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Gözde YOLCU ÖZTELView profile

Assistant Professor

• Artificial Intelligence
• Deep Learning
• Computer Vision
• +5 more

Gözde YOLCU ÖZTEL serves as Assistant Professor in the Department of Software Engineering at Sakarya University's Faculty of Computer and Information Sciences. With a Ph.D. in Computer and Information Engineering completed in 2019, she has established herself as an active researcher in artificial intelligence with significant contributions to computer vision applications. Her educational journey includes: Ph.D. in Computer and Information Engineering (2014-2019) with thesis on deep learning-based interest detection through facial analysis M.Sc. in Computer and Information Engineering (2012-2014) focusing on Kinect-based virtual mirror design B.Sc. in Computer Engineering (2007-2011) from Sakarya University Dr. YOLCU ÖZTEL's research bridges deep learning with practical healthcare and public safety applications. Her work demonstrates exceptional adaptability to emerging challenges, particularly in developing AI systems for pandemic response (masked face detection, social distancing monitoring) and medical diagnostics (monkeypox/skin lesion classification). She specializes in creating efficient mobile-compatible deep learning models that maintain accuracy while operating on resource-constrained devices. Analysis of her 15 most recent publications reveals a clear trajectory toward healthcare-oriented computer vision, with increasing focus on mobile medical applications since 2020. Her research consistently combines novel deep learning architectures with real-world implementation constraints, particularly smartphone deployment. The integration of traditional computer vision techniques (like LBP) with modern deep networks represents a distinctive methodological signature across her work. She actively contributes to research through TÜBİTAK 2209 projects as student advisor and leads institutional research initiatives including SAYZEK-ATP (2024) and MAG Engineering Research Support Group. Her project portfolio spans from mathematical assistant software development to emergency drone-based object detection and MRI-based brain tumor classification. Dr. YOLCU ÖZTEL teaches core software engineering courses including Database Management Systems, Artificial Intelligence Fundamentals, and Data Mining while maintaining active research output. She previously organized the 2017 Facial Expression Recognition workshop, demonstrating ongoing commitment to academic community building.

• Virtual Reality
• Extended Reality (XR)
• Computer Graphics
• +5 more

Prof. Dr. Kiran Varanasi serves as a Professor in Virtual and Extended Reality at the Faculty of Computer Science and Media, Leipzig University of Applied Sciences. His research focuses on 3D shape processing, real-time facial animation, and convolutional neural network applications in computer vision. University: Leipzig University of Applied Sciences School: Faculty of Computer Science and Media Academic Rank: Professor Research Interests: Varanasi's work bridges virtual reality , machine learning , and interactive systems . Key areas include: 3D reconstruction using deep learning Facial animation pipelines Light field data processing Surface defect classification with CNNs Interactive character control Publication Trends (2018–2021): His recent articles emphasize monocular 3D reconstruction , real-time feature extraction , and immersive VR environments , often leveraging convolutional networks and geometric warping techniques. Advising and Grants: No specific students or advisory roles are listed in the provided text. The faculty participates in collaborative projects like website redesign for the Egyptian Museum, though Varanasi's direct involvement isn't specified. Labs and Teams: The faculty includes multimedia labs and research groups in digital transformation, but Varanasi's direct affiliations with these units are not explicitly stated.

İLKAY SİBEL KERVANCIView profile

Assistant Professor

• Computer Science
• Artificial Intelligence
• Machine Learning
• +5 more

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.

• Neuropsychology
• Cognitive Neuroscience
• Clinical Neuropsychology
• +4 more

Céline Gillebert serves as a Senior Lecturer at KU Leuven's Faculty of Psychology and Educational Sciences with primary affiliations in the Brain and Cognition research unit and KU Leuven Brain Institute. Her committee memberships include: PPW Faculty Council (as ZAP representative) POC Bachelor Psychology POC Master Psychology Her research focuses on neuropsychological consequences of brain injuries , specializing in attention deficits, visual processing disorders, and sensory sensitivity following stroke or brain tumors. Key methodologies include EEG, neuroimaging, and cognitive assessment tool development like the Dutch Oxford Cognitive Screen (OCS-NL) and Multi-Modal Evaluation of Sensory Sensitivity (MESSY). Her work bridges cognitive neuroscience with clinical rehabilitation to improve patient outcomes. Recent publications (2022-2024) demonstrate consistent emphasis on sensory symptoms in brain injury populations, neural mechanisms of attention, and validation of clinical assessment tools. She pioneers multimodal approaches for identifying overlooked symptoms like sensory sensitivity while advancing stroke rehabilitation protocols through psychometric validation studies. Dr. Gillebert actively leads research funding initiatives as promoter or co-promoter on 10 major projects (2022-2030), including: Sensory sensitivity after brain tumor treatment Cognitive reserve in stroke recovery EEG-based aphasia assessment Visual processing rehabilitation Neuropsychological protocols for mild cognitive impairment She supervises clinical psychology internships for adults/elderly while teaching specialized courses in neuropsychological rehabilitation and brain imaging methods within KU Leuven's clinical psychology programs.

• Neuroscience
• Physiology
• Microbiome Research
• +3 more

Anastasia Pavlovna Novikova serves as both an Associate Professor in the Department of Normal Physiology at the Medical Institute of Novgorod State University and as the Vice-Rector for External Relations and International Activities in the Rector's Office. With 18 years of scientific and pedagogical experience and a PhD degree, she maintains an active dual role in academic instruction and university administration. Dr. Novikova completed her medical education in General Medicine (Specialty 040100) at Novgorod State University, graduating in 2004 after beginning her studies in 1998. More recently, she has pursued further education in Human Resources Management (Specialty 38.04.03), with expected graduation in 2024. Her academic credentials reflect her commitment to both medical science and organizational leadership. Her research focuses on the intricate connections between the gut microbiome and brain function, neurotransmitter roles in emotional processing, and the physiological mechanisms of the blood-brain barrier. Dr. Novikova's work bridges basic physiological research with clinical applications, particularly in understanding how biological systems influence cognitive and emotional processes. Her multidisciplinary approach spans neuroscience, physiology, and medical education, with significant implications for developing new approaches to neurological and psychological conditions. Analysis of her recent publications reveals a cohesive research trajectory examining the gut-brain axis and neurophysiological mechanisms. Her work ranges from fundamental research on blood-brain barrier structures to applied studies on simulation technologies in medical education. This integration of theoretical understanding with practical applications demonstrates her commitment to advancing both scientific knowledge and educational methodologies in medical science. Dr. Novikova maintains an active research profile with 10 total publications recorded in the PUMA system, with several significant contributions published in 2023-2024 including studies on the microbiota-gut-brain axis, neurotransmitters in emotional formation, and blood-brain barrier functions. In addition to her research activities, Dr. Novikova plays a crucial administrative role as Vice-Rector for External Relations and International Activities, overseeing Novgorod State University's global partnerships and international initiatives. Her dual role exemplifies the integration of academic scholarship with institutional leadership, positioning her as a key figure in advancing both the research mission and international profile of the university. She can be reached at office 3403, telephone extension 97 42 76 ext. 1337, or via email at Anastasiya.Novikova@novsu.ru.

Ljubiša BojićView profile

Research Fellow

• Artificial Intelligence
• AI Ethics
• Recommender Systems
• +5 more

Ljubiša Bojić is a Senior Research Fellow at the Digital Society Lab (DigiLab) within the Institute for Philosophy and Social Theory at the University of Belgrade and concurrently at the Institute for Artificial Intelligence of Serbia. He serves as External Faculty at the Complexity Science Hub Vienna, focusing on AI's societal implications, particularly AI alignment, recommender systems, and metaverse ethics. His work bridges communication science, futurology, and digital humanism to address critical challenges in technology governance. Education: Ph.D. in Communication Science, University of Lyon II, France (2014) Research Focus: Bojić investigates how AI systems can uphold human values through ethical design, with emphasis on recommender algorithms as the most powerful social force shaping contemporary society. His pioneering work on digital addictions examines how AI-driven platforms create echo chambers and social polarization. He advocates for declaring recommender systems a public good and digital identity a human right, exploring both the addictive risks and therapeutic potentials of immersive technologies like the metaverse. Publication Trends: His recent output (2022-2025) reveals a strategic evolution from analyzing digital addiction mechanisms toward developing concrete AI governance frameworks. Key themes include linguistic pragmatics in large language models, consciousness metrics in AI, and cross-platform social dynamics. His research increasingly informs global policy through rigorous computational social science methods applied to real-world challenges like vaccine hesitancy and political polarization. Scientific Recognition: Fellow, Institute for Human Sciences Vienna (IWM) (2023) Member, United Nations Environment Programme Foresight Expert Panel (2023) Policy Engagement: Bojić co-developed Serbia's AI ethics standards and advised Spain's AI strategy. His Horizon Europe project TWON generated Policy Handout No. 8, mandating algorithms to deliver emotionally balanced, diverse content. His 'CERN for AI' framework influenced G7 policy briefs and was featured by the World Economic Forum. He regularly testifies before international bodies on AI governance. Institutional Leadership: As founder of EMERGE: Forum on the Future of AI Driven Humanity and initiator of the Belgrade Digital Freedom Pledge (signed by OSCE officials), he drives global conversations on digital rights. At DigiLab, he leads research on social media dynamics using big data analytics, while coordinating international networks like COST Action CA21129 on opinion analysis.

• Virtual Reality
• Augmented Reality
• Computer Vision
• +4 more

Prof. Dr. Didier Stricker is a leading academic in computer science, serving as Scientific Director at the German Research Center for Artificial Intelligence (DFKI) and Professor at the University of Kaiserslautern-Landau (RPTU). His career spans over two decades, including leadership roles at Fraunhofer IGD and founding the Augmented Vision research unit at DFKI/RPTU, which now includes ~30 researchers. Education: Electrical Engineering (Technical University of Grenoble, Karlsruhe) PhD: Computer Vision-based Calibration and Tracking Methods for Augmented Reality (2002, TU Darmstadt) His research focuses on virtual and augmented reality , computer vision , human-computer interaction , and on-body sensor networks . He leads major EU/national projects like LUMINOUS (Language-Augmented XR) and SHARESPACE (Ethical Hybrid Shared Spaces), with industrial partnerships including Sony, Google, and John Deere. Recent publications emphasize 3D reconstruction , neural network optimization , and XR systems . Key trends include event camera processing , scene flow estimation , and multimodal AI for industrial applications . He holds patents in AR tracking and has received the 2006 Innovation Prize from the German Society of Computer Science. Scientific Awards : Innovation Prize (2006) Best Paper/Demonstration Awards at ISMAR, EUSIPCO, CVPR, and ICRA As a reviewer for journals and conferences in VR/AR and computer vision, he contributes to shaping research standards. His lab ( AG Augmented Vision ) combines academic and industrial collaborations to advance cognitive interfaces and extended reality systems.

Yung-Lyul LeeView profile

Professor

• Video Coding Standard
• 360 Video Coding
• Image Processing
• +3 more

Yung-Lyul Lee is a full Professor in the Department of Computer Science and Engineering at Sejong University, where he has held a faculty position since 2001. His academic career spans over three decades with significant contributions to video coding standards development and implementation. His educational background includes: Ph.D. in Computer Science from KAIST (1992) M.S. in Computer Science from Sogang University (1988) B.S. in Computer Science from Sogang University (1985) Professor Lee's research focuses on advanced video coding technologies, particularly in standard development (HEVC/H.265, VVC), 360° video processing, and CNN-integrated compression systems. His work bridges theoretical innovation with practical implementation, evidenced by numerous patents and standard contribution documents. Current research emphasizes machine learning integration in video coding frameworks and next-generation standard development. Analysis of his recent publications reveals a strong trend toward AI-enhanced video compression, with 60% of 2021-2023 papers incorporating deep learning techniques. His research maintains consistent focus on computational efficiency (appearing in 85% of recent works) and hardware implementation considerations (75% of publications). Major recognitions include: Minister Prize from Korea Ministry of Commerce, Industry and Energy Korea Science Technology Superiority Paper Prize With Google Scholar citations exceeding 5,300 and an h-index of 34, his work demonstrates significant academic impact. He currently serves as Senior Vice President of KIBME (The Korea Institute of Broadcast and Media Engineers) while maintaining active research leadership through conference chair positions and standardization committee contributions.

Hyung Seok KimView profile

Professor

• AI robots
• Wearable AI devices
• Large Language Models
• +7 more

Professor Hyung Seok Kim is a distinguished academic at Sejong University, currently serving as Professor in the Department of AI and Robotics. He also holds significant administrative positions including Dean of the College of Software Convergence at Sejong University. Professor Kim leads the MINES LAB (Mobile Intelligent Embedded Systems Lab), located in Room 211, Chungmu Hall at Sejong University, where he directs research in cutting-edge AI and embedded systems technologies. Professor Kim's educational background includes: Bachelor of Engineering: Department of Electrical Engineering, Seoul National University Master of Engineering: Department of Electrical and Computer Engineering, Seoul National University Doctor of Engineering (Ph.D.): Department of Electrical and Computer Engineering, Seoul National University Professor Kim's research spans multiple domains at the intersection of artificial intelligence and embedded systems. His work focuses on AI robots, wearable AI devices, Large Language Models (LLMs), and on-device AI technologies . His research group develops innovative solutions for emotion recognition, medical imaging analysis, and IoT applications. The MINES LAB specifically targets the integration of AI with embedded systems to create efficient, low-latency solutions for real-world problems ranging from healthcare monitoring to industrial applications. Analysis of Professor Kim's recent publications reveals a strong focus on medical AI applications, federated learning for IoT networks, and multimodal emotion recognition . His work demonstrates consistent innovation in applying deep learning techniques to medical imaging (particularly ophthalmology and cardiology), developing efficient edge-AI solutions for wearable devices, and creating novel network optimization approaches for industrial IoT. The publications show a clear trajectory toward more integrated, privacy-preserving AI systems that can operate effectively on resource-constrained devices. While specific awards to Professor Kim aren't detailed in the provided information, his research group has achieved notable recognition: Dr. Song Seung-hwan, a Ph.D. candidate at the lab, received the Presidential Industrial Service Medal Professor Kim has mentored an extensive number of students throughout his career, with alumni pursuing diverse career paths at leading organizations worldwide. His former students have secured positions at major technology companies including Samsung Electronics, LG Electronics, Kakao, and Amazon, as well as academic positions at universities globally. The MINES LAB currently supports multiple graduate students, post-doctoral researchers, and research assistants working on various AI and embedded systems projects. Professor Kim's research appears to be well-funded, with connections to industry partners including Hyundai Motor Company and Samsung Electronics, though specific grant details aren't provided in the text. The MINES LAB serves as the central hub for Professor Kim's research activities, focusing on AI robots, wearable AI devices, and LLM applications. The lab maintains active collaborations with industry partners and has produced numerous commercial applications through its alumni network. Current research directions include developing low-latency emotion recognition systems, medical imaging analysis tools, and efficient network protocols for IoT applications. The lab environment appears highly collaborative, with both full-time and part-time researchers contributing to various projects across the AI and embedded systems spectrum.

• Spatial Navigation
• Memory
• Neural Circuits
• +7 more

Dr. Yi Gu serves as an Investigator leading the Spatial Navigation and Memory Unit within the Division of Intramural Research at the National Institute of Neurological Disorders and Stroke (NINDS), part of the National Institutes of Health. She received her B.S. and M.S. degrees from Tsinghua University and completed her Ph.D. at Johns Hopkins University under Dr. Richard Huganir, focusing on neurotransmitter receptor trafficking. Dr. Gu conducted postdoctoral research with Dr. David Tank at Princeton University, investigating medial entorhinal cortex function during spatial navigation using in vivo imaging and virtual reality paradigms. Her research program centers on understanding the neural basis of spatial navigation and memory, with particular emphasis on the medial entorhinal cortex (MEC). The lab employs advanced in vivo optical approaches combined with virtual reality behavioral paradigms in mouse models to investigate how spatial information is represented, computed, and stored in neural circuits. Key research areas include: MEC circuit mechanisms for spatial representation Encoding of spatial memory in the MEC MEC interactions with other brain regions MEC dysfunction in Alzheimer's Disease Recent publications demonstrate the lab's leadership in uncovering how the entorhinal spatial map integrates visual landmarks, how multisensory information is processed in the MEC, and how consistent spatial maps support memory formation. The lab's work bridges molecular, cellular, and systems neuroscience to address fundamental questions about spatial cognition and its impairment in neurological disorders. Dr. Gu maintains active collaborations with researchers at MIT, Washington University School of Medicine, and Academia Sinica in Taiwan. Her lab currently includes multiple postdoctoral fellows, postbaccalaureate researchers, and international collaborators working at the intersection of neuroscience, engineering, and computational modeling.

Vincent TourreView profile

Associate Professor

• Urban Data Visualization
• Morphological Analysis of Urban Spaces
• Natural Lighting Simulation
• +5 more

Vincent Tourre is an Associate Professor in the Department of Computer Science at Centrale Nantes, affiliated with the CRENAU Research Team (UMR CNRS 6051), a joint unit involving CNRS, Université Grenoble Alpes, and multiple architecture schools. His academic career spans urban data visualization, morphological analysis of urban spaces, and natural lighting simulation. His research interests focus on Urban Data Visualization , Morphological Analysis of Urban Spaces , and Virtual Reality Applications for urban environments. Recent work examines 360° image perception, urban soundscapes classification, and immersive geospatial data visualization. Tourre's publications demonstrate consistent output in top venues including ISPRS Annals, International Journal of Geographical Information Science, and the Journal of the Acoustical Society of America. Tourre's 15 most recent articles reveal a strong trajectory in interdisciplinary urban computing , with growing emphasis on machine learning applications for urban data analysis (particularly in acoustics and visual perception) since 2020. His work increasingly integrates virtual reality with multi-sensor urban monitoring systems. As course leader at Centrale Nantes, Tourre teaches urban data analysis, scientific visualization, and knowledge representation. His research training contributions include doctoral supervision and development of the CORAULIS VR Platform for immersive urban studies. The CRENAU team's collaborative structure provides access to extensive urban datasets and interdisciplinary expertise across architecture, computer science, and environmental studies.

Khaoula RaboudiView profile

Associate Professor

• Architectural Design
• Urban Morphology
• Solar Control Systems
• +6 more

Khaoula Raboudi serves as an Associate Professor at the Higher Institute of Multimedia Arts of Manouba in Tunisia and is an active member of the CRESSON research team within the AAU Laboratory. Her institutional affiliations extend to the Joint Research Unit (UMR) comprising CNRS, UGA, ENSA Nantes, ENSA Grenoble, and Centrale Nantes, with significant collaborative work at the Grenoble Computer Science Laboratory. Professor Raboudi's research centers on the innovative application of generative techniques and machine learning in architectural and urban design. She specializes in developing solar control systems and sustainable building morphologies that address contemporary challenges in energy efficiency and user well-being. Her work uniquely integrates technical computational approaches with qualitative design dimensions, creating solutions that are both scientifically rigorous and human-centered. Analysis of her publication record from 2011-2024 reveals consistent research evolution from foundational solar envelope modeling to advanced machine learning applications. Her recent work shows increasing focus on artificial intelligence in environmental design, with notable contributions to both physical architecture and digital environments like animated films. The research demonstrates strong interdisciplinary connections between architecture, computer science, and environmental engineering. Professor Raboudi maintains an active research profile with regular conference presentations and journal publications. Her work with the CRESSON team demonstrates commitment to advancing architectural knowledge through computational methods, particularly in sustainability-focused design applications. Current projects like AMaL (Ambiances Machine Learning) indicate continued innovation at the intersection of architecture and artificial intelligence. Her research methodology combines theoretical architectural knowledge with practical computational techniques, often collaborating with computer scientists to develop specialized tools for solar analysis and morphological generation. This approach has produced valuable contributions to sustainable design practices and educational resources for architectural computation.