جستجوی استاد راهنمای دانشگاهی

• Blockchain Technology
• Cryptocurrency Forensics
• Decentralized Finance (DeFi)
• +۴ مورد دیگر

Bernhard Haslhofer is a Professor at the University of Vienna, affiliated with the Department of Information Systems within the Faculty of Computer Science and Mathematics. His research focuses on blockchain technology, cryptocurrency analytics, and digital libraries, with a particular emphasis on forensic applications of blockchain, decentralized finance (DeFi), and semantic web technologies. He co-leads the Cryptoasset Analytics Workshop (CAAW) and contributes to projects like GraphSense, a cryptoasset analytics platform. His work spans academic collaborations with institutions worldwide, including contributions to standards like ResourceSync and Open Annotation Collaboration (OAC) models. Research interests include machine learning applications in finance, decentralized governance mechanisms, and improving the interoperability of digital resources. Publications highlight trends in DeFi composability, blockchain forensics for crime mapping, and ethical considerations in AI-driven cryptocurrency analysis. He has organized major workshops at venues like The Web Conference (WWW) and Financial Cryptography (FC), fostering interdisciplinary dialogue between academia and industry. No notable scientific awards are explicitly listed in the provided text, though his extensive publication record reflects significant contributions to the field.

• Human-Computer Interaction
• Eye Tracking
• Educational Technology
• +۳ مورد دیگر

Susanne Hummel is a Researcher at the University of Hamburg (UHH), contributing to interdisciplinary projects in Human-Computer Interaction and Digital Humanities. Her work focuses on eye tracking in online learning contexts and collaborative research data curation initiatives like the Beta maṣāḥǝft project. She has co-authored studies exploring eye movement synchronicity's impact on test performance and visualizing collective attention in educational settings. Her research integrates technologies like eye tracking with educational theory, while her data science contributions emphasize open science practices through structured datasets. Collaborations span international teams in computer science, linguistics, and education.

Antonio Solanas Perezمشاهده پروفایل

استاد

• Quantitative Psychology
• Multivariate Analysis
• Research Methods in Behavioral Sciences
• +۳ مورد دیگر

Antonio Solanas Perez is a University Professor at the Department of Social Psychology and Quantitative Psychology in the Faculty of Psychology at the University of Barcelona. He specializes in quantitative research methodologies, particularly in single-case experimental designs and multivariate analysis. His work intersects behavioral sciences, health sciences, and work psychology. Education includes a Bachelor's in Philosophy and Science (1985), a Licentiate in Psychology (1987), and a Doctorate in Taught Teaching (1990), all from the University of Barcelona. Research interests focus on statistical methodologies in behavioral sciences, including data analysis in single-case designs, simulation of social behavior, and brain connectivity networks in fMRI. He has led projects like "Learning analytics and big data" (2015-2021) and contributed to the Maria de Maetzu Excellence Mention for UBNEURO (2018-2022). Key publications explore analytical approaches for single-case experiments, meta-analysis methods, and applications in brain impairment studies. His textbook Statistics for the Behavioral Sciences: Annotated Exercises (2016) is widely used. Advising includes directing doctoral theses such as Rejina Mary Selvam's work on group-level indices. Grants and collaborations span educational sciences, health systems, and neuroscience through institutions like the Ministry of Science and AGAUR.

Peter Brusilovskyمشاهده پروفایل

استاد

• Adaptive Web Systems
• Social Web
• Adaptive Hypermedia
• +۵ مورد دیگر

Peter Brusilovsky is a Professor of Information Science and Intelligent Systems at the University of Pittsburgh's School of Computing and Information. He directs the Personalized Adaptive Web Systems (PAWS) Lab and the Learning Technologies Lab (LTL), focusing on adaptive educational systems, user modeling, and intelligent interfaces. His roles include Associate Editor-in-Chief of IEEE Transactions on Learning Technologies and board memberships in journals like User Modeling and User-Adapted Interaction. Brusilovsky earned his PhD from Moscow State University and has held visiting positions at institutions worldwide, including Carnegie Mellon University. He is a recipient of prestigious awards, including the Alexander von Humboldt Fellowship and NSF CAREER Award, and holds an honorary doctorate from the Slovak University of Technology. His research spans adaptive web systems, social computing, and e-learning, with contributions to intelligent tutoring systems and student modeling. Over 20 years, he has authored numerous papers and books on adaptive hypermedia and the adaptive web. Professional service includes roles with ACM SIGWEB and IEEE, and leadership in User Modeling Inc. Brusilovsky's grants include NSF-funded projects, and his work emphasizes learner control, explainable AI, and the integration of educational technologies. His labs develop tools like QuizGuide and Progressor, advancing personalized learning and open social student modeling.

• Bacteriophage Therapy
• Antibiotic Resistance
• Biofilm Control
• +۳ مورد دیگر

Sanna Sillankorva is a Staff Researcher and Principal Investigator at the International Iberian Nanotechnology Laboratory (INL), where she has pioneered bacteriophage-based antimicrobial strategies since joining in 2019. Her work focuses on combating antibiotic-resistant pathogens through innovative delivery systems targeting biofilm populations, with applications in chronic wound care and otitis media treatment. Her academic foundation includes a Ph.D. in Chemical and Biological Engineering from the University of Minho (2009), with doctoral research conducted at the Universities of Oulu and Helsinki. She completed postdoctoral work at the Centre of Biological Engineering (University of Minho) and a research visit to MIT's Synthetic Biology group. From 2013-2018, she held an FCT Investigator position before joining INL as a Marie Skłodowska-Curie Research Fellow. Her research integrates microbiology , biomaterials science , and pharmaceutical technology to develop phage-lysing solutions against biofilm-associated infections. Key innovations include microneedle-based delivery platforms, liposomal encapsulation for transtympanic permeation, and biopolymer scaffolds for sustained antimicrobial release—addressing critical challenges in treating multidrug-resistant pathogens in wounds and ear infections. Analysis of her recent publications reveals a strategic shift toward translational applications of phage therapy, with emphasis on delivery system engineering (microneedles, liposomes), biofilm-specific targeting , and combination therapies . Her work bridges fundamental virology with clinical needs, particularly in chronic wound management and pediatric otitis media—where biofilm persistence complicates conventional antibiotic treatments. Her scientific recognition includes: Marie Skłodowska-Curie Research Fellowship (COFUND-FP program) Scientific Employment Stimulus program award (2020.03171.CEECIND) Dr. Sillankorva has secured competitive funding for developing topical phage delivery systems for chronic wounds (Marie Curie COFUND-FP) and ongoing research on antimicrobial strategies (Scientific Employment Stimulus grant). She has authored over 75 research articles, edited two Phage Therapy books (ISBN: 978-1-0716-3522-3; 978-2-88945-789-2), and developed novel in vitro/ex vivo biofilm models for therapeutic evaluation. As a core member of the Bañobre Research Group at INL, she leads interdisciplinary collaborations integrating nanotechnology, microbiology, and pharmaceutical sciences to advance phage-based antimicrobial solutions for clinical and food safety applications.

• Temporal Information Extraction
• Computational Linguistics
• Natural Language Processing (NLP)
• +۳ مورد دیگر

Marc Verhagen is a computational linguist and programmer affiliated with Brandeis University's Computer Science Department. He holds a PhD in Computer Science (2004) and MA degrees in Computational Linguistics (University of Utrecht, 1991) and Social Geography (1985). His roles include Senior Research Scientist (2007-2008) and Postdoctoral Fellow (2004-2006). He co-founded LingoMotors Inc. (1997-2002), leading software development for natural language processing (NLP) tools. Research Focus: Temporal information extraction, TimeML standards, and computational linguistics infrastructure. Key Projects: TARSQI toolkit for temporal processing, LAPPS Grid, ISO-Space spatial annotation, and Medstract biomedical NLP. He has expertise in programming languages (Python, Perl), database systems (Oracle), and tools like Dreamweaver. His work bridges academia and industry, emphasizing NLP tool development and semantic interoperability. Beyond technical roles, he coordinated large-scale events (300–800 attendees) and served as a free-lance DJ and theater participant. Research interests span temporal semantics, event modeling, and spatial NLP, with contributions to TimeML, annotation frameworks, and semantic visualization. Over 30 publications since 1990, including his 2004 PhD thesis Times Between the Lines .

Rafael Valencia Garciaمشاهده پروفایل

استاد

• Computer Science
• Knowledge Management
• Natural Language Processing
• +۲ مورد دیگر

Rafael Valencia Garcia is Full Professor in the Department of Computer Science and Systems Engineering at Universidad de Murcia's Faculty of Informatics. His research develops modeling, processing and knowledge management technologies through semantic approaches. He leads the Tecnomod research group focusing on knowledge extraction and semantic technologies. His 2005 PhD thesis 'Un entorno para la extracción incremental de conocimiento desde texto en lenguaje natural' pioneered incremental knowledge extraction methods under Dr. Jesualdo Tomás Fernández Breis and Dr. Rodrigo Martínez Béjar's supervision. His recent publications demonstrate strong focus on NLP applications for social good: Advanced hate speech detection using multi-task learning Multimodal emotion recognition in Spanish Few-shot learning strategies for low-resource scenarios AI moderation systems for inclusive communication The research consistently integrates transformer architectures with linguistic features across diverse tasks including author profiling, persuasion detection, and emotion analysis. He regularly contributes to SemEval and IberLEF evaluation campaigns, developing state-of-the-art systems for detecting harmful content and analyzing emotional patterns in digital communication.

Marcus Winterمشاهده پروفایل

مدرس

• Human-Computer Interaction
• Artificial Intelligence
• Museum Studies
• +۳ مورد دیگر

Marcus Winter is a Principal Lecturer at the University of Brighton's School of Arch, Tech and Eng Computing and Mathematical Sciences. His research focuses on human-computer interaction and AI applications in education, cultural heritage, and museum engagement. He is particularly interested in how new technologies enhance visitor experiences and informal learning in museums. Winter is a Fellow of the Higher Education Academy (since 2019) and actively contributes to academic conferences and industry collaborations. He leads undergraduate and postgraduate computing modules, emphasizing practical, constructionist learning approaches based on Papert's principles. His teaching includes constructive alignment strategies and involves students in research prototype development. Winter also serves as an External Examiner at the Norwegian University of Science and Technology (NTNU). His research spans projects like 'Archives Alive' (2019–2020), exploring locative media and local history, and has produced over 25 peer-reviewed articles. His work often involves interdisciplinary collaboration, aiming to bridge academic research with real-world applications in museums and education.

Jan Eric Lenssenمشاهده پروفایل

پژوهشگر

• Computer Vision
• Machine Learning
• 3D Reconstruction
• +۴ مورد دیگر

Jan Eric Lenssen is a Senior Researcher at the Max Planck Institute for Informatics leading the Geometric Representation Learning Group , and a Founding Engineer at Kumo.ai . He holds a Dr.-Ing. in Computer Science from TU Dortmund University (2022) and has conducted research internships at Facebook Reality Labs and Nnaisense. Affiliations: ELLIS Unit Faculty Member and Saarland Informatics Campus Fellow Education: PhD under Prof. Heinrich Müller, TU Dortmund (2022) His research focuses on differentiable algorithms , neural fields , and graph-based methods for 3D perception and generative modeling. Key contributions include frameworks like SplineCNN , PyTorch Geometric , and methods for scalable 3D reconstruction, human-object interaction modeling, and relational deep learning. Publications span top venues ( CVPR, ECCV, ICCV, NeurIPS ) with highlights including Best Paper Honorable Mention (ECCV 2022) and Best Paper Award (BIOSIGNALS 2018) . He co-founded the RelBench benchmark for relational database learning and leads development of geometric deep learning tools. Key Labs/Teams: Geometric Representation Learning Group (MPI), Kumo.ai Research Team, and the Computer Vision and Machine Learning department at MPI.

Tom Ballمشاهده پروفایل

استاد

• Climate Change
• Flood Risk Management
• Ecosystem Restoration
• +۳ مورد دیگر

Dr. Tom Ball is a Reader in Geography and Head of the Department of Social Sciences at the University of Winchester. His research focuses on climate change, flood risk management, functional ecosystem restoration, and catchment management, with a particular emphasis on the interface between human and physical geography. He leads major projects such as the Scottish Natural Heritage-funded Cairngorms blanket bog restoration and the Eddleston Water Project, collaborating with institutions like the University of Dundee, SEPA, and the British Geological Survey. His teaching includes modules on Global Risks and Local Environmental Change, and he has supervised doctoral research in flood risk adaptation and peatland ecology. Ball’s work integrates hydrological modeling, policy analysis, and interdisciplinary collaboration. His research on carbon sequestration in peatlands and floodplain restoration has contributed to both scientific understanding and practical policy solutions. He has secured over £500K in funding for projects addressing flood risk reduction, climate adaptation, and environmental governance. His expertise spans environmental law, GIS applications, and sustainable water management. Ball’s contributions extend to policy development, including annotations of the Flood Risk Management (Scotland) Act 2009 and guidance documents for coastal practitioners. He is a member of the British Hydrological Society and UK Environmental Law Association, reflecting his commitment to both scientific rigor and legal frameworks in environmental management.

• Software Engineering
• Requirements Engineering
• Natural Language Processing
• +۵ مورد دیگر

Liping Zhao is a Senior Lecturer in Information Management. His expertise spans Software Engineering (SE), Requirements Engineering (RE), Natural Language Processing (NLP), Machine Learning (ML), and Deep Learning (DL). He holds a PhD and MPhil from the University of Leeds. He is a founding member of the Asian Pacific Conference on Pattern Languages of Programs and led the UK’s first multi-disciplinary service science network (2007–2010). He has received IBM Faculty Awards (2004, 2005, 2008) and a Best Tool Demo Award for TRAM (Transforming Textual Requirements into Analysis Models). He serves as an Associate Editor for Springer’s Requirements Engineering and Wiley’s Expert Systems , and guest-edited special issues on topics like Knowledge Intensive Service Systems and Problem Orientation. His research focuses on applying NLP/ML to software engineering tasks, blockchain systems, and service-oriented computing. Key contributions include a theory of pattern-based software design and datasets like FN-REQ (frame-annotated requirements) and TRAM. His work addresses challenges in requirements classification, blockchain architectures, and business problem validation through ML. Recent projects include process mining for blockchain applications, hierarchical classification of software requirements, and developing taxonomies for blockchain systems. His datasets and tools are publicly available via Zenodo and Mendeley, supporting reproducible research and industry collaboration.

Cresten Mansfeldtمشاهده پروفایل

استادیار

• Environmental Engineering
• Environmental Microbiology
• Bioinformatics
• +۷ مورد دیگر

Cresten Mansfeldt is an Assistant Professor in Environmental Engineering at the University of Colorado Boulder, affiliated with the Civil, Environmental, and Architectural Engineering Department. He holds a B.C.E. from the University of Minnesota (2007) and a Ph.D. from Cornell University (2013). Previously, he worked as a Research Scientist at Eawag (Swiss Federal Institute of Aquatic Science and Technology). His research focuses on microbial processes interacting with human-engineered systems, including wastewater treatment, riverine ecosystems, and bioremediation. Key interests include microbial community dynamics, contaminant biotransformation, and reactor design for resource recovery. Mansfeldt’s lab actively recruits students at all levels, emphasizing interdisciplinary approaches combining computational modeling, molecular microbiology, and environmental analytics. Research highlights include developing frameworks for wastewater-based public health surveillance, analyzing wildfire impacts on water quality, and modeling microbial community responses to environmental perturbations. Awards include the 2025 '40 Under 40' recognition from the American Academy of Environmental Engineers and Scientists, and a 2008 NSF Graduate Research Fellowship. Recent work addresses ethical considerations in wastewater sampling and integrates ecological risk assessment with synthetic biology. His publications span topics like microbial enzyme biomarkers, micropollutant fate in activated sludge systems, and lipid-accumulating algae for biofuel production. Current projects aim to optimize wastewater treatment for trace organic contaminant removal while enhancing microbial community resilience.

• Data Science
• Machine Learning
• Distributed Computing
• +۴ مورد دیگر

Chris Mattmann is an academic affiliated with the University of Southern California, specializing in data science, machine learning, and high-performance computing. His work spans interdisciplinary applications in Earth sciences, climate modeling, and distributed systems. He has contributed to projects like the Earth System Grid Federation and Apache open-source software. His research emphasizes scalable data management, secure parser development, and automated machine learning systems. Education & Background: While specific educational details aren't listed, his extensive publication record in top-tier journals and conferences indicates advanced training in computer science and computational science. Research Interests: Focus areas include distributed computing infrastructure for scientific applications, machine learning for geosciences, and data-intensive systems. He explores challenges in data movement, scalable supercomputing, and open-source tools for scientific workflows. Publications: Over 150 peer-reviewed articles since 2003, with recent work on super-resolution satellite data, deep learning in geosciences, and secure parser systems. His contributions highlight advancements in both foundational computing and applied environmental science.

• Semantic Web
• Knowledge Graphs
• Linked Data
• +۳ مورد دیگر

Mohamed Ahmed Sherif is an academic affiliated with Paderborn University's Department of Computer Science and the University of Leipzig's Institute of Computer Science. His primary research focuses on semantic web technologies, knowledge graph integration, and machine learning applications in data integration. Key affiliations: Paderborn University, University of Leipzig Specialization: Link discovery, knowledge graph construction, semantic web standards His work emphasizes scalable methods for linking heterogeneous data sources, explainable AI techniques for knowledge integration, and geospatial semantic technologies. Recent contributions include the ANTS system for entity summarization in knowledge graphs and the NELLIE framework for continuous linked data integration. Notable projects include LIMES - a widely-used link discovery framework - and contributions to the HOBBIT benchmarking platform. His research has been applied in disaster management (I-AID system), Quran semantification, and global health data integration.

• Human-Computer Interaction
• Affective Computing
• Multimedia Systems
• +۲ مورد دیگر

Andrej Kosir is a researcher at the University of Ljubljana's Faculty of Electrical Engineering, specializing in multimedia systems and human-computer interaction. His core research examines physiological signal analysis for affective computing applications, including advertising engagement measurement and cognitive load prediction in automotive interfaces. Research Focus: His work spans multimedia ad exposure modeling, physiological computing, and recommender systems. Recent studies analyze biometric responses to advertising content and develop metrics for evaluating user engagement through physiological signals like heart rate and pupillary response. Publication Trends: Kosir's 15 most recent publications (2010-2025) demonstrate consistent focus on affective computing applications. Earlier work centered on recommender systems and affective labeling, while recent publications emphasize physiological signal processing in automotive and advertising contexts. Methodologically, his work combines machine learning with psychophysiological measurement techniques.