جست‌وجوی استاد و پژوهشگر

Joergen Kornfeldمشاهده پروفایل

استاد پژوهشی

• Connectomics
• Machine Learning
• Deep Learning
• +۵ مورد دیگر

Dr. Joergen Kornfeld is a Research Group Leader at the MRC Laboratory of Molecular Biology in Cambridge, UK, where he leads the 'Connectomics of learned behaviour' group. He also maintains a connection with the Max Planck Institute for Biological Intelligence as a Guest Scientist. Previously, he was a Group Leader at the Max Planck Institute of Neurobiology in Martinsried, Germany (2021-2024). Dr. Kornfeld received his PhD in Biology (summa cum laude) from the Max Planck Institutes of Medical Research and Neurobiology, with research stays at NYU. He completed his Master's in Computational Biology and Bioinformatics at ETH Zurich and his Bachelor's in Molecular Cell Biology at Heidelberg University. Dr. Kornfeld's research focuses on understanding how animals store learned behaviors in their neuronal networks. His work centers on the zebra finch songbird as a model system, investigating how song memories are stored and retrieved from underlying brain circuits. His laboratory employs high-throughput 3D electron microscopy to map brain circuits at synaptic resolution, generating massive datasets that require advanced deep learning techniques for analysis. His team develops and applies state-of-the-art machine learning algorithms to reconstruct neural circuits and infer connectivity patterns. His recent publications demonstrate a clear trajectory toward increasingly sophisticated methods for neural circuit reconstruction and analysis, combining volume electron microscopy with deep learning approaches. The research spans multiple model organisms including zebra finches, mice, and zebrafish, with applications to understanding memory formation, behavioral control, and neural computation. Dr. Kornfeld leads a research team including Caitlin Gillespie, Nelson Medina, and Anastasia Sorokina. His laboratory is at the forefront of developing new methodologies for connectomics that bridge neuroscience, computer science, and engineering. He maintains active collaborations with multiple institutions including MIT, NYU, and the Max Planck Society.

Luis Antonio Belanche Muñozمشاهده پروفایل

استاد

• Machine Learning
• Kernel Methods
• Neural Networks

Luis Antonio Belanche Muñoz is a Professor at the Department of Computer Science , Faculty of Informatics of Barcelona (FIB) , Universitat Politècnica de Catalunya (UPC) . He is affiliated with research groups SOCO - Soft Computing and IDEAI-UPC - Intelligent Data Science and Artificial Intelligence Research Group . His career spans over 25 years, with 216 documented activities. His research focuses on Machine Learning , Kernel Methods , and Neural Networks . He has pioneered techniques in feature selection, similarity measures, and hybrid models connecting deep learning with kernel methods. His work applies to diverse domains including finance, microbiology, cancer diagnostics, and environmental engineering. Recent publications highlight trends in kernel matrix analysis using entropy, microbiome data integration , and drug resistance prediction in HIV. Earlier work includes knowledge-based systems for wastewater treatment diagnostics and educational technologies for MOOC environments. He has collaborated with 75+ researchers across UPC's research network, contributing to projects funded under Spain's State Research Plans and Catalonia's RIS3CAT strategy. His 2011 thesis on Feature selection in brain tumor MRS data demonstrates interdisciplinary applications.

• Hospitality
• Tourism
• Events Education
• +۱۱ مورد دیگر

Dr Ahmed Hassanien is a Lecturer and College Lead in the Business School at Edinburgh Napier University, with over 30 years of international experience in teaching and research. He is internationally recognized for his work in hospitality, tourism, and events education, with a focus on curriculum innovation, student transition, and inclusive pedagogy. Position: Lecturer and College Lead Institution: Edinburgh Napier University School: Business School Email: a.hassanien@napier.ac.uk Dr Hassanien's research centers on hospitality education, student engagement, and facilities management. His work on the Direct Entrant Transition Programme (DETP) has enhanced student belonging and informed institutional strategies. He has contributed extensively to pedagogical literature, particularly in group learning and inclusive curriculum design. His recent publications span destination image analysis, social enterprise in hospitality, innovation in events venues, and knowledge management. These works reflect a consistent engagement with both theoretical and practical aspects of tourism and hospitality management. Scientific awards include: Best Teacher of the Year (ENSA) Dr Hassanien has supervised numerous doctoral students to completion, serving as Director of Studies or second supervisor on topics ranging from heritage site development to digital wellbeing of migrants. He is also an external examiner for doctoral theses across the UK and Gulf institutions. He serves as Associate Editor of the International Journal of Customer Relationship Marketing and Management and on the editorial board of The International Journal of Human Capital and Information Technology Professionals . His leadership extends to industry partnerships, professional training, and international academic collaborations. He has co-edited and contributed to key texts such as Facilities Management and Development for Tourism, Hospitality and Events and co-authored the widely adopted textbook Hospitality Business Development , now in its third edition.

• Co-reference identification
• Computer Vision
• Robotics
• +۷ مورد دیگر

Dr. David Croft is a Research Fellow at De Montfort University, affiliated with the Faculty of Arts, Design and Humanities and the School of Design. His work bridges computer science and digital humanities, focusing on advanced data integration techniques for cultural archives. Research Fellow, School of Design, De Montfort University Member, Knowledge Media Design Research Group Dr. Croft's research expertise lies in co-reference identification , record linkage , and semantic similarity in short texts, particularly applied to museum and photohistory collections. His technical approaches include Fuzzy Logic , Clustering , and Term Comparison . He also explores Computer Vision and Robot Navigation , demonstrating a broad engagement with intelligent systems. His publications reflect a consistent focus on improving data matching in uncertain and heterogeneous historical datasets. Key themes include hybrid co-reference systems, efficient semantic metrics, and the application of color-based FIRE algorithms in robotics. These works span disciplines such as digital humanities, artificial intelligence, and information retrieval. Dr. Croft has taught C++ Programming and holds advanced degrees in computer science and robotics. He is actively contributing to interdisciplinary research that enhances access to and integration of cultural heritage data.

Anouk van der Hoornمشاهده پروفایل

استاد

• Neuro-oncology
• Brain tumor imaging
• Posttreatment imaging
• +۳ مورد دیگر

Prof. Anouk van der Hoorn holds a Professorship at the University of Groningen within the Faculty of Medical Sciences , affiliated with the University Medical Center Groningen (UMCG) . She leads research in Neuro-oncology, Brain tumor imaging, and Stroke intervention , focusing on advanced imaging modalities like MRI and PET/CT. Her work addresses critical topics such as post-treatment tumor monitoring, endovascular stroke therapies, and radiation therapy effects. Key Affiliations: Medical Annotation Specialist at Quantitas (since Mar 2023) Honorary Visiting Senior Research Fellow at the Brain Tumour Imaging Group, University of Cambridge (since Oct 2015) Principal Investigator in Innovative Clinical Studies and Long-term Treatment Consequences in Cancer (InClinS) and BASIC and Translational Research and Imaging Methodology Development in Groningen (BRIDGE) groups Research Interests: Her interdisciplinary work spans neuroimaging advancements for oncological and neurological disorders, emphasizing translational research and clinical impact. Key areas include: Diffusion-weighted imaging validation in glioma patients CT perfusion strategies for late-window stroke treatment Proton therapy’s neurocognitive effects in glioma Artefact reduction in DSC perfusion MRI Publications & Recognition: Over 140 peer-reviewed articles (e.g., in J Neurointerventional Surgery , JAMA Neurology ). Awards include the Hauw The-penning (2014) and contributions to the NWO-Veni grant (2020) . Grants & Supervision: Co-led the MR CLEAN-NO IV and MR CLEAN-LATE trials. Supervised 4 PhD candidates focusing on radiation therapy outcomes and imaging methodologies. Labs & Teams: Leads the BRIDGE group and collaborates internationally in neuroradiology and oncological imaging networks.

• Computer Science
• Cryptography
• Blockchain
• +۶ مورد دیگر

Giulia Fanti is an academic researcher affiliated with Carnegie Mellon University in the Computer Science Department . Her research focuses on privacy-preserving technologies, blockchain systems, and machine learning mechanisms, with significant contributions to federated learning, differential privacy, and cryptocurrency network design. Key Research Areas : Privacy in blockchain, Generative Adversarial Networks (GANs), Federated Learning, Game Theory applications to decentralized systems. Recent Publications : Her work explores liquidity provisioning in decentralized finance, truncated consistency models for image generation, and private data valuation frameworks. She has contributed to venues like NeurIPS, ICLR, and SIGMETRICS, often addressing privacy-utility tradeoffs.

• Data Mining
• Social Network Analysis
• Machine Learning
• +۳ مورد دیگر

Jaideep Srivastava is a Professor affiliated with Qatar Foundation (Doha, Qatar), University of Minnesota (Minneapolis, USA), and holds a PhD from University of California Berkeley. His research spans data mining, social network analysis, time series modeling, and health informatics. Recent work focuses on clinical deterioration prediction, sleep research, and computational analysis of pandemic behaviors. Key contributions in clustering algorithms and graph neural networks Active in multimodal learning and misinformation detection His publications from 2024-2022 demonstrate expertise in hierarchical clustering , large language model applications , and health data analytics . Articles often integrate machine learning , social network dynamics , and clinical monitoring systems . Current projects involve Covid-19 in-hospital mortality prediction , virtual influencer analysis , and low-light imaging techniques . Collaborations span institutions in Qatar, USA, and India with applications in urban mobility and precision medicine.

Stephen D. Christmanمشاهده پروفایل

استاد

• Cognitive Psychology
• Handedness
• Hemispheric Asymmetry
• +۲ مورد دیگر

Stephen D. Christman is a Professor in the Department of Psychology at the University of Toledo, affiliated with the College of Arts and Letters. He holds a Ph.D. from the University of California, Berkeley (1988) and leads research in the Cognitive Neuropsychology Lab. Education: Ph.D., University of California, Berkeley, 1988 His research focuses on Cognitive Psychology , particularly Handedness , Hemispheric Asymmetry , Episodic Memory , and Visual Perception . He investigates how interhemispheric interaction influences memory, decision-making, and personality traits. Recent publications highlight trends in handedness research spanning Political Psychology , Memory Encoding , and Interhemispheric Communication . Articles from 2020–2021 examine links between handedness and political behavior, while 2017–2018 studies focus on memory retrieval using fNIRS and motor manipulation. Key co-authors include John D. Jasper, Andrew Geers, and R. E. Propper. His work has been cited extensively in Psychology , Neuroscience , and Public Health domains.

Samel Arslanagicمشاهده پروفایل

دانشیار

• Antenna Engineering
• Metasurface
• Dielectric Materials
• +۱۲ مورد دیگر

Samel Arslanagic is an Associate Professor and Head of the Electromagnetic Systems group at the Department of Space Research and Technology, Technical University of Denmark. His expertise spans antenna engineering, metasurfaces, and microwave technologies, with a focus on advanced measurement techniques and electromagnetic wave control. Lead supervisor for multiple PhD projects on near-field antenna measurements and nano-photonic circuits. Active in developing water-based microwave devices and dielectric resonators for directional scattering. Key contributor to ESA-EurAAP facility comparison campaigns for mm-wave antenna validation. Research interests include metasurface design , bound states in the continuum , all-dielectric materials , and novel antenna architectures . His work bridges theoretical modeling with experimental validation in electromagnetic systems. Recent publications emphasize dielectric metasurfaces , spherical near-field measurement accuracy , and quantum-enhanced nanoantennas . He employs computational methods like finite element analysis and eigenmode decomposition for material parameter extraction and field averaging. He supervises interdisciplinary projects involving optical communication , microwave sensing , and acoustic imaging , often in collaboration with European examiners and industry partners.

• Natural Language Processing
• Computational Linguistics
• Artificial Intelligence
• +۷ مورد دیگر

David Martins de Matos is an Associate Professor at Instituto Superior Técnico (IST), Universidade de Lisboa , and a senior researcher at INESC-ID Lisbon within the Human Language Technology Lab . With a career spanning over three decades, he has taught subjects such as Compilers and Object-Oriented Programming since 1993. His research focuses on Natural Language Engineering , Automatic Natural Language Generation , Music Information Retrieval , and Machine Learning Applications in Healthcare . Education: B.Sc. in Electrical and Computer Engineering (IST, 1990) M.Sc. in Electrical and Computer Engineering (IST, 1995) on object-oriented programming in distributed systems Ph.D. in Systems and Computer Science (IST, 2005) on automatic natural language generation Research Interests: His work bridges Natural Language Processing and Computational Music Analysis , with applications in Health Informatics . He investigates semantic frame induction, dialog act recognition, and multimodal systems for chronic pain assessment, Alzheimer's detection, and music generation. His recent articles explore cross-modal retrieval, deep learning for pain narratives, and embodied semantics via fMRI. Scientific Contributions: He has published over 161 works, including 15 recent articles on chronic pain datasets, dialog act recognition, and music-language correlations. His awards include Senior Member status in ACM (SIGMM, SIGIR) and IEEE (Signal Processing Society, Computer Society) , and membership in the Order of Portuguese Engineers . Advising & Collaborations: He has supervised 111 doctoral and master's theses, mentoring students in topics like Visual Story Generation , Music Summarization , and Health Informatics . He collaborates with institutions such as IBM Research , Northwestern University's Feinberg School , and Universidade de Lisboa .

Emanuel Kitzelmannمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Neurosymbolic AI
• +۴ مورد دیگر

Prof. Dr. Emanuel Kitzelmann is a Professor of Applied Artificial Intelligence at Brandenburg University of Technology and Scientific Director of the AI Laboratory since 2023. His work bridges classical symbolic AI and modern machine learning, with a focus on integrating Large Language Models (LLMs) with structured knowledge bases like knowledge graphs and ontologies to enable reliable, explainable AI. He co-leads the SCALE-C research project on secure AI content generation for cybersecurity and directs the SmartRetrieve project on GraphRAG for campus chatbots. University: Brandenburg University of Technology Department: Computer Science and Media Rank: Professor His research spans hybrid neurosymbolic AI, inductive program synthesis, and robotics as AI application areas. Recent publications explore hallucination mitigation in LLMs, RAG techniques, and AI educational tools. He actively collaborates with industry partners like membraPure and REMINE GmbH, supervising student projects in cybersecurity, chatbots, and image-based analysis. Key initiatives include workshops on machine learning with ZF Getriebe Brandenburg and program committee roles for ECAI 2025 and IJCLR 2025.

Ivan Bratkoمشاهده پروفایل

استاد

• Machine Learning
• Knowledge-Based Systems
• Qualitative Modelling
• +۵ مورد دیگر

Ivan Bratko is a Professor of Computer Science at the University of Ljubljana's Faculty of Computer and Information Science. He founded the Artificial Intelligence Laboratory in 1985 and served as its head until 2017, remaining an active member. Until 2002, he also directed the AI group at the Jožef Stefan Institute. His academic journey includes B.Sc., M.Sc., and Ph.D. degrees in electrical engineering and computer science, all from the University of Ljubljana. Bratko's research spans machine learning, knowledge-based systems, qualitative modeling, intelligent robotics, heuristic programming, and computer chess. His work focuses on learning from noisy data, combining learning with qualitative reasoning, constructive induction, Inductive Logic Programming, and applications in medicine and dynamic system control. He has authored over 200 scientific papers and influential books including Prolog Programming for Artificial Intelligence (third edition, 2001), KARDIO: A Study in Deep and Qualitative Knowledge for Expert Systems (MIT Press, 1989), and Machine Learning and Data Mining: Methods and Applications (Wiley, 1998). His publication portfolio demonstrates consistent contributions to AI, with recent work emphasizing argument-based machine learning, qualitative modeling applications, and medical AI systems. These publications reveal strong interdisciplinary connections between theoretical AI and practical applications in environmental science, healthcare, and robotics. Fellow of the European Coordinating Committee for Artificial Intelligence (ECCAI) Member of the Slovene Academy of Arts and Sciences (SAZU) Former editorial board member of Artificial Intelligence , Machine Learning , Journal of AI Research , and other leading journals Co-founder and first chairman of the Slovenian AI Society (SLAIS) Bratko has secured numerous research projects including ARRS programs on artificial intelligence (2009-2020), the PARKINSCHECK project for Parkinson's disease detection, and European projects like X-MEDIA and XPERO. His laboratory serves as the central hub for AI research at the University of Ljubljana, fostering collaborations across medical, environmental, and industrial domains. He has mentored numerous researchers and maintained active collaborations through visiting positions at institutions including Edinburgh University, University of New South Wales, and Delft University of Technology.

• Planetary atmospheres
• Exoplanet observations
• Martian atmospheric dynamics
• +۳ مورد دیگر

Siteng Fan is an Associate Professor in the Department of Earth and Space Sciences at Southern University of Science and Technology. His research focuses on planetary atmospheres, exoplanet observations, and Martian atmospheric dynamics, combining observational data from missions like TGO and EMM with climate models such as Mars PCM. Education: B.S. in Space Science and Technology from Peking University (2015), Ph.D. in Planetary Science from Caltech (2020). Research Interests: Dynamics of planetary atmospheres (Mars, Titan, Pluto), photochemistry, microphysical processes, and light curve analysis for exoplanet surface inversion. Scientific Awards: Xie Yibing Young Scientist Award (2023), Marie Skłodowska-Curie Postdoctoral Fellowship (2022), NSFC Excellent Young Scientists (Overseas) (2022). Publication Trends: Recent work spans Mars climate modeling, Pluto/Titan atmospheric haze, exoplanet proxy studies, and radiative transfer algorithms.

• Snow Hydrology
• Mountain Studies
• Climate Change Impacts
• +۴ مورد دیگر

Dr. Anne Nolin serves as Professor in the Department of Geography at the University of Nevada, Reno, and contributes to the Graduate Program in Hydrologic Sciences. With three decades of expertise, she conducts pioneering research in snow hydrology and climate change impacts across global mountain systems from the Sierra Nevada to Greenland. Education Ph.D. in Geography, University of California, Santa Barbara (1993) Research Focus Dr. Nolin investigates snow-forest-fire interactions , snowpack vulnerability in warming climates , and mountains as social-ecological systems . Her methodological approach integrates satellite remote sensing (MISR, InSAR), field measurements, and computational modeling to address critical questions about "at-risk" snow and glacier dynamics. Publication Trends Recent work (2023-2025) reveals strong emphasis on citizen science integration for precipitation phase detection, multi-sensor fusion (optical/radar) for snow water equivalent mapping, and transdisciplinary frameworks for mountain sustainability. Key themes include post-wildfire snow hydrology, atmospheric river impacts on snowpack, and accessible tools like SnowCloudMetrics for democratizing snow data. Collaborative Leadership Co-leads NASA SnowEx field campaigns Principal investigator for Mountain Rain or Snow citizen science initiative Mentors graduate students in Geography and Hydrologic Sciences programs Active contributor to Mountain Sentinels Collaborative Network Research Infrastructure Directs the Computational Mountain Studies group and develops open-access platforms including SnowCloudMetrics and SnowCloudHydro, enabling community-driven snow data analysis. Her work bridges technical innovation with societal applications for water security in changing mountain environments.

• Computational Photonics
• Metamaterials
• Inverse Design
• +۵ مورد دیگر

Taavi Repän is an Associate Professor of Computational Photonics at the Institute of Physics, Faculty of Science and Technology, University of Tartu. He has held this position since December 2021, with his current appointment running until December 2025. Prior to this, he worked as a Post-Doc at Karlsruhe Institute of Technology from 2019 to 2021. Repän earned his Doctoral Degree in Physics from the Technical University of Denmark (DTU) in 2019, with his dissertation titled 'Dark-field hyperlens: High-contrast subwavelength imaging in optics and acoustics' supervised by Andrei Lavrinenko and Morten Willatzen. He received his Master's Degree in Physics from the University of Tartu in 2014, with a thesis on 'Sub-wavelength imaging with hyperbolic metamaterials' supervised by Siim Pikker and Sergei Zhukovsky. His educational background also includes a Bachelor's Degree in Physics from the University of Tartu (2009-2012). His research focuses on computational photonics, metamaterials, inverse design, and the application of neural networks to optical simulations. He leads the project 'Inverse design methods for integrating nanophotonic structures with gas sensors' (2022-2026) and participates in several other research initiatives related to wood valorization and structural optimization. His work bridges theoretical physics, computational methods, and practical applications in optical sensing and imaging. His publication record shows a consistent trajectory in computational photonics, with recent work heavily emphasizing the integration of machine learning techniques with electromagnetic simulations. The most recent publications demonstrate a strong focus on neural network applications for inverse design problems in nanophotonics, hyperbolic metamaterials, and plasmonic structures, indicating his leadership in applying AI to complex optical design challenges. Repän currently leads or participates in multiple research projects funded by the Estonian Research Council and other institutions, demonstrating his active role in the research community. His work spans fundamental theoretical investigations to applied research with potential industrial applications. His laboratory work appears to focus on computational modeling of photonic structures rather than experimental setups, with emphasis on numerical methods for designing and analyzing optical systems. His collaborations span multiple institutions across Europe, reflecting the international nature of his research.