Computer Physics Communications , and Future Generation Computer Systems . Acosta Cobos has been involved in collaborative projects such as the Joint-Laboratory of Extreme Scale Computing and contributed to HighResMIP, focusing on high-resolution climate models. His research bridges computational science with environmental applications, emphasizing reproducibility, scalability, and sustainable computing practices.

Minge Xieمشاهده پروفایل

استاد

• Statistical Inference
• Data Science Foundations
• Meta-Analysis
• +۶ مورد دیگر

Minge Xie is a Distinguished Professor and Director of the Office of Statistical Consulting at Rutgers, The State University of New Jersey . His research focuses on foundational aspects of statistical inference, data science, and interdisciplinary applications. He serves as a leader in the field of statistics, contributing to methodologies in meta-analysis, reproducibility, and computational inference. Education : Ph.D. from the University of Illinois at Urbana-Champaign Xie’s research spans statistical theory, computational methods, and practical applications in data science. He has pioneered approaches in confidence distributions , fusion learning , and high-dimensional modeling , with notable work on zero-inflated data, conformal prediction, and reproducibility frameworks. His recent publications emphasize the integration of Bayesian, frequentist, and fiducial paradigms, advancements in meta-analysis for rare events, and scalable methods for big data analysis. Xie’s work bridges theoretical rigor with real-world applications, including microbiome studies, nuclear detection, and maritime threat modeling. Scientific Awards : ASA Fellow IMS Fellow ISI Fellow Xie actively contributes to statistical education and interdisciplinary collaboration, leading the Office of Statistical Consulting at Rutgers. His methodological innovations continue to influence fields requiring robust and interpretable statistical inference.

Karelle Siellezمشاهده پروفایل

مدرس

• Gravitational-wave physics
• High-energy astrophysics
• Multi-messenger astrophysics
• +۶ مورد دیگر

Dr. Karelle Siellez is a Lecturer in Physics at the University of Tasmania's School of Natural Sciences. Her research focuses on gravitational-wave physics, high-energy astrophysics, and time-domain astronomy, particularly in studying explosive cosmic events like gamma-ray bursts and neutron star mergers through multi-messenger approaches. She is a key member of the LIGO/Virgo/KAGRA collaboration and Associate Investigator at OzGrav. Academic Rank: Lecturer University: University of Tasmania School: School of Natural Sciences Department: Department of Physics Email: Karelle.Siellez@utas.edu.au Dr. Siellez's research encompasses: Gravitational-wave physics High-energy astrophysics Multi-messenger astrophysics Time-domain astronomy Gamma-ray bursts Kilonovae Neutron star mergers Cosmology Dark sky preservation Her academic contributions include: Over 130 refereed publications 2017 Breakthrough Prize in Fundamental Physics Leadership in citizen science platform development Collaboration with Tahitian institutions on French Polynesian telescope installation Advocacy for dark sky preservation in Tasmania Development of real-time pipelines for southern-hemisphere observatories Teaching & Supervision: Lecturer in first- to third-year physics Focus on multi-messenger astrophysics and time-domain astronomy Coordinator of "Electromagnetism and Thermodynamics" unit Honours and Doctoral supervision in gamma-ray bursts and neutron star mergers Pioneer in Aboriginisation of physics curriculum Planned 2026 course on General Relativity's astrophysical impact

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.

David E. Robbinsمشاهده پروفایل

دانشیار

• Healthcare Informatics
• Biospecimen Informatics
• Data Science
• +۷ مورد دیگر

David E. Robbins is an Associate Professor and Program Director of the Master of Health Informatics and Analytics at Samford University, affiliated with the School of Public Health and the Department of Healthcare Administration and Informatics. He holds a PhD and MSEE in computer engineering from the University of Alabama at Birmingham and a BS in computer engineering from LeTourneau University. His research focuses on healthcare informatics, biospecimen informatics, data science, and machine learning, with a strong emphasis on applying software engineering and semantic web technologies to biomedical data challenges. He has developed tools for cancer genomics analysis and digital biorepositories, contributing significantly to translational research infrastructure. His recent publications demonstrate a consistent trend in leveraging big data and web-based frameworks for cancer genomics, biospecimen collaboration, and innovative applications of virtual reality in healthcare empathy training. These works reflect interdisciplinary efforts at the intersection of computer science, public health, and clinical research. Scientific Awards: Seng-jaw Soong Award for Excellence in Biostatistics and Bioinformatics Research (2013) Outstanding Service to the Society for Design and Process Science (2012) Outstanding Graduate Student Engineer of the Year (2010-2011) Dr. Robbins has served as a key contributor in developing national-level informatics tools and frameworks. While no formal advisees are listed, his role as Program Director indicates leadership in graduate education. He has not disclosed grant funding or lab affiliations in the provided text. He is actively involved in professional societies and has presented at major conferences such as Translational Science and BCB. His work supports collaborative, data-driven advancements in public health and biomedical research.