جست‌وجوی استاد و پژوهشگر

Carla Teixeira Lopesمشاهده پروفایل

استادیار

• Information Retrieval
• Human-Computer Interaction
• Web Search Behavior
• +۳ مورد دیگر

Carla Teixeira Lopes is an Assistant Professor in the Department of Informatics Engineering at the University of Porto, Portugal, and a Senior Researcher at INESC TEC's Human-Centered Computing and Information Science Centre since May 1, 2014. She completed her PhD in Informatics Engineering from the University of Porto in 2013. Her research focuses on the intersection of information retrieval and human-computer interaction, with particular emphasis on information search behavior and the development of tools to enhance search effectiveness. Recently, she has concentrated on how context can improve health consumers' web search experience. Her recent publications demonstrate strong expertise in natural language processing, particularly in text readability assessment, text simplification using LLMs, cross-lingual entity linking in medical contexts, and Wikipedia quality assessment. Her work spans multiple disciplines including computer science, medical informatics, and information systems. Dr. Lopes has supervised multiple graduate students on diverse topics including non-machine-readable document analysis, linked data in cultural heritage, FAIR principles implementation, and health-related message categorization in online communities.

• Quaternary Geology
• Geomorphology
• Permafrost and Rock Glaciers
• +۴ مورد دیگر

Luc Braillard is a Lecturer in the Department of Geosciences at the University of Fribourg's Faculty of Science and Medicine. His work bridges geological and archaeological research, focusing on Quaternary processes, rock glacier dynamics, and human-environment interactions in Alpine and Jura regions. Primary Affiliation: Department of Geosciences, University of Fribourg Research Themes: Paleoenvironmental reconstruction, periglacial geomorphology, rock shelter archaeology His research integrates sedimentological, micromorphological, and tectonic analyses to study long-term landscape evolution. Recent work examines climate change impacts on xerothermic ecosystems and sediment cascades in high Alpine regions. Publications span from 2000 to 2024, with a 2024 Global Change Biology paper on cliff-edge forests as climate change models. Key methodologies include automatic camera monitoring of rock glaciers (2018), multi-method permafrost ridge analysis (2022), and lichenometry/geochronology for reconstructing postglacial landscape transitions (2020). Collaborative projects involve cross-border Swiss/French teams studying sediment dynamics in Molasse Basin contexts.

Pasi Fräntiمشاهده پروفایل

استاد

• Machine Learning
• Data Mining
• Pattern Recognition
• +۷ مورد دیگر

Pasi Fränti is a Professor of Computer Science at the University of Eastern Finland (UEF) since 2000, affiliated with the School of Computing within the Faculty of Science, Forestry and Technology. He holds MSc and PhD degrees from the University of Turku (1991 and 1994). His research focuses on machine learning, data mining, pattern recognition, and location-based systems, with notable contributions to clustering algorithms, image compression, and speech technology. Fränti leads the Machine Learning research group at UEF and has supervised 25 PhD students, covering topics like clustering, image/audio compression, and location-aware systems. His work bridges theoretical advances with practical applications in healthcare optimization, mobile services, and intelligent systems. He has published extensively, with over 79 journal articles and 167 conference papers, including 14 IEEE Transactions papers. Recent research highlights include optimizing health station locations via clustering algorithms, outlier detection improvements, and gamified location-based services (e.g., Mopsi). His interdisciplinary projects address challenges in urban planning, energy systems, and biomedical signal processing. Fränti actively critiques academic publishing practices, advocating for open science and peer review reforms. Key projects include the IMPRO initiative (2018–2023) supporting health/social services reform, and contributions to journals like Applied Computing and Intelligence . His lab develops tools for real-time data clustering and spatial analysis, with applications in mobile user behavior prediction and smart city infrastructure planning.

• Artificial Intelligence
• Cybersecurity
• Internet of Things
• +۴ مورد دیگر

Dr. Mabrouka Abuhmida is a Senior Lecturer in Computing and Mathematical Sciences at the University of South Wales. She holds dual leadership roles as Research and Innovation Group Leader and Course Leader for MSc/BSc AI programs. Her research focuses on Artificial Intelligence applications in Digital Healthcare Transformation, Cybersecurity, and Internet of Things (IoT), particularly in medical and infrastructure contexts. She leads initiatives spanning Computer Science, Data Science, and AI, emphasizing collaborations between academia and industry. Her research interests include Deep Learning, Natural Language Processing (NLP), and empowering women in STEM. Notable projects include developing Annie the chatbot, an empathy-focused NLP tool, and frameworks for IoMT security. She has pioneered work on AI-based intrusion detection in Industrial Control Systems (ICS) and cybersecurity dynamics in critical infrastructure. Publications highlight her cross-disciplinary work across AI ethics, structural health monitoring, and forensic data automation. Recognized for advancing women's participation in STEM, she actively bridges academic research with real-world tech adoption through student-business partnerships. Responsibilities include spearheading: IoT and Machine Learning initiatives Deep Learning in healthcare cybersecurity Natural Language Processing for case management Her lab focuses on AI-driven solutions for digital transformation challenges in healthcare and infrastructure sectors.

Dimitrios Kantzosمشاهده پروفایل

استاد

• Signal Processing
• Pattern Recognition
• Machine Learning
• +۲ مورد دیگر

Dimitrios Kantzos is Professor at University of West Attica's Department of Industrial Design and Production Engineering. His research centers on signal processing applications for environmental monitoring, biomedical systems, and seismic forecasting. Holds a PhD from University of Southern California with prior research funded by NASA and US Army. Key research domains include pattern recognition algorithms for earthquake precursor detection, machine learning approaches to air pollution analysis, and fractal characterization of environmental time-series data. Recent work focuses on multifractal techniques for seismic risk assessment. Publications demonstrate strong methodological focus on Detrended Fluctuation Analysis (DFA) and Multifractal DFA (MFDFA) applied to geophysical and environmental datasets. Article themes cluster around signal processing innovations for natural hazard prediction. No awards, supervised students, or laboratory affiliations are documented.

Hsiao-Chun Wuمشاهده پروفایل

استاد

• Statistical Learning
• Digital Signal Processing
• Wireless Communications
• +۹ مورد دیگر

Hsiao-Chun Wu is a Professor and holder of the Michel B. Voorhies Professorship at Louisiana State University (LSU), affiliated with the Division of Electrical & Computer Engineering within the School of Electrical Engineering and Computer Science. He earned his Ph.D. in 1999 from the University of Florida. His research focuses on statistical learning, embedded algorithms, digital signal processing, and wireless communications. Key areas include optimization techniques for detection and estimation, image/speech processing, and networked systems design. He has contributed to advancements in sensor deployment strategies, tensor-based signal processing, and machine learning applications for posture recognition and multimedia classification. Notable technical interests span computational photography, robust multichannel decorrelation, and energy-efficient data collection in wireless sensor networks. His work often integrates tensor analysis and graph convolutional networks to address challenges in high-dimensional data processing. Dr. Wu's academic contributions include pioneering methods for indoor line-of-sight coverage optimization and developing novel modulation recognition techniques. His research also addresses industrial IoT performance optimization and biomedical applications such as noninvasive activity recognition using mmWave radar.

Ira Assentمشاهده پروفایل

استاد

• Machine Learning
• Data Mining
• Clustering Algorithms
• +۵ مورد دیگر

Professor Ira Assent is affiliated with the Department of Computer Science at Aarhus University. Their research focuses on machine learning, data mining, and visualization, with applications in climate science, medical informatics, and computer vision. Professor Assent leads projects such as Light-IoT (analytics on compressed IoT data), WallViz (interactive visualization for massive datasets), and eData (anomaly detection in e-science). Their work emphasizes scalable algorithms, explainable AI, and interdisciplinary applications. Recent publications address rainfall prediction using deep learning, entity summarization via knowledge graphs, and efficient clustering techniques. Projects like RainAI demonstrate contributions to weather modeling and satellite data analysis. Collaborative efforts span academic and industrial domains, with a strong emphasis on practical, user-centric solutions. Selected research contributions include advancements in density-based clustering (e.g., AnyDBC, DISCO), parallel algorithms optimized for GPUs (HUNIPU), and visualization frameworks (AVID). Their work bridges theoretical computer science with real-world challenges, such as improving decision-making through interactive visualizations and enhancing medical information retrieval systems. Ongoing projects aim to address computational efficiency in large-scale data analytics while maintaining interpretability. Key areas of innovation include explainable AI (e.g., InteDisUX), climate modeling (DROPP), and hardware-accelerated algorithms (GPU-FAST-PROCLUS). These efforts reflect a commitment to advancing both foundational methods and applied technologies that impact diverse fields from environmental science to healthcare.

• Additive Manufacturing
• Laser Powder Bed Fusion
• Materials Science
• +۳ مورد دیگر

Kyle Perkins is a Research Fellow at the Institute of Photonics and Quantum Sciences within the School of Engineering & Physical Sciences at Heriot-Watt University, Edinburgh, United Kingdom. His research focuses on advancing metal additive manufacturing through laser-based processes and thermal modeling. Research Interests Perkins' work centers on critical challenges in industrial additive manufacturing: Laser Powder Bed Fusion (LPBF) process optimization Heat and mass transfer dynamics in melt pools Advanced in-situ monitoring techniques (Schlieren/X-ray imaging) Novel powder bed and sheet-based manufacturing methodologies Laser defocusing effects on metal processing Thermal calibration for high-precision metal 3D printing His research bridges fundamental thermal physics with practical manufacturing solutions to enhance part quality and process reliability in metal additive systems. Publication Trends From 2022-2025, Perkins' publications reveal a strategic progression from foundational heat source calibration (2022) to innovative process developments. His recent work (2024-2025) emphasizes multi-modal process monitoring, spatial laser control, and next-generation powder handling – demonstrating a clear trajectory toward real-time process control and industrial scalability in metal additive manufacturing. Scientific Awards No scientific awards were documented in the provided materials. Research Environment Perkins operates within Heriot-Watt's Institute of Photonics and Quantum Sciences, collaborating extensively on laser-material interaction studies. His work integrates experimental imaging, thermal modeling, and process engineering to address industry bottlenecks in metal additive manufacturing.

Volker Märgnerمشاهده پروفایل

پژوهشگر

• Image Processing
• Machine Learning
• Digital Humanities
• +۳ مورد دیگر

Dr Volker Märgner is a researcher at the Institute for Communications Technology, Technische Universität Braunschweig. He was actively involved in the Collaborative Research Center (SFB) 950 ‘Manuscript Cultures in Asia, Africa and Europe’ (2011–2020), leading Project Z03 on image processing methods for manuscript and character analysis from 2015 to 2019. His work focuses on applying computational techniques to cultural heritage preservation.

Ira Rabinمشاهده پروفایل

استاد

• Material Analysis
• Ancient Inks
• Manuscript Conservation
• +۲ مورد دیگر

Professor Dr. Ira Rabin is a leading researcher at the University of Hamburg's Centre for the Study of Manuscript Cultures and the Bundesanstalt für Materialforschung und -prüfung. Her work focuses on material analysis of written artefacts using advanced scientific methods. She leads projects including 'History of Writing: Black Inks and Writing Surfaces' and 'Recovery of Damaged Writing', which reconstruct historical writing technologies and develop conservation strategies. Her research investigates: Historical ink composition and degradation processes Non-invasive analysis techniques for fragile manuscripts Material properties of writing surfaces from papyrus to parchment Authentication methods for cultural heritage objects Environmental impact on manuscript preservation Recent publications demonstrate a strong focus on analytical techniques applied to diverse historical materials, from Carolingian manuscripts to Coptic documents. Her work consistently integrates material science with humanities research to understand scribal practices and historical contexts. Dr. Rabin directs laboratory facilities including specialized equipment for spectroscopic analysis and collaborates internationally on manuscript conservation projects. Her research group develops protocols for handling culturally significant artefacts while advancing scientific understanding of historical materials.

• Migration Technologies
• Border Surveillance
• Human Rights Law
• +۳ مورد دیگر

Petra Molnar is a lawyer and anthropologist at York University's Osgoode Hall Law School , serving as Associate Director of the Refugee Law Lab . She directs the global Migration and Technology Monitor project and holds a Faculty Associate position at Harvard's Berkman Klein Center. Her work bridges law, technology, and human rights, focusing on migration governance. As a Governor General's Literary Award finalist , she authored The Walls Have Eyes , analyzing AI's impact on border crossings across Mexico, Greece, and Palestine. Her current research explores community resistance to border technologies. Her key research areas include: Migration technologies and digital surveillance AI ethics in immigration enforcement Carceral technology critique Human rights impacts at borders Surveillance capitalism in asylum processes Techno-political collaboration with the US Her scientific engagement spans 15 recent publications analyzing: AI surveillance trends (2023-2025) Digital border walls and ecological impacts Crypto-rewarded border vigilantism (ICERAID) Biometric data in criminal databases UN human rights frameworks for tech governance Generative AI's role in migration control She has advocated for: Transparency in migration technology Centering lived experiences of migrants Accountability mechanisms for AI systems Decolonizing border tech discourse

Atul Raiمشاهده پروفایل

دانشیار

• Machine Learning
• Remote Sensing
• Hydrology
• +۷ مورد دیگر

Atul Rai is an Associate Professor at Wichita State University, specializing in interdisciplinary research spanning machine learning, remote sensing, and optimization algorithms. His work integrates computational methods with real-world applications in healthcare, environmental science, and distributed computing systems. Key areas of focus include medical imaging analysis, satellite data-driven hydrological modeling, and task scheduling in fog-cloud environments. Research emphasizes innovations such as fuzzy logic-based medical image segmentation, deep learning for emotion classification in music, and genetic algorithm optimizations. Rai's contributions extend to ecological assessments using satellite technology, particularly in monitoring endangered species like the Ganga River dolphin. His publications reflect a blend of technical advancements and methodological innovations, addressing challenges in transdisciplinary collaboration frameworks and corporate financial disclosure practices. While no specific awards are documented, his extensive academic output highlights sustained contributions to multiple fields.

• Evolutionary Computation
• Machine Learning
• Neurodegenerative Disease Prediction
• +۴ مورد دیگر

Dr. Francesco Fontanella is an Associate Professor at the Department of Electric and Information Engineering (DIEI) at the University of Cassino and Southern Lazio. His research focuses on evolutionary computation, machine learning, and their applications in neurodegenerative disease detection (e.g., Alzheimer’s and Parkinson’s), cultural heritage preservation, and environmental monitoring. He leads the AIDA Lab and has contributed to major initiatives like the HAND project for neuromuscular disease diagnosis and the C4E environmental monitoring system. Fontanella holds editorial roles in journals such as Pattern Recognition Letters , Genetic Programming and Evolvable Machines , and Applied Soft Computing . He has organized international workshops including the XVI International Workshop on Artificial Life and Evolutionary Computation (2022) and chairs the IEEE Computational Intelligence Society’s Task Force on Evolutionary Computer Vision. His teaching responsibilities include courses on computer science fundamentals, operating systems, and cybersecurity at both undergraduate and graduate levels. Notable projects include developing handwriting analysis systems for medical diagnostics and AI-driven tools for medieval manuscript analysis. Fontanella’s work bridges computational techniques with real-world challenges, emphasizing interdisciplinary collaboration between computer science and fields like healthcare, archaeology, and environmental science.

Angelos Amanatiadisمشاهده پروفایل

استادیار

• Robotics
• Autonomous Vehicles
• Computer Vision
• +۳ مورد دیگر

Angelos Amanatiadis is Assistant Professor of Robotics at the Department of Production and Management Engineering, Democritus University of Thrace (DUTH), Greece. He received his Diploma and Ph.D. (Hons.) from the Department of Electrical and Computer Engineering at DUTH in 2004 and 2009, respectively. His research focuses on autonomous robotic systems , computer vision , machine learning , and real-time embedded systems . His publications (35+ journal papers, 50+ conference papers) cover topics like autonomous vehicle localization, 3D object detection, educational robotics, and image processing. He has received the Stavros Niarchos Award for Promising Young Scientists and IKY Postdoc Scholarship . He contributes to the field as Associate Editor of the International Journal of Advanced Robotic Systems and HardwareX Journal, and as a program committee member for 10+ international conferences. His recent work includes leading the development of Greece’s first Hellenic Autonomous Vehicle (HAV) using deep learning and AI. He teaches courses on Control Systems, Big Data Analytics, Robotics, and Computational Intelligence. His research has been featured in media outlets like BBC, Bloomberg, IEEE Spectrum, and Popular Science.

• Statistical Learning
• Machine Learning
• Probability
• +۶ مورد دیگر

Stephan Clémençon is a Professor at Télécom Paris, working within the Information Processing and Communication Laboratory (LTCI) where he leads the Signal, Statistics and Learning (S2A) research team. His academic career spans multiple prestigious institutions including University of Paris X (2000-2005) and INRA Met@risk research unit (2005-07), with membership in the LPMA (Stochastic Modeling and Probability) laboratory of Paris 6 and Paris 7. Clémençon earned his PhD in Applied Mathematics from University of Paris 7 Denis Diderot with a visiting period at Stanford University's Department of Statistics (1997-1998). He currently serves as the responsible for the Specialized Master's in 'Big Data' at Télécom Paris and previously held the 'Machine Learning for Big Data' industrial chair (2013-2018), now actively involved in the 'Data Science and AI for Digitalized Industry and Services' chair. His research interests focus on statistical learning, probability, statistics, machine learning, stochastic processes, and nonparametric statistics, with applications spanning quantitative finance, biosciences, and signal/image processing. Clémençon has developed an 'immoderate taste for stochastic modeling and statistics applied in various application areas.' His extensive publication record (270 documents in HAL) demonstrates consistent research productivity with 15 recent publications (2020-2024) primarily focused on anomaly detection, ranking algorithms, statistical learning theory, and bias correction in machine learning. His work bridges theoretical statistics with practical applications in computer vision, telecommunications, and ethical AI considerations. Clémençon teaches advanced courses including Martingale Theory, Machine Learning, Advanced Nonparametric Statistics, and Big Data Projects at Télécom Paris, while also contributing to programs at Ensae Paris, Paris Diderot University, ENS Paris Saclay, and other institutions. His research team (S2A) operates within the Image, Data, Signal (IDS) department at Télécom Paris, focusing on the intersection of signal processing, data science, and statistical learning methodologies.