Find Academic Supervisors

• Visual perception
• Attentional selection
• Perceptual organization
• +4 more

Melanie Palomares serves as an Instructor in the Department of Psychology at the McCausland College of Arts and Sciences, University of South Carolina. Her academic foundation includes a Ph.D. from Johns Hopkins University (2007), establishing her expertise in visual cognitive processes and developmental psychopathology. Education: Ph.D. in Cognitive Psychology/Neuroscience from Johns Hopkins University (2007) Research Focus: Dr. Palomares investigates visual perception mechanisms through multiple lenses including attentional selection , perceptual organization , and subitizing . Her work bridges letter recognition processes with visual development in both typically developing children and clinical populations, particularly those with Williams Syndrome. Utilizing visual-evoked potentials (VEPs) as a key methodology, she explores neural correlates of perceptual abnormalities in neurodevelopmental disorders. Publication Trends: Analysis of her 2000-2011 publications reveals sustained investigation into visual cognition's intersection with developmental disorders. Core themes include attention's role in enumeration limits, visuospatial integration deficits in Williams Syndrome, and neural mechanisms underlying letter identification. Her collaborative network prominently features Howard Egeth and Denis Pelli, indicating strong ties to foundational visual cognition research. Research Infrastructure: Dr. Palomares operates within the C-Panda Lab at USC, which specializes in developmental visual cognition research using psychophysical and electrophysiological approaches. The lab environment supports her dual focus on typical perceptual development and Williams Syndrome phenotypes.

Julie WallView profile

Professor

• Artificial Intelligence
• Advanced Computing
• Deep Learning
• +12 more

Professor Julie Wall is a faculty member at the University of West London, serving as Professor of AI and Advanced Computing in the School of Computing and Engineering. She is actively engaged in research, teaching, and professional service, including her role as an expert at the British Standards Institution (BSI) in the domain of artificial intelligence. Research Interests: Julie Wall's research is centered on the design and application of intelligent systems for processing and modeling temporal data, particularly in speech and language. She leverages neural network architectures—ranging from biologically inspired models to computationally efficient deep learning systems—to analyze diverse data types such as audio, video, images, tabular data, and 3D features. Her work extends to developing production-grade deep learning and natural language understanding systems for immersive environments like virtual and augmented reality. Publications and Research Trends: Her body of work, comprising over 50 high-quality papers and multiple patents, reflects a strong trajectory in AI systems that integrate multimodal data with temporal dynamics. The research spans core areas of machine learning, natural language processing, and applied AI, with increasing focus on real-world deployment, efficiency, and intelligent interaction in extended reality platforms. Scientific Awards: US Patent UK Patent Advising and Grants: Julie Wall supervises research students across disciplines including forensic science and artificial intelligence. She contributes extensively to academic programs, teaching courses such as BSc and MSc in Computer Science, Artificial Intelligence, Data Science, and specialized AI programs in cybercrime and criminal justice. While specific grant details are not listed, her patent holdings and publication volume suggest sustained research funding and project leadership. Labs and Teams: As a leading researcher in AI and advanced computing, she is likely involved in or leads research groups focused on intelligent systems, deep learning, and multimodal AI at the University of West London, though specific lab names are not mentioned in the text.

Claudia Poch Pérez BotijaView profile

Researcher

• Cognitive Neuroscience
• Working Memory
• Neurophysiology
• +5 more

Claudia Poch Pérez Botija is a researcher affiliated with the School of Language and Education at Universidad Nebrija. Her work focuses on Psychobiology , particularly in cognitive neuroscience and neurophysiological mechanisms of memory and attention. Doctorate: Universidad Autónoma de Madrid (2015) Thesis: Neural oscillatory activity associated with attention orientation in working memory Supervisors: Dr. Pablo Campo Martínez-Lage, Dr. Luis Carretié Arangüena Key research areas include working memory dynamics , emotional processing , neural oscillations (alpha/gamma), and language cognition . Her studies often employ EEG/ERP techniques to explore memory-perception interactions and bilingual cognitive control. Recent publications reveal trends in: Pattern separation mechanisms during memory encoding Cross-modal integration in illusory perception Emotional primacy effects in memory recognition Lesion studies of temporopolar regions in epilepsy patients Neurogenetic influences on fear conditioning (D2 receptor variants) Alpha/gamma oscillation modulation in attentional processes She contributes to understanding mnemonic discrimination , semantic memory impairment in neurological conditions, and neural correlates of bilingualism . Her work bridges basic cognitive mechanisms with clinical applications in temporal lobe epilepsy. Active in the CINC Nebrija Research Center on Cognition and CEDI Research Group , she investigates interrelations between cognition, education, and individual differences.

Angela BezoldView profile

Lecturer

• Mathematics Education
• Primary School Didactics
• Argumentation Competencies
• +5 more

Dr. Angela Bezold is a Lecturer at the Chair of Mathematics V (Mathematics Education) at the University of Würzburg . She specializes in primary mathematics didactics , with a focus on fostering argumentation competencies and self-differentiated learning environments . Her work is closely tied to the SINUS and CO-SINUS projects, which aim to improve mathematics education through research tasks and teacher training. Project leader of CO-SINUS (2024) for primary school research tasks Co-creator of the mathematics textbook series Zahlenzauber (2015–2021) Founder of Emil's Research Camp for children Her research emphasizes exploratory learning in geometry and arithmetic, including projects like Mathematik und Kunst and Modellieren und Problemlösen . Recent activities include workshops on LehrplanPlus implementation and digital teaching models like the Inverted Classroom . She actively collaborates with regional institutions on teacher training programs.

Ari KorhonenView profile

Senior Lecturer

• Computing Education Research
• Educational Technology
• Algorithm Visualization
• +6 more

Ari Korhonen is a Senior University Lecturer in the Department of Computer Science at Aalto University's School of Science. His academic career spans over two decades with a focus on computing education research and educational technology. Korhonen leads research in algorithm visualization, automatic assessment systems, and learning analytics within computer science education. Education: Doctoral degree in Engineering and Technology, Helsinki University of Technology (2003) Licentiate degree in Engineering and Technology, Helsinki University of Technology (2000) Master's degree in Engineering and Technology, Helsinki University of Technology (1997) Research Interests: Korhonen specializes in computing education research with particular expertise in algorithm visualization and automatic assessment systems . His work explores how visual representations of algorithms impact student learning, with significant contributions to tools like TRAKLA2 for data structures education. He investigates learning analytics approaches to understand programming misconceptions and develop targeted interventions. His recent research focuses on prerequisite skill assessment and differentiated learning pathways in computer science education, examining how fragile knowledge in foundational concepts affects advanced topic mastery. Publication Trends: Analysis of Korhonen's recent publications (2017-2025) reveals a strong focus on algorithm education, particularly Dijkstra's algorithm and data structures. His work increasingly incorporates learning analytics to identify student misconceptions, with growing emphasis on automated assessment systems that provide personalized feedback. The research demonstrates a progression from tool development (TRAKLA2) to sophisticated analysis of student learning patterns and prerequisite knowledge gaps, with recent work exploring JSON-based algorithm animation languages and the cognitive aspects of visualization. Awards and Recognition: Docentship at University of Turku (2012-present) Academic Service and Mentorship: Korhonen has supervised 8 theses and collaborates extensively with researchers across Finland and internationally. He serves on editorial boards for key journals including Computer Science Education and ACM Transactions on Computing Education. His service includes program committee memberships for major conferences like ICER (International Computing Education Research). Korhonen actively contributes to building educator networks, particularly through the Finland-US Network for the Study of Engagement and Learning in Games (FUN) and software engineering teacher networks in Finland. Research Groups: Korhonen leads the LeTech Learning Technology Group at Aalto University, focusing on educational technology development and evaluation. His fingerprint analysis shows strong activity in Data Structures (100%), Early Research in Computer Science (37%), Dijkstra Algorithms (37%), Learning Analytics (37%), and Case Studies (37%). He's deeply involved with the Computing Education Research community in Finland, contributing to national initiatives that strengthen computer science pedagogy across institutions.

Giovanni Di FrancoView profile

Professor

• Social Research Methodology
• Electoral Sociology
• Attitudes and Values of Young People
• +3 more

Giovanni Di Franco serves as Full Professor in the Department of Social and Economic Sciences at Sapienza University of Rome's Faculty of Political Science, Sociology and Communication. He directs the book series "La cassetta degli attrezzi" (Franco Angeli Milano) and holds affiliations with CIRES at Roma Tre University and Bonanno publishing house's scientific committee. His research centers on Social Research Methodology , emphasizing data collection/analysis techniques, historical evolution of methods, qualitative-quantitative integration, and micro-macro data synthesis. Specialized in Electoral Sociology , he examines polling methodologies in digital contexts, while his work on Youth Attitudes explores values during economic crises. Additional expertise spans Europeanization Processes , Social Representations of eating disorders, and Social Cohesion frameworks. Recent publications reveal methodological innovation through machine learning applications and critical analysis of intergenerational inequality, reflecting his commitment to advancing sociological research in contemporary digital landscapes. Professor Di Franco has led significant projects including immigrant family-school interactions (2009-10), food education integration (2009), Campanian legality culture (2008), and European identity formation (2007-09), alongside European Commission-funded e-inclusion research. He supervises doctoral candidates through Sapienza's PhD program in Sociology and Applied Social Sciences. He contributes to interdisciplinary research through LARFAD laboratory and CIRES center, developing methodological tools for social analysis while directing academic publications that shape methodological discourse in Italian sociology.

• Federated Learning
• Edge Computing
• Mobile Computing
• +4 more

Zimu Zhou is an Assistant Professor at Tsinghua University's School of Software, Department of Computer Science and Technology, specializing in federated learning, edge computing, and mobile systems. With an extensive publication record spanning from 2018 to 2025, Dr. Zhou has established themselves as a leading researcher in distributed machine learning systems. Dr. Zhou's research focuses on federated learning systems , mobile AI optimization , and spatial data processing . Their work addresses critical challenges in distributed machine learning including data heterogeneity, communication efficiency, privacy preservation, and practical deployment constraints. Recent research has expanded into federated large language models and advanced personalization techniques for mobile environments. Analysis of Dr. Zhou's publication trends reveals a strong emphasis on practical deployment of federated learning systems, with increasing focus on real-world applications in mobility, urban computing, and AIoT. Their work bridges theoretical advances with practical implementations, as evidenced by multiple publications in top-tier systems and AI conferences. Best Paper Award, MobiCom 2024 ACM SIGSPATIAL Best Paper Honorable Mention, 2022 Dr. Zhou has supervised several PhD students who have become active contributors in the field, including Xiaochen Li, Sicong Liu, and Yexuan Shi. Their research has been supported by multiple grants focused on edge intelligence and privacy-preserving distributed learning. Current projects include federated reasoning with large language models and resource-efficient AIoT systems. Dr. Zhou leads the Distributed Intelligence Lab at Tsinghua University, which focuses on building practical frameworks for decentralized machine learning across mobile and edge environments. The lab collaborates with industry partners to deploy federated learning solutions in real-world settings.

Azelle CourtialView profile

Researcher

• Map generalization
• Deep learning
• Multi-scale maps
• +3 more

Azelle Courtial is a Researcher at Institut Géographique National (IGN) , specializing in deep learning applications for cartographic generalization within the MEIG research team (Modélisation des Environnements Inhabités et Généralisation). Her work focuses on multi-scale mapping automation and spatial data representations , with recent contributions in GAN-based mountain road generalization and deep learning benchmark development . Education: PhD in Geographic Information Sciences (Université Paris-Est, 2023), Master's in GIS (2019), Licence in Math & Informatics (2017) Projects: ERC Consolidator Grant recipient (2021-2026) Software: Developer of CartAGen4Py and DeepMapGen libraries Her publications demonstrate a research trajectory combining deep learning for map generalization multi-scale cartography principles constraint-based evaluation frameworks spatial relation modeling in GIS Scientific recognition includes: Best short paper award at AGILE Conference 2022 Teaching contributions span spatial databases , webmapping , and GIS programming , with responsibilities in course material development and student project supervision.

• Cryptography
• Network Security
• Distributed Systems
• +2 more

Antonio Nicolosi is an Associate Professor of Computer Science at Stevens Institute of Technology's Schaefer School of Engineering, specializing in cryptography and secure distributed systems. His NSF CAREER award-winning research explores lattice-based cryptography, non-commutative cryptographic protocols, and future internet architectures like NEBULA. Education PhD in Computer Science, New York University MS in Computer Science, Università di Catania Research Focus: Develops provably secure cryptographic systems using group-theoretic approaches, with applications in network security and privacy-preserving protocols. Honors: NSF CAREER Award (2014), EU Marie Curie Fellowship. Recent publications address pseudorandom functions, broadcast steganography, and internet architecture security. Teaches courses including Foundations of Cryptography and Secure Systems. Professional Service: Program Director for Cybersecurity Masters, editorial roles, and conference organization. Research funded by NSF, Air Force Research Lab, and DOD.

• Computer Vision
• Computer Graphics
• Artificial Intelligence
• +3 more

Alexei (Alyosha) Efros is a Professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley, where he holds the Howard Friesen Professorship and is a core member of the Berkeley Artificial Intelligence Research Lab (BAIR). Prior to joining UC Berkeley in 2013, he spent a decade as faculty at Carnegie Mellon University and maintained affiliations with École Normale Supérieure/INRIA and the University of Oxford. His educational background includes: PhD in Computer Science, University of California, Berkeley (2003) BS in Computer Science, University of Utah (1997) Efros's research fundamentally explores how machines can understand and recreate the visual world using vast unlabeled data, with pioneering contributions at the intersection of computer vision and computer graphics. He champions data-driven and self-supervised learning approaches, emphasizing slow science principles while advancing applications in computational photography, visual data mining, robotics, and interdisciplinary humanities projects. His work consistently bridges theoretical innovation with practical impact, as evidenced by his prolific publication record and industry collaborations. Analysis of his 2024-2025 publications reveals three dominant trajectories: 1) Generative model interpretability (CLIP analysis, diffusion model auditing), 2) 3D scene understanding through novel representations (Gaussian splatting, persistent state modeling), and 3) Self-supervised techniques for video and multiview consistency. These threads demonstrate his lab's strategic focus on making generative systems more controllable, interpretable, and spatially coherent while maintaining strong connections to human vision principles. His exceptional contributions have been recognized with: ACM Prize in Computing (2016) Five ICCV Helmholtz Test-of-Time Prizes (1999-2017) SIGGRAPH Significant New Researcher Award (2010) NSF CAREER Awards (2006, 2010) Multiple teaching honors including the Jim and Donna Gray Award (2023) As a dedicated mentor, Efros has advised 19 PhD students to completion (including current faculty at CMU, TTIC, and Stanford) and numerous MS/BS researchers, with his trainees consistently securing prestigious fellowships and industry positions. His research has been supported by sustained NSF funding, industry partnerships with Adobe and NVIDIA, and collaborative grants through BAIR's multi-institutional initiatives. The lab maintains active international collaborations with Oxford, École Normale Supérieure, and leading AI institutes worldwide. His research group operates within BAIR's collaborative ecosystem, featuring dedicated computational resources for vision and graphics research. The lab emphasizes interdisciplinary teamwork, regularly partnering with robotics and cognitive science researchers to explore human-AI visual interaction. Current projects focus on foundational challenges in visual representation learning, with increasing emphasis on ethical AI development and societal impact through initiatives like visual data attribution frameworks.

Yinghan ChenView profile

Associate Professor

• Bayesian Statistics
• Educational Measurement
• Cognitive Diagnosis Models
• +3 more

Yinghan Chen is an Associate Professor in the Department of Mathematics and Statistics at the University of Nevada, Reno (UNR), within the College of Science. Their research focuses on Bayesian statistics, cognitive diagnosis models, and educational measurement, with applications in public health and behavioral science. Key areas include latent variable modeling, health disparities among Asian American populations, and statistical methods for educational interventions. Dr. Chen’s work bridges theoretical statistical methodologies with practical applications in education and healthcare. Recent publications explore topics such as Bayesian hierarchical modeling for cognitive diagnostic assessments, multimodal data integration, and health behavior analysis using frameworks like the transtheoretical model. Their research also addresses algorithm optimization for network motif detection and model reduction techniques in psychometrics. No scientific awards or grants are explicitly listed in the provided materials. They currently advise no formally listed students but contributes to academic advising within their department. The full description provides additional details on their affiliations, educational background, and research trajectory.

• Statistics
• Machine Learning
• Data Science
• +3 more

Diego Nascimento is an Assistant Professor in Statistics at NEOMA Business School. His research focuses on statistical modeling, machine learning, spatio-temporal dependencies, and data visualization, with applications in supply chain management, neurosciences, and industrial projects. He has published in top-tier journals like the Journal of the Royal Statistical Society Series C and Pattern Recognition , collaborating with companies in South America and healthcare institutions. His work bridges theoretical statistics with practical industry challenges, emphasizing Bayesian methods and dynamic models. Key research areas include spatio-temporal modeling, neural data analysis, and optimization techniques. Recent studies involve clinical trials on transcranial stimulation effects and predictive analytics for supply chains during the pandemic. He actively contributes to interdisciplinary projects, combining statistical rigor with real-world applications in healthcare and environmental monitoring. Award-winning research includes advancements in imbalanced classification, safety-stock prediction, and entropy analysis in clinical trials. His collaborative projects reflect a commitment to both academic innovation and industrial relevance, with ongoing work in neuroimaging and supply chain resilience.

Paul VickersView profile

Professor

• Sonification & Auditory Display
• Network Security
• Cyber Security
• +4 more

Professor Paul Vickers holds the position of Professor of Computer Science and Sonification at Northumbria University since 2001, previously at Liverpool John Moores University from 1989-2001. He specializes in sonification, auditory display, cybersecurity, and interdisciplinary applications of computing. His research explores intersections between sonification, network security, and aesthetic computing, with notable projects including SoNSTAR (network traffic sonification) and SoniFRED (healthcare sonification). Education: PhD in Computing Science (2000), BSc (Hons) in Computer Studies (1989). Professional qualifications include Chartered Engineer (CEng), Fellow of the Higher Education Academy (FHEA), and membership of the Institution of Engineering and Technology (MIEE). Research focuses on sonification for cyber situational awareness, structural biology, and healthcare applications. His work emphasizes transdisciplinary collaboration, combining art, science, and technology. Key contributions include auditory monitoring systems for network security, sonification of biomedical data, and interactive tools for molecular visualization. Professional activities include organizing international conferences (e.g., ICAD), serving as a PhD examiner, and leading research initiatives such as the Leverhulme Trust-funded RADICAL project. His work is disseminated through academic publications, open-source software (e.g., GitHub repositories), and platforms like YouTube and ResearchGate. Current projects include the Northumbria University Sonification Network (nuson), exploring applications of sonification in healthcare, cybersecurity, and artistic collaboration. He actively engages in public outreach and STEM education initiatives.

• Graph Neural Networks
• Time Series Analysis
• Anomaly Detection
• +5 more

Cesare Alippi is a Professor at Politecnico di Milano in the Department of Electronics, Information and Bioengineering within the School of Industrial and Information Engineering. With an extensive publication record spanning over three decades from 1991 to 2025, he has established himself as a leading researcher in machine learning, particularly in graph neural networks, time series analysis, and anomaly detection. His research interests focus on developing advanced machine learning methodologies for time series analysis, graph-based learning, and anomaly detection systems. Professor Alippi's work bridges theoretical foundations with practical applications across various domains including smart systems, healthcare, and cybersecurity. His recent publications demonstrate a strong emphasis on graph-based approaches to time series forecasting, structural anomaly detection, and spatiotemporal modeling. His publication trends from 2023-2025 reveal a concentration on graph neural networks for time series applications, with significant contributions to forecasting, anomaly detection, and representation learning. These works often address challenges in spatiotemporal data, missing values, and structural pattern recognition, demonstrating both theoretical depth and practical utility. Professor Alippi has mentored numerous researchers who have become prominent collaborators, including Andrea Cini, Daniele Zambon, and Lorenzo Livi, among others. His collaborative network extends across multiple institutions and research domains.

Taotao CaiView profile

Lecturer

• Graph Data Processing
• Social Network Analytics
• Data Mining
• +6 more

Dr. Taotao Cai serves as an Honorary Lecturer and Postdoctoral Research Fellow in the School of Computing at Macquarie University, bringing expertise in graph analytics and machine learning. Previously, they held an Associate Research Fellow position at Deakin University's School of Information Technology. Their academic foundation includes a Ph.D. from Deakin University and a Master's in Computer Science from Shenzhen University, China. Education: Ph.D. in Computer Science, Deakin University, Australia Master of Computer Science, Shenzhen University, P.R. China Research spans graph data processing, social network analytics, and data mining with significant contributions to causal graph neural networks, influence maximization, and privacy-preserving machine learning. Recent work demonstrates strong interdisciplinary applications in remote sensing, healthcare decision systems, and battery management, while maintaining core focus on dynamic network analysis. The research portfolio reveals increasing sophistication in causal reasoning within graph structures and practical implementations for real-world systems. Publication trends from 2023-2025 show concentrated activity in graph neural networks (40%), privacy/security (25%), and remote sensing applications (20%), with emerging work in causal classification and medical AI. This evolution reflects both theoretical advancement and practical problem-solving across diverse domains. No formal scientific awards are documented in available sources. While no formal student advising is recorded, collaborative patterns indicate active mentorship within research teams. Grant activity remains unspecified, but extensive cross-institutional collaborations (particularly with Deakin University and international partners) suggest participation in multiple funded projects. Current trajectories point toward causal graph neural networks for explainable AI and privacy-enhanced federated learning systems as primary research frontiers. Active participation in international research networks is evident through co-authorship spanning Australia, China, and global institutions, with particular strength in graph algorithm development and social network analysis communities.