جست‌وجوی استاد و پژوهشگر

• Actuarial Science
• Machine Learning
• Insurance Pricing
• +۳ مورد دیگر

Roel Henckaerts is a Research Fellow at the Insurance Research Group, KU Leuven. His work centers on integrating advanced computational methods with actuarial science to optimize insurance pricing models. Research Focus: Henckaerts specializes in machine learning applications for insurance, including telematics-based dynamic pricing, interpretable AI for regulated industries, and data-driven risk assessment. His methodologies emphasize neural networks, tree-based models, and transparent surrogate systems to balance accuracy with regulatory compliance. Publication Trends: Recent articles (2019–2025) demonstrate a shift toward neural networks and model interpretability, while earlier work (2016–2018) established foundations in tree-based methods and risk factor engineering. Thematic evolution shows consistent focus on bridging machine learning scalability with actuarial rigor. Affiliations: As part of KU Leuven's Insurance Research Group, Henckaerts collaborates on industry-facing projects, though specific lab/team structures are undisclosed.

Ron Mangunمشاهده پروفایل

استاد

• Cognitive Neuroscience
• Visual Attention
• Neural Mechanisms of Attention
• +۳ مورد دیگر

Ron Mangun is a Professor of Psychology and Neurology at the University of California, Davis, and co-director of the Center for Mind and Brain . His work bridges cognitive neuroscience, attentional mechanisms, and the interplay between free will and neural processes. Mangun has significantly shaped neuroscience education through his co-authored textbook, Cognitive Neuroscience: The Biology of the Mind , which has sold over 150,000 copies across five editions. 2024 Award for Education in Neuroscience Fulbright US Distinguished Scholar (2024-2025) Mangun's research focuses on visual attention , neural oscillations , and predictive cognitive processes , often integrating EEG-fMRI for high-resolution neural mapping. His recent work explores alpha oscillations, rhythmic environmental sampling, and the ventral attention network's role in reorienting attention. His scientific awards include: 2024 Award for Education in Neuroscience Fulbright US Distinguished Scholar (2024-2025) Mangun has mentored numerous trainees globally through the Summer Institute in Cognitive Neuroscience , a program funded by NIMH, NIDA, and the Kavli Foundation. His research is supported by the National Science Foundation , focusing on attention and free will.

Wendy Suzukiمشاهده پروفایل

استاد

• Neuroscience
• Cognitive Science
• Brain Plasticity
• +۳ مورد دیگر

Dr. Wendy Suzuki is a Professor of Neural Science and Psychology at New York University's Center for Neural Science and Dean of the College of Arts and Science. She holds a Ph.D. in Neuroscience from the University of California, San Diego (1993). Her research focuses on brain plasticity, particularly the effects of exercise on cognitive functions, including learning, memory, and executive functions. She leads the Suzuki Lab, which investigates how aerobic exercise enhances brain function in healthy adults and clinical populations. Roles: Professor of Neural Science, Dean of Arts and Science, Author Affiliations: NYU Center for Neural Science, NYU College of Arts and Science Her work bridges basic neuroscience and translational research, with studies on memory formation in the medial temporal lobe and exercise's impact on mood, creativity, and neurological conditions. She authored best-selling books like Healthy Brain, Happy Life and Good Anxiety , emphasizing practical neuroscience for everyday well-being. Publications span over 100 articles, focusing on exercise neurobiology, mindfulness, and cognitive enhancement. She actively engages in public science communication through media appearances, TED Talks, and educational initiatives.

• Work & Organisational Psychology
• Human-Computer Interaction
• Eye Tracking
• +۶ مورد دیگر

Patrik Pluchino is an Assistant Professor at the Department of Psychology, University of Padua, affiliated with the Human Inspired Technology Research Centre. His research focuses on cognitive processes in human-computer interaction, psychophysiological measures of attention, and IoT applications in workplace and living environments. He investigates attention allocation, multimedia learning, and ethical decision-making through eye-tracking and neurophysiological methods. Research interests include cognitive workload assessment using EEG/ERP, IoT systems for safety and sustainability, and VR-based training for disaster response. He leads projects like SAFE GYMS and DOMHO, developing smart solutions for elderly care and workplace safety. His work bridges psychology, technology, and design to enhance human-centered systems in Industry 5.0 contexts. Pluchino collaborates on EU-funded initiatives addressing aging workforce needs and accessible technologies for disabled users. His publications span 20+ years, emphasizing interdisciplinary approaches to human factors, educational technology, and assistive systems. Key contributions include studies on attentional blink, implicit learning mechanisms, and ethical behavior through attention dynamics.

Katharina von Kriegsteinمشاهده پروفایل

استاد

• Neuroscience
• Cognitive Science
• Clinical Neuroscience
• +۵ مورد دیگر

Katharina von Kriegstein is a Professor of Cognitive and Clinical Neuroscience at Technische Universität Dresden (since 2017). Previously, she held positions at Humboldt University Berlin (2013–2017) and led a Max Planck Research Group (2009–2018). Her research focuses on sensory processes underlying human communication, employing neuroimaging, neurostimulation, and behavioral techniques. She explores communication impairments in developmental disorders and neurological patients, particularly investigating auditory and visual thalamic dysfunction in dyslexia and autism. Her work is supported by an ERC Consolidator Grant (2015) and has contributed to understanding sensory pathway roles in speech-in-noise recognition and voice identity processing. Key findings include thalamic structural abnormalities linked to dyslexia and autism, as well as predictive coding mechanisms in auditory pathways. Education: Medical License (Approbation), 2003 Dr. med., summa cum laude, University of Göttingen (1994–2001) Philosophy studies, University of Göttingen (1995–1997) Studium generale, Leibniz-Kolleg, Tübingen (1993–1994) Key Projects: Leading Subproject A11 in CRC 940 (goal-directed behavior) Investigating thalamic roles in communication (e.g., auditory/visual speech perception) Her research integrates clinical, cognitive, and computational approaches. Recent work highlights multisensory learning benefits for auditory processing and the causal role of sensory cortices in vocabulary translation. Awards include the Max Planck Research Group (2009) and best doctoral thesis in Experimental Medicine (2002).

Martial Hebertمشاهده پروفایل

استاد

• Computer Vision
• Robotics
• Autonomous Systems
• +۶ مورد دیگر

Martial Hebert is the Dean and University Professor of Robotics at Carnegie Mellon University's School of Computer Science (SCS), leading since August 2019. His career spans decades at CMU's Robotics Institute (RI), where he served as Director (2014-2019) and Professor (1999-present). Broad research interests in computer vision, perception for autonomous systems, and 3D environment modeling. Current PhD advisees include Zhipeng Bao, with numerous past PhD and Master's students listed. Editor-in-Chief of the International Journal of Computer Vision and member of IEEE Robotics and Automation Society. His research focuses on computer vision and robotics , emphasizing 3D data interpretation, object recognition, and machine learning applications. Recent articles highlight advancements in 3D vision , diffusion models , and disaster response robotics , reflecting a trajectory from foundational algorithms to applied autonomous systems. Notably, he pioneered the first master's program in computer vision in the U.S. Hebert's leadership in academic and research spheres includes directing the RI and securing an operating budget peak during his tenure. His work bridges perception, intelligence, and autonomous systems, with applications in disaster scenarios , LiDAR point cloud detection , and video forecasting .

• Bioinformatics
• Health Informatics
• Affective Computing
• +۳ مورد دیگر

Dr. Stamos Katsigiannis is an Associate Professor in the Department of Computer Science at Durham University. His research focuses on bioinformatics, health informatics, affective computing, and GPU computing. He holds a BSc, MSc, and PhD in Computer Science from the University of Athens and the Athens University of Economics and Business. Before Durham, he was a Postdoctoral Research Fellow and Lecturer at the University of the West of Scotland (2016–2020). His work spans biometric identification via EEG signals, medical image analysis, and deep learning applications in healthcare. Education: BSc (Hons.) in Informatics and Telecommunications from National and Kapodistrian University of Athens MSc in Computer Science from Athens University of Economics and Business PhD in Computer Science from National and Kapodistrian University of Athens Research interests include bioinformatics (e.g., microarray analysis), health informatics (e.g., ECG-based emotion modeling), and GPU computing for real-time applications. He has pioneered studies on EEG-based biometrics and affect recognition in human-horse interaction. Recent articles explore chest X-ray classification using deep learning , AI-generated content detection , and trajectory prediction with graph networks . His research emphasizes interdisciplinary applications of machine learning in healthcare and education. He advises six postgraduate students and collaborates on UK parliamentary AI governance reports. Notable contributions include the BED EEG dataset and the DREAMER emotion recognition database.

• Bioinformatics
• Health Informatics
• Affective Computing
• +۴ مورد دیگر

Dr. Stamos Katsigiannis is an Associate Professor in the Department of Computer Science at Durham University. His research focuses on bioinformatics, health informatics, affective computing, machine learning, and GPU computing applications. He holds a PhD in Computer Science from the National and Kapodistrian University of Athens (Greece), with prior roles including Postdoctoral Research Fellow and Lecturer at the University of the West of Scotland (2016-2020). Education: BSc (Hons) Informatics and Telecommunications, National and Kapodistrian University of Athens (Greece) MSc Computer Science, Athens University of Economics and Business (Greece) PhD Computer Science, National and Kapodistrian University of Athens (Greece) Research Interests: Bioinformatics, health informatics, affective computing (e.g., emotion recognition using EEG/ECG signals), machine learning applications in medical imaging and video quality, GPU-accelerated algorithms for biomedical data analysis, and biometric identification systems. His work bridges computational methods with healthcare, education technologies, and human-computer interaction. Advising & Students: Supervising postgraduate students in AI-driven healthcare, computer vision, and affective computing. Recent collaborations include projects on AI-generated content detection (De-Factify 4.0), chest X-ray image analysis (CLN network), and trajectory prediction using graph neural networks. Labs/Teams: Active in Durham's AI research groups, contributing to interdisciplinary projects in medical imaging, cybersecurity, and educational technology. Collaborates internationally in EU-funded initiatives and UK parliamentary AI governance consultations (AGENCY project).

Nikola Simidjievskiمشاهده پروفایل

پژوهشگر ارشد

• Machine Learning
• Biomedical Data Integration
• Neuroscience
• +۴ مورد دیگر

Dr. Nikola Simidjievski is a Research Fellow at the University of Cambridge's Department of Computer Science and Technology. His research focuses on machine learning applications in natural sciences, including oncology, neuroscience, and biomedical informatics. He specializes in multimodal data integration, dynamic systems modeling, and explainable AI techniques. Simidjievski contributes to interdisciplinary projects such as AI-driven space research and Earth observation systems (e.g., AIAtlas). His work emphasizes computational methods for small-sample biomedical data and interpretable neural networks. Education background not explicitly stated, but his research spans computational biology, aerospace telemetry analysis (e.g., Mars Express spacecraft), and environmental modeling. He collaborates on projects like GalaxAI for spacecraft data analysis and PATHS for medical imaging. Key tools developed include Healnet for biomedical data fusion and RO-FIGS for tabular ensemble methods. No scientific awards listed. Advising no listed students, but contributes to academic initiatives like the Human Brain Project's neuroscience training programs. Engages in open-source projects (e.g., AITLAS toolbox for Earth observation). Current research trends include tabular data augmentation methods (TabEBM, TabMDA) and efficient transformer-based models for medical imaging (PATHS).

Dario L Ringachمشاهده پروفایل

استاد

• Neurobiology
• Visual Cortex
• Population Coding
• +۵ مورد دیگر

Dario L Ringach is a Professor in the Department of Neurobiology at the University of California Los Angeles (UCLA) School of Medicine , specializing in the neural mechanisms of visual processing. His research focuses on understanding how populations of neurons in the visual cortex encode and process sensory information, with particular emphasis on energy efficiency, adaptation, and the relationship between neural dynamics and perceptual coding. Key Research Areas : Visual Cortex, Population Coding, Sensory Discrimination, Cortical Adaptation, Receptive Field Structure Grants : NIH R01NS116471 (2020-2023), NIH R01EB022915 (2016-2021), NIH R01EY018322 (2007-2019), NIH R01EY012816 (2000-2011) Recent Publications (2022-2025) demonstrate his work on cortical adaptation mechanisms, energy-efficient coding in mouse visual cortex, and the role of ON/OFF domains in shaping receptive fields. His theoretical contributions include power laws for neural dynamics and normalization models of population responses. Grants have supported studies on Bayesian estimation of network connectivity and computational modeling of visual cortex function. Contact : dario@ucla.edu

Amin Rezaeiمشاهده پروفایل

استادیار

• Computer Security
• Machine Learning
• Computer Architecture
• +۳ مورد دیگر

Amin Rezaei is an Assistant Professor in the Department of Computer Engineering and Computer Science at California State University, Long Beach (CSULB). He directs the Computer Architecture, Reliability, and Security Laboratory (CARS-Lab) and serves as the undergraduate advisor for Computer Engineering. His expertise spans computer security, machine learning, and computer architecture, with a focus on hardware security and protecting hardware intellectual property. Rezaei holds a Ph.D. in Computer Engineering from Northwestern University. He is a senior member of IEEE and a lifetime member of ACM and AAAI. His research has been funded by NSF grants totaling $500,000, and he has published over 50 peer-reviewed articles in top venues like DAC, ICCAD, and DATE. He has also served on technical program committees for major conferences such as IPDPS and GLSVLSI. His research interests include hardware security, machine learning applications in secure systems, and novel logic locking techniques. Key projects include developing CycPUF (a cyclic physical unclonable function) and frameworks for detecting hardware Trojans using multimodal deep learning. His lab emphasizes democratizing research by publicly sharing source code. Education: Ph.D. in Computer Engineering, Northwestern University Awarded Academic Excellence Award, University of Louisiana at Lafayette Awards: Walter P. Murphy Fellowship, Northwestern University Royal E. Cabell Fellowship, Northwestern University Grants: NSF CISE-MSI and CRII programs. Rezaei’s advising focuses on undergraduate Computer Engineering students, with Spring 2025 hours on Zoom. His lab’s current research includes projects on hardware Trojan prediction, ML-based security evaluations, and dynamic logic locking.

• Behavioral Neuroscience
• Neurobiology of Cognition
• Neurodegenerative Disorders
• +۳ مورد دیگر

Bill DeCoteau is the Absalom Graves Gaines Professor in the Psychology Department at St. Lawrence University. His research focuses on the neurobiology of cognition and the application of nanoparticle-based therapies for neurodegenerative disorders (e.g., ALS, Parkinson’s) and neurodevelopmental disorders (e.g., autism spectrum disorder). He holds a B.A. in Psychology from Mercyhurst University and a Ph.D. in Cognition and Neural Science from the University of Utah, followed by postdoctoral training at MIT’s McGovern Institute for Brain Research. He teaches courses in Behavioral Neuroscience, Human Neuropsychology, and Animal Behavior, and has led a Neuroscience of Fear summer program in Rome, Italy. His research uses rodent models to study spatial and motor behaviors, with recent emphasis on evaluating cerium oxide nanoparticles to mitigate motor/cognitive decline in disease models. He has contributed to over 30 peer-reviewed publications on nanoparticle efficacy, striatal-hippocampal theta rhythm coordination, and neurocognitive lesion studies. Professional service includes roles as Faculty Council Secretary and membership in the Institutional Strategy and Assessment Committee. Key research themes include: Antioxidant nanoparticle development for neuroprotection Neural mechanisms of spatial/temporal cognition Behavioral modeling of neurodegenerative diseases Recent work emphasizes translational applications of nanotechnology in neurological disease treatment.

• Medical Imaging
• Pediatric Cardiology
• MRI Reconstruction
• +۳ مورد دیگر

Dr. Qing Zou is an Assistant Professor in the Division of Pediatric Cardiology, Department of Pediatrics at the University of Texas Southwestern Medical Center (UTSW). He holds secondary appointments in the Department of Radiology and the Advanced Imaging Research Center (AIRC). His research focuses on medical imaging techniques, particularly magnetic resonance imaging (MRI), with an emphasis on pediatric cardiology applications. Dr. Zou’s work integrates deep learning and advanced reconstruction algorithms to address challenges in dynamic imaging, motion compensation, and undersampling problems. Education: Ph.D. in Mathematics/Computer Science (University of Iowa) Postdoctoral Training in Cardiac MRI (University of Iowa) Research Interests: Dr. Zou explores cutting-edge methodologies in MRI reconstruction, including deep learning-driven approaches for free-breathing and non-contrast-enhanced imaging. His projects address pediatric-specific needs, such as congenital heart disease diagnostics and rapid whole-heart angiography. He also investigates mathematical foundations of sampling theory and high-dimensional data analysis. Recent Research Trends: His publications highlight advancements in motion-resolved imaging, Gaussian representation-based reconstruction, and generative models for dynamic MRI. Key themes include improving temporal resolution, reducing imaging time, and enhancing vascular visualization without contrast agents. Awards: No honors or awards explicitly listed in the provided text. Advising & Grants: No formal advisees/students or grant details mentioned. His research is likely funded through institutional resources or collaborative grants. Labs/Teams: Dr. Zou is affiliated with the Advanced Imaging Research Center (AIRC) at UTSW, a hub for innovative imaging technologies and interdisciplinary research.

Aline Villavicencioمشاهده پروفایل

استاد

• Lexical Semantics
• Multilinguality
• Cognitively Motivated NLP
• +۴ مورد دیگر

Professor Aline Villavicencio is a Chair in Natural Language Processing at the University of Sheffield's School of Computer Science. She holds a PhD and MPhil from the University of Cambridge and an MSc from the Federal University of Rio Grande do Sul (Brazil). Her research focuses on lexical semantics, multilinguality, and cognitively motivated NLP, with applications including Multiword Expression (MWE) treatment and text simplification for English and Portuguese. She has held a CNPq Research Fellowship (2007–2017) and currently leads projects like Modeling Idiomaticity in Human and Artificial Language Processing (EPSRC grant). Her roles include PC co-chair for CoNLL-2019, Area Chair for ACL-2019 and NAACL-2018, and co-chair of PROPOR 2018. She advises WiNLP, contributes to TACL, JNLE, and other journals, and collaborates with the Neurocomputational and Language Processing Lab in Brazil. Notable work includes corpus development (e.g., BRWAC for Brazilian Portuguese), idiomaticity detection, and cross-lingual transfer learning. Recent grants include £446k from EPSRC and £370k from Horizon Europe (Atrium). Key Projects: Modeling Idiomaticity, Cross-lingual Adaptation, Speech Segmentation Grants: EPSRC (£446k), Horizon Europe (£370k), Royal Society (£74k) Publications: Over 100 papers in ACL, EMNLP, and journals like Natural Language Engineering . Her lab work involves developing NLP tools for under-resourced languages and exploring neural models' limitations in idiomatic understanding. She co-edited books on cognitive aspects of language acquisition and MWE processing, and her teaching includes advanced NLP modules.

Anda Degeratuمشاهده پروفایل

پژوهشگر

• Geometric Analysis
• General Relativity
• Mathematics for Theoretical Neuroscience

Dr. Anda Degeratu is a Research Associate at the University of Stuttgart's Department of Mathematics, affiliated with the Institute for Discrete Structures and Symbolic Computing (Chair of Differential Geometry). She previously held positions including Junior Scientist at the Max Planck Institute for Gravitational Physics and Visiting Assistant Professor at Duke University. Her education includes a Ph.D. in Mathematics from MIT (2001) and a B.A. from the University of Bucharest (1996). Her research spans geometric analysis, general relativity, and mathematical neuroscience. She investigates emergent dynamics in neural networks, Calabi-Yau manifolds, and geometric PDEs, with applications to theoretical physics and computational neuroscience. Her publications focus on geometric analysis (Calabi-Yau manifolds, quasi-asymptotic spaces), mathematical physics (Witten spinors, positive mass conjecture), and neuroscience (competitive neural networks, activity encoding). Recurring themes include nonlinear dynamics, spectral theory, and singularity resolution. Awards: Lecturer Award for best advanced lecture (2022): Fractal Geometry and Chaos Lecturer Award for best advanced lecture (2021): Neural Networks She advises bachelor's and master's theses in mathematics and physics, covering topics like black hole geometry, fractal dynamics, and neural networks. As Deputy Equal Opportunities Officer at the university, she contributes to institutional policy development.