جست‌وجوی استاد و پژوهشگر

• Evolutionary Computation
• Genetic Algorithms
• Optimization
• +۵ مورد دیگر

Dirk Thierens is an Associate Professor in the Department of Computer Science at Utrecht University's Faculty of Science, specializing in Intelligent Systems within AI & Data Science. His academic career spans over 25 years, with continuous publications from 1996 through 2025, demonstrating sustained research activity and leadership in his field. He maintains an active research program with numerous collaborations, most notably with Peter A.N. Bosman, indicating a long-standing productive research partnership. Thierens' research focuses on evolutionary computation, particularly model-based evolutionary algorithms, genetic algorithms, and optimization techniques. His work has evolved from foundational genetic algorithm research in the late 1990s and early 2000s to more specialized model-based approaches in recent years, including significant contributions to Gene-pool Optimal Mixing Evolutionary Algorithms (GOMEA). His expertise spans single-objective and multi-objective optimization, permutation problems, mixed-integer problems, and real-valued optimization. In recent years, his research has expanded into applications in machine learning, particularly semi-supervised learning and neural network optimization. His publication record shows a consistent output of high-quality research, with numerous papers in top conferences like GECCO and journals in evolutionary computation. His most recent work (2023-2025) demonstrates continued innovation in synthetic data generation, neural network combination techniques, and parameterless evolutionary algorithms. The breadth of his work spans theoretical algorithm development, benchmarking methodologies, and practical applications in healthcare and other domains. While no specific scientific awards are mentioned in the available information, his extensive publication record, tutorial contributions at major conferences, and sustained research productivity over multiple decades indicate recognition within the evolutionary computation community. His tutorial work at GECCO conferences suggests he is considered an authority on model-based evolutionary algorithms. Thierens maintains an active research laboratory focused on evolutionary algorithms and their applications, with recent work exploring the intersection of evolutionary computation and deep learning. His research continues to advance both theoretical understanding and practical applications of optimization techniques in complex problem domains.

Thomas Blaschkeمشاهده پروفایل

پژوهشگر ارشد

• Geosciences
• Geoinformatics
• Remote sensing
• +۳ مورد دیگر

Thomas Blaschke is a Research Fellow at the Department of Geoinformatics - Z_GIS, University of Salzburg. His work bridges geospatial technologies, remote sensing, and sustainable urban systems, emphasizing object-based image analysis (OBIA) as a transformative paradigm in geographic information science. Research Areas : Geoinformatics, Remote Sensing, Spatial Research, Physical Geography, Cartography Blaschke’s publications highlight advancements in OBIA techniques, sustainable landscape management, and urban monitoring using geospatial technologies. His 2014 paper on Geographic Object-based Image Analysis (GEOBIA) demonstrates its integration into modern remote sensing workflows. He has received prestigious awards including the Christian-Doppler-Preis , Marie Curie Research Grant , and Fulbright Professorship , underscoring his international impact. His projects often involve interdisciplinary collaborations, supported by grants from institutions like the European Commission and University of South Carolina. Scientific Awards : Christian-Doppler-Preis des Landes Salzburg Marie Curie Research Grant Förderungspreis der Österreichischen Geographischen Gesellschaft Fulbright Professorship Provost Grant of the University of South Carolina

Stavros Vologiannidisمشاهده پروفایل

استادیار

• Control Theory
• Robotics
• Machine Learning
• +۳ مورد دیگر

Stavros Vologiannidis serves as an Assistant Professor in the Department of Informatics, Computer and Telecommunications Engineering at the International University of Greece. His academic career spans both teaching and research in control theory, robotics, and machine learning applications. Previously, he was associated with the Mathematics Department at Aristotle University of Thessaloniki where he completed his education and conducted postdoctoral research. Education: B.Sc. in Mathematics from Aristotle University of Thessaloniki (1997) Ph.D. in Control Theory from Aristotle University of Thessaloniki (2005) with dissertation titled 'ALGEBRAIC-POLYONYMICAL COMPUTING METHODS IN CONTROL THEORY' Dr. Vologiannidis' research focuses on Classical and Intelligent Control Theory, Robotics, and Machine Learning, with particular expertise in polynomial matrices and automatic control systems. His work bridges theoretical mathematics with practical engineering applications, especially in educational robotics and industrial control systems. He has developed several educational platforms including EUROPA, a ROS-based educational robot for teaching sensor integration and data acquisition. His publication record shows a clear evolution from theoretical control systems research toward applied machine learning and educational technology. Recent work demonstrates increasing focus on practical applications of AI in education, urban feature recognition, industrial monitoring, and robotics education across multiple educational levels from middle school through university. His research combines mathematical rigor with real-world implementation. Scientific Recognition: Excellence Scholarship in the 'Excellence Scholarships 2010' program of the Research Committee Total citations exceeding 250 with Scopus H-index of 9 Dr. Vologiannidis has secured numerous research grants and led multiple projects including 'Rapid Earthquake Damage Assessment Consortium – REDACt', 'Predictive Maintenance 4.0', and 'Development of computational methods for optimization of eigenvalue assignment problems'. He has collaborated extensively with institutions across Europe including UTIA Foundation in Prague and has participated in EU-funded projects like GALENOS and GN4-1 GÉANT Research and Education Networking. His laboratory work centers around the EUROPA educational robotics platform and the StreetScouting urban feature detection system, both of which integrate hardware, software, and educational applications. These projects demonstrate his commitment to translating theoretical research into practical educational and industrial tools.

Luis Miguel Bergasaمشاهده پروفایل

استاد

• Perception Systems
• Autonomous Vehicles
• Deep Learning
• +۴ مورد دیگر

Luis Miguel Bergasa is a Full Professor at the Department of Electronics, University of Alcalá (UAH), with a career spanning over 25 years. He leads the RobeSafe Lab (since 2010) and serves as Director of Digital Transformation at UAH (since 2022). His academic roles include heading the Department of Electronics (2004–2010) and coordinating multiple educational programs. He teaches Perception Systems (Master in Industrial Engineering) Intelligent Control Systems (Computer Science) Computer Vision (Computer Science) His research focuses on Perception Systems for Intelligent Vehicles , emphasizing driver behavior analysis, scene understanding, and sensor fusion via deep learning. He has authored over 300 papers and holds 9 patents. Notable recognitions include being ranked #81 in Computer Science (Spain) by Research.com (2025) and receiving 30+ awards in Robotics/Automotive fields. Recent publications highlight advancements in Transformer-based driver action recognition (2025) Infrastructure-vehicle cooperative frameworks (2025) 3D semantic segmentation for autonomous perception (2024) Simulation-to-reality gap bridging (2024) Scientific Leadership Senior Editor, IEEE Transactions on ITS (2025) International Program Committee roles in 15+ conferences (2024–2025) Co-founder of Vision Safety Technologies Ltd (2009–2016) He supervises 9 active PhD students and has advised 14 former PhD candidates. His group collaborates with institutions in Germany, USA, China, and Spain, including KIT, UC Berkeley, and Northwestern Polytechnic University.

Sven Mayerمشاهده پروفایل

استاد

• Human-AI Interaction
• Human-Robot Interaction
• Machine Learning
• +۷ مورد دیگر

Sven Mayer is a Professor at TU Dortmund University’s Faculty of Computer Science and leads the Chair for Human-AI Interaction. He previously held a postdoctoral position at LMU Munich’s Media Informatics group and completed his PhD at Carnegie Mellon University (2014–2018). His research focuses on Human-AI/Robot Interaction , machine learning for touch systems , and sensor fusion . Recent work includes super-resolution capacitive touchscreens, pose estimation via smartphones, and city-scale sensing using retroreflective markers. The 15 most recent publications highlight trends in AI-driven interaction design , robot expressions , and smartphone-based sensing , with keywords spanning robotics, machine learning, and smart cities. Subdomains include contextual awareness, ergonomic constraints, and novel touch input paradigms. Scientific contributions recognized with Honorable Mention for 'Flyables' (2022) Honorable Mention for ISS piano learning paper (2022) . He has supervised projects in augmented reality and human-robot interaction courses , and frequently contributes to conferences like CHI, UIST, and MobileHCI as both author and organizational leader (e.g., Publications Chair at CHI 2022).

Katharina Dobsمشاهده پروفایل

پژوهشگر

• Visual Cognition
• Computational Neuroscience
• Face Perception
• +۴ مورد دیگر

Dr. Katharina Dobs is a faculty-level researcher at Justus Liebig University Giessen, based in the Department of Psychology and Sports Science and soon moving to the Department of Mathematics & Computer Science. She leads the Visual Cognition & Computational Neuroscience (VCCN) Lab, where she integrates state-of-the-art machine-learning models with human behavioural and neural data to decipher the computations underlying rapid visual scene and face perception. Education & Career: While explicit degree details are not provided, Dr. Dobs currently holds a prestigious LOEWE Start-Professorship and an ERC Starting Grant (DEEPFUNC), underscoring her leading role in computational vision research. Research Interests: Computational principles of human visual perception Functional organisation of the visual cortex Deep neural network models of object and face recognition Integration of facial form and motion cues Temporal dynamics of face perception using MEG and fMRI Across her recent publications, a clear trend emerges toward leveraging deep convolutional networks to explain category-selective responses in the human brain, particularly for faces. Her work repeatedly demonstrates how artificial networks trained on ecologically relevant tasks spontaneously develop brain-like functional specialisation, providing computational evidence against domain-specific expertise hypotheses. Scientific Awards & Funding: ERC Starting Grant “DEEPFUNC” LOEWE Start-Professorship Supervision & Collaboration: Dr. Dobs currently supervises PhD students Elaheh Akbarifathkouhi and Hilal Nizamoglu. She co-leads multiple SFB (Collaborative Research Centre) projects, including Project S “Deep Learning: Unleashing the Potential” with Prof. Roland Fleming and Project C9 “Factors influencing categorical face processing” with Dr. Benjamin de Haas. The VCCN Lab is additionally affiliated with the CRC Cardinal Mechanisms of Perception, the research cluster The Adaptive Mind, the Center for Mind, Brain and Behavior, and the Center for Applied Computer Science and Data Science (ZAD).

• Visual Perception
• Color Vision
• Sensorimotor Integration
• +۴ مورد دیگر

Karl R. Gegenfurtner is a Professor of General Psychology at the Department of Psychology, Justus Liebig University Giessen. His research focuses on information processing in the visual system, particularly the interplay between low-level sensory processes, high-level visual cognition, and sensorimotor integration. He investigates how complex scenes are perceived, represented in the brain, and used to drive motor systems, with a specialization in color perception, material property recognition, and eye movement dynamics. Ph.D. in Experimental Psychology, New York University (1990) Diploma in Psychology, University of Regensburg (1986) Habilitation in Medical Psychology and Behavioral Neurobiology, University of Tübingen (1998) His work bridges visual neuroscience with computational modeling, examining color categorization in neural networks, cortical mechanisms of color vision, and dynamic recalibration of visual perception during eye movements. Recent projects include Color 3.0: An object-oriented approach to color (ERC Advanced Grant) and Dynamics in Vision and Touch (Marie Curie Actions). Publications highlight advancements in understanding saccadic suppression, predictive eye movements, and chromatic adaptation timelines. Scientific awards include the Wilhelm Wundt Medal (2016), Rank Prize Funds Lecture (2014), and ERC Advanced Grant (2020–2025). He has served on editorial boards of Journal of Vision , Vision Research , and Perception , and led initiatives like the Neuroscientific Workflow Assistance (NOWA) project. Collaborations span institutions in Germany, the U.S., Australia, and the U.K., with a focus on perception-action loops and neural mechanisms underlying visual stability.

• Design Computing
• Spatial Modeling
• Machine Learning
• +۷ مورد دیگر

Sean Hanna is a Professor of Design Computing at The Bartlett School of Architecture , University College London , and a member of the UCL Space Syntax Laboratory . His interdisciplinary work bridges architecture, computational modeling, and machine learning.

• Visual Perception
• Object Recognition
• Visual Crowding
• +۷ مورد دیگر

Dr. Ramakrishna Chakravarthi is a Senior Lecturer at the School of Psychology, University of Aberdeen, where he leads the Consciousness, Attention, and Perception (CAP) Lab. His research focuses on the neural processes underlying visual perception, particularly object recognition, attention, short-term memory, and visual crowding. He employs psychophysics, EEG, and computational techniques to investigate fundamental questions about how we perceive and process visual information. Chakravarthi's research interests center on visual crowding as a window into object recognition mechanisms. His work examines how visual features are detected and integrated to form coherent object representations, with particular attention to spatial attention, numerosity processing, and time perception. His lab investigates neural oscillations in attention and awareness, the neural basis of crowding, subitization processes, and time perception using advanced methodologies including Steady State Visual Evoked Potentials, Multivariate Classifiers, and Representational Similarity Analysis. Analysis of his recent publications reveals a consistent focus on visual crowding as a fundamental phenomenon for understanding object recognition. His work spans multiple levels of analysis from behavioral psychophysics to neural mechanisms, with increasing integration of computational approaches. Recent research has expanded into social aspects of perception, particularly social agency and temporal binding in social interactions, while maintaining his core focus on visual processing mechanisms. Dr. Chakravarthi has successfully secured significant research funding, including a £344,000 BBSRC grant as co-PI on 'Neural mechanisms of long-range spatial vision' and multiple smaller grants totaling over £20,000. He has supervised numerous PhD students to completion and currently mentors several doctoral candidates across diverse projects related to visual perception, attention, and consciousness. The CAP Lab, which he directs, is part of the Perception and Attention theme at the University of Aberdeen's School of Psychology. The lab maintains active research programs in visual crowding, attentional mechanisms, object recognition, and time perception, utilizing state-of-the-art psychophysics and EEG facilities. Chakravarthi also coordinates the Perception and Attention group meetings, fostering collaboration among researchers with related interests.

Nunung Nurul Hidayahمشاهده پروفایل

دانشیار

• Ethics and Religion in Organizations
• Audit
• Islamic Accounting and Finance
• +۲ مورد دیگر

Dr. Nunung Nurul Hidayah is an Associate Professor in Accounting at Southampton Business School , specializing in ethics, religion, and hybrid organizations within accounting and finance. She serves as a senior personal tutor for postgraduate accounting programs and contributes to the school's Ethics Committee . Her interdisciplinary work bridges Islamic finance, auditing practices, and organizational complexity. Education : PhD in Accounting (Aston Business School, funded by IRTI-IDB) Previous Institutions : Lecturer at Aston Business School; Lecturer and Head of Accounting Department at University of Al Azhar Indonesia Her research focuses on ethical governance , religious influence in organizations , and hybrid operational models , particularly in government audit work, philanthropic organizations during crises, transnational academia, and women's leadership challenges. Recent publications analyze emotion in auditing, sustainability assurance gaps, and spiritual dynamics in Islamic finance. Key research trends include: Emotional labor in public sector auditing Hybridity in microfinance and philanthropy Gender dynamics in corporate leadership Shariah compliance mechanisms Temporal structuring in commodity processing Quasi-judicial auditing technologies Scientific recognitions include: Best Researcher 2018 - Aston Business School Journal Business Ethics Best Paper Award (Apira Melbourne 2016) British Accounting Review Social Media Impact Award (2021) She supervises PhD students in business studies and teaches Business Research at undergraduate level, with prior doctoral-level instruction in financial accounting and research design. Her curriculum development spans Islamic finance, accounting frameworks, and IFRS implementation across three institutions.

Kazumi Matsumotoمشاهده پروفایل

دانشیار

• Fluency in Second Language Learners
• Second Language Writing
• Intercultural Competence of Second Language Learners

Kazumi Matsumoto serves as Associate Professor of Japanese within the Department of Modern Languages and Classics at Ball State University's College of Sciences and Humanities. Her academic profile centers on empirical research in second language acquisition with specialized focus on Japanese language pedagogy and learner performance metrics. Her educational credentials include: Ph.D. in Applied Linguistics (Japanese), Purdue University (2010) Dr. Matsumoto's research program investigates fluency development , second language writing , and intercultural competence through rigorous quantitative methodologies. She pioneered the application of computational tools like the Fluency Calculator to objectively measure oral production characteristics, challenging traditional subjective fluency assessments. Her scholarly trajectory reveals evolving methodological sophistication—from early kanji recognition studies examining L1 transfer effects to current corpus-based analyses of oral dysfluency patterns across proficiency levels. This progression demonstrates consistent commitment to objective measurement in second language performance, particularly through computational analysis of learner corpora and tool development for fluency assessment. Dr. Matsumoto's advising activities and grant funding details are not specified in available institutional materials.

Jan Salmenمشاهده پروفایل

پژوهشگر

• Machine Learning
• Computer Vision
• Neural Networks
• +۳ مورد دیگر

Dr. Jan Salmen is a researcher at Ruhr University Bochum's Faculty of Computer Science, affiliated with the Institute of Neuroinformatics (INI). His work focuses on real-time systems, computer vision, and machine learning. Doctoral thesis: Efficient video-based driver assistance systems Salmen's research spans autonomous driving, traffic sign recognition, stereo vision, and sports analytics. He has contributed to benchmarks in traffic sign detection and soccer analysis. Publications highlight his expertise in image processing, pattern recognition, and sensor fusion for autonomous systems. Key trends include optimization of machine learning algorithms for real-time applications. He collaborates with interdisciplinary teams at INI, which integrates experimental psychology, neurophysiology, and robotics into artificial cognitive systems research.

Jo Herstadمشاهده پروفایل

دانشیار

• Human-Computer Interaction (HCI)
• Universal Design
• Inclusive Technology
• +۴ مورد دیگر

Jo Herstad is an Associate Professor in the Department of Information Systems Design at the University of Oslo , where she has worked since 2002. With a background in industry at Ericsson (1990-2003), Herstad bridges technical expertise with academic rigor. She teaches courses including: IN1030 - Introduction to Design, Use, Interaction (since 2017) IN2000 - Software Engineering with Project Work IN5480 - Interaction with AI and Autonomous Systems Research Focus: Her work explores Human-Computer Interaction through: Universal Design principles in digital environments Human-Robot Interaction for domestic applications Inclusive Technology across diverse populations Participatory Design methodologies Privacy Dynamics in smart homes Multimodal Systems for elderly care Key Publications (2017-2024) examine motion profiling in robotics, equitable digital feedback, and socio-technical systems. Her supervision includes 15+ master's theses on topics ranging from chatbot ethics to universal design in education. Current projects focus on: Artificial Intelligence and Sensor Technology Multimodal Elderly Care Systems (MECS) Podcast Accessibility (PUD) RHYME: Music Technology for All

Nicholas Warrenمشاهده پروفایل

استاد

• Polymer Science
• Flow Chemistry
• Online Monitoring
• +۲ مورد دیگر

Professor Nicholas Warren is a Chair in Sustainable Materials at the School of Chemical, Materials and Biological Engineering at the University of Sheffield. With a PhD from Sheffield and academic experience at Leeds University (2016-2024), his research integrates polymer chemistry with automation technologies. Education: University of Bristol (2005), University of Sheffield (PhD) Academic Positions: Postdoc at Sheffield (2005-2016), University Academic Fellow at Leeds (2016-2024), Associate Professor (2021-2024) Current Role: Chair in Sustainable Materials (2024-present) Research focuses on polymer science with flow chemistry , online monitoring , and artificial intelligence to advance sustainable materials. Key article trends include self-driving laboratories , multi-objective optimization , and nanostructured polymer systems . Scientific recognitions include: 2022 Macro Group UK Young Researchers Medal 2023 RSC Reaction Chemistry & Engineering Outstanding Early Career Paper Award Advisees span current and alumni PhD students like Dr Stephen Knox , Anna Morrell , and Dr Charlotte Pugsley . His team employs self-driving lab platforms that combine robotics, AI, and online analytics for accelerated materials discovery.

• Wireless Communication
• Machine Learning
• Signal Processing
• +۴ مورد دیگر

Prof. Dr. Haris Gačanin is a faculty member at RWTH Aachen University, affiliated with the Institute for Distributed Signal Processing under the College of Electrical Engineering. His research focuses on integrating machine learning with wireless communication systems, particularly in industrial IoT, edge computing, and network optimization. Current academic rank: Professor Contact: harisg@dsp.rwth-aachen.de Research Interests: Wireless systems, machine learning, signal processing, and network optimization. Key contributions include: Adaptive resource allocation in IIoT and vehicular networks AI-driven channel estimation and feedback mechanisms Security-oriented emitter identification via metric learning Federated/transfer learning for edge environments Hardware-efficient deep learning models for mmWave and THz communications Methodological Focus: Combines reinforcement learning, attention mechanisms, and robust neural architectures with practical implementations on FPGA and vehicular systems.