جست‌وجوی استاد و پژوهشگر

André Niemannمشاهده پروفایل

استاد

• Hydraulic Engineering
• Water Management
• Flood Protection
• +۶ مورد دیگر

Professor André Niemann at the University of Duisburg-Essen's Institute of Hydraulic Engineering and Water Management is a leading expert in water resources management, focusing on flood protection, dam control systems, and AI-driven hydrological forecasting. His work bridges practical engineering challenges with advanced data science applications. Academic Leadership: Coordinated projects like interSim (interactive simulation for vocational training) and PROWAVE (forecast-based dam control) Research Impact: Pioneered ensemble optimization methods for reservoirs and LSTM models for inflow forecasting Technological Innovation: Developed AI frameworks for sensor data quality control in water management His research addresses critical intersections between hydraulic engineering and climate resilience, with recent projects analyzing flood forecasting systems ( HÜProS ), urban drainage optimization, and sustainable hydropower solutions using legacy mining infrastructure. Collaborations span institutions like Harz Waterworks, Deltares, and international conferences (IAHR, ICOLD, EGU). Publications since 2012 cover topics from underground pumped storage feasibility to real-time control of urban reservoirs, with a growing emphasis on machine learning applications since 2023. He actively engages in fieldwork, including excursions to dams and control centers, and teaches modules ranging from hydromechanics to environmental monitoring.

• Image Processing
• Artificial Intelligence
• Machine Learning
• +۶ مورد دیگر

Doç. Dr. Emre Sümer, currently serving as an Associate Professor at the Department of Computer Engineering within Baskent University , focuses on image processing, deep learning, and their applications in medical diagnostics and computer vision. His work bridges artificial intelligence with healthcare and geographic information systems. PhD (2011) : Geodesy and Geographic Information Technologies, Middle East Technical University MS (2004) : Geodesy and Geographic Information Technologies, Middle East Technical University BS (2001) : Computer Engineering, Baskent University His research spans medical imaging (e.g., Parkinson's and Alzheimer's detection), steganography for secure data transmission, and machine learning applications in healthcare and urban planning. Recent publications highlight CNN-based diagnostics, sign language recognition, and innovative advertising systems. Scientific awards include the First Oral Presentation Award at the International Congress on Artificial Intelligence in Health (2020). He has supervised multiple PhD/MSc theses on topics like zero-shot sign language recognition and steganography methods. Email: esumer@baskent.edu.tr

• Artificial Intelligence
• Image Processing
• Signal Processing

İclal Çetin Taş is an Assistant Professor in the Computer Engineering department at Başkent University. She holds a PhD (2019), Master's (2012), and Bachelor's (2009) in Electrical and Electronics Engineering from Çukurova University. Her research spans three core domains: Artificial Intelligence : Applications in predictive modeling, classification, and deep learning systems. Image Processing : Focus on medical imaging (brain tumors, dental X-rays), forgery detection, and aerial image analysis. Signal Processing : Techniques for noise reduction and signal decomposition in medical and technical contexts. Recent publications (2022–2025) demonstrate a strong emphasis on deep learning applied to interdisciplinary challenges. Key themes include healthcare (cancer diagnosis, dementia classification), sustainability (battery life estimation), agriculture (crop detection), and digital security (image forgery). Her work frequently employs convolutional neural networks (CNNs) and regression models. She advises Master's students on topics like deep learning, image analysis, and software quality, though specific student names are not listed.

• Geometric Deep Learning
• Computer Graphics
• Computer Vision
• +۲ مورد دیگر

Dr. Simone Foti is a Research Fellow at the Department of Computing, Faculty of Engineering in Imperial College London . He holds a PhD from University College London (UCL) and has held internships at Disney Research Studios and Adobe Research. Education : PhD (2023), MRes (2019), MSc (2017), BSc (2015) His research spans Geometric Deep Learning , Computer Graphics , and Computer Vision , focusing on improving 3D generative models for applications in AR/VR, the metaverse, medical imaging, and digital human creation. Recent work explores problems in non-Euclidean domains like graphs and meshes, with trends including latent disentanglement , 3D reconstruction , and medical applications of geometric AI. Collaborations include institutions like Carnegie Mellon University, Politecnico di Milano, and UCL, with publications across top venues such as NeurIPS , CVPR , and Computer Graphics Forum . Current projects include UV-free texture generation and mesh sampling techniques .

• Numerical Analysis
• Machine Learning
• Optimization
• +۳ مورد دیگر

Luca ZANNI is a Full Professor at the Department of Physical, Computer and Mathematical Sciences, Università di Modena e Reggio Emilia. His academic career spans numerical analysis, machine learning, and optimization methods, with a focus on gradient projection algorithms, stochastic gradient techniques, and their applications in imaging and deep learning. His research includes developing adaptive steplength rules for gradient methods, variance control in stochastic optimization, and GPU-accelerated reconstruction techniques for medical imaging. He teaches courses such as Numerical Calculation with Python, Fundamentals of Machine Learning, and Mathematical Methods for Machine Learning, covering topics like numerical optimization, support vector machines, and deep learning. He has contributed to methodologies for hyperparameter-free training, active set identification in constrained optimization, and edge-preserving image reconstruction. His work bridges theoretical advancements in optimization with practical implementations in large-scale machine learning and medical imaging applications.

• Image Processing and Computer Vision
• Machine Learning
• Evolutionary Computation
• +۱ مورد دیگر

Seyed Jalaleddin Mousavirad (Jalal) serves as a Postdoctoral researcher at Mid Sweden University in Sundsvall, Sweden, within the Department of Computer and Electrical Engineering (DET) and affiliated with the STC Research Centre. His research focuses on advancing AI-driven solutions for sustainable technologies and complex optimization problems. He earned his PhD in Computer Engineering specializing in Artificial Intelligence from the University of Kashan, Iran. Previous academic appointments include Assistant Professor at Hakim Sabzevari University (Iran), instructor roles at the University of Tehran (2018-2019) and Azad University (2019-2020), and a Research Fellow position at the University of Beira Interior (Portugal) where he contributed to the European GreenStamp project on sustainable Android applications. Dr. Mousavirad's research spans Image Processing and Computer Vision, Machine Learning, Evolutionary Computation, and Applied Artificial Intelligence, with significant contributions in pattern recognition, metaheuristic algorithms, and neural network optimization. His work demonstrates strong interdisciplinary applications in healthcare diagnostics, power systems, and medical imaging. Recent publications reveal a pronounced trend toward federated learning frameworks for privacy-preserving medical analysis, adversarial robustness in diffusion models, and hybrid optimization techniques for ECG classification and brain tumor detection. This reflects a strategic focus on translating AI innovations into practical healthcare and sustainability solutions. He actively contributes to the academic community as a guest editor for journals including Computational Intelligence and Neuroscience, Entropy, and Mathematical Biosciences and Engineering. His editorial leadership extends to organizing special sessions at IEEE CEC and EvoApplications conferences. Dr. Mousavirad maintains extensive peer-review commitments across 50+ prestigious venues including IEEE Transactions on Evolutionary Computation and IEEE Transactions on Cybernetics. His collaborative research includes international engagements at Xi'an Jiaotong-Liverpool University (China) and current work within Mid Sweden University's STC Research Centre on energy-aware computing and neural network optimization.

Mary Lauren Bentonمشاهده پروفایل

استادیار

• Computational Biology
• Gene Regulation
• Biomedical Informatics
• +۳ مورد دیگر

Dr. Mary Lauren Benton is an Assistant Professor in the Department of Computer Science at Baylor University's College of Engineering and Computer Science. She holds a Ph.D. and M.S. in Biomedical Informatics from Vanderbilt University (2020, 2018) and a B.S.I. in Bioinformatics from Baylor University (2015). Education: Ph.D. (Vanderbilt, 2020), M.S. (Vanderbilt, 2018), B.S.I. (Baylor, 2015) Her research focuses on computational biology and gene regulation, particularly how DNA sequences influence genome function and human disease risk through integrated datasets. Recent work explores enhancer-gene interactions, epigenetic mechanisms, and the application of graph neural networks to genomic problems. Selected publications highlight interdisciplinary trends across genomics (e.g., Cis-regulatory landscapes , CTCF motif accessibility ) and computer science (e.g., Dynamic graph attention , Node classification ).

Sukalpa Chandaمشاهده پروفایل

دانشیار

• Machine Learning
• Zero-Shot Learning
• One-Shot Learning
• +۷ مورد دیگر

Sukalpa Chanda is an Associate Professor at the Department of Computer Science and Communication, Halden University College. His research focuses on Machine Learning with applications to Document Image Analysis, Computer Vision, and Video Image Analysis, including advanced methods like Zero-Shot Learning, Deep Learning, and Transformer Networks. PhD in Computer Science from NTNU Appointments: Postdoctoral Researcher at Uppsala University (2018-2019) and Groningen University (2016-2018) Research Interests: Chanda specializes in Zero-Shot and One-Shot Learning for document and image analysis, with applications in handwriting recognition, face generation, and biomedical imaging. His work bridges theoretical machine learning with practical implementations in cultural heritage preservation and healthcare diagnostics. Scientific Collaboration: He collaborates with institutions like Indian Institute of Technology (Pallakad/Patna) and leads the Hugin Munin Project under The Digital Society research priority area. His team includes Master’s students and research assistants working on Transformer Networks and generative models. Key Publications: Recent works include frameworks for zero-shot action recognition (T2L, 2025), Nordic manuscript writer identification (2023), and advanced medical image segmentation networks (PAANet, 2021). His research spans document analysis, deep metric learning, and biomedical applications.

Stephen McKennaمشاهده پروفایل

استاد

• Computer Vision
• Biomedical Image Analysis
• Pattern Recognition
• +۳ مورد دیگر

Stephen McKenna is a Professor of Computer Vision at the School of Computing, University of Dundee, UK. His research spans computer vision, biomedical image analysis, and machine learning, with a focus on medical diagnostics and assistive technologies. Doctor of Philosophy, Automated Analysis of Papanicolaou Smears, University of Dundee (1994) Bachelor of Science, University of Edinburgh (1990) McKenna's work integrates artificial intelligence with biomedical applications, including skin lesion classification, gland segmentation, and multimodal risk stratification for diabetes patients. His projects often bridge computer science with healthcare and social care challenges. His recent publications highlight advancements in deep learning for medical diagnostics, such as automated lipoprotein profiling and porcine pathology inspection. Collaborations with institutions like the University of Cambridge and funding from the Engineering and Physical Sciences Research Council underscore his interdisciplinary impact. McKenna actively contributes to academic service through editorial roles in journals like Scientific Data and Machine Vision and Applications. He has supervised research teams and datasets, including the Multimodal Focused Interaction Dataset and QMB Morning Dataset.

Surangi W Punyasenaمشاهده پروفایل

دانشیار

• Palynology
• Ecology
• Evolutionary Biology
• +۵ مورد دیگر

Surangi W Punyasena is an Associate Professor at the University of Illinois Urbana-Champaign, affiliated with the School of Integrative Biology, Department of Plant Biology, and the Center for Latin American and Caribbean Studies. She is also an affiliate of the Carl R. Woese Institute for Genomic Biology, Geography and Geographic Information Science, and Earth Science and Environmental Change. BA, Yale University SM, University of Chicago PhD, University of Chicago Her research integrates palynology, machine learning, and microscopy to study the impact of climate on Neotropical plant communities. The lab develops automated methods to enhance pollen/spore count accuracy, focusing on deep learning, superresolution imaging, and phylogenetic analysis. Key projects include CuticleTrace for leaf cuticle imaging and frameworks for open-world pollen detection. Recent publications highlight the application of deep learning to fossil pollen classification, phylogenetic placement of extinct morphotypes, and climate-driven evolutionary patterns in Podocarpus and grasses. Her work emphasizes improving reproducibility and taxonomic resolution in paleoecological data. The lab is transforming paleoecology into a data-intensive discipline by leveraging big data from fossil microstructures. Collaborative efforts span computational tools, fossil curation, and ecological hypothesis testing, with implications for understanding long-term vegetation dynamics.

Giorgos Bouritsasمشاهده پروفایل

پژوهشگر ارشد

• Geometric Deep Learning
• Graph Deep Learning
• Weight Space Learning
• +۵ مورد دیگر

Giorgos Bouritsas is a machine learning scientist and postdoctoral fellow at the Archimedes AI unit / Athena Research Center, affiliated with the Department of Informatics and Telecommunications at the National and Kapodistrian University of Athens. He also serves as an adjunct lecturer at NCSR Demokritos. His research focuses on Geometric and Graph Deep Learning, with contributions spanning theoretical foundations of graph/geometric neural networks and generative modeling of graph/geometric data. His educational background includes a PhD in computer science from Imperial College London (2023), supervised by Prof. Michael Bronstein and Prof. Stefanos Zafeiriou, and an MEng in electrical and computer engineering from the National Technical University of Athens (2017). Dr. Bouritsas' research interests encompass Geometric Deep Learning, Graph Neural Networks, Weight Space Learning, Self-Supervised Learning, and Machine Learning applications in biology and chemistry. His work intersects with areas such as computer vision, network science, and physics, with publications in leading conferences (NeurIPS, CVPR, ICCV, ECCV) and journals (TPAMI). His recent publications demonstrate trends in geometric deep learning, graph neural networks, and applications in bioinformatics and computer vision. His research on Scale Equivariant Graph Metanetworks was accepted for an oral presentation at NeurIPS 2024, highlighting his contributions to advancing the theoretical foundations of graph neural networks. Outstanding reviewer award, NeurIPS '21 Outstanding reviewer award, NeurIPS '23 Outstanding reviewer award, ICML '22 Outstanding reviewer award, ICML '24 Dr. Bouritsas regularly engages in educational activities, teaching postgraduate courses such as Deep Learning at the MSc in AI program at NCSR Demokritos. He also provides academic service as a reviewer for major machine learning conferences and journals. His current work at Archimedes AI centers on weight space learning, with applications in automating machine learning processes and predicting ML model behavior.

• Age-related macular degeneration
• Optical Coherence Tomography
• Retinal imaging densitometry
• +۴ مورد دیگر

Dr. Ashley Wood is a Senior Lecturer and Director of Learning and Teaching at the School of Optometry and Vision Sciences, Cardiff University. An experienced optometrist and Cardiff graduate, Dr. Wood has been with the institution since 2012, contributing significantly to both research and teaching in the field of vision sciences. Dr. Wood's educational background includes a BSc in Optometry & Vision Science (2003-2006) and a PhD in "Retinal structure and function in Age-related Maculopathy" (2007-2011), both from Cardiff University. Professional credentials include Fellowship of the Higher Education Academy (FHEA), Accreditation for Eye Health Examination Wales (EHEW), and registration as an Optometrist with the General Optical Council (GOC: 01-32560). Research interests focus primarily on age-related macular degeneration (AMD), the leading cause of vision loss in the EU/UK. Dr. Wood employs objective assessment methods including Optical Coherence Tomography (OCT), ocular electrophysiology, and retinal imaging densitometry. Current work involves using a prototype long wavelength OCT device that enables visualization of the choroid, a layer of blood vessels beneath the retina not visible with standard OCT devices. This technology also allows retinal imaging under dark-adapted conditions, facilitating studies of physiological and pathological changes in the dark-adapted eye. Dr. Wood has secured significant research funding including a Macular Society PhD Studentship (£99,858), AltRegen Ltd. Clinical Trial (£1.2M), and multiple studentships from the College of Optometrists and Abbeyfield Research Foundation. Current research projects focus on improving AMD detection and monitoring to ultimately support better treatments and prevent vision loss. Scientific contributions include numerous publications on AMD detection, OCT imaging, and electrophysiological assessment. Key awards and recognitions include multiple CUROP Summer Studentships, Abbeyfield Research Foundation PhD Studentship, and ISCEV Travel Award. Teaching responsibilities are extensive, including leadership of the Investigative Optometry and Case Studies module (OP3206) and Abnormal Ocular Conditions module (OP3104). Dr. Wood also delivers color vision lectures, supports clinical sessions, and supervises research projects. International teaching experience includes collaboration with HU University of Applied Sciences in Utrecht, Netherlands for the "Eye Care and Diabetes; the Bigger Picture" summer school. Administrative roles include serving as Director of Learning and Teaching, Exams Liaison Officer, and chairing the Student-Staff Panel. Dr. Wood currently supervises multiple postgraduate students working on AMD-related research projects.

Xiaoming Huoمشاهده پروفایل

استاد

• Statistics
• Machine Learning
• Data Science
• +۵ مورد دیگر

Xiaoming Huo is the A. Russell Chandler III Professor at the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Tech, where he also serves as Associate Director for Research at the Institute for Data Engineering and Science (IDEaS). He holds executive leadership positions including Director of the NSF-funded Transdisciplinary Research Institute for Advancing Data Science (TRIAD) and oversees Georgia Tech's Master of Science in Analytics program in Shenzhen. Education includes: Ph.D. in Statistics from Stanford University (1999) M.S. in Electrical Engineering from Stanford University (1997) B.S. in Mathematics from University of Science and Technology of China (1993) Dr. Huo's research integrates statistical theory with computational methods, focusing on: Foundational machine learning : Theoretical analysis of deep neural networks, adversarial training frameworks High-dimensional statistics : Sparse modeling, regularization techniques, and minimax optimization Data science applications : Anomaly detection, generative modeling, and domain adaptation methods His work consistently bridges theoretical rigor with practical implementations. Recent publications (2023-2025) demonstrate strong focus on: adversarial learning frameworks, neural network theory, anomaly detection systems, and high-dimensional statistical methods. Common themes include theoretical guarantees for deep learning architectures, optimization in statistical estimation, and robust model formulations. Significant scientific recognition includes: Golden Prize, International Mathematical Olympiad (1989) IEEE Senior Member (2004) Sigma Xi Young Faculty Award (2005) Emerging Research Fronts in Mathematics (2006) Multiple competitive fellowships during academic training Leadership in major NSF initiatives includes directing TRIAD and contributing to the NSF AI Institute: ACTION. Manages interdisciplinary teams across data engineering, statistical theory, and machine learning applications.

• Machine Learning
• Big Data
• Knowledge Discovery
• +۷ مورد دیگر

Dr. Borja Sanz Urquijo serves as a Senior Lecturer at the Faculty of Engineering, University of Deusto, and has been a core researcher at DeustoTech-Computing since 2008, including a tenure as Head Researcher (2015-2018). He holds a cum laude PhD in Information Systems (2012) from the University of Deusto, specializing in Android malware detection. Education PhD in Information Systems, University of Deusto (2012, cum laude) His research spans machine learning, big data, and knowledge discovery, with critical expansion into AI ethics, fairness, accountability, and societal impact. He investigates AI applications in domestic violence intervention, health rights, law enforcement transparency, and Edge Computing optimization, consistently bridging technical innovation with social responsibility. His work demonstrates rigorous methodology in small-dataset machine learning and genomic sequence analysis. Dr. Sanz Urquijo's publication trajectory reveals evolving expertise from foundational cybersecurity (Android malware analysis, spam filtering) to contemporary societal challenges (feminist AI frameworks, quantum software security). Recent articles emphasize interdisciplinary collaboration, particularly in feminist technology studies and ethical AI governance, while maintaining technical depth in Edge Computing and genomic analytics. Advising and Projects He has supervised multiple theses including doctoral work on Edge Computing for digital twins and cybersecurity competency frameworks. As lead researcher in over 50 projects (H2020, national, private), he currently directs BEACON (industrial AI systems) and contributes to EU initiatives like IMPROVE (domestic violence response) and ELKARTEK (Industry 5.0 ethics). His collaborations span social organizations, enterprises, and research centers globally. Research Environment As a pillar of DeustoTech-Computing, he operates within a multidisciplinary unit advancing AI, cybersecurity, and Edge Computing applications. His leadership in projects like AI-Driven Cognitive Robotic Platforms and REal tiME control systems demonstrates integration of theoretical research with industrial implementation in smart manufacturing contexts.

Majid Zamaniمشاهده پروفایل

مدرس

• Biomedical Engineering
• Artificial Intelligence
• Neuroscience
• +۳ مورد دیگر

Dr Majid Zamani is a Lecturer in the School of Electronics & Computer Science at the University of Southampton. His research focuses on implantable and wearable biomedical devices, applied AI in biomedical engineering, and hardware-efficient processing frameworks. Member of: Digital Health and Biomedical Engineering Institute for Life Sciences Member of: Centre for Internet of Things and Pervasive Systems Member of: Centre for Health Technologies Member of: UKRI AI Centre for Doctoral Training in AI for Sustainability (SustAI) Current research addresses key challenges in scalability, signal processing, sensing, energy efficiency, and miniaturization for next-generation implantable brain-machine interfaces (iBMI). He explores hardware-efficient computational platforms for biomedical applications, including AI-driven algorithms for neural signal/image processing, augmented navigation in constrained anatomical spaces, and low-power real-time processors in 180/90/45 nm CMOS technologies. Recent publications focus on deep learning for spike sorting, binarized neural networks, noise-aware speech enhancement, and AI integration in surgical navigation. His work bridges biomedical engineering with AI and hardware optimization. Teaching: Digital system design (ELEC6236) 42+ publications in journals like IEEE Transactions on Medical Imaging, Journal of Neural Engineering, and IEEE Access