جست‌وجوی استاد و پژوهشگر

Mahdi Jampourمشاهده پروفایل

پژوهشگر

• Computer Vision
• Artificial Intelligence
• Digital Humanities
• +۳ مورد دیگر

Dr. Mahdi Jampour is a Researcher at the Centre for the Study of Manuscript Cultures (CSMC), University of Hamburg, and a member of the Cluster of Excellence ‘Understanding Written Artefacts’ (UWA). He holds a Ph.D. in Computer Science (Artificial Intelligence) from Graz University of Technology (2016), with postdoctoral research at Iran Telecommunication Research Center (ITRC) (2016–2017). He previously served as Assistant Professor at Quchan University of Technology (2017–2024) and led Project RFA05 (2022–2025) focusing on visual pattern similarity in written artefacts. Education: Ph.D. in Computer Science (Artificial Intelligence), TU Graz, Austria (2016) Postdoctoral Fellowship, ITRC, Iran (2016–2017) Assistant Professor, Quchan University of Technology (2017–2024) Research Interests: Dr. Jampour specializes in applying AI and computer vision to cultural heritage preservation, including palimpsest analysis, historical document digitization, and pattern recognition. His work integrates generative models, deep learning, and semi-supervised methods to address challenges in manuscript analysis and multispectral imaging. Publications Trends: Recent work focuses on generative AI for palimpsest deciphering, dataset creation for sports and cultural heritage analysis, and facial expression recognition surveys. His articles bridge computer science with digital humanities, emphasizing cultural artifact preservation through technological innovation. Awards: Kazemi-Ashtiani Award (2019) Chamran Award (2017) KUWI Prize (2015) Marshal Plan Fellowship (2015) Best MSc Thesis Award (2009) Advising & Grants: Led UWA’s Project RFA05 (2022–2025) and contributed to international preservation initiatives like the Timbuktu Manuscript Training Project. His research is supported by grants from the Iran National Elites Foundation and the Iranian Ministry of Science. Labs & Collaborations: Active in CSMC’s labs, including the Written Artefact Profiling Guide and Mobile Lab Container projects. Collaborates with institutions globally on digitization and cultural heritage safeguarding.

Murat Kirtayمشاهده پروفایل

استادیار

• Humanoid Robotics
• Cognitive Systems
• Machine Learning
• +۵ مورد دیگر

Murat Kirtay is an Assistant Professor at Tilburg University in the Department of Cognitive Science and Artificial Intelligence within the Tilburg School of Humanities and Digital Sciences. He leads the Cognitive Developmental Robotics (CoDeRs) group and co-directs the AI for Robotics Laboratory (AIR-LAB). Dr. Kirtay's research focuses on Humanoid Robotics, Cognitive Systems, and Machine Learning with particular interest in human-robot interaction, imitation learning, and cognitive modeling. His work explores how robots can learn from human behavior through observation and interaction, with applications in social robotics and collaborative systems. His research integrates insights from neuroscience, psychology, and artificial intelligence to develop more natural and effective robot behaviors. His recent publication output demonstrates consistent research activity with multiple papers accepted at top robotics conferences including IEEE Humanoids, IEEE ICDL, and IEEE Ro-MAN. His work spans theoretical contributions to cognitive modeling as well as practical applications in robot behavior and interaction design. Best paper award nomination at IEEE ICDL 2023 Dr. Kirtay is actively involved in research projects including STEADFAST: Swarm Technology Enabling Advanced Drone-Facilitated Active Support Tactics for Military and First Responder Operations, where he serves as a Co-Principal Investigator. He leads research groups focused on cognitive developmental robotics and AI for robotics applications. His laboratory work involves humanoid robots including the iCub platform for conducting experiments in human-robot interaction and collaborative manipulation.

Zahabul Islamمشاهده پروفایل

استادیار

• Additive Manufacturing
• Material Characterization
• Advanced Manufacturing Techniques
• +۷ مورد دیگر

Zahabul Islam is an Assistant Professor and Program Coordinator in Mechanical and Manufacturing Engineering at Bowling Green State University, School of Engineering. His research focuses on additive manufacturing (3D printing) and material characterization, particularly for energy and biomedical applications. Ph.D., M.S., and B.S. in Mechanical Engineering from Penn State University His expertise spans Laser Powder Bed Fusion (LPBF), Selective Laser Melting (SLM), Direct Energy Deposition (DED), and Stereolithography. Current research includes process map optimization for polymer/metal additive manufacturing, radiation damage studies, and multiscale modeling of fused filament fabricated composites. Recent publications highlight work on tungsten lattice structures, MoSiB alloys, and Haynes 282 superalloys. He actively collaborates on projects involving supercritical CO2 heat-to-power systems and ultrahigh-temperature refractory metal alloys for aerospace applications.

• High-Performance Computing
• Energy-Efficient Computing
• Parallel and Distributed Systems
• +۵ مورد دیگر

Ravi Reddy Manumachu is an Assistant Professor in the School of Computer Science at University College Dublin (UCD), Ireland. He holds a B.Tech from IIT Madras (1997) and a PhD in Computer Science from UCD (2005), specializing in high-performance heterogeneous computing and energy-efficient systems. His research focuses on optimizing performance and energy efficiency in modern heterogeneous platforms like clouds, grids, and supercomputers through novel models and algorithms. Key contributions include functional performance/energy models, energy-prediction frameworks, and extensions like Heterogeneous MPI and ScaLAPACK for heterogeneous clusters. He has published over 69 articles in top journals/conferences, with recent works addressing data transfer energy measurement, scalable allreduce algorithms (SUARA), and portable programming models (OpenH). Professional roles include Assistant Professor at UCD (2023–present), SEAI Research Fellow (2022–2023), and prior industrial experience at Ansys, Siemens, and IONA Technologies. He has certifications in university teaching, GDPR, and research integrity. Languages include English (fluent), Telugu, and Hindi. Research trends emphasize bi-objective optimization (performance-energy), hardware heterogeneity challenges, and scalable communication algorithms for deep learning. His work addresses energy non-proportionality in CPUs and GPU-CPU interactions, with practical solutions for real-world applications like matrix operations and gene sequencing.

Vasilios Kelefourasمشاهده پروفایل

مدرس

• Compiler Optimizations
• Deep Neural Networks
• Matrix/Tensor Computations
• +۲ مورد دیگر

Dr. Vasilios Kelefouras is Lecturer in Computer Science at the University of Plymouth's School of Engineering, Computing and Mathematics. His research optimizes software applications for execution time, energy consumption, and memory footprint across CPUs, GPUs and FPGAs. Notable contributions include analytical methodologies accelerating convolution layers in Deep Neural Networks (x1.1-7.2 speedup over Intel oneDNN) and optimization techniques for image processing algorithms (x2.8-40 speedup over Intel IPP). His compression work for Deep Neural Networks includes Tensor Train Decomposition methodologies for edge devices. Honors include HiPEAC Technology Transfer Award (2022) and Best Paper at SAMOS XXII. Teaches Parallel Computing, Computer Systems, and Computing Practice. Current PhD supervision focuses on accelerating ML algorithms on heterogeneous architectures.

Ziad Kobtiمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Data Science
• +۶ مورد دیگر

Ziad Kobti is a Professor and Director of the School of Computer Science at the University of Windsor. His work focuses on advancing artificial intelligence, machine learning, and data science with applications in healthcare, cybersecurity, and social network analysis. He leads initiatives such as the Graph Neural Network Lab and collaborates with industry partners like Sterling Information Technologies to foster cybersecurity talent. Kobti has organized programming competitions, co-op education programs, and research workshops, emphasizing experiential learning for students. His research spans recommendation systems, graph neural networks, pandemic modeling, and medical diagnostics, with a focus on impactful real-world solutions. He is involved in academic leadership, program development, and community outreach through events like Computer Science Demo Day. Research interests include: Graph Neural Networks and Dynamic Link Prediction Recommender Systems and Anomaly Detection Social Network Analysis and Team Formation Healthcare Informatics and Medical Imaging Cybersecurity and Privacy Risk Analysis Machine Learning for Public Health and Pandemic Preparedness Recent projects include collaborations on AI-driven crop disease detection, electric vehicle research, and palliative care simulation frameworks. He has developed frameworks like UDIS and BeComE, and contributes to open-source tools for social network analysis and anomaly detection. His work integrates multi-disciplinary approaches, combining computational models with domain-specific expertise in healthcare, agriculture, and social systems.

Zervakis Michalisمشاهده پروفایل

استاد

• Digital Image Processing
• Signal Processing
• Biomedical Engineering
• +۳ مورد دیگر

Zervakis Michalis is a full Professor at the Technical University of Crete (TUC), serving as Rector and Director of the Digital Image and Signal Processing Laboratory (DISPLAY). He holds a Ph.D. in Electrical Engineering from the University of Toronto (1990) and has been a faculty member at TUC since 1995. His expertise spans Digital Image/Signal Processing, biomedical applications, and neural network implementations in automation. Education: Ph.D., Electrical Engineering, University of Toronto (1990) M.Sc., Electrical Engineering, University of Toronto (1985) B.Sc., Electrical Engineering, Aristotle University of Thessaloniki (1983) Research Interests: Signal/image processing for biomedical applications Machine learning for healthcare diagnostics EEG/ECG analysis and seizure detection Neural network architectures for automation Multi-sensor systems and aerial surveillance His research emphasizes clinical applications like epilepsy monitoring and cardiovascular diagnostics, leveraging deep learning and multimodal data fusion. He has led over 20 international projects and published >90 papers in image/signal processing. Current work focuses on AI-driven medical systems and UAV-based infrastructure monitoring. Labs/Teams: Director of the DISPLAY Lab, collaborating on projects like the BorderUAS semiautonomous surveillance platform and the Pulsense cardiovascular monitoring system. Grants/Contributions: Extensive involvement in EU-funded initiatives and industry partnerships, advancing smart farming, power line inspection, and emergency response systems.

Minghua Chenمشاهده پروفایل

استاد

• Machine Learning with Performance Guarantee
• Online Optimization and Algorithms
• Energy Systems (Smart Grids, Data Centers)
• +۵ مورد دیگر

Minghua Chen is a Presidential Chair Professor and Director of the Design and Analysis of Networked, Computing, and Energy (DANCE) Systems Lab at the School of Data Science, The Chinese University of Hong Kong (Shenzhen). He is also affiliated with the Department of Data Science at City University of Hong Kong (on leave). His research spans machine learning, optimization, energy systems, and networked systems, with a focus on performance guarantees and real-time applications. Education: PhD in Electrical Engineering and Computer Sciences, UC Berkeley (2006) Master in Electrical Engineering, Tsinghua University (2001) Research Interests: Minghua Chen investigates machine learning with performance guarantees, online optimization algorithms, energy systems (smart grids, energy-efficient data centers), intelligent transportation systems, distributed optimization, delay-constrained network coding, and data-driven prediction in system design. His work bridges theoretical rigor with practical implementations in wireless networks, power systems, and peer-to-peer communications. Publication Trends: His recent publications demonstrate a strong focus on machine learning applications in energy and transportation systems, particularly AC optimal power flow problems and emission-minimizing truck routing. He integrates neural networks with optimization theory, emphasizing feasibility guarantees and low-complexity algorithms. His work also explores age-of-information (AoI) optimization and multi-armed bandit approaches for model freshness. Scientific Awards: IEEE Transactions on Multimedia Prize Paper Award (2009) Eli Jury Award, EECS Department, UC Berkeley (2007) Best Paper Awards at ACM e-Energy 2023, IEEE INFOCOM 2022 (Poster), and ACM Multimedia 2012 Multiple Best Paper Finalists and Runner-ups Labs and Teams: He leads the DANCE Systems Lab, which develops theoretical frameworks and practical systems for networked, computing, and energy optimization. His team collaborates on projects like WISER (indoor white space networking) and DeepOPF series for power flow optimization.

• Computer Systems
• Soft Computing
• Combinatorial Optimization
• +۵ مورد دیگر

Kazushi Kawamura is an Assistant Professor at the Tokyo Institute of Technology 's AI Computing Research Unit , with prior roles at Waseda University and the Institute of Science Tokyo. His academic journey began at Waseda University, where he earned a Dr. Eng in 2016. 2025.04 - Now: Assistant Professor, School of Fundamental Science and Engineering, Tokyo Tech 2024.10 - 2025.03: Assistant Professor, School of Fundamental Science and Engineering, Waseda 2020.04 - 2025.03: Specially Appointed Assistant Professor, Institute of Integrated Research, Institute of Science Tokyo His research spans combinatorial optimization , annealing processors , Ising machines , and FPGA-based computing systems . He has contributed extensively to LSI design methodology and high-level synthesis . Key publication trends include neural network compression , parallel annealing algorithms , edge AI inference , and sparse matrix operations on FPGAs. His work often integrates theoretical optimization with practical hardware implementations . 2023 CS Achievement Award 2016 ISOCC Best Paper Multiple Algorithm Design Contest Prizes (2014-2019) IPSJ SLDM Outstanding Student Awards Kawamura teaches courses like Physical Electronics Laboratory and Machine Learning at Waseda, with recent projects such as Amorphica (metamorphic annealer) and Pianissimo (sub-mW DNN accelerator). He serves on committees for IEEE , IPSJ , and IEICE , focusing on system design and circuit optimization.

Ziad Ibbiniمشاهده پروفایل

پژوهشگر ارشد

• Developmental Biology
• Phenomics
• Marine Biology
• +۳ مورد دیگر

Ziad Ibbini is a Research Fellow at the University of Plymouth's School of Biological and Marine Sciences, affiliated with the Embryo Phenomics research group. His work focuses on advancing bioimaging and computational methodologies to study developmental physiology in aquatic organisms. University: University of Plymouth School: School of Biological and Marine Sciences Position: Research Fellow Email: ziad.ibbini@plymouth.ac.uk His research integrates cutting-edge technology with developmental biology, emphasizing environmental adaptation and automated phenotyping systems. Key contributions include open-source tools like LabEmbryoCam and HeartCV for cardiovascular and behavioral analysis in embryos. Recent work applies deep learning (Dev-ResNet) and computer vision to detect developmental events and predict thermal responses in aquatic embryos, advancing understanding of physiological resilience under climate change stressors. The Embryo Phenomics research group develops spectral and imaging techniques to quantify thermodynamic and environmental responses across species, supporting comparative studies in developmental physiology.

Lei Shiمشاهده پروفایل

پژوهشگر

• Machine Learning
• Artificial Intelligence
• Human-Robot Interaction
• +۱۲ مورد دیگر

Lei Shi is a Researcher at the Institute for Visualization and Interactive Systems (VIS) within the Collaborative Artificial Intelligence department at the University of Stuttgart, Germany . Their work focuses on integrating machine learning, human-robot interaction, and safety control systems. Research Areas: Human-Robot Interaction, Bayesian Deep Learning, Visual Servoing, Eye Tracking Key Techniques: Graph Convolutional Networks, Predictive Modeling, Feature Descriptors Recent publications highlight their contributions to gaze-based interaction safety, dropout placement in neural networks, and predictive tuning methodologies. They operate from Room 01.029, Pfaffenwaldring 5a, 70569 Stuttgart.

Thomas Bocklitzمشاهده پروفایل

استاد

• Machine Learning
• Spectroscopy
• Medical Imaging
• +۳ مورد دیگر

Thomas Bocklitz is a Professor at the Leibniz Institute of Photonic Technology, leading the Photonic Data Science working group. His research focuses on integrating machine learning with spectroscopic and medical imaging techniques to enhance diagnostic accuracy and analytical workflows. University: Leibniz Institute of Photonic Technology School: Research Department Photonic Data Science Academic Rank: Professor Email: thomas.bocklitz@leibniz-ipht.de Research Interests : Bocklitz specializes in applying machine learning to spectroscopy (Raman, SERS, LIBS) and biomedical imaging. His work addresses challenges like spectral artifacts correction, image quality assessment, and class imbalance in histopathology data. He also explores explainable AI for spectroscopy and medical image registration across modalities. Publication Trends : His recent publications (2025) emphasize spatially aware deep learning for hyperspectral data, improved feature extraction methods in medical imaging, and custom CNN architectures for LIBS analysis. Earlier works (2024) focus on systematic reviews of SERS biomedical applications, Raman spectroscopy artifacts, and endomicroscopic AI for tumor diagnostics. Labs and Collaborations : Bocklitz collaborates with institutions like Universitätsklinikum Jena and researchers such as Jürgen Popp, Elena Corbetta, and Jhonatan Contreras. He leads a working group at Leibniz-IPHT, focusing on photonic data science for clinical and analytical applications.

Martin Eigelمشاهده پروفایل

پژوهشگر

• Numerical Mathematics
• Uncertainty Quantification
• Stochastic Partial Differential Equations
• +۵ مورد دیگر

Martin Eigel is a Researcher at the Weierstrass Institute for Applied Analysis and Stochastics (WIAS) , specializing in numerical methods for stochastic partial differential equations, uncertainty quantification, and machine learning applications in computational mathematics. His work bridges tensor networks, Bayesian inversion, and quantum simulations. Research Interests : Adaptive stochastic Galerkin finite element methods Low-rank tensor approximations for high-dimensional problems Machine learning integration with PDE solvers Quantum circuit simulation techniques Bayesian inverse problems and error control Key Article Trends : His recent publications focus on merging deep learning architectures (e.g., ResNet, CNNs) with stochastic and tensor-based numerical methods for solving parametric PDEs, Bayesian inversion, and quantum systems. Topics include Hamilton-Jacobi-Bellman equations, Langevin dynamics, and risk-averse optimization under uncertainty.

• Process Systems Engineering
• Chemical Process Optimization
• Hybrid Modeling
• +۷ مورد دیگر

Vladimir Mahalec is a Professor in the Department of Chemical Engineering within the Faculty of Engineering at McMaster University. With a career spanning over four decades, his scholarly activity shows consistent publication output from 1977 through 2025, with particular intensity in recent years. He maintains an active teaching schedule, offering graduate courses including Systems Modeling and Optimization (CHEMENG 753, SEP 752), Leadership for Innovation (SEP 773), and M.Eng. Project courses. Professor Mahalec's research interests focus on the intersection of chemical engineering process optimization, hybrid modeling techniques, and sustainable energy systems. His work bridges traditional chemical engineering with modern computational approaches, particularly in refinery planning, distillation tower modeling, and carbon footprint reduction. He has developed innovative methodologies such as inventory pinch algorithms for gasoline blend planning and hybrid models combining first-principles with data-driven approaches for process monitoring and optimization. Analysis of his recent publications reveals a strong trend toward integrating machine learning with traditional process engineering. His work increasingly focuses on sustainability challenges, particularly zero-emission energy systems and greenhouse gas reduction strategies. The publications show a progression from purely process engineering topics toward interdisciplinary work combining chemical engineering, computer science, and environmental science. Professor Mahalec has maintained a productive research program with consistent publication output across multiple high-impact journals including Computers and Chemical Engineering , AIChE Journal , and Energy . His co-author network includes notable researchers such as Prashant Mhaskar (8 publications), Robert Fleisig, and Christopher Swartz. In the classroom, Professor Mahalec teaches advanced courses focused on systems modeling, optimization, and innovation leadership. His teaching portfolio demonstrates commitment to both technical depth in chemical engineering systems and broader skills development for engineering leadership. He has supervised numerous capstone design projects and M.Eng. projects, guiding students through complex process engineering challenges.

Paul Campagnolaمشاهده پروفایل

عضو هیئت علمی

• Biomedical Engineering
• Cell Biology
• Medical Imaging
• +۳ مورد دیگر

Paul Campagnola is an Affiliated Professor in the Department of Physics at the University of Wisconsin-Madison. His research focuses on advanced optical imaging techniques, particularly second harmonic generation (SHG) microscopy, to study collagen architecture alterations in diseases such as ovarian cancer, idiopathic pulmonary fibrosis, and lichen sclerosus. He specializes in developing biomimetic models and computational tools to analyze extracellular matrix dynamics and cell-matrix interactions. His work bridges physics, biomedical engineering, and oncology, emphasizing translational applications in diagnostics and therapeutics. Education: No explicit education details provided. Research Interests: Optical microscopy innovations for biomedical applications Collagen structure analysis in disease progression 3D tissue imaging and modeling Machine learning for image data interpretation Recent Research Trends: Recent articles emphasize SHG imaging applications in cancer pathology, fibrotic diseases, and biomaterials. Studies highlight collagen's role as both a structural and functional biomarker, with focus on computational modeling and multiscale imaging techniques. Grants & Advising: No grants or advisees explicitly listed. Active in interdisciplinary collaborations involving physics, engineering, and clinical medicine. Labs & Teams: Engaged in tissue engineering and biophotonics research groups, though specific lab affiliations are not detailed in provided text.