جست‌وجوی استاد و پژوهشگر

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

Mideth B. Abisado is an active researcher in the fields of machine learning, data science, and natural language processing. Their work focuses on applications such as pedestrian re-identification, recommendation systems, sentiment analysis, federated learning, and privacy-preserving techniques. Key contributions include enhancing deep learning models for computer vision tasks, optimizing feature selection algorithms, and developing systems for citizen participation and education policy evaluation. Research Interests Machine Learning & Deep Learning Computer Vision & Image Processing Natural Language Processing (Sentiment Analysis, Text Classification) Federated Learning & Privacy-Preserving Methods Data Science Applications in Social & Educational Systems Notable Trends in Publications Recent work emphasizes hybrid deep learning architectures, multimodal data analysis, and real-world applications of AI in education and smart infrastructure. There is consistent focus on improving model accuracy through attention mechanisms, feature engineering, and optimization algorithms. Labs/Teams Collaborations involve interdisciplinary teams addressing challenges in digital systems, citizen participation platforms, and sustainable education systems through data-driven approaches.

• Machine Learning
• Computer Vision
• Medical Image Analysis
• +۳ مورد دیگر

João Paulo Papa is a Professor at the Department of Computing within the Institute of Biosciences, Humanities and Exact Sciences at São Paulo State University (UNESP), Brazil. His research spans machine learning, computer vision, and quantum computing with significant applications in medical diagnostics and environmental monitoring. Over the past three years, he has published extensively in top-tier journals including IEEE Access, ACM Computing Surveys, and Neural Computing and Applications. His research interests focus on developing innovative machine learning approaches for healthcare applications, particularly in Parkinson's disease detection through speech and facial analysis, medical image processing for cancer detection, and quantum-classical hybrid models. His work demonstrates strong integration of theoretical machine learning advancements with practical medical and environmental applications. Papa has established a productive research group that has produced numerous publications in computer vision conferences and medical informatics venues. Analysis of his recent publications reveals a strong emphasis on medical applications of AI, with approximately 60% of his work focused on healthcare diagnostics, 25% on fundamental machine learning advancements, and 15% on environmental and remote sensing applications. His research shows increasing collaboration with international partners while maintaining strong roots in Brazilian academic networks. Among his notable contributions is the development of specialized machine learning architectures for medical image analysis, including TransConv for esophageal cancer detection and quantum-classical hybrid models for breast cancer diagnosis. He has also made significant contributions to the Portuguese language processing community through adaptations of large language models for Brazilian Portuguese medical applications. Prof. Papa actively supervises graduate students and collaborates with medical professionals across Brazil, translating AI research into practical clinical tools. His research group maintains strong connections with hospitals and medical research centers to ensure clinical relevance of their technical developments.

• Image Processing
• Computer Vision
• Signal Processing
• +۴ مورد دیگر

Sos S. Agaian is a Professor affiliated with City University of New York (CUNY), USA, with a former position at the University of Texas at San Antonio's Department of Electrical and Computer Engineering. His research focuses on advanced image and signal processing techniques, including thermal imaging, medical imaging, neural networks, and computer vision applications. He has authored numerous publications in top-tier journals and conferences, contributing to fields like adversarial attack defenses, solar panel segmentation, and quantum-inspired algorithms. His work emphasizes practical solutions for image enhancement, deblurring, and segmentation, with applications in healthcare, energy systems, and cybersecurity. Research Interests: Image Processing and Computer Vision Signal Processing and Machine Learning Medical and Thermal Imaging Neural Networks and Deep Learning Quantum Computing Applications in Imaging Recent Trends in Articles: Recent work highlights advancements in quaternion-based neural networks for weather removal, lightweight networks for solar panel fault detection, and novel entropy models for thermal imaging. Collaborations span medical diagnostics, cybersecurity, and energy systems, reflecting interdisciplinary impact. Grants and Collaborations: While specific grants are not detailed, his extensive publication record suggests active research funding in areas like computer vision and quantum information processing. Collaborators include institutions globally, emphasizing translational research. Labs/Teams: Not explicitly mentioned, but his work implies involvement in imaging and machine learning research groups focused on practical solutions for real-world challenges.

Ceyhun Yildizمشاهده پروفایل

مدرس

• Renewable Energy Systems
• Power Electronics
• Artificial Intelligence
• +۳ مورد دیگر

Ceyhun Yildiz is a Doctor Lecturer at the Circuits and Systems Department within the Faculty of Engineering and Natural Sciences at Bandırma Onyedi Eylül Üniversitesi. Previously, he held academic positions at Kahramanmaraş İstiklal University and Kahramanmaraş Sütçü Imam University, serving as Department Head at Kahramanmaraş İstiklal University's Elbistan Vocational School. His academic career spans over a decade with roles including research assistant (2005-2008) and lecturer positions in Electrical Engineering. His research focuses on renewable energy systems, power electronics, and artificial intelligence applications in energy sectors. Key interests include wind energy optimization, machine learning-driven forecasting (e.g., wind power, solar output, electricity prices), and hybrid energy system integration. He has led projects on optimal turbine placement, energy storage solutions, and grid stability analysis. Yildiz has authored/co-authored over 40 publications, with recent works emphasizing deep learning models for electricity load forecasting and wind farm layout optimization. He teaches graduate courses on machine learning applications in engineering and has supervised master's theses on solar power estimation and hybrid energy systems. His work integrates theoretical and applied research, addressing challenges in renewable energy scalability and grid compatibility.

• Artificial Intelligence
• Internet of Things
• Computer Vision
• +۴ مورد دیگر

Miloš Dobrojević serves as a Professor at Singidunum University's Faculty of Informatics and Computing in Belgrade, Serbia. His academic foundation includes doctoral, postgraduate, and bachelor studies at the University of Belgrade's Faculty of Mechanical Engineering. His research spans Artificial Intelligence , Internet of Things , and Computer Vision with emphasis on practical applications. Key focus areas include metaheuristic optimization of neural networks for tasks like crop yield prediction, medical diagnostics, and cybersecurity. Recent work demonstrates strong industry relevance in precision agriculture, drone detection systems, and sustainable homestead automation. Analysis of his 15 most recent publications reveals a consistent pattern of applying modified metaheuristics to enhance deep learning models across diverse domains. His work bridges theoretical AI advancements with real-world implementations in agriculture, healthcare, and energy systems. Scientific Contributions: Author of 3 textbooks including 'Veb informacioni sistemi' (2024) and 'Veb programiranje' (2021) Published 40+ journal/conference papers between 2003-2025 Active contributor to Springer book chapters on AI applications Dobrojević's work demonstrates strong practical orientation with projects in Serbian renewable energy transition, flood prevention systems, and municipal e-government solutions. His recent collaborations with researchers like N. Bacanin and M. Zivkovic show consistent output in high-impact journals. Current projects focus on generative AI for medical data and computer vision for agricultural optimization. His laboratory work centers on IoT-based monitoring systems for environmental applications, particularly water management in mountainous regions. Ongoing research explores drone detection networks and waste classification systems using advanced computer vision techniques.

• Biomedical Signal Processing
• Decision-Making Methodologies
• Frailty Prevention in Elderly
• +۳ مورد دیگر

Aly CHKEIR is an Associate Professor at the University of Technology of Troyes (UTT), holding the 'SilverTech' chair focused on technologies for healthy aging and elderly autonomy support. He earned his PhD in biomedical sciences from the University of Technology of Compiègne (UTC) in 2011, specializing in uterine electrical activity modeling. His research emphasizes biomedical signal processing, decision-making methodologies, and applications in frailty prevention among the elderly. Education: PhD in Biomedical Sciences, UTC, France (2011). Research interests include signal segmentation/classification, biomedical engineering, and solutions for aging populations. Notable projects include the SilverTech chair's holistic approach to aging technologies and the PiCADo initiative for in-home medical monitoring systems. Recent work focuses on AI-driven diagnostics (e.g., MRI segmentation, cough analysis), radar-based mobility assessment, and voice recognition for health monitoring. His contributions span sensor fusion, gait analysis, and non-invasive health tracking tools. His lab collaborations include Doppler radar systems for gait speed measurement, thermal imaging for health indicators, and AI-enhanced geriatric assessment tools. Projects like ARPEGE and PiCADo highlight his commitment to deploying innovative healthcare solutions for aging populations.

Hadj Batatiaمشاهده پروفایل

دانشیار

• Computational Imaging
• Machine Learning
• Medical Imaging
• +۶ مورد دیگر

Hadj Batatia is an Associate Professor at the School of Mathematical and Computer Sciences (MACS) at Heriot-Watt University Dubai (HWUD) since 2020, serving as Associate Director of Research. He previously held roles at the National Polytechnic Institute of Toulouse (INP Toulouse), Conservatoire National des Arts et Métiers (CNAM Toulouse), and University of Toulouse, where he led research teams and departments. His academic journey includes a PhD in Computer Vision from the University of Toulouse (1992) and an MSc in Artificial Intelligence (1987), with undergraduate studies at the University of Algiers. His research focuses on computational imaging, machine learning, and statistical modeling applied to medical imaging and computer vision. Key areas include deep learning, inverse problems, and biomedical image processing. He has supervised 15 PhD theses and contributed to 82 research outputs, achieving an h-index of 26. Notable grants include the ESMS project (2021-2023) for healthcare smart home systems and CAMM4D (2008-2012) for skin oncology imaging. Dr. Batatia teaches Advanced Machine Learning, Biologically Inspired Computation, and Big Data Management. He has led international collaborations across Europe, Latin America, and Asia, addressing challenges in medical imaging, cybersecurity, and education. His work aligns with UN SDGs, particularly in health and innovation. Awards include qualifications to professorship in Computer Science and Engineering.

Marta Vallejoمشاهده پروفایل

استادیار

• Machine Learning
• Deep Learning
• Evolutionary Algorithms
• +۷ مورد دیگر

Dr. Marta Vallejo is an Assistant Professor in the Department of Computer Science at the School of Mathematical & Computer Sciences, Heriot-Watt University. She also serves as Programme Director for the Graduate Apprenticeship in Data Science. Previously, she held a tenure-track Research Fellow position in biomedical signal/image processing and was Head of AI at ClearSky Medical Diagnostics Ltd. She is a Fellow of the Higher Education Academy and has contributed to EU-funded projects in Spain. Education: PhD in Computational Intelligence and Predictive Modelling MSc in Artificial Intelligence Her research focuses on machine learning and evolutionary algorithms applied to healthcare, specifically neurodegenerative diseases (Parkinson’s, Huntington’s, ALS) and cancer. Key projects include developing AI-driven clinical assessments, biomarker detection platforms, and image co-registration systems using deep learning. She leads the ML-Healthcare group and collaborates with ClearSky MD and the Proteus project. Her work emphasizes translating academic research into clinical tools, such as virtual staining techniques and wearable tremor monitoring devices. In public engagement, she organizes initiatives like Rubbish Robots (recycled materials for robotics) and contributes to events like the UK-RAS Robot Lab Live. She has received awards for research culture and outward-looking collaborations, including the Spirit of Heriot-Watt Awards 2017 and the 2024 Research Culture Recognition Award. Grants & Leadership: Principal Investigator: Neuro BioMark (EPSRC IAA) Co-Investigator: Lung Cancer Registration (Cancer Research UK) Successfully secured 96,432 GPU hours via EPSRC for high-performance computing Organized EvoStar workshops and led the SPECIES society for evolutionary computation She hosts educational programs at the Robotic Assisted Living Testbed, introducing students to robots like Pepper and Tiago. Her research also aligns with UN Sustainable Development Goals, particularly in advancing healthcare technologies and promoting STEM education.

Steven Waslanderمشاهده پروفایل

دانشیار

• Autonomous Vehicles
• Robotics
• Simultaneous Localization and Mapping (SLAM)
• +۱۲ مورد دیگر

Steven Waslander is an Associate Professor at the University of Toronto Institute for Aerospace Studies and an Adjunct Professor in Mechanical and Mechatronics Engineering at the University of Waterloo. He previously directed the Waterloo Autonomous Vehicles Laboratory (WAVELab). Education: Doctorate in Aeronautics and Astronautics from Stanford University (2007) Master's in Aeronautics and Astronautics from Stanford University (2002) Bachelor's in Applied Mathematics and Mechanical Engineering from Queen's University (1998) His research focuses on autonomous vehicles , including aerial and ground systems, with an emphasis on Simultaneous Localization and Mapping (SLAM) , nonlinear estimation , multi-robot systems , and autonomous driving . He also explores convolutional and recurrent neural networks for motion planning and object detection . Recent articles highlight trends in 3D detection , LiDAR-based tracking , neural radiance fields for autonomous driving, and model-agnostic pretraining for motion prediction. These works span journals like IEEE Transactions on Robotics and conferences such as ICCV and NeurIPS . Professor Waslander has advised the University of Waterloo Robotics Team, mentored students like Evan Cook, Barza, and Marc, and collaborated with industry partners including Aeryon Labs and Clearpath Robotics. He is a member of the NSERC Canadian Field Robotics Network .

Javad Khazaeiمشاهده پروفایل

استادیار

• Smart Grid Dynamics & Control
• Data-Driven Modeling and Control
• Microgrid Systems
• +۳ مورد دیگر

Javad Khazaei is an Assistant Professor in the Electrical & Computer Engineering Department at Lehigh University and director of the INTEGRITY Laboratory. Previously, he held an Assistant Professor position at Penn State Harrisburg and was affiliated with Penn State University Park's Architectural Engineering Department. He earned his Ph.D. in Electrical Engineering (Power and Energy) from the University of South Florida in 2016. Research Interests : His work focuses on smart grid dynamics and control, data-driven modeling and control in power systems, water-energy microgrids, smart grid cybersecurity, and renewable energy integration. His laboratory, INTEGRITY Lab, addresses challenges in resilient and intelligent energy systems. Recent Work Trends : His publications emphasize data-centric approaches for system identification, model predictive control (MPC) for microgrids, and enhancing resilience through advanced control strategies. Key themes include cybersecurity in energy systems, hybrid energy-water nexus optimization, and naval/marine microgrid resilience. Grants & Collaborations : Awarded a $12M DOE grant to expand marine energy initiatives, with partnerships focusing on naval microgrid resilience and offshore renewable integration. His work often involves interdisciplinary collaborations between electrical engineering, cybersecurity, and infrastructure systems. Lab & Infrastructure : Directs the INTEGRITY Lab, which develops cutting-edge solutions for cyber-physical energy systems. Research includes hardware-software integration for real-time monitoring and control of microgrid components.

• Machine Learning
• Spatial Data Mining
• AI in Mental Health
• +۴ مورد دیگر

Dr. Saida Elmi is an Assistant Professor Adjunct at Yale University School of Medicine and an Assistant Professor at the University of New Haven. She holds a Ph.D. in Computer Science from the National School of Mechanics and Aerotechnics (2017), followed by postdoctoral research at Korea University of Technology and Education (2017–2018) and National University of Singapore (2018–2021). Her research focuses on AI applications in healthcare, transportation systems, and spatial data mining. She developed the Automated Test of Embodied Cognition (ATEC), an AI-driven system using motion capture and machine learning for mental disease detection. Key contributions include predicting taxi fares, travel times, and energy consumption in transportation networks using deep learning architectures like RNNs and CNNs, and designing group-oriented recommendation systems for points-of-interest (POIs) using graph convolution networks and attention mechanisms. Education : Ph.D., Computer Science, National School of Mechanics and Aerotechnics (2017) Postdoctoral Research, Korea University of Technology and Education (2017–2018) Postdoctoral Research, National University of Singapore (2018–2021) Her research interests span machine learning, spatial data mining, and AI ethics. She has pioneered frameworks for spatio-temporal data analysis, including a transfer learning approach for travel time prediction in data-scarce regions. Her work on energy consumption modeling and POI prediction has been published in top-tier conferences like WWW, MOBIQUITOUS, and ECIR. Dr. Elmi’s interdisciplinary projects integrate AI with healthcare and urban systems, emphasizing automated cognitive assessment and smart city technologies. Her current efforts focus on refining action recognition algorithms for clinical applications and optimizing recommendation systems for group mobility decisions.

Wonjun Leeمشاهده پروفایل

استادیار

• Artificial Intelligence
• Security Analytics
• Deep Learning
• +۴ مورد دیگر

Wonjun Lee is an Assistant Professor of Artificial Intelligence at the Katz School of Science and Health at Yeshiva University . His academic journey includes a Ph.D. and M.S. from Purdue University (2011 and 2002, respectively) and a B.A. from Korea University (2000). Educational Background Ph.D., Purdue University, 2011 MS, Purdue University, 2002 BA, Korea University, 2000 Dr. Lee specializes in AI-based analytics platforms for multidisciplinary applications, focusing on knowledge-based response systems inspired by human immunology. His work explores scalable AI models for cloud-based information and communication systems (ICS), leveraging lightweight virtualization and microservice architecture. He collaborates with institutions like NOAA , University of Texas at San Antonio (UTSA) , Auburn University , and University of Georgia . His research spans AI-driven cybersecurity, including analysis of malign network traffic and IoT privacy issues. At Samsung , he previously led cybersecurity projects such as mobile app penetration testing and cloud computing assessments. Recent publications highlight his expertise in encrypted network traffic detection using Perlin Noise and CNNs, container rootkit detection, and cache event analysis on AMD Zen micro-architecture. These works reflect his integration of AI with cybersecurity and hardware-level analysis.

Liliana Caldeiraمشاهده پروفایل

پژوهشگر

• Artificial Intelligence in Medicine
• Medical Image Analysis
• Radiology
• +۴ مورد دیگر

Dr. Liliana Caldeira serves as a Core Scientist and team leader at the Center for Data and Simulation Science (CDS) within the University Hospital of Cologne, University of Cologne. She directs innovative data science research in the Department of Diagnostic and Interventional Radiology, specializing in AI algorithm development for 3D medical imaging analysis including Computed Tomography (CT) and Magnetic Resonance Imaging (MRI). Research Focus: Her work centers on clinical AI implementation in radiology, with expertise in deep learning for medical image segmentation (brain tumors, lymph nodes, organs), image denoising, and disease classification. Key applications include brain metastasis detection in melanoma, lymph node staging for cancer, kidney segmentation in polycystic disease, and pulmonary hypertension assessment—emphasizing clinical validation and multicenter study designs to bridge AI research with practical medical workflows. Publication Trends: Analysis of her 2020-2023 publications reveals consistent focus on clinically validated AI tools, particularly using 3D convolutional neural networks for segmentation tasks. Her research demonstrates strong collaboration across radiology subspecialties, with shared last authorship indicating principal investigator leadership in translating algorithms to clinical practice. Scientific Awards: No awards were documented in the provided text. Advising and Grants: Student mentorship and grant funding details were not specified in the source material. Labs and Teams: Dr. Caldeira leads a dedicated data science team within the radiology department, collaborating with CDS under Scientific Domain 2 (Computational Biology and Medicine). The team focuses on high-quality dataset creation, AI evaluation frameworks, and algorithm implementation for medical imaging diagnostics, operating from the University Hospital campus at Kerpener Str. 62.

Hussein Abdullahمشاهده پروفایل

استاد

• Biomedical Robotics
• Assistive Devices
• Mechatronic Systems Design
• +۳ مورد دیگر

Hussein Abdullah is a Full Professor at the School of Engineering, University of Guelph, specializing in biomedical robotics and mechatronics. He holds a BEng from the University of Technology in Baghdad, and an MSc and PhD from the University of Glasgow. His career includes roles as Associate Professor at Sultan Qaboos University (Oman) and Vice President of Academics and Research at the Australian University in Kuwait (2020–2021). At UoG, he served as Associate Director for Graduate Studies (2008–2009), School Director (2010–2018), and has led over 30 years of academic and industry contributions. Research focuses on assistive robotics, therapeutic devices, and mechatronic systems, funded by NSERC, NRC, and OMAFRA. His 90+ publications include work on robotic exoskeletons, spinal injury treatment models, and thermal energy storage. He has mentored 34 master’s and PhD students, many entering academia and industry globally. He serves as Associate Editor of the Journal of Frontiers in Robotics and AI and chairs committees in engineering education and accreditation. Teaching includes graduate courses in advanced robotics and manufacturing systems. His professional affiliations include the Canadian Council of Deans of Engineering and membership in the Professional Engineers Ontario (PEO).

Tae-Hoon Kimمشاهده پروفایل

استاد

• Cybersecurity Education
• Network Security
• Data Science
• +۸ مورد دیگر

Tae-Hoon Kim is a Professor in the Department of Computer Information Technology and Graphics at Purdue University Northwest. He holds a Ph.D. in Information Science from the University of Pittsburgh, an M.S. in Interdisciplinary Telecommunications from the University of Colorado Boulder, and a B.S. in Mechanical Engineering from Oklahoma State University. His research focuses on cybersecurity education, wireless sensor networks, data-driven analysis using machine learning, and AR-based educational tool development. Current projects are funded by the Army Research Office, NSF, NSA, and industry partners. Research interests span network security, data science, and innovative educational technologies. He actively develops interactive tools for K-12 and college cybersecurity education using augmented reality and data visualization techniques. Teaching specialties include network protocols, IoT, blockchain, and big data analytics. His publications address topics ranging from biomass energy prediction models to cultural heritage glyph analysis. Teaching responsibilities include courses on LAN/WAN design, network security, AI, and mobile applications. Over 20 years of academic experience includes advising student projects in cybersecurity and data science.