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Klara DolkView profile

Senior Lecturer

• Gender pedagogy
• Equal treatment
• Children's rights
• +5 more

Klara Dolk is a Senior Lecturer at Stockholm University's Department of Child and Youth Studies, specializing in early childhood education. She teaches in the Programme in Early Childhood Education and is an active member of the Practice-based research in and about early childhood education and care (PraFF) research group. Her work focuses on integrating critical perspectives into preschool educational practices across Sweden. Dr. Dolk's research interests center on gender pedagogy, equal treatment, children's rights, and norm-critical perspectives in early childhood education. Her work examines how preschools address sensitive social issues including poverty, economic inequality, and social vulnerability. She employs poststructuralist and feminist theoretical frameworks to analyze power relations between children and adults within educational settings, with particular attention to how children's resistance can challenge and transform educational practices. Her recent publications (2020-2023) reveal a growing focus on how preschools handle controversial social phenomena like begging, economic inequality, and class perspectives. These works demonstrate her commitment to examining how norm-critical approaches can be effectively implemented in early childhood education to address societal challenges while maintaining educational integrity. Her research shows consistent engagement with ethical dilemmas in preschool settings and the practical challenges of implementing fundamental values education. Dr. Dolk has contributed significantly to scholarly discourse through numerous publications in both Swedish and international journals. Her work spans theoretical explorations of gender and power to practical examinations of how preschools address real-world social issues. She has published in journals such as Lambda Nordica, International Journal of Equity and Innovation in Early Childhood, and Tam-Revy, demonstrating her interdisciplinary approach to early childhood education research. As a dedicated educator and researcher, Dr. Dolk actively contributes to advancing critical approaches in early childhood education through her teaching, research projects on preschool attendance and social vulnerability, and participation in the PraFF research group. Her work bridges theoretical frameworks with practical educational challenges, providing valuable insights for both academic and practitioner communities in the field of early childhood education.

• Historical Linguistics
• Grammaticalization
• Collocations
• +5 more

Joop van der Horst is an associate professor at Károli Gáspár Reformed University, Faculty of Humanities and Social Sciences, specializing in Dutch Studies. Previously, he held professorial roles at KU Leuven and the University of Amsterdam. PhD in Linguistics (University of Amsterdam, 1986) MA in Dutch Language and Literature (Leiden University, 1975) His research spans historical linguistics, focusing on grammaticalization, collocations, and language change, as well as descriptive linguistics (syntax, morphology) and sociolinguistics. He has extensively studied fixed verb constructions, Dutch-French grammatical comparisons, and long-term language-society interactions. His publications and conference contributions from 2002-2013 highlight trends in Dutch syntax evolution, comparative German-Dutch verb patterns, and the decline of Renaissance language culture in Europe. Key themes include grammatical theory, European multilingualism, and language education reforms. LOT Publication Award Our Language Award He has supervised PhD students Freek Van de Velde, Judith Schonenboom, and Bert Cappelle, while serving as an opponent in doctoral defenses including Evie Coussé and Rinaldo Neels. His guest professorships include institutions in Berlin, Maastricht, Cologne, Münster, and Wroclaw.

Mahir ArzokyView profile

Lecturer

• Artificial Intelligence
• Search Based Software Engineering
• Intelligent Data Analysis
• +4 more

Dr Mahir Arzoky is a Lecturer in the Department of Computer Science at Brunel University London's College of Engineering, Design and Physical Sciences. He holds a PhD from Brunel University London (2015) and has extensive research experience in artificial intelligence and software engineering. His research focuses on: Artificial Intelligence and Intelligent Data Analysis Search Based Software Engineering (SBSE) Clustering algorithms and heuristic search methods Software refactoring and quality assessment Data mining applications in healthcare and education Analysis of his 15 most recent publications (2018-2022) reveals strong interdisciplinary work bridging computer science with healthcare (diabetes patient modeling, medical imaging) and education (chatbot design, algorithm visualization). His technical focus centers on clustering optimization, refactoring impact analysis, and explainable AI, with frequent use of empirical validation methods. Key collaborations include researchers like Stephen Swift, Steve Counsell, and Giuseppe Destefanis. Dr Arzoky has secured significant research funding through EPSRC grants including: AQUATIC project (EP/M024083/1): Assessing Test Suite Quality in Industrial Code FIAR-NET (EP/N011627/1): Fault Analyses in Industry and Academic Research Network His professional network includes active collaborations across computer science, healthcare informatics, and educational technology domains, with recent work extending into transformer models for healthcare SQL conversion and graph partitioning for software modularization.

• Biometrics
• Machine Learning
• Deep Learning
• +5 more

Raghavendra Ramachandra is a Professor at the Department of Information Security and Communication Technology (IIK) , within the Faculty of Information Technology and Electrical Engineering at the Norwegian University of Science and Technology (NTNU) in Gjøvik. His research focuses on biometric systems, particularly in face, fingerprint, and finger vein recognition, with emphasis on presentation attack detection, morphing attack detection, and deep learning applications. Current research projects include: SALT (2022-2026) : Developing privacy-preserving facial biometric authentication systems. OffPAD (2022-2025) : Creating cryptographic tools and presentation attack detection for fingerprint biometrics. SWAN (2015-2020) : Developing biometric countermeasures against presentation attacks. His recent publications demonstrate technical expertise in: Face morphing attack detection using vision transformers and point cloud networks Image fusion techniques for multispectral biometrics GAN-based synthetic data generation for security evaluation Explainable AI approaches for biometric verification Professor Ramachandra also supervises PhD and Master’s students, and has extensive experience in leading national and EU research initiatives.

Anthony ClarkView profile

Assistant Professor

• Autonomous Robotics
• Evolutionary Robotics
• Computer Vision
• +7 more

Anthony Clark is an Assistant Professor of Computer Science at Pomona College, where he has been teaching since 2020. Previously, he served as an Assistant Professor at Missouri State University from 2016 to 2020. He directs the ARCS (Autonomous Robotics and Complex Systems) Lab, which focuses on improving the robustness and adaptability of autonomous robots, particularly small-scale systems that can navigate unpredictable terrain and adapt to potential damage. Clark earned his Ph.D. in Computer Science from Michigan State University in 2016, where he worked under Dr. Philip K. McKinley, and his B.S. in Computer Engineering from Kansas State University, graduating magna cum laude. His research centers on making autonomous robots more robust and adaptive through optimization algorithms and multimodal systems. He specializes in evolutionary robotics, computer vision, neural networks, and simulation methods for developing control systems that leverage multiple locomotion mechanisms. His recent work demonstrates strong trends across several domains: developing hybrid locomotion systems (wheel/leg transformations), applying deep learning to terrain classification and pathfinding, using simulation environments for training, and exploring pretraining techniques for evolutionary robotics. His research shows a consistent focus on bridging simulation and real-world applications while addressing challenges in robot adaptability and robustness. Faculty Excellence in Teaching, Missouri State University (2018) Best Paper Award, Workshop on Evolutionary and Reinforcement Learning (2013) Best Paper Award, ALIFE Conference, Behavior and Intelligence Track (2012) Outstanding Reviewer, Elsevier (2018) Master Advisor Certification, Missouri State University (2017) Clark has advised numerous undergraduate and graduate students through the ARCS Lab, with current research involving projects like the Adabot (a robot with multiple locomotion mechanisms) and thermal semantic segmentation for aerial field robots. His teaching portfolio includes courses on data structures, algorithms, neural networks, computer systems, and mobile robotics. He has also served as a Visiting Associate at Caltech's ARC Lab from 2023-2024, working with Dr. Soon-Jo Chung. The ARCS Lab develops simulation environments, optimizes control systems, and fabricates physical robots. Current projects include the Adabot with its geared coaxial shaft mechanism for hybrid locomotion, thermal semantic segmentation using satellite data, and creating dynamic simulation environments with Unreal Engine 5. The lab emphasizes practical applications of theoretical research while training students in both hardware and software aspects of robotics.

Jason GaigerView profile

Professor

• Aesthetics
• Art Theory
• Theories of depiction and visual meaning
• +2 more

Jason Gaiger is Professor of Aesthetics and Art Theory at the University of Oxford and a Fellow of St Edmund Hall. He serves as Head of Art History and Theory at the Ruskin School of Art and acts as the Undergraduate Admissions Co-Ordinator. Previously, from 2010 to 2014, he was Head of the Ruskin School of Art and currently serves as Director of Graduate Studies. Professor Gaiger received his education in art history and philosophy at the universities of St Andrews, Essex and Heidelberg. Before joining Oxford in 2010, he was Senior Lecturer and Director of Research in the Art History Department at the Open University. Jason Gaiger's research spans aesthetics and art theory from the seventeenth century through to the present day, with additional focus on theories of depiction and visual meaning, and contemporary art practice and theory. His scholarly work is informed by both the historical resources of the German aesthetic tradition and debates in contemporary analytic philosophy. He has published extensively on topics including painting theory, sculpture, rhythm in visual art, visual perception, and the historical development of art theory. His most significant contribution is Philosophy of Painting: Ancient, Modern, Contemporary (2022), which provides a historically informed analysis of painting's contemporary relevance. Professor Gaiger's publication record demonstrates a consistent focus on the philosophical dimensions of visual art, particularly examining the temporal aspects of viewing, the relationship between materiality and meaning, and the historical continuity of aesthetic concepts. His work bridges historical analysis with contemporary philosophical inquiry, often drawing connections between eighteenth and nineteenth-century aesthetic theories and current debates in visual culture. The recurring themes across his publications include the nature of pictorial representation, the cognitive processes involved in art viewing, and the philosophical foundations of art historical interpretation. Professor Gaiger has supervised numerous graduate students, with recent theses under his direction exploring topics such as The Epistemic Value of Contemporary Art , Modes of Presence in the Contemporary Sculptural Encounter , and Affective Vision: Urban Landscape Photographs and their Paratexts . His mentorship reflects his broad expertise across historical and contemporary art theory.

Brian WeeksView profile

Associate Professor

• Evolutionary Ecology
• Climate Change Biology
• Biodiversity Conservation
• +5 more

Brian Weeks is an Associate Professor in the School for Environment and Sustainability at the University of Michigan, where he joined as an Assistant Professor in 2019. His research focuses on understanding how species and communities respond to human-induced environmental changes, with particular emphasis on avian systems. Weeks leads an active research group that integrates museum specimen-based work, genomics, and field studies to investigate biodiversity responses to global change. Weeks' research interests span evolutionary ecology, climate change biology, and biodiversity conservation. His work primarily examines how bird species and communities have responded to environmental change through morphological adaptations. He combines museum-, field-, and lab-based approaches to study evolutionary processes across multiple scales, from macroevolutionary patterns in the Solomon Islands to contemporary changes in North American migratory birds. His lab has developed innovative methods like Skelevision for high-throughput measurement of functional traits from museum skeletal specimens. His publication record shows a strong focus on climate-driven morphological changes in birds, with recent work demonstrating how warming temperatures drive size reductions while simultaneously increasing wing length. His research has revealed that smaller-bodied species change at faster rates, and that migration timing shifts are decoupled from morphological changes. Weeks' lab also investigates biodiversity-ecosystem functioning relationships and extinction risk prediction. Packard Fellowship in Science and Engineering (2022) Ecological Society of America's George Mercer Award (2022) Katma Award, American Ornithological Society ISI Highly Cited paper (2021) Weeks advises multiple PhD and Master's students, and his lab collaborates extensively with researchers across institutions. His work has received significant media attention, with coverage in Science, The Wall Street Journal, The Washington Post, BBC News, and numerous international outlets. His research on birds shrinking due to climate change achieved an Altmetric score higher than 99.98% of papers tracked, reflecting its substantial scientific and public impact.

Le LuView profile

Researcher

• Medical Imaging
• Artificial Intelligence
• Oncology Imaging
• +3 more

Dr. Le Lu serves as Researcher & Senior Director leading global Medical AI R&D efforts at Alibaba DAMO Academy since August 2021. His career spans prestigious institutions including the National Institutes of Health, Clinical Center, Radiology and Imaging Science Department, NVIDIA AI-Infra division, and Siemens corporate research where he was a senior staff scientist from 2006 until 2013. Ph.D. in Computer Science from Johns Hopkins University (2007) Dr. Lu specializes in medical imaging AI with significant contributions to oncology imaging and computer vision applications in healthcare. His research bridges advanced AI techniques with clinical applications, particularly in cancer screening and diagnostic imaging. He has published over 250 peer-reviewed articles in top-tier journals including Nature Medicine, Nature Communications, and IEEE Transactions on Pattern Analysis and Machine Intelligence, with more than 23,880 citations as of January 2024. Dr. Lu has received numerous accolades including IEEE Fellowship and recognition for publishing the most cited papers in IEEE TMI and CVPR medical imaging categories over the past decade. His innovative work has resulted in over 100 patent filings across multiple jurisdictions. IEEE Fellow (Computer Society) for contributions to medical imaging, AI and oncology imaging IEEE Signal Processing Society Distinguished Industrial Speaker (2021-2023) Associate Editor for IEEE Trans. Pattern Analysis and Machine Intelligence (IF: 24.3) Associate Editor for IEEE Signal Processing Letters and Frontiers in Oncology MICCAI society board member Through his leadership at Alibaba DAMO Academy, Dr. Lu directs research initiatives focused on making AI-driven healthcare solutions accessible globally, with recent presentations on 'AI for Health for All' and cancer screening paradigms.

Ahmad LotfiView profile

Professor

• Computational Intelligence
• Ambient Intelligence
• Robotics
• +7 more

Ahmad Lotfi is a Professor of Computational Intelligence and Head of Department of Computer Science at Nottingham Trent University , with a Visiting Professor role at Tokyo Metropolitan University . He leads the Computational Intelligence and Applications (CIA) research group and has supervised over 30 PhD students to completion. PhD in Learning Fuzzy Systems (University of Queensland, 1995) MTech in Control Systems (Indian Institute of Technology, India) BSc in Control Systems (Isfahan University of Technology, Iran) His research spans computational intelligence , ambient intelligence , robotics , and machine learning , with applications in dementia monitoring , smart environments , and healthcare technology . Recent work focuses on using thermal sensor arrays for privacy-preserving human activity analysis. He has secured funding from Innovate UK , EPSRC , The Royal Society , and Horizon 2020 , with projects like iCarer (assistive living), SmartBerry (agricultural AI), and BigSpark (financial data augmentation). His 15 most recent articles demonstrate expertise in Wi-Fi-based activity recognition , EEG fall detection , and thermal sensor fusion . Senior Member IEEE Member of British Computer Society (MBCS) Editorial roles in Soft Computing and Journal of Ambient Intelligence and Smart Environments He has served as Program Chair for conferences like PETRA and ICCRT , and as Keynote Speaker at PETRA 2023 . His 28+ years of academic leadership include organizing UKCI and UKRAS conferences.

Bradley HayesView profile

Associate Professor

• Human-Robot Interaction
• Explainable AI (XAI)
• Collaborative Robotics
• +5 more

Bradley Hayes is an Associate Professor of Computer Science and Director of the Collaborative AI and Robotics (CAIRO) Laboratory at the University of Colorado Boulder. His work focuses on enabling autonomous agents and robots to collaborate safely and effectively with humans through techniques in human-robot interaction, explainable AI, and learning from demonstration. PhD in Computer Science, Yale University (2015) Postdoctoral Associate, MIT Interactive Robotics Group Research interests span collaborative robotics, dependable AI systems, and imitation learning for human-robot teaming, with applications in manufacturing, healthcare, and autonomous vehicles. His lab develops methods for task planning, motion prediction, and trust calibration in human-machine partnerships. Recent publications emphasize explainable sequential decision-making, neuromorphic learning architectures, and augmented reality interfaces for robotic training. Awards include Sustainability Recognition (2025) for motion planning efficiency Best Student Paper Runner-up (AAMAS 2022) Best Technical Paper Runner-up (HRI 2019) As lab director, he has mentored 12 PhD/Master students to completion, including Matthew Luebbers, Aaquib Tabrez, and Christine Chang. Funding sources include NSF grants and industry partnerships like Circadence, where he serves as Chief Technology Officer.

• Human Factors Engineering
• Neuroergonomics
• Robotic Surgery
• +3 more

Denny Yu is an Associate Professor at the Edwardson School of Industrial Engineering, Purdue University. His work bridges human factors, neuroergonomics, and healthcare safety through advanced sensor systems and AI. Primary Affiliation : Edwardson School of Industrial Engineering, Purdue University Research Themes : Surgical ergonomics, autonomous vehicle human factors, cognitive workload assessment, multimodal physiological sensing Dr. Yu's research focuses on neuroergonomics and human-robot interaction , particularly in surgical and transportation contexts. His team develops sensor-based systems for workload monitoring, including: EEG-eye tracking fusion for situation awareness Wearable exoskeletons for surgical posture support Computer vision tools for lifting task risk analysis Smart infusion pump usability frameworks AI-driven surgical coaching systems Recent publications emphasize deep learning applications in soft tissue deformation estimation and real-time adaptive systems for robotic surgery augmentation. His work spans both occupational health (veterinary surgeons, airport workers) and medical device innovation domains.

• Digital Humanities
• Human-Centered Computing
• Data Visualization
• +5 more

Dr. Almila Akdag is an Assistant Professor in Human-Centered Computing at Utrecht University's Faculty of Science, Department of Information and Computing Sciences. As a Digital Humanities scholar with expertise spanning new media analysis, bibliometrics, and information visualization, she combines qualitative and quantitative methods to study humanities and social data. Research Focus Her interdisciplinary research bridges: Digital Humanities : Computational analysis of cultural artifacts including paintings, poetry, and oral histories Human-Centered AI : Developing empathetic and accessible AI systems Trauma Informatics : Analyzing breathing patterns in oral histories of trauma survivors Cultural Analytics : Studying art communities and online creative platforms Following the 2023 Turkey/Syria earthquake, she is creating an oral history archive to document collective memory of the disaster, with particular focus on Turkish, Kurdish and Syrian diaspora experiences. Awards and Recognition NWO VENI laureate for 'deviantArt: Mapping the Alternative Art World' project Co-initiator of Mellon Grant: 'Tools for Analysis and Visualization of Large Image/Video Collections for Humanities' Leadership and Service Workgroup leader of 'Visual Media and Interactivity' for DARIAH-EU Founding member of International Society for Knowledge Organization (ISKO LC Low Countries Chapter)

Sanna SundqvistView profile

Associate Professor

• Export Performance
• International Business
• Entrepreneurship
• +3 more

Sanna Sundqvist serves as Associate Professor in the Department of Marketing at LUT University, with a research focus spanning international business, export performance, and entrepreneurial strategy. Her work bridges empirical analysis with practical applications in global markets. Her academic credentials include a Doctoral degree in Engineering and Technology (awarded November 29, 2002) and a Master's degree in the same field (awarded December 21, 2000), both from LUT University. Professor Sundqvist's research examines how entrepreneurial orientation and cognitive biases influence international business decisions, with particular emphasis on structural equation modeling techniques. Her work explores the interplay between prior experience and export performance, Finnish market dynamics, and sales strategy optimization in global contexts. Recent investigations extend into consumer behavior analytics, examining online data patterns and high-involvement service interactions. Analysis of her 2018-2025 publications reveals a concentrated trajectory in international entrepreneurship, with increasing attention to cognitive microfoundations of business decisions. Her methodological approach combines bibliometric analysis with computational text mining, while maintaining strong empirical grounding in structural equation modeling for export performance studies. As principal investigator, she has secured multiple research projects including Wisdom2 (2022-2024), KATAJA/New Product Development (2022), KATAJA/LISREL (2021-2022), and KATAJA/Asikainen (2020), demonstrating consistent funding success in international business research.

• Statistics
• Applied Data Science
• Veterinary Epidemiology
• +2 more

Hans Vernooij is a Lecturer in Farm Animal Health at the Faculty of Veterinary Medicine, Utrecht University. He specializes in statistical methods and data science applications in veterinary epidemiology and animal health. His areas of expertise include: Statistical methods for veterinary research Applied Data Science in Life Sciences Epidemiological modeling Machine learning applications in animal health Vernooij has extensive experience in developing statistical models for animal health applications. His research focuses on applying advanced statistical techniques and data science methods to solve problems in veterinary epidemiology and farm animal health. He has particular expertise in Random Forest models, as demonstrated during his sabbatical at the Human Sciences Research Council in Pretoria where he developed a model for HIV status prediction based on demographic information and knowledge of HIV prevention from large-scale survey data. His publication record shows consistent contributions across veterinary epidemiology, with recent work emphasizing machine learning applications and big data analytics in animal health surveillance. The research demonstrates a clear trajectory from traditional statistical methods toward more advanced data science approaches. Vernooij is actively involved in teaching and mentoring: Teaches statistics to Bachelor students at the veterinary faculty Supports PhD candidates and Master students during data analysis phases of their research Provides statistics education for the Master of Epidemiology program at the Julius Centre of University Medical Centre

Silvia CascianelliView profile

Researcher

• Computer Vision
• Document Analysis
• Handwritten Text Recognition
• +4 more

Silvia Cascianelli is an AI and Computer Vision Researcher at the University of Modena and Reggio Emilia (UNIMORE). She actively contributes to the computer vision and document analysis communities through research, conference organization, and academic mentorship. She serves as Area Chair for major computer vision conferences including CVPR2025, BMVC2025, and ECCV2024, demonstrating her standing in the field. Her research focuses on several key areas within computer vision and document analysis: Image Generation : Developing efficient and lightweight methods for image generation with desired characteristics, particularly using diffusion models Handwriting Imitation : Creating algorithms for generating images of text with specific content and handwriting styles, along with evaluation methods Document Understanding : Extracting information from 2D and 3D document images, ranging from modern documents to historical artifacts like carbonized Roman papyri Dr. Cascianelli's work shows a clear progression toward more sophisticated generative models and evaluation frameworks, with recent publications focusing on diffusion models for handwritten text generation, efficient token reduction for multimodal tasks, and innovative approaches to historical document analysis. Her research bridges theoretical advancements with practical applications across diverse document types. Her scientific contributions have been recognized through invitations to serve as Area Chair for top-tier computer vision conferences (CVPR, ECCV, BMVC) and opportunities to organize specialized workshops including VisionDocs at ICCV, AI4DH at ECCV, and ADAPDA at ICDAR. Area Chair at CVPR2025 Area Chair at BMVC2025 Area Chair at ECCV2024 Organizer of VisionDocs Workshop at ICCV2025 Organizer of AI for Digital Humanities Workshop at ECCV2024 Organizer of ADAPDA Workshop at ICDAR2024 Dr. Cascianelli actively mentors the next generation of researchers: Vittorio Pippi - PhD Student at UniMoRe (National PhD program in AI) Fabio Quattrini - PhD Student at UniMoRe (ICT program) Carmine Zaccagnino - Research Intern at UniMoRe (formerly MSc student) Kostantina Nikolaidou - PhD Student at Luleå University of Technology Pau Torras Coloma - PhD Student at Computer Vision Center, Universitat Autònoma de Barcelona Bram Vanherle - CV Engineer at Colruyt Group Smart Innovation (formerly PhD student) She is actively involved in several research initiatives including the AI Governance Lab where she serves as a lecturer, and collaborates with institutions worldwide. Her current projects focus on advancing diffusion models for image generation, improving handwritten text recognition systems, and developing novel methods for document understanding across historical and contemporary contexts.