جست‌وجوی استاد و پژوهشگر

• Educational Technology
• Technology-Enhanced Learning
• Learning Design
• +۴ مورد دیگر

Dr. Amanda Gigliotti is a Senior Lecturer at the School of Education, University of Wollongong , with expertise in technology-enhanced learning and teacher design practice. Her research investigates how educators integrate technology into pedagogy, focusing on design decisions and professional development. Current role (2025–present): Senior Lecturer, University of Wollongong Previous appointments: Lecturer (2023–2024), Career Development Fellow (2020–2023) PhD in Education (2020), Bachelor of Primary Education (Honours) (2011) Her work emphasizes inclusive digital learning , with grants totaling $292,797.25 as lead investigator. She co-authored 6 journal articles, 2 book chapters, and 2 reports, often collaborating on topics like online curriculum design and WIL simulations. Key awards include the University of Wollongong's Vice Chancellor (Early Career) Award for teaching excellence. She chairs UOW’s Early Career Researcher Committee and founded ASSH’s Early Career Academic Network, mentoring over 100 international academics.

• Machine Learning
• Artificial Intelligence
• Virtual Reality
• +۳ مورد دیگر

Simon Colreavy Donnelly is an Associate Professor in the Department of Computer Science & Information Systems at the University of Limerick. He is a member of the Interaction Design Centre and focuses on interdisciplinary research at the intersection of artificial intelligence, educational technology, and healthcare informatics. His work spans machine learning applications in medical data analysis, virtual reality (VR) and extended reality (XR) for inclusive education, and deep learning techniques in chemical analysis and spectroscopy. Research Interests: His primary areas of investigation include generative AI for education equity, semisupervised learning algorithms, virtual learning environments design, and the ethical deployment of immersive technologies in healthcare and palliative care. He also explores NMR spectroscopy analysis using deep learning and develops tools for nutritional content estimation through image processing. Collaborations: His recent collaborations span international teams addressing challenges in toxicity-free online discourse (PAN 2024), semisupervised learning distribution mismatches, and VR applications for post-pandemic blended learning. His work integrates computational methods with real-world applications in education, healthcare, and chemical analysis. Labs/Teams: Active within the Interaction Design Centre at UL, his research group develops practical solutions for accessibility in digital education and healthcare systems, emphasizing user-centered design principles for extended reality applications.

Georg Langsمشاهده پروفایل

استاد

• Machine Learning
• Medical Imaging
• Precision Medicine
• +۳ مورد دیگر

Georg Langs is a Full Professor of Machine Learning in Medical Imaging at the Medical University of Vienna and Founding Director of the Computational Imaging Research Lab (CIR). He leads a 25-member interdisciplinary team focusing on machine learning methodologies for medical image analysis. Key roles include Director of the Joint Initiative on AI in Medical Imaging (European Institute of Biomedical Imaging Research) and Scientific Lead of the Respiratory Disease Phenotype Observatory (ZODIAC, UNO/IAEA). He is affiliated with MIT’s CSAIL and serves on advisory boards for global AI initiatives. Education: PhD in Computer Science, Graz University of Technology (2007) M.Sc. in Mathematics, Vienna University of Technology (2003) Research Interests: Machine learning-driven precision imaging, neuroimaging, clinical data phenotyping, and cross-species brain connectivity analysis. His work bridges imaging biomarkers with biological mechanisms and large-scale clinical data integration. Grants & Funding: Over €6M in competitive grants as Principal Investigator in the last two years. Projects include ARTEMIS (fatty liver disease digital twins) and AI-POD (personalized risk scores via imaging). Awards: 2022 IS3R Emerging Leaders Club 2022 National Academy of Medicine Emerging Leader Programme 2018 Advisor, AI Mission Austria 2030 Lab & Teams: CIR Lab focuses on AI-driven medical imaging solutions. Co-founded contextflow GmbH , a MedUni spin-off developing AI software for imaging analysis.

Patrick T. Brandtمشاهده پروفایل

استاد

• Political Dynamics
• Time Series Analysis
• Machine Learning
• +۸ مورد دیگر

Patrick T. Brandt is a Professor of Political Science, Public Policy, and Political Economy at the University of Texas at Dallas , affiliated with the School of Economic, Political and Policy Sciences . His work integrates advanced statistical methods with political science, focusing on time series analysis, machine learning, and Bayesian modeling to study political dynamics. His research spans international relations , political economy , terrorist targeting , and conflict forecasting . He specializes in developing novel models for event count time series, including the Bayesian Poisson Vector Autoregression and MS-BVAR packages for R. His NSF-funded projects focus on event data generation and real-time conflict forecasting. Recent publications emphasize domain-specific language models (ConfliBERT variants), graph neural networks for conflict prediction, and machine translation challenges in political text analysis. He maintains the OpenEvent Data Repository and develops software like MSBVAR and PESTS for academic use. Scientific Awards : Robert H. Durr Award for Best Methodology Paper, Midwest Political Science Association (2006)

Afshin Ashariمشاهده پروفایل

استادیار

• Computational Design
• Mixed-Reality Environments
• Public Space Design
• +۶ مورد دیگر

Afshin Ashari is an Assistant Professor in Landscape Architecture at the School of Environmental Design and Rural Development , University of Guelph. Prior to academia, he worked at BrookMcIlroy Inc., an interdisciplinary firm in Toronto, on architectural and landscape projects in public and private sectors. Education : Masters in Landscape Architecture, University of Toronto Bachelor of Computer Engineering, Azad University of Tehran Research Interests : Afshin explores the intersection of computational design and mixed-reality environments, focusing on: Art-Technology Unity in Public Spaces Algorithmic and Parametric Modeling Data-Driven Design Approaches Interactive Immersive Environments Biophilic Design Agricultural Urbanism Article Trends : His publications emphasize: AI tools for design processes Parametric modeling in urban rehabilitation Climate change communication via social media Drones for visual impact assessments Augmented reality in public spaces Historical and future-oriented design frameworks

• Neural Video Communication
• Smart Cameras
• Computational Vision Models
• +۳ مورد دیگر

Zhan Ma is a Professor and PhD Advisor at the School of Electronic Science and Engineering, Nanjing University. He leads research in Neural Video Communication, Smart Cameras, and Computational Vision Models. His work focuses on end-to-end learning for compression, networking, and hardware-software co-design. Dr. Ma holds a PhD from New York University's Tandon School of Engineering (2010), and prior to his current role, he served as Senior Staff Researcher at Huawei (2013-2015) and Senior Researcher at Samsung (2011-2013). Research highlights include pioneering work in point cloud compression (adopted into IEEE standards) and dual-camera systems for high-resolution video acquisition. His algorithms are deployed in WeChat/WeChat Video for rate-quality optimization and in ISO standards for video complexity indicators. Recent work emphasizes machine learning-driven approaches for image/video compression and adaptive streaming frameworks. Honors include the 2023 IEEE CAS Society Outstanding Young Author Award and multiple best paper awards at IEEE WACV, BMSB, and other venues. He leads the Vision Lab at Nanjing University and collaborates with industry partners on practical implementations of his research.

• Radiomics
• Medical Imaging Analysis
• Machine Learning in Healthcare
• +۷ مورد دیگر

Adrien Depeursinge is a Professor at HES-SO Valais-Wallis - Haute Ecole de Gestion, affiliated with the School of Economics and Services and the Management Information Systems department. His research focuses on radiomics, personalized medicine via image-based analysis, and clinical workflow optimization. He leads the development of the QuantImage platform, a physician-centered web-based tool for radiomics research, and contributes to radiomics standardization efforts through initiatives like the Image Biomarker Standardization Initiative (IBSI). His work emphasizes machine learning applications in healthcare, including tumor segmentation, biomarker extraction, and improving diagnostic accuracy through computational models. Key research themes include: 1) Radiomics – developing quantitative imaging features for cancer diagnosis/prognosis; 2) Medical Imaging Analysis – advancing texture-based models, multi-modal fusion, and automated lesion detection; 3) Physician-AI Collaboration – designing user-centric tools for clinical integration. His contributions span neuro-oncology (brain metastases), head-and-neck cancer, and multiple sclerosis imaging. Publications emphasize methodological advancements (e.g., kernel optimization in CNNs, steerable detectors) and clinical validation (e.g., reproducibility of radiomics features across imaging protocols). He collaborates with institutions like the University Hospital of Lausanne (CHUV) and international teams on projects like the HECKTOR challenge for PET/CT tumor segmentation. His work bridges technical innovation with clinical impact, aiming to translate radiomics into actionable clinical tools. QuantImage v2, his flagship tool, enables no-code development of machine learning models using clinical imaging data. Research also includes phantom-based validation of radiomics features and addressing challenges in feature stability across imaging modalities. Current projects explore improving contour quality for radiomics studies and optimizing AI explainability in medical decision-making.

Claire Dambrinمشاهده پروفایل

استاد

• Management Control
• Accounting
• Performance Measurement
• +۳ مورد دیگر

Claire Dambrin is a Professor of Management Control at ESCP Business School, specializing in qualitative and critical research rooted in the social studies of accounting and control. She investigates performance measurement tools, including accounting systems and incentive frameworks, with a focus on gendered aspects and feminist perspectives. Her work explores how these tools can both empower and constrain individuals. She earned her PhD from Paris Dauphine University in 2005 and previously served as a professor of accounting at HEC Paris (2004–2012). Her research contributions span leading journals in accounting and management. She holds editorial roles at Accounting, Organizations and Society , Management Accounting Research , and Critical Perspectives on Accounting , and co-edits Comptabilité Contrôle Audit / Accounting Auditing Control . Her research interests include performance measurement systems, organizational sociology, and gender studies in business contexts. She critically examines how metrics shape organizational behavior and societal structures, particularly in the pharmaceutical industry and professional service sectors. Her recent work interrogates sustainability, ethical dimensions of control, and technological mediation in management practices. Key projects address topics such as ERP system lifecycle dynamics, CRM software adoption barriers, and the sociopolitical implications of quantification. Her contributions bridge theoretical frameworks (e.g., Latour’s actor-network theory) with empirical insights to challenge conventional managerial practices.

Marcus Dillenderمشاهده پروفایل

استادیار

• Health Economics
• Labor Economics
• Public Economics
• +۳ مورد دیگر

Dr. Marcus Dillender is an Assistant Professor in the Department of Economics at Vanderbilt University, a Faculty Research Fellow at the National Bureau of Economic Research (NBER), and a Research Fellow at the IZA Institute of Labor Economics. His research focuses on the intersection of health, labor, and public economics, with particular attention to occupational health, workers' compensation insurance, and healthcare labor markets. PhD in Economics from the University of Texas at Austin (2013) Prior affiliations: W.E. Upjohn Institute for Employment Research, University of Illinois at Chicago School of Public Health Dr. Dillender's recent work investigates gender disparities in medical evaluations and policy impacts on healthcare markets . He has also studied the effects of air pollution on workplace safety , computerization on employment , and public health funding efficacy . His research appears in journals such as the Journal of Health Economics and Journal of Public Economics. Office hours: Tuesdays, 3-5pm at Vanderbilt University.

Jayson Pauloseمشاهده پروفایل

دانشیار

• Theoretical Soft Matter Physics
• Biophysics
• Evolutionary Dynamics
• +۳ مورد دیگر

Jayson Paulose is an Associate Professor of Physics at the University of Oregon, affiliated with the College of Arts and Sciences and the Institute for Fundamental Science. His research spans theoretical soft matter physics, biophysics, evolutionary dynamics, and metamaterials design, focusing on the intersection of topology, geometry, and statistical mechanics in artificial and biological systems. Academic Roles: Faculty member in the Department of Physics, Director of the MSTC Program. Research Themes: Designer matter (topological and active metamaterials), biological systems (elasticity of thin shells, evolutionary genetics), and mechanical principles in soft structures. Collaborations: Partners with experimentalists and theorists in physics, materials science, and biology at UO and institutions like Leiden University. Lab: Paulose Group, which includes graduate and undergraduate researchers, explores problems such as topological protection in metamaterials and genetic propagation in populations. Research Interests include: Topological soft matter: Using symmetry and geometry to design materials with robust mechanical properties. Evolutionary dynamics: Modeling the impact of long-range dispersal on genetic diversity and population structure. Elastic mechanics: Analyzing thin shells and membranes relevant to biological systems. Active matter: Studying non-equilibrium systems like rotating dimer particles and synthetic metamaterials. Publications reflect his interdisciplinary focus, with recent work (2023–2024) on mechanical metamaterials, elastic shells, and stochastic population genetics, and earlier contributions to active spinner materials and topological protection in biological systems. Education & Training of students in his lab emphasizes theoretical rigor and computational techniques, with alumni transitioning to postdoctoral positions and industry roles in mechanical engineering, data science, and software development. Contact: jpaulose@uoregon.edu | Office: 375 Willamette Hall, University of Oregon.

Steffi Colyerمشاهده پروفایل

مدرس ارشد

• Biomechanics
• Sports Biomechanics
• Motion Analysis
• +۷ مورد دیگر

Steffi Colyer is a Senior Lecturer in Biomechanics at the Department for Health, University of Bath. She is affiliated with the Centre for Health and Injury and Illness Prevention in Sport and the Bath Institute for the Augmented Human. Her research is supported by major grants from EPSRC and ESA, focusing on elite athletic performance, rehabilitation, and motion analysis technologies. Her research interests center on biomechanics of athletic performance, particularly in sports such as skeleton, badminton, and sprinting. She investigates the kinetic and kinematic determinants of elite performance, develops markerless motion capture systems for real-world analysis, and applies musculoskeletal modelling to understand internal loading and adaptation in normal and simulated gravity environments. Her work bridges sports science, engineering, and rehabilitation. The recent trend in her publications shows a strong focus on markerless motion analysis, pose estimation, musculoskeletal modelling, and the biomechanics of sprinting and racket sports. She leverages advanced computational methods, including deep learning and in silico simulations, to improve performance analysis and injury prevention. Her scientific awards include: ISBS New Investigator Award finalist (oral) (co-author), 2022 Departmental Staff Award for Innovation in Learning and Teaching, 2025 She has supervised multiple research students and projects, including PhD and postdoctoral work, and is actively involved in peer review for journals such as Journal of Sports Sciences , Scientific Reports , and Journal of Biomechanics . She leads the IAA project on markerless motion capture for skeleton push-start analysis and contributes to the CAMERA initiative, a major interdisciplinary research center focused on motion analysis and virtual reality applications. Her research is conducted within the Centre for the Analysis of Motion, Entertainment Research and Applications (CAMERA), where she collaborates with computer scientists, engineers, and sports scientists to develop and apply cutting-edge motion capture technologies in real-world settings.

Tommy Löfstedtمشاهده پروفایل

دانشیار

• Machine Learning
• Computer Vision
• Medical Image Analysis
• +۴ مورد دیگر

Tommy Löfstedt is an Associate Professor at Umeå University , affiliated with the Department of Computing Science and the Department of Mathematics and Mathematical Statistics. His research focuses on machine learning , computer vision , and medical image analysis , with applications in life sciences, radiation therapy, and biomedical imaging. He leads multiple research projects, including AI-driven delineation in radiation therapy, quantitative MRI for radiotherapy, and machine learning for plant nutrient uptake. Current research emphasizes structured regularization methods to improve model interpretability and robustness. Key applications include medical image segmentation , Alzheimer's classification , and uncertainty estimation in MRI . Recent publications highlight his work on morphological regularization , adversarial attack mitigation , and multi-task learning in medical imaging contexts. His projects span 2022–2026 with funding for pediatric oncology automation and gynecological cancer staging. Affiliated with both computing and mathematical departments, he bridges algorithm development with applied mathematical frameworks in medical and life science domains.

• Wireless Communications
• Network Security
• Quality of Experience (QoE)
• +۷ مورد دیگر

Tasos Dagiuklas is a Professor in the Department of Computer Science and Technology within the School of Engineering and Technology at the University of Bedfordshire. With over 168 publications spanning from 1995 to 2025, he has established himself as a leading researcher in telecommunications and network systems. His extensive publication record demonstrates continuous scholarly contribution across multiple decades in the field. Professor Dagiuklas' research focuses on wireless communications, edge computing, 5G/6G networks, quality of experience (QoE), and federated learning . His work bridges theoretical networking concepts with practical applications, particularly in multimedia delivery and security. He has developed significant expertise in video streaming optimization, network security mechanisms, and resource management in emerging network architectures. His research consistently addresses the evolving challenges of modern communication systems, with recent work increasingly focusing on AI integration in networking. Analysis of his recent publications (2023-2025) reveals a strong trend toward edge computing, federated learning, and security applications in next-generation networks. His work demonstrates a strategic shift from traditional networking concerns to more complex systems involving AI integration, energy efficiency, and heterogeneous environments. The publications show consistent collaboration with researchers across multiple institutions, with particularly strong partnerships with Muddesar Iqbal, Ilias Politis, and Stavros Kotsopoulos. Professor Dagiuklas has made substantial contributions to the academic community through his extensive publication record in high-impact venues including IEEE journals and conferences. His work has evolved from foundational networking research to cutting-edge investigations of AI-enhanced communication systems, reflecting the broader trajectory of the field itself. His research demonstrates both technical depth in specific networking challenges and breadth across multiple application domains.

Jeeseop Kimمشاهده پروفایل

استادیار

• Robotics
• Autonomy
• Control Theory
• +۳ مورد دیگر

Jeeseop Kim is an Assistant Professor in the Department of Aerospace and Mechanical Engineering at The University of Texas at El Paso (UTEP), College of Engineering, specializing in robotics, autonomy, and control theory. His research focuses on safety-critical planning and control, with emphasis on bipedal/quadrupedal locomotion, hybrid dynamical system control, and whole-body planning and control. Education: B.S. in Mechanical and Aerospace Engineering, Seoul National University (2014) M.S. in Intelligence and Information (Robotics), Seoul National University (2017) Ph.D. in Mechanical Engineering, Virginia Tech (2022) Postdoctoral Scholar, Mechanical and Civil Engineering, Caltech (2022–2025) His research spans safety-critical control systems for legged robots, including obstacle-aware nonlinear model predictive control (MPC), control barrier functions, and distributed coordination algorithms. Recent work explores adaptive delay estimation, tactile sensing for robotic grasping, and hardware-software co-design for humanoid robots. Key article trends highlight advancements in autonomous inspection robotics, hybrid control architectures, and real-time planning for quadrupedal systems. His work integrates control theory with practical applications in industrial and healthcare domains. Awards: ASME DSCD Rudolf Kalman Best Paper Award (2022) IEEE ICRA Outstanding Paper Award (2023) Jeeseop teaches MECH 4332: Mechanical Computational Applications in Vision and Robotics (Fall 2025). He actively recruits Ph.D. students for Spring/Fall 2026 and seeks motivated undergraduates/MS students with skills in robotics kinematics, programming (C/C++, Python, MATLAB), and CAD design. The AIGIS Lab welcomes applicants with interests in robotics, controls, and autonomous systems.

Andrew O. Arnoldمشاهده پروفایل

استاد مدعو

• Machine Learning
• Natural Language Processing
• Quantitative Trading
• +۵ مورد دیگر

Andrew O. Arnold is a Principal Applied Machine Learning Engineer at Shopify and an Adjunct Professor at New York University's Tandon School of Engineering, Department of Finance and Risk Engineering. He earned his Ph.D. in Machine Learning from Carnegie Mellon University and a BA in Computer Science and Artificial Intelligence from Columbia University. Education Ph.D., Machine Learning, Carnegie Mellon University BA, Computer Science and Artificial Intelligence, Columbia University His research focuses on robust machine learning , developing models that perform well in low signal-to-noise regimes, handle distributional shifts (transfer learning), and extract features from unstructured data. Key applications include time series analysis and natural language processing in financial and other domains. Recent publications highlight work on large language models (LLMs) for code generation, including multitask pretraining, contrastive learning, and quantization techniques for efficiency. He has contributed to understanding model robustness and adapting NLP methods to dynamic market conditions. Arnold teaches NYU FRE GY 7871: News Analytics and Machine Learning , covering NLP and ML techniques for quantitative trading strategies. The course emphasizes practical applications of sentiment analysis, text relevance, and novelty detection in financial contexts. He has led teams at Amazon Web Services (AI Labs), served as Chief Scientist at Oracle Alpha, and worked at Microsoft Research, IBM Research, and other institutions. His technical expertise spans code generation , anomaly detection , and NLP for commerce , with patents in these areas.