جست‌وجوی استاد و پژوهشگر

• Data Mining
• Big data
• Fuzzy Clustering
• +۱۰ مورد دیگر

HINDAWI Mohammed is a Researcher-Lecturer at CESI (France) specializing in Computer Science with a focus on Data Mining, Big Data, and Machine Learning. He holds a PhD in Computer Science from INSA Lyon (2013) and teaches at the engineering level. His current research emphasizes Dimensionality Reduction in semi-supervised learning and frugal/embedded AI applications. Education: PhD in Computer Science (2013), INSA Lyon MSc in Knowledge and Decision (2008), INSA Lyon BSc in Software Engineering (2005), Aleppo University Research interests include semi-supervised learning, feature selection, ensemble methods, and clustering techniques. He leads the Engineering and Numerical Tools research team and contributes to the Advanced Automation: AI and Data Science group. His work addresses practical applications like patient monitoring via machine learning and battery state estimation. Publications span over 15 peer-reviewed articles in journals like Knowledge and Information Systems (KAIS) and conferences such as IEEE MMSP and ACM CIKM. He supervises PhD students like Trésor Yao Koffi and collaborates on projects involving embedded systems and healthcare tech.

Raimondo Bettiمشاهده پروفایل

استاد

• Structural Health Monitoring
• Civil Engineering
• Structural Dynamics
• +۵ مورد دیگر

Raimondo Betti is a Full Professor and Chair of the Department of Civil Engineering and Engineering Mechanics at Columbia University. He holds affiliations with the Center for 'Sense, Collect and Move Data' and 'Smart Cities.' His expertise spans structural health monitoring, bridge engineering, and damage assessment through vibration analysis. Education: Laurea (magna cum laude) in Civil Engineering from University of Rome 'La Sapienza' (1985), M.Sc. in Structural Mechanics (USC, 1988), Ph.D. in Civil Engineering (USC, 1991) Research focuses on structural dynamics, corrosion effects in infrastructure, and novel monitoring technologies. He pioneered vibration-based damage detection and developed the Substructure Deletion Method. Awards include NSF Young Investigator Award (1995) and Columbia’s Distinguished Teaching Award (1996). His work is funded by NSF, NYC DOT, and FHWA. He leads projects for the Metropolitan Transportation Authority, focusing on NYC bridge dynamics and corrosion monitoring. Key contributions include machine learning approaches for damage detection, cable strain analysis, and resilience strategies for critical infrastructure. Awards: NSF Young Investigator Award, Foreign Specialist Award (Japan), Distinguished Teaching Award, Great Teacher Award Advising and grants: Over 50 journal articles and 100 conference papers, with active roles in ASCE committees and editorial boards. Current projects address main cable corrosion in suspension bridges and AI-driven structural health monitoring.

• Computational Biology
• Machine Learning
• Network Analysis
• +۳ مورد دیگر

Preetam Ghosh, Ph.D. , is a Research Professor in the Department of Computer Science at Virginia Commonwealth University (VCU) , with cross-disciplinary focus in computational biology, machine learning, and network science. His work bridges engineering and biomedical research , particularly in pandemic modeling, multiomics data integration, and bioinformatics tool development. Academic Role: Research Professor, VCU School of Engineering Key Research Areas: Computational Biology, Network Analysis, Pandemic Modeling, Multiomics Data, Bioinformatics Algorithms Recent Research Trends include: Application of machine learning to biomedical data (e.g., drug-target affinity prediction, breast cancer subgroup classification) Development of physics-informed models for pandemic propagation and chemical reaction networks Innovations in network science , such as link prediction and vulnerability analysis for biological and IoT systems Advancing single-cell genomics through consensus algorithms (COFFEE, CHAI, CORTADO) Design of adaptive routing protocols for disaster-resilient IoT networks (ADRIN, ADRIN2.0)

Janusz A. Starzykمشاهده پروفایل

استاد

• Machine Learning
• Neural Networks
• Robotics
• +۷ مورد دیگر

Janusz A. Starzyk is a Professor of Electrical Engineering and Computer Science at Ohio University's Russ College of Engineering and Technology, and holds a concurrent professorship at the University of Information Technology and Management in Rzeszów, Poland. He earned his M.Sc. and Ph.D. in Electrical Engineering from Warsaw University of Technology and a habilitation from Silesian University of Technology. His research focuses on embodied intelligence, machine learning, neural networks, and VLSI design, with notable contributions to self-organizing systems and reinforcement learning. Starzyk has supervised 47 M.Sc. and 16 Ph.D. students, and his work spans over 190 peer-reviewed publications. He leads the Embodied Intelligence Lab at Ohio University and collaborates with institutions worldwide, including Nanyang Technological University and the Institute for Artificial Intelligence in Switzerland. His awards include the Best Research Paper Award from Ohio University and nominations for IEEE Fellow. He has secured $3.8 million in research grants, with projects in GPS signal processing, radar target recognition, and machine learning applications. His patents include innovations in object identification systems and self-organizing learning hardware. Starzyk's expertise bridges academia and industry, with roles as a consultant for companies like Magnolia Broadband and Sarnoff Research.

Mete Ahishaliمشاهده پروفایل

پژوهشگر

• Machine Learning
• Remote Sensing
• Medical Imaging
• +۴ مورد دیگر

Mete Ahishali is a Postdoctoral Researcher affiliated with the School of Forest Sciences at the University of Eastern Finland's Faculty of Science, Forestry and Technology. His research focuses on advanced machine learning techniques applied to remote sensing, medical imaging, and environmental monitoring. He is a core member of the Global Ecosystem Health Observatory (GEHO) research group since January 2024. His technical expertise spans hyperspectral image analysis, SAR image classification, domain adaptation networks, and medical image restoration. Key contributions include developing neural network architectures for tree mortality mapping, anomaly detection in industrial systems, and COVID-19 classification using X-ray images. Recent work demonstrates innovations in autoencoder-based band selection for hyperspectral imagery, self-attention fusion networks for cardiac diagnostics, and operational U-nets for wildfire detection. His research bridges theoretical machine learning advancements with practical applications in environmental science and healthcare. Notable technical skills include deep learning frameworks, remote sensing data processing, and medical imaging modalities. Current projects focus on scalable solutions for large-scale Earth observation data analysis and cross-modal medical imaging systems.

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

Ethem Alpaydin is a Professor of Computer Science at Özyeğin University in Istanbul, Turkey. He is a prominent researcher in machine learning and artificial intelligence, with affiliations including membership in The Science Academy, Turkey, and Academia Europaea. He also holds a fellowship with the Asia-Pacific Artificial Intelligence Association. His research focuses on foundational aspects of machine learning, including statistical methods, neural networks, and deep learning. He has contributed to areas such as generative adversarial networks (GANs), decision pathways in neural networks, and distributed decision trees. His work bridges theoretical advancements with practical applications in domains like natural language processing and computer vision. Alpaydin has authored influential textbooks such as Introduction to Machine Learning and Maschinelles Lernen , which are widely used in academic curricula. His publications emphasize model interpretability, regularization techniques, and cross-lingual learning for languages like Turkish. Awards include recognition from leading scientific institutions for his contributions to AI and data science.

Edith Alice Kovácsمشاهده پروفایل

دانشیار

• Multivariate Statistics
• Probability Graph Models
• Artificial Intelligence
• +۸ مورد دیگر

Edith Alice Kovács is an Associate Professor at the Department of Differential Equations, Budapest University of Technology and Economics (BME). She holds a PhD from the West University of Timisoara (2005) and has taught at multiple institutions, including the General College of Entrepreneurship (ÁVF) and universities in Arad. Her research focuses on multivariate statistics, probability graph models, information theory, AI/ML, network reliability, and mathematical modeling in finance/medicine. She has received the BME Innovation Award 2020 for anomaly detection work. Kovács has presented at conferences worldwide, including the European Conference on Operational Research and the IEEE SACI conference. Her teaching spans subjects like economic modeling, operations research, and data science across BME and ÁVF programs. Education: BSc and MSc in mathematics from the West University of Timisoara (1994), PhD in Mathematics (2005). Professional roles include full-time employment at BME since 2015 and prior experience at Aurel Vlaicu and Vasile Goldis Universities (1994–2000). Research interests include decision-making under uncertainty, metaheuristic algorithms, neural halos, and social network modeling. Recent work explores copula-based anomaly detection and vine copula structures. Kovács has contributed to projects like stochastic inventory modeling for TESCO and VNF fingerprinting for Nokia.

Ronald Haynesمشاهده پروفایل

استاد

• Numerical Analysis
• Scientific Computing
• Partial Differential Equations
• +۴ مورد دیگر

Ronald Haynes is a Full Professor and Chair of the Scientific Computing Graduate Programs at the Department of Mathematics and Statistics, Memorial University of Newfoundland. He holds a PhD from Simon Fraser University (2003) and has held academic roles at Acadia University and Memorial University. His research focuses on numerical methods for partial differential equations, domain decomposition, parallel computing, and optimization, with applications in industrial mathematics. Education: B.Sc. (Hons) in Applied Mathematics (Memorial University, 1996), M.Sc. (Simon Fraser University, 1998), PhD (Simon Fraser University, 2003). Research interests include numerical analysis, scientific computing, and computational methods for PDEs. He has led initiatives such as the 25th International Domain Decomposition Conference and contributed to software libraries like RIDC-DD and DD-CPM. Dr. Haynes is an active editor for CAIMS journals and a leader in the Canadian Applied and Industrial Mathematics Society. Awards include the President's Award for Outstanding Research (2018) and the Dean of Science Distinguished Teaching Award (2017). He advises graduate students in numerical methods and optimization, and collaborates with industry on projects like drilling strength prediction and reservoir simulation. His work also emphasizes inclusive teaching practices and STEM education.

Hien Nguyenمشاهده پروفایل

دانشیار

• Machine Learning
• Artificial Intelligence
• Biomedical Image Analysis
• +۹ مورد دیگر

Hien Nguyen is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Houston's College of Engineering. His research focuses on machine learning, artificial intelligence, and their applications in biomedical image analysis, computational medicine, and drug discovery. He specializes in developing AI systems to enhance radiology workflows, improve diagnostic accuracy, and model radiologists' cognitive processes. His work spans domains like medical imaging segmentation, domain adaptation, and interpretable AI for clinical trust. Nguyen's research integrates multimodal data from LIDAR, satellite imagery, and histopathology slides to address challenges in urban mapping, kidney disease classification, and cellular analysis. He has contributed to frameworks like MIRAM for breast lesion risk prediction and ItpCtrl-AI for radiologist intent modeling. His team explores meta-learning, self-supervised learning, and diffusion models for tasks ranging from apoptosis forecasting to seismic data interpolation. Key technical strengths include domain invariant learning, medical dataset synthesis (e.g., MorphSet), and explainable AI systems. His work bridges human expertise with machine intelligence, emphasizing clinical adoption through collaboration between radiologists and AI algorithms.

Ioana Cristina PLAJERمشاهده پروفایل

مدرس

• Image processing
• Multispectral imaging
• Artificial Intelligence
• +۷ مورد دیگر

Ioana Cristina PLAJER is a Lecturer at the Department of Mathematics and Computer Sciences, Faculty of Mathematics and Computer Sciences, Transilvania University of Brașov. Her work focuses on image processing, multispectral imaging, artificial intelligence, machine learning, formal languages, and compiler design. She has contributed to both theoretical and applied research, including advancements in spectral data analysis, medical imaging segmentation, and automata theory applications in diverse fields like dog breeding risk assessment and game design. Dr. PLAJER's research explores the intersection of computational methods and real-world challenges, particularly in remote sensing, medical diagnostics, and compiler optimization. Her publications span topics such as satellite data fusion, neural cellular automata for texture synthesis, and deep learning models for improving X-ray angiogram quality. She actively participates in international conferences, presenting work at venues like the IEEE Emerging Technologies and Factory Automation (ETFA) and the International Symposium on Signals, Circuits and Systems (ISSCS). Her technical contributions include developing algorithms for NDVI computation from hyperspectral imagery, creating datasets like DACIA5 for agricultural monitoring, and advancing techniques for tumor segmentation in CT scans. These efforts highlight her commitment to bridging algorithmic innovation with practical applications in healthcare and environmental science.

• Machine Learning
• Pattern Recognition
• Computer Vision
• +۵ مورد دیگر

David M. J. Tax is a researcher affiliated with Delft University of Technology, Netherlands. His work spans machine learning, pattern recognition, and computer vision, with a focus on neural networks, anomaly detection, and medical image analysis. He has collaborated extensively with researchers like Marco Loog, Robert P. W. Duin, and Marcel J. T. Reinders on topics including multiple instance learning, dissimilarity-based methods, and physics-informed neural networks. Key research contributions include advancements in incremental learning for neural networks, personalized anomaly detection in biomedical signals, and frameworks for evaluating time-series anomalies. His recent work (2023–2025) emphasizes physics-informed neural networks, stochastic scheduling algorithms, and proximity-aware evaluation techniques. Tax’s publications reflect a strong emphasis on interdisciplinary applications, integrating computer science with biomedical engineering and operations research. His methodologies often address challenges in scalability, robustness, and interpretability, with applications ranging from healthcare monitoring to automated project scheduling. Tax maintains an active presence in top-tier conferences like AAAI, ICPR, and NeurIPS, contributing to both theoretical foundations and practical implementations of machine learning systems.

Gemma Moranمشاهده پروفایل

استادیار

• Bayesian Statistics
• Machine Learning
• Deep Generative Models
• +۴ مورد دیگر

Gemma Moran is an Assistant Professor in the Statistics Department at Rutgers University. Her office is located at Hill Center 475, 110 Frelinghuysen Road, Piscataway, NJ 08854. She teaches courses including MSDS597 Data Wrangling with Python, MSDS534 Statistical Learning for Data Science, and STAT588 Data Mining. Dr. Moran received her PhD in Statistics from the University of Pennsylvania, where she was advised by Edward George and Veronika Rockova. Prior to joining Rutgers, she was a postdoctoral researcher at the Columbia Data Science Institute working with David Blei. Dr. Moran's research focuses on developing flexible Bayesian models for analyzing high-dimensional data. Her recent work includes developing identifiable and interpretable deep generative models, particularly variational autoencoders, and creating improved tools for Bayesian model criticism. Her methodological contributions bridge statistics, machine learning, and data science, with applications spanning computer vision, biomedical imaging, and materials science. Analysis of Dr. Moran's publication record reveals a strong emphasis on Bayesian methodology, particularly in variable selection (spike-and-slab approaches), deep generative modeling, and model criticism. Her work consistently addresses the challenge of maintaining statistical rigor while developing practical machine learning solutions, with a growing focus on model interpretability and identifiability in complex models. Dr. Moran actively mentors students at multiple levels. Prospective PhD students are encouraged to apply to the Rutgers Statistics PhD program and mention her name. For Masters of Data Science students, she supervises capstone projects requiring independent revisions of group work using different statistical methods or datasets. She also encourages students interested in research to take her MSDS-534 Statistical Learning for Data Science course as preparation for research projects.

Gemma Roigمشاهده پروفایل

استاد

• Computer Vision
• Artificial Intelligence
• Neuroscience
• +۷ مورد دیگر

Gemma Roig is a Professor of Brain-inspired Artificial Intelligence at the Computer Science Department of Goethe University Frankfurt since 2020. She holds an honorary research affiliate position at MIT's Center for Brains Minds and Machines and is a member of hessian.ai. Her research focuses on computational models of human vision and their application to AI systems. Previously, she served as an Assistant Professor at Singapore University of Technology and Design (SUTD) and conducted postdoctoral research at MIT, exploring connections between AI and neuroscience. Education: BEng and MSc in Telecommunication Engineering from Ramon Llull University (Barcelona), PhD in Computer Vision from ETH Zurich, and extensive postdoctoral training at MIT and EPFL. Career highlights include establishing a research group in Singapore and securing a tenure position in Frankfurt after relocating for family reasons. Research interests span computational vision, neural networks, and the intersection of AI with human cognition. She leads the CVAI lab, developing tools like Net2Brain to bridge artificial and biological neural systems. Her work emphasizes ethical AI, explainability, and multimodal learning. Labs/Teams: Director of the CVAI lab; collaborator with MIT's CBMM and Ernst Strüngmann Institute. Active in interdisciplinary projects like the Algonauts Challenge. Supervises 10+ PhD students and postdocs in topics ranging from neuro-symbolic AI to molecular dynamics simulations. Grants & Funding: Supported by DFG, ERC, and DAAD scholarships. Net2Brain receives funding from German Research Foundation (DFG) and European Research Council (ERC).

• Optics
• Materials Science
• Biophysics
• +۳ مورد دیگر

Julie Bouhy is a Researcher at the University of Namur, affiliated with the Faculty of Sciences, Institute of Structured Matter, and the Research Unit in Physics. She holds a Doctorate in Sciences. Her research focuses on optics and materials science, particularly in birefringence imaging, polarized light microscopy, and quantitative analysis of anisotropic media. She has collaborated internationally and applied her methods to interdisciplinary fields like historical document analysis and animal species identification in parchments. Her work emphasizes topological data analysis and machine learning integration. Active in academic events, she participated in symposia on quantum technologies and exhibitions on historical parchment studies. Notable contributions include peer-reviewed articles and conference presentations.

• Machine Learning
• Data Science
• Computer Vision
• +۳ مورد دیگر

Dr. Andreas Streich is a Lecturer at the Department of Computer Science at ETH Zurich. His research focuses on Machine Learning, Data Science, and their applications in fields like medical imaging and signal processing. He teaches courses including Computer Science I, Building ML/AI Applications, and Data Science & Machine Learning. His work spans neural networks, clustering algorithms, and multi-label classification. He has contributed to advancements in medical diagnostics via deep learning and acoustics-based signal processing. No scientific awards or student advisees are explicitly mentioned in the provided texts.