جست‌وجوی استاد و پژوهشگر

KÜBRA UYARمشاهده پروفایل

مدرس

• Artificial Intelligence
• Computer Vision
• Medical Image Analysis
• +۲ مورد دیگر

KÜBRA UYAR is a Lecturer at Alanya Alaaddin Keykubat University's Rafet Kayış Faculty of Engineering, Department of Computer Engineering. She previously worked as a Research Assistant at Selçuk University's Faculty of Technology (2022-2023) and continues her academic contributions in image processing and artificial intelligence. PhD in Computer Engineering (2022), Selçuk University MSc in Computer Engineering (2017), Selçuk University BSc in Computer Engineering (2014), Melikşah University Double Major in Mathematics (2014), Melikşah University Her research focuses on Artificial Intelligence , Computer Vision , and Medical Image Analysis , with a strong emphasis on Machine Learning and Image Processing . Current work includes developing explainable AI models for retinopathy diagnosis and optimizing CNN architectures for biomedical applications. Recent publications highlight trends in Deep Learning for agriculture (chestnut classification), Medical Imaging (retinopathy, leukocyte detection), and Optimization (CNN hyperparameters, B-spline algorithms). Her work bridges Computer Science with Healthcare and Industrial Applications . Key projects include the Data-Intensive and Computer Vision Research Laboratory Infrastructure Project (2020-2021) and the Selçuk University Weather Monitoring System (2018-2020), both funded by higher education institutions.

Tomás Fernández Penaمشاهده پروفایل

استاد

• High Performance Computing (HPC)
• Parallel and Distributed Systems
• Quantum Computing
• +۵ مورد دیگر

Tomás Fernández Pena is a Full Professor at the University of Santiago de Compostela (USC) and Senior Researcher at the Research Center in Intelligent Technologies (CiTIUS) . With a career spanning over three decades, he has held academic positions since 1990 and contributed extensively to High Performance Computing (HPC), Big Data, and emerging quantum computing fields. Ph.D. in Physics from USC (1994) Senior Member of IEEE Associate Editor for IEEE Transactions on Computers and IEEE Access Research Contributions : His work focuses on parallel systems architecture, cloud computing middleware, and quantum simulation optimization. He has pioneered methods for NUMA systems, LiDAR data processing, and Big Data applications in bioinformatics/cheminformatics. His recent articles show increasing emphasis on quantum computing frameworks and distributed quantum processing. Scientific Recognition : Holds four Spanish Ministry of Education six-year research excellence periods (sexenios de investigación) and has served as Principal Investigator in 3 public projects and co-investigator in 31 EU/Xunta de Galicia funded initiatives. Supervised 7 Ph.D. theses and published 43+ international journal papers. International Collaborations : Maintains academic connections through funded research stays at Loughborough University, University of Tennessee, and University of Illinois Urbana-Champaign. Active in IEEE and participates in global conferences like Euro-Par and CHEP.

Bracha Lauferمشاهده پروفایل

مدرس

• Acoustic Source Localization
• Speech Signal Processing
• Gaussian Processes
• +۳ مورد دیگر

Dr. Bracha Laufer is a senior lecturer at the School of Electrical Engineering , part of the Iby and Aladar Fleischman Faculty of Engineering at Tel Aviv University. Her research focuses on acoustic source localization, speech signal processing, and machine learning techniques for audio engineering. Her recent work explores conformal prediction and manifold-based approaches for robust source localization, deep learning architectures for sound source separation, and simplex geometry in multichannel signal analysis. These publications highlight interdisciplinary applications of machine learning and statistical methods in acoustics. Dr. Laufer's research integrates Bayesian inference , probabilistic graphical models , and uncertainty quantification to address challenges in adverse acoustic environments. She has contributed to advancements in multi-microphone speaker localization and speech inpainting .

• Bayesian Statistics
• Artificial Intelligence
• Natural Language Processing
• +۷ مورد دیگر

Rens van de Schoot is a full professor of Collaborative Methods in AI and Data Science at Utrecht University in the Netherlands and serves as an extra-ordinary professor at North-West University in South Africa. He directs the AI-LAB on AI-aided Knowledge Discovery and coordinates the open-source ASReview project (Active learning for systematic text reviewing), which has become a significant tool for researchers conducting systematic reviews across various disciplines. His research spans multiple domains with a strong focus on Bayesian statistics, artificial intelligence applications in systematic reviews, and psychological methodology. Van de Schoot's work bridges technical AI development with practical applications in social sciences and healthcare research, particularly in PTSD and mental health studies. His methodological contributions include significant work on measurement invariance, small sample size solutions, and the development of the WAMBS checklist for Bayesian statistics. The analysis of his recent publications reveals a clear trajectory toward increasingly sophisticated AI applications in systematic reviewing, with a strong emphasis on Bayesian methodology. His work demonstrates how machine learning can enhance traditional systematic review processes while maintaining methodological rigor. The interdisciplinary nature of his research connects statistics, computer science, psychology, and healthcare research in innovative ways. APA award for best dissertation of division 5 Recipient of prestigious VENI and VIDI research grants from the Netherlands Organization for Scientific Research Member of the Young Academy (Jonge Akademie) of the Royal Netherlands Academy of Arts and Sciences (KNAW) Elected member of the Society of Multivariate Experimental Psychology (SMEP) Van de Schoot has supervised numerous PhD candidates, as evidenced by his research on PhD delays and academic careers. His research program has been supported by multiple competitive grants, including his VENI project on integrating trauma-related background knowledge into statistical models and his VIDI project on expert knowledge and limited data. His work bridges theoretical methodological advances with practical applications across multiple domains. As director of the AI-LAB, he leads a team developing cutting-edge AI tools for knowledge discovery. His coordination of the ASReview project has created an international community of researchers applying active learning to systematic reviewing. He's also actively involved with the Utrecht Platform for Applied Data Science, fostering interdisciplinary collaboration on data-intensive research projects.

Dragos Truscanمشاهده پروفایل

استاد

• Software Testing
• Model-Based Testing
• AI in Test Automation
• +۷ مورد دیگر

Prof. Dragos Truscan is a Professor of Computer Science at the Faculty of Natural Sciences and Engineering within Åbo Akademi University , focusing on Information Technology and Mission Critical Software Systems . Research interests include validation and verification , automated test generation using model-based technology and AI , test left-shifting , and runtime monitoring of industrial software systems Active in Security Requirements Engineering , DevOps , Deep Learning , and Software Component analysis Recent work explores: 2025 - Reinforcement learning for security testing and visual fault localization 2024 - Explainability in network attack detection and hyperparameter optimization 2023-2024 - Model-based requirements engineering in large collaborative projects Scientific Recognition Best Paper Award (2024) Best Paper Award (2013) Academic Activities Organizing member of IEEE International Conference on Software Services Engineering (2025) Hosted scholars like Victoria Zsók and Dr. Dumitru Iulian Nastac

• Developmental Biology
• Cell Biology
• Image Analysis
• +۷ مورد دیگر

Karsten Siller is an Associate Professor at the University of Virginia's School of Data Science. He previously served as an Assistant Professor in the Department of Biology (College of Arts & Sciences) and held the role of Associate Director for Research Computing User Services. His work bridges data science with biology, focusing on scalable, reproducible data workflows and computational methods. Ph.D., Biology , University of Tübingen, Germany Dipl. Biol., Biology , University of Tübingen, Germany His research interests include: Building reproducible and resilient, scalable data analysis workflows for diverse computing environments. Developing user-friendly, extensible interfaces for modular data pipelines with integrated visualization. Advancing open-source software and computational tools for biology and image analysis. Key trends in his publications show expertise in: Fluorescence Lifetime Imaging (FLIM) for cellular metabolism studies. High-performance computing (HPC) for data pipeline management. Mechanistic insights into cell division and neurodegenerative processes via imaging and computational modeling.

Maciej Hukمشاهده پروفایل

استادیار

• Contextual Systems
• Data Mining
• Artificial Neural Networks
• +۹ مورد دیگر

Maciej Huk is an Assistant Professor at Wroclaw University of Science and Technology, working in the Department of Applied Informatics within the Faculty of Information and Communication Technology. He has been affiliated with the university since 2007, previously working in the Department of Artificial Intelligence and Department of Security of Computer Systems before moving to his current department in 2016. His academic credentials include Ph.D. and D.Sc. degrees in Computer Science from Wroclaw University of Technology (2007, magna cum laude) and an M.Sc. Eng. degree in Computer Science (magna cum laude). Dr. Huk's research spans multiple areas of artificial intelligence and computer science, with particular emphasis on contextual systems, artificial neural networks, and machine learning. His work explores contextual neural networks, selective attention mechanisms, genetic algorithms, and ensembles of classifiers. He has developed several research software systems including H2O Snowflake for distributed training of contextual neural networks, CxNNS, GACS for context-sensitive text mining, and DYDO for educational applications. His research bridges theoretical computer science with practical applications in IoT, embedded systems, and robotics. His publication record shows consistent productivity with research spanning from 2006 to the present (2025), demonstrating evolving focus from foundational neural network research to more applied contextual systems. The publication trends indicate a strong specialization in contextual neural networks, with recent work expanding into biomedical applications (CRISPR-Cas9 analysis), educational technology (attendance systems), and energy optimization. His work consistently addresses the challenge of context representation and utilization in machine learning systems. Dr. Huk serves as an Academic Editor for PLOS ONE Journal and Evolving Systems Journal, and has reviewed over 130 papers since 2016 for journals registered in ISI Web of Science. He has been actively involved in the academic community as a program and organization committee member for numerous conferences including ACIIDS (2016-2021), ICCCI (2016-2020), and others. He has chaired special sessions on Intelligent and Contextual Systems at IEEE conferences. His professional activities extend beyond pure academia, including serving as Software Architect/Data Analyses Architect at Gigaset Communications since 2008, and organizing clinical research including the Phase 3 randomized multicenter clinical study NCT04952519 on amantadine treatment for COVID-19 patients. He is certified to organize clinical trials with human subjects through NIH NIAID Good Clinical Practices 2.0.

Nejat YUMUŞAKمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Bioinformatics
• +۵ مورد دیگر

Prof. Nejat YUMUŞAK is a full Professor in the Department of Computer Engineering at Sakarya University's Faculty of Computer and Information Sciences, where he has built his academic career since 1995. His progression includes roles as Research Assistant (1993-1998), Assistant Professor (1998-2004), Associate Professor (2005-2011), and Professor (2011-present). Education PhD in Electrical-Electronics Engineering, Sakarya University (1995-1998) Thesis: Analysis of Power System Circuit Elements Using Artificial Neural Networks MSc in Electrical-Electronics Engineering, Dumlupinar University (1994-1995) Thesis: Calculation and Measurement of Transformer Parameters at High Frequencies BSc in Electrical-Electronics Engineering, Istanbul Technical University (1989-1993) Research Focus Prof. YUMUŞAK's research integrates artificial intelligence with multidisciplinary applications. Primary domains include: Bioinformatics & Medical AI : Developing deep learning models for COVID-19 mutation prediction, leukemia classification via blood smear analysis, and gastrointestinal anomaly detection using endoscopic data. Signal Processing & Security : Advancing acoustic event detection for security systems and vulnerability testing through intelligent fuzzing techniques. Optimization & Industrial Applications : Creating metaheuristic algorithms (e.g., enhanced Wild Horse Optimizer) for energy efficiency and Industry 4.0 digitalization. Publication Trends His recent articles (2022-2025) demonstrate strong emphasis on transformer architectures for virology, ensemble methods for medical diagnostics, and CNN/LSTM networks for genomic and acoustic data analysis. Over 50% of recent works focus on healthcare applications, particularly cancer and pandemic-related prediction models. Contact & Recognition Contact via nyumusak@sakarya.edu.tr or phone (0264) 264 58 94. His work has received 793 citations (Web of Science) with an H-index of 14.

Alexander W. Dowlingمشاهده پروفایل

دانشیار

• Chemical Engineering
• Computational Optimization
• Machine Learning
• +۷ مورد دیگر

Prof. Alexander W. Dowling is an Associate Professor at the Department of Chemical and Biomolecular Engineering within the College of Engineering at the University of Notre Dame. He holds joint appointments with the Department of Applied & Computational Mathematics & Statistics and is affiliated with the Center for Sustainable Energy at Notre Dame (ND Energy) and the Center for Nano Science and Technology (ND nano). His research combines chemical engineering, computational optimization, machine learning, and data science across three core themes: (1) molecular-to-systems (multiscale) modeling and optimization, (2) optimal design of experiments and statistical inference, and (3) machine learning for bridging timescales. Dowling earned his Ph.D. in Chemical Engineering from Carnegie Mellon University (2015) and a B.S.E. from the University of Michigan-Ann Arbor (2010). His research applications span energy markets and infrastructure, integrated energy systems, carbon capture and sequestration, circular economies for refrigerants, sustainable hydrogen production, critical minerals, and advanced separation technologies like membranes and ionic liquids. Education Ph.D., Carnegie Mellon University, 2015 B.S.E., University of Michigan-Ann Arbor, 2010 His recent publications focus on multiscale modeling frameworks, Bayesian hybrid models for uncertainty quantification, and machine learning applications in energy systems and materials design. These works emphasize techno-economic analysis, dynamic process optimization, and uncertainty mitigation strategies. Scientific awards include the 2025 James A. Burns, C.S.C., Award and the 2020 NSF CAREER Award (CBET-1941596). He actively mentors graduate students and postdoctoral researchers, with lab alumni including Dr. Bridgette Befort (2022), Dr. Xian Gao (2022), Dr. Ke Wang (2025), and Dr. Jialu Wang (2024).

İLKAY SİBEL KERVANCIمشاهده پروفایل

استادیار

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

İLKAY SİBEL KERVANCI serves as an Assistant Professor in the Department of Computer Engineering at Gaziantep University's Faculty of Engineering. Her academic career spans teaching and research in artificial intelligence, machine learning, and data mining with practical applications across finance and bioinformatics sectors. PhD in Computer Engineering, Çukurova University (2023) MSc in Informatics, Kahramanmaraş Sütçü İmam University (2017) BSc in Computer Engineering, Kocaeli University (2004) Her research centers on machine learning applications for cryptocurrency price forecasting using LSTM and GRU networks, neutrosophic logic implementations in regression problems, and drug-target interaction prediction. Recent work demonstrates expertise in hyperparameter optimization, time series analysis, and handling imbalanced datasets through hybrid feature reduction techniques. She actively contributes to advancing neural network architectures for financial and biomedical challenges. Publication trends reveal consistent focus on Bitcoin price prediction (6 publications since 2017), expanding into drug-target interaction modeling and neutrosophic applications. Her work bridges theoretical machine learning with industrial applications in cement manufacturing, stock markets, and pharmaceutical research through recurrent neural networks and optimization frameworks. Dr. Kervanci teaches graduate courses including Introduction to Artificial Intelligence Methods and Introduction to Data Mining Methods, alongside undergraduate courses such as Artificial Intelligence in Engineering and Discrete Mathematics, demonstrating commitment to computational education across academic levels.

Serkan Özbayمشاهده پروفایل

دانشیار

• Electrical and Electronics Engineering
• Signal Processing
• Computer Vision
• +۴ مورد دیگر

Serkan Özbay serves as an Associate Professor in the Department of Electrical and Electronics Engineering at Gaziantep University's Faculty of Engineering, Turkey. With academic appointments since 2003, he progressed from Lecturer to his current position (2024), maintaining continuous affiliation with the institution where he also completed all his degrees. His career demonstrates deep institutional integration and vertical advancement within the same department. His academic credentials were entirely earned at Gaziantep University: Doctorate (2007-2015): Institute of Science, Electrical and Electronic Engineering Master's (2003-2006): Institute of Science, Electrical and Electronics Engineering (Thesis) Licence (1997-2002): Faculty of Engineering, Electrical and Electronics Engineering Özbay's research centers on signal processing and machine learning applications, with significant contributions to computer vision for medical diagnostics (sinus segmentation, lung cancer detection) and security systems (image/video forgery analysis). His work bridges theoretical algorithms with practical implementations in edge computing and biomedical devices, evidenced by projects like real-time fall detection systems and metamaterial antennas. Recent publications show intensified focus on deep learning since 2020, particularly convolutional neural networks for medical imaging and object tracking. His publication trajectory reveals strategic interdisciplinary expansion: early work (2007-2015) concentrated on spectrum sensing and wireless communications, while post-2020 output pivoted toward AI-driven medical applications and digital forensics. Approximately 70% of his 22 publications (2019-2023) involve deep learning implementations, with strong institutional collaboration patterns—85% co-authored with Gaziantep University researchers. Journal publications predominantly appear in Q2-Q4 SCI-indexed engineering journals, while conference papers target international venues in computer vision and biomedical engineering. As an academic advisor, Özbay has supervised 16 graduate theses (1 PhD, 15 Master's) between 2017-2025, with 11 completed in 2020-2023 alone. Current advisees include students developing Raspberry Pi-based plate recognition systems and real-time fall detection solutions. He additionally mentors 15 undergraduate students in senior design projects annually, as evidenced by 2024 EEE498/499 project meetings. Administrative leadership includes Deputy Head of Department (2016), Erasmus Coordinator (2015-2016), and Institute Board membership (2020-2023).

• Machine Learning
• Artificial Intelligence
• Cybersecurity
• +۷ مورد دیگر

Prof. Dr. Virginijus Marcinkevičius is a Professor at Vilnius University , serving as the head of the Smart Technologies Research Group and the Artificial Intelligence Laboratory within the Institute of Data Science and Digital Technologies . He is also a Senior Researcher , Project Lead Researcher , and Group Leader . Based in Vilnius, Lithuania, he has been instrumental in advancing research in machine learning , artificial intelligence , cybersecurity , and natural language processing . Research Interests: Machine Learning & AI Cybersecurity & Threat Detection Natural Language Processing Hyperspectral Imaging & Remote Sensing Autonomous Systems & Robotics Big Data & Cloud Computing His work spans both theoretical and applied aspects, including IoT security , visual analytics , and intelligent decision support systems . Recent projects include the development of propaganda detection systems , hyperspectral unmixing algorithms , and autonomous driving agents . Doctoral Supervision: He has supervised 19+ PhD students and 5+ consultants , covering topics from machine learning in cybersecurity to neural machine translation and autonomous UAV navigation . Projects & Grants: He has led or contributed to 15+ national and EU-funded projects , including: CognitiveSTATS – COVID-19 data literacy platform Propaganda and Disinformation Research – ML-based detection DAMIS – Data mining system for national research Raštija 2 – Lithuanian language resource integration Publications: He has authored or co-authored 60+ peer-reviewed publications in journals like IEEE Access , Informatica , Frontiers in Psychology , and Machine Vision and Applications . Professional Memberships: He is a member of the Lithuanian Computer Society , Lithuanian Mathematical Society , and Lithuanian Operations Research Society .

• X-ray Computed Tomography
• Image Reconstruction Algorithms
• Medical Imaging Physics
• +۷ مورد دیگر

Professor Frederic Noo is a leading academic in the field of Medical Imaging at the University of Utah 's Radiology & Imaging Sciences department. With a PhD from the University of Liege, his work focuses on advancing X-ray Computed Tomography (CT) through innovative image reconstruction algorithms , cone-beam CT , and dual energy CT methods. He contributes to the CT Research Lab and collaborates internationally with experts like Michael Defrise and Hiroyuki Kudo. Research Interests : CT physics, beam hardening correction, deep learning in imaging, iterative reconstruction, and noise/resolution analysis. Collaborations : Extensive work with institutions in Germany (J. Hornegger), Japan (H. Kudo), and France (M. Defrise). Teaching : Co-author of a book chapter in Informatics in Medical Imaging (2012), demonstrating expertise in CT principles.

Jong-Weon Leeمشاهده پروفایل

استاد

• Augmented Reality
• Virtual Reality
• 3D Interaction
• +۴ مورد دیگر

Jong-Weon Lee is a Professor in the Department of Contents Software at Sejong University and a researcher at the eXtended Reality Research Center. With a focus on immersive technologies and human-computer interaction, he actively contributes to advancements in Augmented Reality (AR), Virtual Reality (VR), and 3D interaction systems. Ph.D. in Computer Science (2002) from the University of Southern California M.S. in Computer Science (1991) from the University of Wisconsin-Madison B.S. in Computer Science (1989) from Ohio University His research explores the intersection of AR/VR, deep learning, and practical applications in agriculture and crisis response. He develops lightweight CNN architectures for plant disease detection in low-resource environments, while also creating multimodal frameworks like CBRCL to detect hate speech in text-image pairs during geopolitical crises. His work extends to VR-based emotion detection systems for healthcare, leveraging neural networks to alleviate negative emotional states in real-time. Recent publications highlight his expertise in optimizing YOLO models for fire detection via Bayesian hyperparameter tuning, enhancing detection accuracy in large-scale datasets. His collaborations span international institutions, reflecting a commitment to cross-disciplinary innovation.

• Unmanned Aerial Vehicles
• Control Systems
• Sliding Mode Control
• +۴ مورد دیگر

Xuan Mung Nguyen serves as Professor in the Department of Aerospace Engineering at Sejong University, where he leads research in advanced control systems for unmanned aerial vehicles and spacecraft. His work bridges theoretical control engineering with practical aerospace applications, maintaining active collaborations across multiple engineering disciplines. Dr. Nguyen's research focuses on disturbance observer techniques , finite-time control systems , and attitude tracking algorithms for UAVs and spacecraft. His expertise spans both theoretical development of control laws and experimental validation, with particular emphasis on handling external disturbances and system constraints. The fingerprint analysis reveals dominant specialization in Quadcopter Engineering (100%) and Unmanned Aerial Vehicle systems (39%), with significant contributions to Sliding Mode Control (28%) and Computer Simulation (25%). Analysis of his recent publications shows an evolving research trajectory that increasingly integrates machine learning with traditional control theory. While maintaining core focus on aerospace control systems, his 2024-2025 work demonstrates expanding applications into computer vision for agriculture and cybersecurity, reflecting the transferable nature of his disturbance observer methodologies. Professor Nguyen maintains active research collaborations across national boundaries, as evidenced by his co-authorship on interdisciplinary projects spanning plant disease recognition and malware propagation modeling. His publication metrics show growing impact, with several papers receiving notable attention including blog mentions and news coverage.