جست‌وجوی استاد و پژوهشگر

Zhixiang Chenمشاهده پروفایل

مدرس

• Computer Vision
• Machine Learning
• Human Analysis
• +۲ مورد دیگر

Dr. Zhixiang Chen is a Lecturer in Machine Learning at the Department of Computer Science, University of Sheffield, and an active member of both the Machine Learning and Computer Vision research groups. Previously, he held postdoctoral positions at Imperial College London and Tsinghua University. Education: PhD in Computer Science, Tsinghua University, China B.Eng. in Computer Engineering, Xi'an Jiaotong University, China His research focuses on advancing Computer Vision and Machine Learning through human analysis, object detection, and image retrieval systems. Recent work integrates deep learning for industrial applications like PCB inspection and autonomous systems, while earlier contributions pioneered video hashing techniques for efficient multimedia search. His methodology emphasizes robust feature representation and scalable algorithm design. Publication trends reveal a strategic shift from foundational hashing research (2016-2019) toward applied 3D vision and industrial automation (2020-2024), with increasing emphasis on uncertainty-aware models and sensor fusion for real-world deployment. Scientific Awards: China Society of Image and Graphics Doctoral Dissertation Award (2019) IEEE International Conference on Multimedia and Expo Best Paper Award (2018) China National Postdoctoral Program for Innovative Talents (2017) Dr. Chen currently leads the EPSRC-funded project 'VIM: towards a Vision based System for Inventory Management with Deep Learning' (2024-2026, £46,354) as Principal Investigator. He serves as a Grant Reviewer for UKRI-FLF and NSFC, Senior Program Committee Member for AAAI 2023, and regularly reviews for top-tier conferences (CVPR, ICCV, NeurIPS) and journals (IEEE T-PAMI, IEEE T-MM). As a core member of Sheffield's Machine Learning and Computer Vision research groups, he contributes to collaborative projects involving industrial partners and interdisciplinary teams focused on AI-driven solutions for healthcare, manufacturing, and autonomous systems.

• Empirical Legal Studies
• Law and Economics
• Law and Technology
• +۶ مورد دیگر

Jerrold SOH is an Assistant Professor of Law at the Yong Pung How School of Law, Singapore Management University (SMU), serving concurrently as Deputy Director of the Research Programme in Computational Law at the Centre for Digital Law. He holds an LL.M. from Harvard University and dual first-class honors degrees in Law (LL.B.) and Economics (B.Soc.Sci.) from the National University of Singapore (NUS). His academic journey includes roles as Deputy Director at the Centre for Computational Law (2021–2024) and Lecturer of Law at SMU (2018–2020). SOH’s research focuses on empirical legal studies, law and technology intersections, and computational methods in jurisprudence. He investigates topics such as judicial citation patterns, algorithmic accountability, AI-driven legal systems, and tort liability for automated vehicles. His work often bridges legal theory with data science, exemplified by projects like legal text formalization, network analysis of case law, and blockchain applications in property law. SOH has published widely in journals like the Asian Journal of Comparative Law , Frontiers in Physics , and American Business Law Journal , with recent work addressing AI legal implications, blockchain land transfers, and antitrust regulation. He is a Chief Data Scientist at Lex Quanta Pte Ltd and has received honors such as the SMU START Overseas Postgraduate Scholarship and multiple Dean’s Lists at NUS. His professional contributions span computational law infrastructure development, legal innovation advocacy, and educational initiatives in technology law. Current research priorities include AI ethics in legal contexts, control-centric liability frameworks, and empirical analyses of judicial decision-making processes.

Bogdan Vrusiasمشاهده پروفایل

استاد مهمان

• Generative AI
• Large Language Models
• Foundation Models
• +۶ مورد دیگر

Bogdan Vrusias is a Visiting Professor at the University of Surrey's School of Computer Science and Electronic Engineering (part-time). He concurrently serves as Global Head of AI and Data Engineering at The Economist. His academic journey began at Surrey as an undergraduate (BSc Computing and Information Technology, 1998), followed by a PhD in multi-modal information retrieval (2004) and an MBA (2010). He is a Fellow of the Higher Education Academy (FHEA). Education: PhD, University of Surrey (2004) MBA, University of Surrey (2010) BSc Computing and Information Technology, University of Surrey (1998) His research spans Generative AI, Large Language Models, Foundation Models, Machine Learning, Deep Learning, Cloud Computing, Natural Language Processing, and Computer Vision . His work focuses on developing scalable AI architectures and semantic technologies for real-world applications. Publications consistently explore semantic systems, neural networks, and multimodal information retrieval . Recent articles emphasize video annotation frameworks and P2P network architectures, while earlier work established foundations in self-organizing maps and feature selection for high-dimensional data. Awards: FHEA (Fellow of the Higher Education Academy) He previously directed UG/PG programs at Surrey and created pioneering courses like Web Hacking Countermeasures. His industry leadership includes founding tech startups and heading Amazon Web Services' AI/ML Specialist Solutions Architects team for EMEA. At The Economist, he drives AI and data transformation.

Ioannis Chatzigeorgiouمشاهده پروفایل

مدرس ارشد

• Communication theory
• Network coding
• Error decoding
• +۴ مورد دیگر

Dr. Ioannis Chatzigeorgiou is a Senior Lecturer at the School of Computing and Communications, Lancaster University , with a focus on communication theory and network coding. He holds a PhD from the University of Cambridge and has held academic roles at NTNU, Cambridge University, and Lancaster since 2010. Dipl.-Ing. in Electrical Engineering, Democritus University of Thrace (1997) MSc in Satellite Communications Engineering, University of Surrey (2000) PhD in Electrical Engineering, University of Cambridge (2007) Postgraduate Certificate in Academic Practice, Lancaster University (2011) His research centers on communication theory , particularly random linear network coding , noise decoding (e.g., GRAND), and physical-layer security . Recent work explores throughput optimization in massive MIMO systems, burst error resilience, and security implications of partial packet recovery. Key scientific awards include: Fellow of the Higher Education Academy (FHEA) since 2012 IEEE Senior Member since 2015 Full member of the EPSRC Peer Review College since 2018 Member of Royal Society International Exchanges Panel (2025-2027) He has supervised PhD students and co-edited a EURASIP Journal special issue on network coding. His grants include EPSRC-funded Network error control for Rapid and Reliable Data Delivery (R2D2) (2014-2015) and Royal Society collaboration Improving wireless information security via physical layer dynamics (2015-2017).

Margo Seltzerمشاهده پروفایل

استاد

• Computer Systems
• Data Management
• Machine Learning
• +۵ مورد دیگر

Margo Seltzer is the Canada 150 Research Chair in Computer Systems and Cheriton Family Chair in Computer Science at the University of British Columbia (UBC), where she is a Professor in the Department of Computer Science, Faculty of Science. She currently serves as co-Head of the UBC Computer Science Department, managing its academic and operational affairs. Her research focuses on systems, provenance, machine learning, and security, with notable contributions to graph databases, program synthesis, and reproducibility frameworks. She holds prestigious awards including election to the Royal Society of Canada (2023) and was nominated for the ACM Athena Lecturer Award. Her work emphasizes interdisciplinary applications, such as healthcare anomaly detection and interpretable AI models for datasets with missing values. Dr. Seltzer leads the Systopia research group, which explores systems and machine learning intersections. Recent projects include Velosiraptor (code synthesis for memory translation), HyperBrain (anomaly detection in brain networks), and NetShaper (differentially private network security). She actively publishes in top conferences like NeurIPS, ICML, and USENIX. Her lab develops tools like Provenance-Aware Storage Systems (PASS) and frameworks for reproducible research. She mentors postdoctoral researchers but is not currently admitting new graduate students, emphasizing alignment between student interests and her research areas.

• Legal Information Management
• Law Librarianship
• Technology in Legal Practice
• +۳ مورد دیگر

David Whelan serves as Assistant Professor and Director of Law Library & Instructional Technology at the University of Illinois Chicago School of Law, where he integrates academic instruction with leadership in legal information systems and technology infrastructure. His research focuses on practical applications of technology in legal environments, including digital library management, cloud-based legal practice solutions, and cost-effective research methodologies. Key areas include electronic licensing negotiation, wireless networking implementation in libraries, and digital marketing metrics for legal services, reflecting his commitment to enhancing accessibility and efficiency in legal information systems. Whelan's publication trajectory from 2000-2024 demonstrates evolving expertise from foundational internet research tools to contemporary leadership development for law librarians and DIY digital analytics, consistently addressing technological adaptation challenges within legal practice and education. No scientific awards or fellowships are documented in the available materials. He has managed research grant projects as evidenced by 2002-03 completed reports, though no student advisement or current grant funding details are provided. His administrative role as Library Director encompasses oversight of instructional technology services at UIC Law. Whelan leads the Law Library and Instructional Technology team, driving the integration of digital resources and infrastructure within the law school's academic framework.

• Chemical Informatics
• Data Science
• Semantic Integration
• +۳ مورد دیگر

Stuart J. Chalk is a Professor in the Department of Chemistry and Biochemistry at the University of North Florida. His research spans Chemical Informatics and Data Science, with a focus on semantic data integration, AI applications in chemistry, and blockchain for research data management. Dr. Chalk's research interests include chemical informatics, data science, and the development of tools for scientific data management. Notable projects include the KnowLedger digital research notebook (funded by NIST in 2021) and an NSF-funded project on semantic integration of heterogeneous datasets from toxicology, medicine, materials, biodiversity, and chemistry. His work also involves machine accessibility of solubility data, enhancements to the IUPAC Gold Book, and automated extraction of chemical property data from PDF files. His recent publications highlight trends in applying artificial intelligence to deep space research, blockchain technology for scientific workflows, and chemical data evaluation standards. These articles reflect interdisciplinary collaborations across chemistry, biology, computer science, and space medicine. Dr. Chalk leads the Chalk Lab, which has produced 27 datasets including the ChemTwins project (digital twins of chemical substances) and tools for processing thermophysical property data. He has secured significant grants from NSF and NIST, demonstrating the impact of his work on scientific data infrastructure.

• Linguistics
• Computational Linguistics
• Cognitive Science
• +۷ مورد دیگر

Aniello De Santo is an Assistant Professor in the Department of Linguistics at the University of Utah, where he has been serving since July 2020. He earned his PhD in Linguistics from Stony Brook University in 2020 and is fluent in Italian, which informs his research on Italian syntax and processing. His research spans multiple disciplines with key interests in: Computational Linguistics and Syntax Cognitive Science and Theoretical Frameworks Artificial Intelligence and Machine Learning Applications Formal Language Theory and Complexity Psycholinguistic Processing Models De Santo's scholarly work demonstrates remarkable interdisciplinary integration, with publications in top journals across linguistics, cognitive science, and computer science. His recent work focuses on theoretical foundations in cognitive science (2024 publications on plausibility criteria and theoretical virtues), empirical investigations of Italian relative clause processing (2024), and innovative applications of machine learning techniques to linguistic problems. His research often bridges formal theoretical analysis with computational modeling and empirical validation. He has developed innovative educational approaches including an asynchronous general-education course "Language in the United States" (2023) and teaches a diverse range of courses in computational linguistics, syntax, and language studies. His extensive teaching portfolio includes Computers & Language, Introduction to Syntax, Computational Linguistics, and various research/thesis courses at both undergraduate and graduate levels. De Santo is actively engaged in community outreach as an organizer of the North American Computational Linguistics Open Competition (NACLO) for high school students, conducting practice sessions and hosting competition rounds since 2022. This work connects academic linguistics with pre-college education and demonstrates his commitment to promoting computational thinking in language studies.

Schahram Dustdarمشاهده پروفایل

استاد

• Adaptive and Self-Organizing Systems
• Distributed and Parallel Computing
• Software Engineering

Schahram Dustdar serves as Full Professor of Computer Science and heads the Distributed Systems Group at Vienna University of Technology (TU Vienna). He holds significant academic distinctions including membership in the Academia Europaea (since 2013) and status as an IEEE Senior Member (2009). His research spans three core domains: Adaptive and Self-Organizing Systems Distributed and Parallel Computing Software Engineering His work focuses on practical implementations of distributed architectures with emphasis on efficiency, scalability, and real-world application in domains like Smart Cities and cloud infrastructure. Analysis of his recent publications reveals strong trends in stream processing optimization, smart city data ecosystems, and containerized deployment frameworks. His research consistently addresses cost-efficiency and adaptive resource management in distributed environments. Major recognitions include: ACM Distinguished Scientist award (2009) IBM Faculty Award (2012) He also contributes significantly to academic discourse as Editor in Chief of Computing (Springer) and editorial board member for IEEE Computer and IEEE Internet Computing. Dustdar maintains active leadership through his role heading the Distributed Systems Group at TU Vienna, driving research in next-generation distributed computing paradigms and mentoring within the academic community.

• Computer Vision
• Machine Learning
• Robotics
• +۳ مورد دیگر

James Hays is affiliated with Brown University . His research spans Computer Vision , Robotics , 3D Reconstruction , and Image Synthesis . His recent work focuses on 3D Object Detection (e.g., CoRL 2024 ), Semantic Scene Flow (e.g., ICLR 2024 ), and Multimodal Image Generation (e.g., CVPR 2024 ). He has contributed to datasets like ContactDB and ContactPose for robotic grasp analysis. Key trends in his publications include 3D Perception , Efficient Neural Architectures , and Cross-modal Learning for autonomous systems. While no explicit scientific awards are listed, his work is frequently published in top-tier venues like CVPR , ECCV , and CoRR .

Jiadong Zangمشاهده پروفایل

استاد

• Materials Science
• Condensed Matter Physics
• Magnetism
• +۳ مورد دیگر

Jiadong Zang is a Professor in the Department of Physics & Astronomy and Director of the Materials Science Program at the University of New Hampshire's College of Engineering and Physical Sciences. He holds a Ph.D. in Theoretical and Mathematical Physics from Fudan University and a B.S. from the same institution. His research focuses on materials science, condensed matter physics, and magnetism, with particular emphasis on skyrmions, topological materials, and spintronic systems. Key areas of research include the dynamics of magnetic skyrmions, topological phenomena in magnetic systems, and the development of novel magnetic materials for energy and technological applications. He teaches advanced courses such as Electronic Materials Science, Quantum Mechanics, and Doctoral Research. His work has contributed to understanding topological effects in nanoscale magnetic structures and their applications in next-generation devices. Dr. Zang's recent publications explore topics like skyrmion motion in confined geometries, anomalous Hall effects in 2D magnets, and the design of correlated ferromagnetic metals. He is actively involved in creating databases (e.g., Northeast Materials Database) to accelerate the discovery of high-performance magnetic materials. His research also involves collaborations on advanced imaging techniques, such as neural vector tomography for magnetization reconstruction.

Steffen Staabمشاهده پروفایل

استاد

• Artificial Intelligence
• Knowledge Graphs
• Semantic Web Technologies
• +۷ مورد دیگر

Steffen Staab is a Professor at the University of Stuttgart's Institute for Artificial Intelligence within the Faculty of Computer Science, Electrical Engineering and Information Technology. His research focuses on knowledge graphs, semantic web technologies, and their applications in interdisciplinary domains like construction, healthcare, and autonomous systems. He actively contributes to advancing AI fairness, geometric relational embeddings, and explainable machine learning. Staab's work bridges theoretical advancements with real-world challenges, such as improving data interoperability in building information modeling and enhancing accessibility for deaf communities via AI-driven captioning. His recent projects span semantic industrial information modeling, blockchain-based fair exchange protocols, and large language models for ontology learning in life sciences. Staab has collaborated on initiatives like the WikiMed-DE dataset for biomedical entity linking and the SEMMA knowledge graph foundation model. He also engages in digital society discussions, addressing ethical implications of AI and societal impacts of web science. Staab's research has been presented at leading conferences including Web Science and ACM SIGKDD. His team develops tools for knowledge graph reasoning, anomaly detection in zero-shot learning, and fair algorithmic decision-making. He is affiliated with the University of Stuttgart's Analytic Computing group and maintains active participation in international academic networks.

Martin Seifridمشاهده پروفایل

استادیار

• Organic Materials
• Self-Driving Labs
• Materials Informatics
• +۷ مورد دیگر

Martin Seifrid is an Assistant Professor at NC State University's Department of Materials Science and Engineering (College of Engineering). His research focuses on designing organic materials with controlled structures for applications in energy storage, healthcare, and neuromorphic computing. He leads the Data-Driven Organic Materials Lab, which develops self-driving labs powered by machine learning to accelerate materials discovery. Key areas include organic mixed ionic-electronic conductors, materials informatics, and automation through tools like Chemspyd. His work integrates synthesis, characterization, and computational methods. Recent efforts emphasize automated experimentation frameworks and open-source robotics interfaces for chemical synthesis. Notable projects include optimizing organic photovoltaic materials and understanding thin film morphology in semiconductors. Publications highlight advancements in self-driving lab systems, machine learning for material design, and novel material characterization techniques. He collaborates widely, contributing to interdisciplinary initiatives in autonomous laboratory development. His lab is equipped with state-of-the-art equipment for automated chemistry and advanced material analysis. While currently no scientific awards are listed, his research has been featured in prominent journals like Science , Chemistry of Materials , and Journal of Materials Chemistry A . He actively mentors graduate students in materials science and engineering through collaborative projects at NC State.

• Visual-vestibular interactions
• Perceiving a stable world
• Motor signals and self-motion perception
• +۷ مورد دیگر

Paul MacNeilage is an Associate Professor and Director of the Cognitive & Brain Science Ph.D. Program at the University of Nevada, Reno, Department of Psychology. His research focuses on human spatial orientation, integrating sensory and cognitive processes to understand how we perceive movement and maintain balance during activities like walking, driving, and virtual reality interactions. Ph.D., Vision Science, University of California Berkeley (2007) B.A., Biological Anthropology, Harvard University (1996) Research in his Self-Motion Lab examines visual-vestibular interactions, head motion statistics, and perceptual stability. Key methods include VR motion simulators, head-mounted eye tracking, and computational modeling. Applications span vertigo diagnosis, VR development, and autonomous agent systems. Recent publications highlight his work on sensory conflict detection, dynamic gaze error, and VR sickness mitigation. His lab has developed open-source tools like GazeMetrics and the Visual Experience Database. Grants supporting his research include UNR Neuroscience COBRE, OIA Track-2 FEC and IIS CHS:Small, Nevada NASA Space Grant Consortium, and Transfer Project DT-1 (BCCN Munich).

• Computational Chemistry
• Molecular Machine Learning
• Catalyst Design
• +۲ مورد دیگر

Clémence Corminboeuf is a Professor at École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the Laboratory of Computational Molecular Design (LCMD). Her research focuses on computational chemistry, machine learning for molecular design, and catalyst optimization using physics-inspired models. Email: clemence.corminboeuf@epfl.ch Scopus Author ID: 6603320500 Researcher ID: I-5973-2018 Current research involves: Developing integer linear programming algorithms for molecular machine learning training set selection Generative inverse design pipelines for singlet fission materials discovery Automated volcano plot analysis tools (SPOCK) for catalyst design Enantiospecific synthesis methods for planar chiral organometallic complexes Neural network potential frameworks for solute-solvent reaction dynamics Recent methodological advances include: Cost-informed Bayesian optimization (CIBO) for efficient chemical experimentation 3DReact geometric deep learning architecture for reaction property prediction MARC software for automated transition state ensemble analysis