جست‌وجوی استاد و پژوهشگر

Johan Lithnerمشاهده پروفایل

استاد

• Mathematics Education
• Problem-Solving Strategies
• Cognitive Science
• +۷ مورد دیگر

Johan Lithner is a Professor in the Department of Science and Mathematics Education at Umeå University, Sweden. He is affiliated with the Umeå Mathematics Education Research Centre (UMERC) and leads the Learning by Imitative and Creative Reasoning (LICR) research group. His work focuses on mathematical reasoning, problem-solving strategies, and their cognitive and educational implications. Key research areas include analyzing how students engage with algorithmic vs. creative reasoning, the role of formative assessment, and the neurobiological basis of learning through studies using fMRI and eye-tracking technologies. He has contributed extensively to textbook analysis, teacher professional development, and curriculum design, emphasizing the importance of fostering deep understanding over rote learning. Dr. Lithner’s research spans over two decades, involving collaborations across disciplines such as cognitive psychology, neuroscience, and educational policy. His projects include evaluating the impact of national educational reforms, designing effective mathematics tasks, and investigating how teaching methods influence brain activity and long-term retention. He has led major grants addressing formative assessment mechanisms and the cognitive benefits of creative reasoning for students of varying abilities. Research Projects: Combatting detrimental lack of understanding in mathematics via formative assessment (2024–2028) LEARNING TO ENGAGE THE BRAIN (2014–2025) Podcasting in School (2008) His publications span peer-reviewed journals like Educational Studies in Mathematics and Frontiers in Psychology , focusing on empirical studies of learning processes and their applications in educational practice. Awards: No specific awards listed, though his sustained contributions to mathematics education research are widely recognized. Grants: Multiple grants from Swedish Research Council and EU frameworks, including projects on cognitive neuroscience and teacher training. Lithner’s work bridges theory and practice, influencing both academic discourse and classroom methodologies globally.

Richard Crowleyمشاهده پروفایل

استادیار

• Financial Accounting
• Text Analysis
• Voluntary Disclosure
• +۹ مورد دیگر

Richard Crowley is an Assistant Professor of Accounting at the School of Accountancy (SOA), Singapore Management University (SMU). He joined SMU in 2016 as full-time faculty, specializing in financial accounting research using archival and analytical methods, particularly leveraging high-powered computing for unstructured textual data analysis. His work addresses challenges in financial reporting, disclosure strategies, and market behavior. Education: PhD in Accountancy, University of Illinois Urbana-Champaign Bachelor's degrees in Accountancy, Finance, and Theoretical Mathematics, University of Illinois Urbana-Champaign (2012) Research Focus: Financial stability, investor behavior, stock market anomalies, text analysis, voluntary disclosure, and the intersection of accounting with artificial intelligence/machine learning. His interdisciplinary work spans corporate governance, auditing, econometrics, and social media regulation. Key Contributions: Pioneering use of NLP for detecting financial misreporting, analyzing social media's impact on corporate strategy and traditional media, and exploring CSR reputation management through Twitter analytics. Awards: Fellow of the Lindau Nobel Laureate Meetings (2014) University Fellowship, University of Illinois (2014, 2015) Professional Service: Reviewer for AAA Annual Meeting, Illinois International Journal of Accounting Symposium, FARS midyear meeting, and University of Illinois Symposium on Audit Research. Discussed at IAS midyear meeting (2014). Labs/Teams: Engaged in cross-disciplinary projects at SMU's School of Computing and Information Systems, focusing on AI applications for financial information scrutiny on platforms like Twitter.

• Data Mining
• Computational Intelligence
• Ordinal Data Analysis
• +۷ مورد دیگر

Rob Potharst is an Assistant Professor at the Econometric Institute of Erasmus School of Economics, Erasmus University. His academic work focuses on data mining and computational intelligence, particularly applied to Internet marketing and decision support systems. His research interests include data mining , ordinal data analysis , censored data , and lognormal distribution modeling . These areas are deeply rooted in both theoretical and applied statistics, with applications in business analytics and marketing. His methodological contributions address challenges in monotonicity, classification, and parameter estimation. The recent articles show a consistent trend in developing algorithms and statistical methods for handling complex data types, especially ordinal and censored data, with applications in real-world decision-making systems. Much of his work bridges econometrics, computer science, and operations research. No scientific awards mentioned in the text. There is no information available about student advising or research grants. Rob Potharst has contributed to technical reports and peer-reviewed journal articles, often in collaboration with researchers such as W. Pijls and U. Kaymak. His publications appear in reputable outlets like Decision Support Systems and Engineering Applications of Artificial Intelligence . No specific lab or research team is mentioned in the provided text.

Oubay Hassanمشاهده پروفایل

استاد

• Computational Fluid Dynamics
• Computational Electromagnetism
• Unstructured Mesh Generation
• +۳ مورد دیگر

Professor Oubay Hassan is a Personal Professor in Civil Engineering at Swansea University's School of Aerospace, Civil, Electrical and Mechanical Engineering. He has been with the university since 2002, advancing from Lecturer to Senior Lecturer to Reader before becoming a Professor. His research focuses on computational methods for engineering analysis and design, particularly in computational fluid dynamics (CFD), computational electromagnetism, and unstructured mesh generation. He co-developed the FLITE system with BAE Systems and Airbus, pivotal for aerodynamic design of supersonic vehicles like Thrust SSC (1997 World Land Speed Record) and Bloodhound SSC. Hassan has led projects funded by grants such as 'Advanced Mesh Generation' and 'CFD Technologies for Bloodhound.' His career highlights include an MBE (1998), Catherine Richards Prize (1999), and Royal Academy of Engineering Fellowship (2012). He teaches modules like Advanced Fluid Flow Simulation and Structural Mechanics, and supervises PhD research in areas like Mach-Uniform schemes and AI-driven mesh optimization. Collaborations span industries and institutions, emphasizing public engagement through projects like Bloodhound's educational outreach. Research Interests: CFD, mesh adaptation, high-speed flows, parallel processing, and aerospace engineering applications. Awards: MBE, Catherine Richards Prize, Royal Academy of Engineering Fellowship. Grants/Projects: Includes collaborations with BAE Systems, Airbus, and projects like 'Unstructured Mesh Technology for Viscous Flows.' Labs/Teams: Leads Swansea's computational engineering teams, integrating CFD, electromagnetics, and meshing innovations.

Mingi Jeongمشاهده پروفایل

استادیار

• Computer Science
• Robotics
• Artificial Intelligence
• +۷ مورد دیگر

Mingi Jeong is a Ph.D. candidate in Computer Science at Dartmouth College and a member of the Reality and Robotics Lab under Prof. Alberto Quattrini Li. Starting December 2025, he will join Virginia Tech as a tenure-track Assistant Professor in the Aerospace and Ocean Engineering Department. His research focuses on autonomous systems for maritime environments, specifically robust decision-making in unstructured aquatic scenarios. Education: B.S. in Nautical Science (Korea Maritime and Ocean University), M.E. in Maritime Safety Engineering (Korea Maritime and Ocean University), and Ph.D. candidate at Dartmouth College. Key research interests include autonomous navigation for ASVs, obstacle detection/avoidance, multi-robot systems, and environmental monitoring. He has developed the SeePerSea dataset—the first multi-modal in-water object perception dataset—and pioneered intention-aware collision avoidance algorithms. His work combines robotics, AI, and ocean engineering for societal impact in search-and-rescue, cargo transport, and environmental surveillance. Notable awards include the 2025 Neukom Prize and MIT Postdoctoral Fellowship (declined). He has presented at top venues like IROS, RSS, and IEEE conferences globally. His lab emphasizes hands-on fieldwork and interdisciplinary collaboration.

• Computer Vision
• Machine Learning
• Neural Networks
• +۴ مورد دیگر

Sridharan Sridha is a Professor at the University of Queensland, specializing in advanced AI and computer vision research. His work spans neural networks, robotics, medical informatics, and surveillance systems. He collaborates extensively with institutions like the University of Queensland’s School of Information Technology and Electrical Engineering. Key research focuses include adversarial machine learning, multimodal fusion, and domain adaptation. His contributions to aerial-ground person re-identification (AG-ReID), LiDAR-based place recognition, and medical signal analysis have been widely recognized. Recent projects emphasize self-supervised learning, transformer-based architectures for hyperspectral imaging, and autism severity detection using physics-augmented models. His work bridges theory and practical applications in autonomous systems, healthcare, and robotics.

Marcel Oliverمشاهده پروفایل

استاد

• Mathematical Fluid Dynamics
• Partial Differential Equations
• Numerical Analysis
• +۳ مورد دیگر

Marcel Oliver is a Professor and Chair of Applied Mathematics at the Mathematical Institute for Machine Learning and Data Science (MIDS), KU Eichstätt-Ingolstadt. His research focuses on Mathematical Fluid Dynamics, Partial Differential Equations, and Numerical Analysis. He has contributed to advancing eddy parameterizations in ocean models and variational balance models for geophysical flows. Oliver is involved in the TRR181 Energy Transfers in Atmosphere and Ocean collaborative research project. He teaches courses such as Models of Weather and Climate and Algorithms and Data Structures, and has organized numerous international conferences and summer schools on topics like dynamical systems and turbulence. His work bridges theoretical mathematics with applied computational methods, emphasizing interdisciplinary approaches to fluid dynamics and climate modeling.

Xiaotong T Shenمشاهده پروفایل

استاد

• Machine Learning
• Data Science
• Statistical Inference
• +۱۲ مورد دیگر

Xiaotong T Shen is a Professor in the Department of Statistics at the University of Minnesota, Twin Cities, within the College of Science and Engineering. He is actively involved in research and currently accepting PhD students. His work is supported by major grants from the National Science Foundation (NSF) and the National Institutes of Health (NIH), including projects on gene regulatory networks, Alzheimer’s disease genetics, and generative learning on unstructured data. Research Interests: Machine Learning and Deep Learning Statistical Inference and Modeling Biomedical Applications in Genomics and Neurodegenerative Diseases Natural Language Processing and Video Interpretation Generative Models and Synthetic Data Causal Inference and Graphical Models His recent publications reflect a strong trend in integrating deep learning with statistical rigor for causal and biomedical discovery, particularly in high-dimensional settings. Articles span applications in hepatology, transcriptomics, manufacturing, and AI-driven data generation, demonstrating interdisciplinary impact. Scientific Projects and Grants: PI, NSF: Generative Learning on Unstructured Data (2020–2024) PI, NIH: Estimation and Inference in Gene Regulatory Networks (2017–2024) Co-I, NIH: Causal Deep Learning for Alzheimer’s Disease Genetics (2021–2026) PI, NIH: Directed Acyclic Graphical Models for Biological Networks (2022–2026) He has advised numerous research projects and collaborated extensively with researchers in biostatistics and computer science. His work contributes to UN Sustainable Development Goals through data science for health and well-being. He leads a research group focused on foundational and applied statistical learning, with ongoing efforts in developing robust, interpretable models for complex scientific data.

Jessica R. Deitzerمشاهده پروفایل

استادیار

• Criminal Decision-Making
• Peer Influence
• Adolescent Delinquency
• +۵ مورد دیگر

Jessica R. Deitzer is an Assistant Professor in the School of Criminology and Criminal Justice at the University of Nebraska Omaha. Her research centers on criminal decision-making, with a focus on how self, social, and environmental contexts shape deviant behavior, particularly among adolescents. Institution: University of Nebraska Omaha School: School of Criminology and Criminal Justice Email: jdeitzer@unomaha.edu Her primary research interests include peer influence, adolescent delinquency, substance use, situational crime theory, and environmental criminology. She examines how immediate social contexts, such as unstructured peer interactions, affect risk-taking and criminal decisions. Recent publications indicate a strong focus on the temporal dynamics of peer effects, including how peer interactions influence short-term mindsets and subsequent behavior. Her work often employs space-time budget data and advanced statistical modeling to understand situational crime patterns. Jessica Deitzer actively collaborates with leading scholars in criminology, including Evelien Hoeben and Jean Marie McGloin, contributing to cutting-edge research on peer dynamics and adolescent behavior.

• Computational Fluid Dynamics
• Aeronautical Systems
• High-Order Numerical Methods
• +۵ مورد دیگر

Dr. Antonios Antoniadis is a Lecturer in Computational Engineering Science and Director of the Computational Fluid Dynamics (CFD) Masters program at Cranfield University. He leads the VR/AR Laboratory within the Aircraft Integration Research Center and a research group focused on computational methods for fluid dynamics. His expertise spans aeronautical systems, turbulence modeling, and renewable energy technologies. Education: BEng in Automotive Engineering, University of Sussex MSc in Mechanical Engineering, University College London PhD in Aerospace CFD, Cranfield University (2013) Research Interests: Dr. Antoniadis develops cutting-edge computational frameworks for engineering challenges. His work includes high-order numerical methods on unstructured grids, data-driven turbulence models, and multi-physics simulations. Applications range from helicopter aerodynamics and wind-farm optimization to supersonic flow analysis and fluid-structure interactions in morphing UAVs. He integrates AI/ML techniques for turbulence modeling and design automation, advancing sustainable aerospace solutions. Publication Trends: Recent articles highlight advancements in rotorcraft CFD, shock absorber fluid dynamics, and high-performance solver development. Predominant themes include validation of high-order schemes for transonic flows, vortex dynamics in hovering rotors, and scalable algorithms for renewable energy systems. His work consistently bridges theoretical computational methods with industrial aerospace applications. Awards: Fellowship of the Higher Education Academy (FHEA) Labs & Teams: Dr. Antoniadis directs a VR/AR laboratory pioneering human-computer interaction for CAE applications. His research group collaborates with industry partners (e.g., BAE Systems, Airbus, Red Bull Racing) on projects involving aerodynamic prediction, multi-phase flows, and HPC parallelization.

Qing Yangمشاهده پروفایل

استاد

• Computer Architectures
• Hardware/Software Designs for AI
• Machine Learning
• +۲ مورد دیگر

Qing Yang is a Professor at the University of Rhode Island's College of Engineering , specializing in Electrical, Computer and Biomedical Engineering. His research spans Computer Architectures , Hardware/Software Designs for AI , Machine Learning , Data Storage , and Computer Networks . Ph.D., Computer Engineering, University of Louisiana, Lafayette, 1988 M.A., Electrical Engineering, University of Toronto, 1985 B.S., Computer Science, Huazhong University of Science and Technology, China, 1982 Yang's work focuses on advanced computing systems, including FPGA optimization, in-sensor processing for ADAS, and secure die-to-die communication architectures. His publications highlight innovations in storage systems, graph processing acceleration, and thermal monitoring for data centers. Recent research trends emphasize AI hardware acceleration , runtime security mechanisms , and smart storage solutions , with articles published in venues like FPGA, IEEE NAS, and ACM Transactions on Storage. Yang has secured multiple National Science Foundation grants for projects such as "Introducing a New In-Sensor Computing Architecture for Intelligent 3-D Imaging Systems." His inventions in data recovery and cache coherence have resulted in 14 U.S. patents , several of which were commercialized through a startup.

• Information Systems
• Big Data Analytics
• Online Customer Reviews
• +۷ مورد دیگر

Wenjie Fan is a Visitor (Faculty) at the Department of Information and Service Management, School of Business, Aalto University, Finland. He holds a Doctor of Science in Economics and Business Administration (majoring in Information Systems Science) from Aalto University, awarded on June 27, 2022. His academic journey includes two master's degrees in Business and Economics from the University of Arizona and City University of Hong Kong, and a bachelor's degree in Engineering and Technology from Beijing University of Posts and Telecommunications. Doctor of Science in Economics and Business Administration (Information Systems Science), Aalto University, 2022 Master's degree in Business and Economics, University of Arizona, 2013 Master's degree in Business and Economics, City University of Hong Kong, 2012 Bachelor's degree in Engineering and Technology, Beijing University of Posts and Telecommunications, 2008 His research focuses on leveraging big data to understand consumer behavior, managerial responses, and digital platform dynamics. Key interests include online customer reviews, crowdfunding, tourism industry analytics, and service management. His work integrates theories from information systems, behavioral economics, and data science to explore how digital interactions shape business outcomes. The most recent articles reflect a consistent trend toward empirical analysis of large-scale datasets from online platforms. These studies employ advanced statistical methods to investigate review helpfulness, environmental impacts on tourism, investor behavior in crowdfunding, and engagement patterns on social media. The research bridges academic rigor with practical implications for businesses and policymakers. No scientific awards were explicitly mentioned in the provided text. There is no public information available about Wenjie Fan’s advisees or research grants. However, his doctoral thesis and publications suggest active involvement in interdisciplinary research projects, likely supported by institutional or collaborative funding. His work has been disseminated through high-impact journals and conferences in information systems and management. Wenjie Fan’s research is conducted within the Department of Information and Service Management at Aalto University’s School of Business. While no specific lab or research group is named, his work aligns with digital service innovation, customer analytics, and sustainable business models — themes central to contemporary information systems research.

• Computer Vision
• Machine Learning
• Artificial Intelligence
• +۵ مورد دیگر

Övgü Öztürk Ergün is an Assistant Professor in the Department of Software Engineering at Muğla Sıtkı Koçman University's Faculty of Engineering. She holds a Ph.D. from The University of Tokyo's Department of Frontier Informatics. Her research focuses on computer vision, machine learning, and AI applications in areas such as augmented reality, cultural heritage digitization, and human behavior analysis. She has led multiple TÜBİTAK-funded projects including 'Customer Tracking and Interest Measurement Technologies for Retail Sector Sales Performance Improvement' and '3D Modeling of Cultural Heritage Sites.' Her work spans academic publications, industry collaborations, and innovative teaching in software engineering and interactive multimedia systems. Education: Middle East Technical University, B.Sc. Electrical & Computer Engineering (2003) University of Tokyo, Ph.D. Frontier Informatics (2010) Research Interests: Her interdisciplinary research integrates computer vision with real-world applications, including privacy-preserving biometrics, smart environment monitoring, and cultural heritage preservation. She has pioneered ontology-based systems for Turkish cuisine and developed AR tools for virtual try-on. Her work emphasizes ethical AI and context-aware systems, with recent focus on food recognition using CLIP models and churn prediction in telecom sectors. Awards & Grants: TÜBİTAK Undergraduate Software Projects Competition (2013) Robot Days 3rd Place (2013) Completed TÜBİTAK 1001/1505 projects totaling over 500k USD funding Advising & Labs: Supervised BENGÜ-ÖZTÜRK's thesis on food image recognition. Active in the University's Research & Application Centers for Computer Vision and AR/VR development. Collaborates with ISTKA and Ministry of Development on cultural heritage digitization projects.

Jason Hartfordمشاهده پروفایل

استاد پژوهشی

• Machine Learning
• Causal Inference
• Deep Learning
• +۳ مورد دیگر

Jason Hartford is a Research Professor and Dame Kathleen Ollerenshaw Fellow specializing in Machine Learning and Robotics. He leads the Research Unit at Valence Labs since April 2023 and holds a Doctor of Science from the University of British Columbia (2021). His research focuses on causal representation learning, causal inference from unstructured data, and deep learning methodologies. Education: Doctor of Science (2021), University of British Columbia, specializing in Architectures and learning algorithms for data-driven decision making. Research Interests: Hartford's work bridges causal inference and machine learning, addressing challenges in representation learning, active learning, and multimodal data integration. He explores how AI can enhance scientific discovery, particularly in biological and medical domains through projects like Virtual Cells and Mendelian randomization applications. Awards: Recipient of the prestigious Dame Kathleen Ollerenshaw Fellowship, highlighting his contributions to causal machine learning and interdisciplinary research. Grants & Projects: Leads the MCAIF: Centre for AI Fundamentals (2021–2026), a collaborative initiative involving over 20 researchers and students, focusing on foundational AI advancements in reinforcement learning, neural networks, and approximation algorithms. Students: Supervises multiple PhD candidates including Hosseinzadeh, Mousa, and Das, fostering innovation in AI-driven scientific exploration.

Bernardo Pereira Nunesمشاهده پروفایل

مدرس ارشد

• Web Science
• Technology-Enhanced Learning
• Semantic Web
• +۷ مورد دیگر

Bernardo Pereira Nunes is a Senior Lecturer and Sub Dean of Educational Programs at the Australian National University (ANU) within the College of Engineering, Computing and Cybernetics. He holds a PhD in Informatics from the Pontifical Catholic University of Rio de Janeiro (PUC-Rio) and has held adjunct roles at PUC-Rio and visiting professorships at institutions such as the University of Pennsylvania. His professional affiliations include the IEEE Technical Committee on Learning Technology and the International Journal of Educational Technology in Higher Education. Education: Bachelor's in Computer Engineering (PUC-Rio, 2006) Master's in Informatics (PUC-Rio, 2009) PhD in Informatics (PUC-Rio, 2014) with a research period at Leibniz Universität Hannover Research focuses on Web Science , Technology-Enhanced Learning , and Semantic Technologies , with emphasis on educational search behavior, bias in information systems, and AI-driven learning tools. Notable projects include the FireMe app (widely reported globally) and the BiasChecker . He has published over 100 papers and serves as an editor/reviewer for top journals like IEEE Transactions on Learning Technologies and conferences such as ACM Hypertext. Key awards include the Senior Fellow of the Higher Education Academy (SFHEA). His work spans educational game design (e.g., Treasure Explorers), social media analysis (e.g., mourning behavior studies), and collaborative tools like TagTheWeb and VocRec. He has collaborated internationally with institutions like L3S Research Center (Germany) and Consiglio Nazionale delle Ricerche (Italy). Grants and labs: Active in securing funding for projects on learning analytics and digital humanities. Leads research teams focused on educational technology innovation and open data applications.