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Richard CrowleyView profile

Assistant Professor

• Financial Accounting
• Text Analysis
• Voluntary Disclosure
• +9 more

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.

Oubay HassanView profile

Professor

• Computational Fluid Dynamics
• Computational Electromagnetism
• Unstructured Mesh Generation
• +3 more

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 JeongView profile

Assistant Professor

• Computer Science
• Robotics
• Artificial Intelligence
• +7 more

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
• +4 more

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 OliverView profile

Professor

• Mathematical Fluid Dynamics
• Partial Differential Equations
• Numerical Analysis
• +3 more

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 ShenView profile

Professor

• Machine Learning
• Data Science
• Statistical Inference
• +12 more

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.

• Computational Fluid Dynamics
• Aeronautical Systems
• High-Order Numerical Methods
• +5 more

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 YangView profile

Professor

• Computer Architectures
• Hardware/Software Designs for AI
• Machine Learning
• +2 more

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.

• Computer Vision
• Machine Learning
• Artificial Intelligence
• +5 more

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.

Bernardo Pereira NunesView profile

Senior Lecturer

• Web Science
• Technology-Enhanced Learning
• Semantic Web
• +7 more

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.

Adrien BousseauView profile

Researcher

• Computer Graphics
• Image Processing
• Non-Photorealistic Rendering
• +5 more

Adrien Bousseau is a Senior Researcher at Inria within the GraphDeco group at Université Côte d'Azur. He earned his PhD from Inria Rhône-Alpes under Joëlle Thollot and François X. Sillion, with internships at Adobe and MIT. His postdoctoral research at UC Berkeley focused on computational design and graphics. Notable roles include leading the ANR DRAO project (2012–2015) and coordinating the CRISP associate team with UC Berkeley. He has received prestigious awards such as the Eurographics 2011 PhD Award and ERC grants for projects on 3D design and circular design. Research interests span image creation, stylization, vector graphics, and sketch-based modeling. Recent work explores computational methods for circular design and AI-driven design tools. He has supervised over 13 PhD students, including Nicolas Rosset (aerodynamics) and Emilie Yu (VR sketching). Key projects include ERC-funded initiatives on drawing interpretation and material acquisition. Service contributions include co-chairing Eurographics 2025 and EGSR 2021, and editorial roles at Computer Graphics Forum. Publicly accessible tools include Photoshop watercolor filters and academic resources like BendFields tutorials.

Mengjie XuView profile

Assistant Professor

• Financial Accounting
• Corporate Governance
• Information Economics
• +4 more

Mengjie Xu is an Assistant Professor of Accounting at Duke University's Fuqua School of Business. Her research focuses on financial accounting, corporate governance, and information economics, particularly analyzing how information creation and dissemination influence market participant strategies and outcomes. She holds a background from Frankfurt School of Finance & Management. Key research areas include high-frequency data analysis, social media-driven insights (e.g., Reddit, Twitter), EDGAR database tracing, and Glassdoor-based workplace studies. She has developed methodologies for parsing satellite imagery, short-sale data, and SEC filings. Technical expertise spans Python, Stata, SAS, and API integrations for data collection. Her work bridges accounting theory with practical data science, emphasizing replicable workflows for complex datasets. No scientific awards are explicitly mentioned in the sources provided.

Kristof CoussementView profile

Visiting Professor

• Business Analytics
• Data Science
• Text Analytics
• +3 more

Kristof Coussement serves as a Visiting Professor at the University of Western Australia's Business School within the Marketing department, while holding a primary position as Professor of Business Analytics at France's IÉSEG School of Management. His expertise centers on developing business-value creating data science frameworks through advanced analytics methodologies. His research specializes in integrating textual data into predictive modeling using text analytics and deep learning, with applications spanning banking, retail, social media, and telecommunications. Key contributions include innovative decision support systems that transform unstructured data into actionable business insights, particularly in customer relationship management and political content analysis. Recent publications demonstrate a concentrated focus on social media dynamics, digital retail engagement, and advertising effectiveness through graphic design elements. These works consistently employ machine learning techniques to decode complex human behaviors in online environments, revealing patterns in political polarization, consumer decision-making, and emotional responses to visual stimuli. Professor Coussement chairs the IÉSEG Center for Marketing Analytics (ICMA) and leads multiple industry research chairs with Crédit Agricole, Enfocus, and Oney Bank. His executive education initiatives have solved data science challenges for global companies including Amazon, AXA, and Orange, translating academic research into practical business solutions across eight industry sectors.

Francis OgokeView profile

Assistant Professor

• Artificial Intelligence
• Deep Learning
• Physics-Informed Machine Learning
• +7 more

Francis Ogoke is an incoming Assistant Professor in the Department of Mechanical Engineering at Carnegie Mellon University, set to begin in Fall 2025. He is currently a postdoctoral associate at the Massachusetts Institute of Technology. His academic journey includes a Ph.D. in Mechanical Engineering from Carnegie Mellon University (2024) and a B.S.E. in Chemical and Biological Engineering from Princeton University (2019). His research lies at the intersection of artificial intelligence and engineering systems, with a focus on developing foundational AI methods for complex engineering problems. Key areas include: Physics-informed deep learning Uncertainty quantification and probabilistic modeling Representation learning for generalization Applications in additive manufacturing, digital twins, and cyber-physical systems The recent articles reflect a strong trend in leveraging deep learning—especially vision transformers, generative models, and reinforcement learning—for accelerating simulations, enhancing in-situ monitoring, and improving control in additive manufacturing. His work consistently bridges AI innovation with real-world engineering challenges, particularly in metal 3D printing and multiphysics modeling. Notable scientific awards include: Presidential Fellowship in the College of Engineering, Carnegie Mellon University G.E.M. Fellowship Francis Ogoke advises emerging researchers and is expected to lead a research group focused on AI-driven engineering systems. His lab will likely focus on developing intelligent frameworks for digital twins and autonomous manufacturing. He has not yet advised any students as per current records. He is actively involved in pioneering research that integrates AI into core engineering workflows, supported by advanced computational and experimental infrastructure. He is affiliated with the College of Engineering at Carnegie Mellon University and conducts research relevant to advanced manufacturing, sensing technologies, and intelligent systems.

Lu HeView profile

Assistant Professor

• Health Informatics
• Human-Computer Interaction
• Natural Language Processing
• +3 more

Lu He is an Assistant Professor in the Department of Health Care Informatics at the University of Wisconsin-Milwaukee (UWM). She holds a PhD in Informatics from the University of California, Irvine (2023) and a BS in Computer Science from the University of Minnesota (2017). Her research focuses on leveraging computational methods like Natural Language Processing (NLP) to analyze health-related data from social media, Electronic Health Records (EHR), and clinical notes. Dr. He’s work examines temporal trends in public health attitudes, patient concerns about healthcare quality, and social determinants of health in rare cancer cases. She has developed NLP pipelines for extracting clinical information and evaluating sentiment analysis tools. Recent Research Trends Large Language Models (LLMs) for healthcare worker burnout analysis Social media image analysis during pandemics AI explainability in health news evaluation Telehealth implementation in developing countries Clinical NLP applications for veterans' health Scientific Awards Editor’s Choice, JAMIA (2021) Graduate Dean’s Dissertation Fellowship, UCI (2022) Best Poster Award, SoCal NLP Symposium (2019) Multiple student paper nominations at AMIA and MedInfo conferences Dr. He serves on the Student Editorial Board for JAMIA and reviews for health informatics journals. She is committed to reducing biases in computational health methods and improving patient outcomes through interdisciplinary collaboration.