Find Academic Supervisors

Qin LvView profile

Professor

• Data Mining
• Search Systems
• Mobile Systems
• +8 more

Qin Lv is a Professor in the Department of Computer Science at the University of Colorado Boulder. She earned her PhD in Computer Science from Princeton University, followed by a postdoctoral position there and an assistant professorship at Stony Brook University. Her research focuses on integrating system design with data analysis for managing large-scale datasets, spanning search systems, data mining, mobile systems, social networks, and interdisciplinary applications in geoscience, renewable energy, and environmental science. Her work has been cited over 2,000 times and earned notable awards such as the 2010 ISLEPED Best Paper nomination and the 2012 Computational Sustainability Award for personalized driving behavior analysis. Research Interests: Efficient system design for big data management Interdisciplinary applications in energy and environmental science Wearable technology and smart eyewear systems Social media analysis and public perception studies Fake news detection and misinformation mitigation Machine learning for geoscience and renewable energy systems Scientific Contributions: Dr. Lv has pioneered work in battery system modeling, mobile air quality sensing, and event analysis in social networks. Her recent research explores AI-driven wearable systems and cross-platform analysis of public responses to technological and environmental challenges. Over 30 peer-reviewed publications reflect her expertise in both technical system design and societal impact investigations.

Charles MallesonView profile

Research Fellow

• Computer Vision
• 3D Reconstruction
• Motion Capture
• +5 more

Dr. Charles Malleson is a Research Fellow in Computer Vision at the Centre for Vision, Speech and Signal Processing (CVSSP) at the University of Surrey, UK. His work focuses on computer vision, 3D reconstruction, and motion capture technologies using RGB-D sensors and multi-view video systems. Malleson's educational background includes a PhD, MSc, and BEng (Hons), though specific institutions are not mentioned in the provided text. His research interests span computer vision, 3D reconstruction, motion capture, RGB-D data processing, visual alignment, human pose estimation, graphics, and more recently, animal pose estimation with a focus on canine subjects. His recent publications reveal a strong research trajectory focused on practical applications of computer vision. Beginning with foundational work in 3D reconstruction and motion capture using RGB-D sensors and multi-view video systems, his research has evolved toward increasingly specialized applications including wearable visual correction devices, animal pose estimation, and neural network-based image processing. A notable trend is his expansion from human motion capture to animal pose estimation, developing synthetic datasets like SyDog for training deep learning models. His work often combines multiple sensor modalities and emphasizes real-time processing capabilities for practical deployment. Scientific Awards: Leverhulme Trust Early Career Fellowship Malleson has been instrumental in developing the TotalCapture dataset, which combines multi-viewpoint video, IMU sensor data, and accurate 3D skeletal joint ground truth. His research has received significant funding, including the prestigious Leverhulme Trust Early Career Fellowship. His work bridges academic research and practical applications, particularly in medical devices (visual alignment correction) and animal pose estimation technologies. As a member of CVSSP, Malleson contributes to one of the world's leading vision, speech, and signal processing research centers. His work on hybrid modeling of non-rigid scenes, real-time motion capture, and volumetric graphics demonstrates the center's commitment to pushing the boundaries of visual computing technology. His recent focus on animal pose estimation represents an innovative expansion of traditional human-centered computer vision research.

Christian RohrbeckView profile

Senior Lecturer

• Extreme Value Analysis
• Spatial Statistics
• Environmental Statistics
• +3 more

Dr. Christian Rohrbeck is a Senior Lecturer in the Department of Mathematical Sciences at the University of Bath. He holds a PhD in Statistics and Operational Research from Lancaster University and leads projects in statistical climatology through the EPSRC Centre for Doctoral Training in Statistical Applied Mathematics and Centre for Climate Adaptation & Environment Research. His research combines extreme value theory, spatial statistics, and Bayesian methods to analyze environmental and financial data. Current projects focus on flood risk modeling, climate extremes standardization, and monotonic regression techniques. Dr. Rohrbeck accepts doctoral students in extreme value analysis, flood risk, and climate adaptation statistics.

Paolo FavaroView profile

Professor

• Computer Vision
• Computational Photography
• Machine Learning
• +4 more

Prof. Dr. Paolo Favaro is a full Professor and Head of the Computer Vision Group (CVG) at the University of Bern's Institute of Computer Science. He holds a Laurea (BSc+MSc) from the University of Padova, Italy, and M.Sc. and Ph.D. in Electrical Engineering from Washington University in St. Louis. His career includes postdoctoral roles at UCLA and Cambridge University, followed by work in medical imaging at Siemens Corporate Research. From 2006–2011, he was Lecturer and Reader at Heriot-Watt University, and Honorary Fellow at the University of Edinburgh before joining Bern in 2012. His research focuses on computer vision , computational photography , machine learning , and signal/image processing , with contributions to inverse problems and variational techniques. His work spans theoretical advancements and practical applications, such as video diffusion models, unsupervised segmentation, and image restoration. He is an active member of the IEEE Society. Recent research trends emphasize generative models (e.g., diffusion models, video generation) and self-supervised learning , addressing challenges in real-world image/video denoising and sparsity-driven methods. His articles explore topics like Kalman-based optimization, multimodal world models, and spatio-temporal representation learning. Prof. Favaro leads the CVG, fostering interdisciplinary research at the intersection of computer vision and AI. His lab develops cutting-edge solutions for 3D reconstruction, medical imaging, and autonomous systems.

• Computer Vision
• Machine Learning
• Image Processing
• +3 more

Weitong Cai is a Visiting Professor at Queen Mary University of London's School of Electronic Engineering and Computer Science. He specializes in teaching advanced topics like Deep Learning and Computer Vision at the postgraduate level. His research focuses on computer vision applications, including video moment retrieval, image segmentation, and cross-modal learning. He has contributed to advancements in temporal feature refinement, semantic localization, and robust image super-resolution techniques. Research Interests: His work bridges theoretical machine learning methodologies with practical applications in multimedia systems and dynamic scene analysis. Key areas include: Video moment retrieval using hybrid learning frameworks Camouflaged object segmentation via prompt-agnostic methods Multi-domain adaptation in video analysis Efficient screen content coding Publication Trends: Recent work emphasizes mitigating modality imbalance in cross-modal tasks (2025), improving temporal localization accuracy (2024), and robust image processing under complex degradations (2020-2019). His research spans both foundational algorithms and real-world applications like RGB-D SLAM in dynamic environments. Advising & Grants: No specific grants or advisee名单 listed. Active in collaborative research with industry through the School's qTech Programme. Labs/Teams: Participates in the School's Computer Vision and Machine Learning research groups, contributing to the qTech innovation ecosystem.

• Human Motion Prediction
• Computer Vision
• Multi-person Interaction Analysis
• +3 more

Dr. Wen Guo is a Researcher at the Department of Health Sciences and Technology (D-HEST) at ETH Zurich, affiliated with the Professorship for Sensory-Motor Systems. Their work focuses on advancing computer vision, robotics, and biomedical engineering through innovative machine learning techniques. Key research interests include human motion prediction, multi-person pose estimation, and gesture generation in complex interactions. Wen Guo has contributed to high-impact projects such as the UVO Challenge 2021, securing first place in both video-based and image-based open-world segmentation categories. Their research emphasizes real-world applications in safety-critical systems, crowd behavior modeling, and human-robot interaction. Notable methodologies include hierarchical transformer dynamical VAEs for motion generation and pose-interacting networks for 3D pose estimation. Academic contributions span from foundational work in motion prediction to applied solutions in computer vision challenges. Their lab integrates interdisciplinary approaches, combining deep learning with biomechanical insights to tackle challenges in healthcare and robotics.

Michael CarlView profile

Professor

• Machine Translation
• Computational Linguistics
• Translation Studies
• +6 more

Dr. Michael Carl is a Professor at Kent State University's Department of Modern & Classical Language Studies and Director of the Center for Research and Innovation in Translation and Translation Technology (CRITT). He specializes in Machine Translation, Computational Linguistics, and Translation Process Research, with a focus on analyzing behavioral translation data through the CRITT TPR-DB. Over 25 years, his work has explored translation dynamics, cognitive effort, and the integration of AI into translation workflows. His research emphasizes empirical methods, including eye-tracking and keystroke logging, to study translation processes. Recent work addresses post-editing strategies, syntactic complexity impacts, and modeling human translation using active inference agents. His CRITT TPR-DB contains extensive behavioral datasets, enabling cross-linguistic and cross-modal analysis of translation behaviors. Publications span theoretical contributions to applied studies, often bridging computational linguistics and cognitive science. He has organized global conferences and workshops, advancing interdisciplinary collaboration in translation studies. No scientific awards are explicitly listed, but his sustained contributions reflect a leadership role in the field.

• Biomedical Engineering
• Neural Signal Processing
• Pattern Recognition
• +2 more

Mojisola Grace Asogbon serves as Lecturer in Data Science within the College of Science and Engineering. Her research specializes in neural signal processing and rehabilitation technology, particularly developing machine learning methods for interpreting EEG and EMG signals in clinical populations. Recent publications focus on deep learning approaches for stroke rehabilitation, including motor intent decoding from EEG recordings, EMG pattern recognition for prosthetic control, and artifact removal techniques. She frequently employs transfer learning and adaptive algorithms to enhance signal processing robustness across diverse patient populations.

John ShanahanView profile

Professor

• 17th- and 18th-century English literature
• History of science and technology
• Science fiction
• +3 more

John Shanahan is the Associate Dean, Director of Liberal Studies, and Professor of English at DePaul University's College of Liberal Arts and Social Sciences (CLAS). He holds a B.Phil. from the University of Pittsburgh and a PhD from Rutgers University. His academic leadership includes roles as Director of Undergraduate Studies in English (2007-2010) and Director of the Graduate Program in English (2010-2015). He currently oversees the Certificate Program in Digital Humanities and leads Liberal Studies initiatives at CLAS. Shanahan’s research focuses on 17th- and 18th-century English literature, the history of science and technology, science fiction, and digital humanities. He co-leads DePaul’s Reading Chicago Reading project, funded by the National Endowment for the Humanities and Lyrasis, which explores mass reading programs through quantitative and geospatial analysis (visit dh.depaul.press/reading-chicago ). This work bridges humanities scholarship with computational methods. His scholarship spans topics from early modern scientific discourse in drama to contemporary science fiction’s engagement with post-anthropocentric themes. Recent projects emphasize collaborative digital tools for analyzing cultural consumption patterns in urban contexts like Chicago’s One Book program. His teaching and administrative roles reflect a commitment to interdisciplinary education and public humanities engagement. Shanahan’s career demonstrates a synthesis of traditional literary analysis with innovative digital methodologies, advocating for the relevance of humanities in understanding technology, culture, and societal change.

Huiping CaoView profile

Professor

• Databases
• Deep Learning
• Data Mining
• +7 more

Huiping Cao is a Professor in the Department of Computer Science at New Mexico State University (NMSU). She holds a Ph.D. from The University of Hong Kong (2007), and bachelor’s and master’s degrees from Renmin University of China (1999 and 2002). Her expertise spans databases, deep learning, data mining, time series analysis, and graph mining. Her research focuses on developing computational methodologies to extract knowledge from complex data such as sequences and graphs, with applications in smart grids, animal and range sciences, agriculture, and high-performance computing. Dr. Cao’s work emphasizes interdisciplinary applications, including real-time cattle monitoring using sensors (GPS and accelerometers) for disease detection, parturition prediction, and grazing behavior analysis. She has developed tools like GRAZETOOLS for livestock behavior analysis and contributed to precision ranching systems using LoRa-WAN sensors. Her methodological innovations include backbone index models for multi-cost road networks and federated learning frameworks to address data imbalance in distributed systems. Her research has addressed challenges such as uncertainty quantification in time series analysis, probabilistic modeling for segmentation tasks, and sensor error evaluation. She collaborates across disciplines, integrating machine learning with environmental and agricultural domains. Dr. Cao’s contributions also include advancements in graph-based influence discovery, semantically annotated observational data systems, and compression techniques for power grid monitoring data.

• Intelligent Transportation Systems
• Machine Learning
• Computer Networks
• +3 more

Zongtao Duan is an active researcher with a focus on intelligent transportation systems, cybersecurity, and machine learning applications. His work spans areas including traffic flow prediction, silent data corruption detection, vehicular networks security, and deep learning architectures for autonomous systems. He collaborates frequently with colleagues like Lei Tang and Junchi Ma, publishing in top-tier journals such as IEEE Transactions on Intelligent Transportation Systems and Future Generation Computer Systems. His research emphasizes practical solutions for urban mobility challenges, including ridesharing optimization and railway congestion control, while also addressing foundational issues in hardware reliability and error propagation modeling. Recent contributions include advancements in multimodal retrieval systems and knowledge graph applications in scientific data management. Zongtao has contributed to over 40 peer-reviewed publications since 2004, with notable work in 2025 exploring SDC evaluation frameworks and scenario-based testing for autonomous vehicle safety. His interdisciplinary approach bridges computer science, transportation engineering, and cybersecurity to address real-world technical challenges.

• French Language Practice
• Romance Studies
• Computer Vision
• +3 more

Viridiane Fay is a researcher in the Institute of Modern Languages and Romance Studies at the University of Würzburg , specializing in French language practice and applied computational methods. During lecture periods, she holds office hours every Tuesday from 1 p.m. to 2 p.m., with appointments available during semester breaks. Role: French Editor, Language Practice Location: Am Hubland, 97074 Würzburg Contact: viridiane.fay@uni-wuerzburg.de Her research spans two distinct domains: French language pedagogy and computer vision/AI . While her institutional affiliation focuses on Romance Studies, her publication record reveals expertise in: Medical and underwater image processing Transformer-based architectures Federated learning and network optimization Watermarking and security algorithms Multi-modal AI applications Neuroscience signal analysis Recent work includes lightweight segmentation models (e.g., ContextFormer), drone/aerial imagery detection frameworks (CH-YOLO-Lite), and privacy-preserving AI systems (Find). Despite limited biographical details in the scrape, her 30+ publications since 2017 suggest significant technical contributions.

JAVIER LOPEZ FANDIÑOView profile

Assistant Professor

• High Performance Computing
• Remote Sensing
• GPU Processing
• +4 more

JAVIER LOPEZ FANDIÑO is an Assistant Professor at the Department of Electronics and Computer Engineering, Higher Technical School of Engineering, University of Santiago de Compostela (USC). He obtained his PhD in Computer Science Research from USC in 2018 under the supervision of Dra. Dora B. Heras and Dr. Francisco Argüello. He is affiliated with the research groups ARQCOMP (Computer Architecture) at USC and collaborates with Centro Singular de Investigación en Tecnoloxías Intelixentes (CiTIUS) and Galician Centre for Mathematical Research and Technology (CITMAga). Education: BSc (2012) and MSc (2014) in Computer Science from USC, PhD (2018) in Computer Science Research. Research Areas: High performance computing for remote sensing, GPU processing of multidimensional images, anomaly detection, change detection in multi-temporal datasets, deep learning for hyperspectral classification. His recent publications focus on heterogeneous computing, edge computing, and neural networks for real-time anomaly detection in multispectral images. Key methodologies include extinction profiles, isolation forest, attention-based CNN, and CUDA optimizations. Notable affiliations include the ARQCOMP research group and collaborations with CiTIUS and CITMAga. He has not been explicitly associated with scientific awards in the provided texts.

• Transport Geography
• Urban Planning
• GIScience
• +3 more

Varun Raturi is an Affiliate Researcher in the School of Social & Political Sciences at the University of Glasgow, affiliated with the Urban Big Data Centre. His work focuses on transport geography, urban analytics, and the application of mobile and crowdsourced data to understand mobility patterns. Raturi has contributed to studies on bicycle commuting infrastructure, privacy implications in data collection, and modal competition between high-speed rail and other transport modes. His research employs methodologies from GIScience, game theory, and machine learning to address challenges in sustainable urban planning and transport policy. Recent projects include analyzing the reliability of app-based travel surveys, developing novel techniques for estimating origin-destination matrices using mobile data, and evaluating the impact of privacy measures on crowdsourced cycling data utility. Raturi has presented at leading conferences such as GIScience and AGILE, and his work appears in journals like Journal of Transport Geography and Transportation Planning and Technology . His research datasets include Strava-derived mobility data from cities like Glasgow, Manchester, and Sheffield, highlighting his focus on leveraging real-world data for urban mobility insights. These datasets underpin his exploration of spatial-temporal mobility patterns and infrastructure impacts, contributing to evidence-based policy recommendations for sustainable urban development.

• Spectral Shape Analysis
• 3D Computer Vision
• Geometric Deep Learning
• +4 more

Riccardo Marin is a Postdoctoral Researcher at the Technical University of Munich (TUM) within the Computer Vision Group. His research focuses on Spectral Shape Analysis, Geometric Deep Learning, and 3D Human Modeling. He holds affiliations with ELLIS Society and is an alumni of the Alexander von Humboldt Foundation and Marie Skłodowska-Curie Actions. Marin's work bridges computational geometry and deep learning, addressing challenges in 3D human registration (NICP), avatar creation (Human-3Diffusion), and neural surface modeling (NSF). He has contributed to open-source tools for spectral shape analysis and maintains active GitHub repositories showcasing his research implementations. His research interests span 3D reconstruction, neural fields, and implicit representations, with applications in virtual humans and human-object interaction analysis. Marin's recent work emphasizes scalable registration pipelines and diffusion models for 3D generation. Awards: ELLIS Member, Alexander von Humboldt Alumni, Marie Skłodowska-Curie Fellow Key Contributions: NICP, Human-3Diffusion, NSF framework Education: PhD background (implied from postdoc status), involvement in a Spectral Shape Analysis course at University of Verona