جست‌وجوی استاد و پژوهشگر

John Liagourisمشاهده پروفایل

استادیار

• Distributed Systems
• Databases
• Secure Analytics
• +۴ مورد دیگر

Dr. John Liagouris is an Assistant Professor at Boston University's Faculty of Computing and Data Sciences, appointed since July 2022. Previously, he served as an Adjunct Assistant Professor (2020–2022) and held roles at BU’s Hariri Institute for Computing, ETH Zurich’s Systems Group, UC Berkeley’s RISELab, and the University of Hong Kong. His research focuses on distributed systems, databases, and secure analytics, with a strong emphasis on privacy-preserving technologies in cloud environments. He earned a 5-year diploma in Electrical and Computer Engineering (2008) and a PhD (2015) from the National Technical University of Athens (NTUA). His academic journey includes visiting scholar positions at UC Berkeley and the University of Hong Kong, alongside research roles at the Athena Research Center in Greece. Liagouris’s research spans cryptographic cloud analytics, streaming state management, and spatial RDF data systems. His work on frameworks like Queryshield and TVA demonstrates expertise in securing distributed data processing. Recent trends in his publications emphasize multi-party computation and latency-conscious distributed systems. His contributions to fault-tolerant systems (e.g., Lineage Stash) and incremental routing logic (DeltaPath) highlight a focus on optimizing distributed workflows. Though no formal advising records are listed, his roles suggest active involvement in training researchers in distributed computing and cybersecurity. Labs and teams associated with his work include BU’s Hariri Institute and collaborations with institutions like ETH Zurich’s Systems Group, reflecting a networked approach to academic research.

• Understandable Language
• Financial Communication
• Corporate Communication
• +۵ مورد دیگر

Dr. Henk Pander Maat is an Affiliate Researcher at the Humanities Institute for Language Sciences (Utrecht Institute of Linguistics OTS) at Utrecht University, specializing in Language and Communication. With a career spanning over three decades from the early 1990s to present, he has established himself as a leading expert in text readability, comprehensibility, and communication between experts and laypeople. His research primarily focuses on understandable language, financial communication, corporate communication , and corpus-based discourse analysis . His work examines how experts can effectively communicate with lay audiences, particularly in government, healthcare, and financial contexts. He has developed practical tools like T-Scan for analyzing text complexity and has contributed significantly to evidence-based guidelines for creating comprehensible texts. Analysis of his extensive publication record (over 125 publications) reveals a consistent focus on text readability across diverse domains including healthcare communication (patient information leaflets), financial communication (pension documents), legal communication (summonses), and government communication. His methodological approach combines corpus linguistics with experimental and qualitative reader studies to understand how textual features affect comprehension across different reader populations, particularly those with lower literacy levels. Dr. Pander Maat has made notable contributions through the development of the Utrecht Readability Model (U-Read) and the T-Scan tool for analyzing Dutch text complexity. His collaborative work spans multiple disciplines, with frequent co-authorship with researchers like L. R. Lentz, T. J. M. Sanders, and S. Kleijn. His research has practical applications in improving communication between organizations and citizens, particularly for vulnerable populations. His media presence includes coverage of his work on text readability tools, such as the December 2017 article 'Nieuwe tool bepaalt leesniveau' (New tool determines reading level) by the Language Union. While specific awards aren't detailed in the provided information, his sustained publication record in reputable journals and the practical implementation of his research findings indicate significant recognition within his field.

Elin Karlssonمشاهده پروفایل

مدرس

• Disability Research
• Audiology
• International Classification of Functioning Disability and Health (ICF)
• +۶ مورد دیگر

Elin Karlsson is an adjunct lecturer at Örebro University's School of Health Sciences, Department of Health Sciences, and a researcher at the Audiology Research Center, Region Örebro County. She teaches in the Master's program in Hearing Science/Occupational Therapy, Audiologist program, and Occupational Therapist program, with upcoming postdoctoral research at Western University, Canada starting fall 2024. Her educational background includes: Master's degree in Audiology (University of Gothenburg) Master's degree in Gerontology (Jönköping University) Doctor of Medicine in Disability Research (Örebro University), focused on audiology Specializing in disability research and clinical audiology , Karlsson's work centers on the International Classification of Functioning, Disability and Health (ICF) framework. She develops and validates patient-reported instruments like the Hearing and Functioning in Everyday Life Questionnaire (HFEQ) and Empowerment Audiology Questionnaire (EmpAQ-15), examining how hearing loss impacts daily activities, social participation, and empowerment. Her research bridges rehabilitation science, psychometrics, and cross-cultural healthcare assessment. Recent publications (2021-2025) reveal a focused trajectory on ICF-based instrument validation for hearing disorders. Key trends include multinational studies of HFEQ, psychometric refinement of hearing assessment tools, and scoping reviews of disability frameworks. Her work consistently addresses hearing loss functional impact, questionnaire translation challenges, and empowerment measurement across diverse populations. Karlsson actively collaborates with researchers in Canada, South Africa, USA, Jönköping University, India, Norway, and Switzerland. She is affiliated with the Interdisciplinary Research in Clinical Audiology (IRCA) group at Örebro University, focusing on evidence-based audiological practices and global hearing rehabilitation strategies.

• Computer Science
• Human Activity Recognition
• Business Process Management
• +۷ مورد دیگر

Ian Mc Chesney is a Senior Lecturer in the School of Computing at Ulster University, with a strong focus on Computer Science and Informatics. His research spans Human Activity Recognition, Process Mining, Autonomic Computing, and Education Technology. He has an h-index of 30 with 303 citations, reflecting impactful contributions to academic and applied domains. Key roles: Senior Lecturer, Researcher, Consultant Research affiliations: School of Computing, Faculty of Computing, Engineering & Built Environment Research interests include IoT device management, smart home systems, transfer learning applications, and improving healthcare outcomes through AI. He co-developed the InSync dataset and contributed to studies on code readability for programmers with dyslexia. Recent work highlights include semi-Markov models for smart home processes, frameworks for authentic assessment in computing education, and digital twin pipelines for marine optimization. He received the 2025 Best Paper Award at ICAS for synthetic data generation in autonomic computing. Awards: Best Paper Award ICAS 2025 Consultancy: TRAXX Fusion Project (2014-2016) His team includes PhD students Ainsley Casement, Owais Hakeem, and Catherine Saunders, focusing on machine learning, healthcare analytics, and robot communication strategies. Lab collaborations involve open data initiatives and sensor platform development (e.g., SensorCentral).

Tobias Isenbergمشاهده پروفایل

پژوهشگر

• Scientific Visualization
• Computer Graphics
• Human-Computer Interaction
• +۵ مورد دیگر

Tobias Isenberg is a Senior Research Scientist (Directeur de recherche) at Inria's AVIZ project group in Saclay, Université Paris-Saclay. He is affiliated with the Johann Bernoulli Institute for Mathematics and Computer Science at the University of Groningen, Netherlands. His research focuses on interactive scientific visualization, illustrative rendering techniques, and human-computer interaction in non-traditional environments like AR/VR. He has pioneered methods for blending dynamic data physicalizations with virtual content and enhancing collaborative AR learning. Key contributions include machine learning-driven visualization tools for cell lineage analysis and perceptually grounded evaluation frameworks for visualizations. His work spans medical visualization, educational AR systems, and reproducibility in visualization research. He has collaborated with institutions such as the Interactive Media Lab Dresden and led projects like Naviscope (Inria) and tVISt (ANR/DFG). Recent awards include the 2024 SciVis Test-of-Time Award for his 2012 selection techniques paper and a Best Paper Award at CHI 2025 for AR transition techniques. He currently seeks a PhD student to advance interactive 3D dataset exploration methods. Notable grants include the tVISt collaboration grant (2024) and institutional funding from Inria. His lab, AVIZ, develops open-source tools like LineageD+ for biological data analysis and interactive physicalization systems. Recent publications emphasize cross-reality visualization, human-AI collaboration, and reproducibility in visualization research.

Stefan Guglerمشاهده پروفایل

پژوهشگر

• Computational Quantum Chemistry
• Materials Science
• Machine Learning
• +۳ مورد دیگر

Dr. Stefan Gugler is a Postdoctoral Researcher at the Technical University of Berlin (TU Berlin), affiliated with the Berlin Institute for the Foundations of Learning and Data (BIFOLD) and the Machine Learning Group led by Prof. Dr. Klaus-Robert Müller. His research focuses on integrating machine learning methods, such as diffusion models and Gaussian processes, into theoretical chemistry to address challenges like molecular similarity, reaction networks, and catalyst design. He holds a PhD in quantum chemistry from ETH Zurich (2023), where he developed machine learning approaches for dispersion interactions, and a Master’s degree in computational inorganic chemistry from MIT (2018). His work has been recognized with an MSDE Award for his thesis on transition metal complexes. Gugler’s contributions include open-source software tools like qcscine/readuct and qcscine/puffin , advancing computational chemistry and machine learning workflows. Education: PhD in Quantum Chemistry, ETH Zurich (2023) Master’s in Computational Inorganic Chemistry, MIT (2018) Interdisciplinary Sciences (BSc/MSc), ETH Zurich (2016–2018) Research Interests: Molecular similarity and chemical reaction networks Diffusion models and generative AI for chemistry Machine learning in quantum chemistry and materials science Explainable AI (XAI) for scientific applications Awards: MSDE Award (2018), MIT Labs/Teams: Active contributor to BIFOLD and the qcscine open-source project, focusing on machine learning-driven computational chemistry tools.

Qunfeng Liaoمشاهده پروفایل

دانشیار

• Financial Accounting
• Capital Markets
• Corporate Governance
• +۳ مورد دیگر

Dr. Qunfeng Liao is an Associate Professor of Accounting at the School of Business Administration, Oakland University, specializing in financial accounting, capital markets, and corporate governance. She holds a Ph.D. in Accounting with a minor in Finance from the University of Texas at Arlington. Her research focuses on environmental reporting, corporate social responsibility (CSR), and governance structures' impact on financial transparency and market outcomes. Dr. Liao has published in prestigious journals like Review of Accounting Studies and serves on the editorial board of International Journal of Accounting, Auditing, and Performance Evaluation . Her notable awards include the 2025 Research Excellence Award from Oakland University and multiple SBA Journal Awards (2022–2024). Her work explores topics such as carbon emissions disclosure, board governance's role in mitigating analyst forecast errors, and family firm dynamics. Recent studies investigate the interplay between executive diversity and earnings management practices, as well as non-profit operational efficiency in healthcare sectors. Dr. Liao has advised no current students listed but maintains an active research agenda focused on transparency and accountability in corporate reporting. Awards: Over 10 awards including SBA Journal Awards (2019–2024) and the Distinguished Paper Award (2014). Advising/Grants: No formal student advisees listed; active in securing research fellowships like the 2022 URC Summer Research Fellowship. Labs/Teams: No dedicated lab mentioned, but collaborates with peers on interdisciplinary projects in accounting and governance.

• Decision Neuroscience
• Computational Modeling
• Social Contexts Research
• +۴ مورد دیگر

Christoph W Korn is a researcher at the University of Hamburg focusing on human decision-making and learning in complex multimodal and social contexts. His work integrates computational modeling , fMRI analysis , and pupillometry to study neural mechanisms underlying decision processes. His research highlights include Investigating hippocampus and medial prefrontal cortex roles in threat minimization Exploring language proficiency's impact on future optimism Developing psychophysiological models for fear learning Examining how font readability affects framing effects He serves as a Review Editor for Cognitive Neuroscience at Frontiers in Human Neuroscience, contributing to editorial roles in computational and cognitive neuroscience domains.

Daniel Wiechmannمشاهده پروفایل

پژوهشگر

• Natural Language Processing
• Computational Psycholinguistics
• Explainable AI
• +۵ مورد دیگر

Dr Daniel Wiechmann is a researcher at the University of Amsterdam's Faculty of Humanities, Department of Literature and Language Science, affiliated with the Institute for Logic, Language and Computation (ILLC). His primary focus lies in Natural Language Processing with applications in mental health informatics and computational psycholinguistics. His research interests center on explainable AI for mental health detection , specializing in hybrid models that integrate transformer architectures with psycholinguistic features. He investigates German-language social media analysis (evidenced by SMHD-GER and FANG-COVID datasets), personality detection through verbal behavior, and text simplification techniques. Recent work emphasizes cross-lingual mental disorder classification and feature fusion strategies for improved model interpretability in clinical contexts. Analysis of his 15 most recent publications (2021-2024) reveals a strong trajectory toward multilingual mental health detection systems, with significant contributions to German-language NLP resources. His work consistently bridges computational linguistics and clinical psychology through Hybrid transformer-psycholinguistic model architectures Large-scale dataset creation for mental health screening Explainability frameworks for clinical decision support Dr Wiechmann maintains active research output with 20+ publications since 2021, primarily through the MANTIS research group, focusing on social media-based mental health detection systems and German-language NLP applications.

Stefanie Krauseمشاهده پروفایل

پژوهشگر

• Explainable Artificial Intelligence
• Educational Robotics
• Artificial Intelligence in Education
• +۳ مورد دیگر

Stefanie Krause is a doctoral researcher at the Doctoral Center Engineering and Information Technologies (IWIT) at Harz University of Applied Sciences and the University of Bamberg. Her work focuses on explainable AI (XAI) and its application in higher education and robotics. She is supervised by Prof. Dr. Frieder Stolzenburg (Harz University) and Prof. Dr. Ute Schmid (University of Bamberg). Education: M.Sc. in Technical Innovation Management (Harz University), B.Sc. in Mathematics (University of Bayreuth) Research interests center on post-hoc explainability of large AI models, educational robotics, and AI's transformative impact on higher education. Her publications (2021–2025) span conferences like Frontiers in Digital Education , AIET , and RiE , addressing topics such as generative AI applications, commonsense reasoning, and interdisciplinary module development. Recent articles highlight trends in AI education, including generative AI's role in curriculum design, large language model explainability, and robotics integration in teaching. Her work emphasizes practical usability and human-readable AI explanations. At Harz University, she has served as a Research Assistant ( Wissenschaftliche Mitarbeiterin ) and Lecturer for Special Tasks ( Lehrkraft für besondere Aufgaben ) since 2021, contributing to projects like InterGradEGD and REGINA. No scientific awards are mentioned.

Mohamed Wiem Mkaouerمشاهده پروفایل

دانشیار

• Software Engineering
• Refactoring
• Artificial Intelligence
• +۷ مورد دیگر

Mohamed Wiem Mkaouer is an Associate Professor of Software Engineering in the Golisano College of Computing and Information Sciences at Rochester Institute of Technology (RIT). He serves as the principal investigator of the SMILE (Software Maintenance and Intelligent Evolution) Research Lab, which focuses on designing intelligent algorithms to tackle software engineering problems. Dr. Mkaouer has published over 130 peer-reviewed papers in top venues including TSE, TOSEM, EMSE, ICSE, FSE, CHI, and ASE, and has secured $1.5 million in externally funded projects as PI/Co-PI. PhD in Computer and Information Science, 2016, University of Michigan MS in Computer Science, 2010, University of Geneva (Switzerland) BS in Computer Science, 2007, University of Tunis (Tunisia) Dr. Mkaouer's research spans the intersection of Software Engineering and Artificial Intelligence, with particular focus on software quality assurance, systems refactoring, and the application of search-based techniques. His work increasingly explores the integration of large language models in software development processes, examining how AI tools like ChatGPT impact developer workflows, code quality, and refactoring practices. His research often employs empirical methods, mining software repositories, and conducting large-scale studies of developer behavior. Analysis of Dr. Mkaouer's recent publications reveals a strong trend toward investigating AI-assisted software development, particularly focusing on how developers interact with large language models for code refactoring and quality improvement. His work bridges traditional software engineering concerns with emerging AI capabilities, examining both infrastructure-as-code quality and mobile application development challenges. His research methodology typically combines empirical studies of developer practices with the development of intelligent tools to support software maintenance. Recipient of 5 best-paper/presentation awards RIT GCCIS Best Emerging Researcher Award (2020) Dr. Mkaouer actively mentors a diverse group of students at multiple levels, from undergraduate to PhD candidates, through the SMILE Research Lab. His externally funded projects demonstrate the practical relevance of his research to industry needs. He also contributes significantly to the software engineering community through service on program committees for major conferences including ASE, ICSE, and ICSME, where he frequently serves in leadership roles for tracks related to software maintenance, refactoring, and AI in software engineering. The SMILE Research Lab, under Dr. Mkaouer's leadership, maintains strong industry connections and collaborates with researchers worldwide. The lab's work has practical implications for improving software quality, reducing technical debt, and enhancing developer productivity through intelligent tooling and evidence-based practices.

Fiorella Zampettiمشاهده پروفایل

پژوهشگر

• Experimental Software Engineering
• Continuous Integration and Delivery
• Static Analysis Tools
• +۴ مورد دیگر

Fiorella Zampetti is a researcher at the University of Sannio, Italy, specializing in empirical software engineering with a focus on continuous integration and delivery, static analysis tools, mining software repositories, and software maintenance and evolution. Her work bridges theoretical research with practical applications in real-world software development environments. Her research interests span multiple critical areas of modern software engineering, with particular emphasis on Experimental methodologies for evaluating software engineering practices Technical debt identification and management, especially in emerging domains like deep learning systems Continuous integration and delivery pipelines, including common anti-patterns and solutions Mining software repositories to extract valuable insights about development processes Software quality assessment through static analysis and empirical studies Analysis of her recent publications reveals an evolving research trajectory that has increasingly incorporated artificial intelligence and large language models into traditional software engineering concerns. Her 2023-2025 work shows a significant focus on how AI-generated code impacts software development practices, licensing concerns, and educational contexts. She has also maintained a strong thread of research on technical debt, particularly examining self-admitted technical debt across different software domains. Dr. Zampetti has been actively involved in the software engineering research community as a program committee member for major conferences including ASE, ICSE, ESEC/FSE, and ICSME. Her service on these committees demonstrates recognition by her peers as a subject matter expert in her research domains. Her research has practical implications for software development teams looking to improve their CI/CD practices, manage technical debt effectively, and understand the emerging challenges posed by AI-assisted development tools. She has conducted numerous empirical studies that provide evidence-based insights into software engineering practices across both open-source and industrial contexts.

• Algorithms for Planning and Scheduling
• Multi-Agent Systems
• Energy Transition
• +۷ مورد دیگر

Mathijs M. de Weerdt is a Full Professor of Algorithms for Planning and Scheduling and head of the Algorithmics Group at TU Delft's Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS). His research focuses on robust, scalable, and multi-agent planning techniques for energy systems, transportation, and logistics. He holds a PhD from TU Delft (2003) and BSc/MSc from Utrecht University (1998). Key research areas include stochastic scheduling, multi-agent pathfinding, energy transition algorithms, and railway optimization. He leads projects on smart grid scheduling, train unit shunting (TORS simulator), and AI-driven energy market design. Notable awards include the 2016 Erasmus Energy Science Award and 2015 Best Teacher Award at TU Delft. Over 196 publications span conferences like AAAI, AAMAS, and journals in Operations Research. Supervised 15 PhD students, with 9 completed. Active in industry collaborations (NS Railways, Alliander), advisory roles (Dutch Rail Scientific Board), and initiatives like the Center for Energy System Intelligence (CESI). Teaching roles include Algorithm Design, Probabilistic AI, and leading the TU Delft AI Initiative's education efforts. Developed open-source tools like B-FELSA (flexible load scheduling benchmark) and TORS (railway shunting simulator).

Sergio Rodriguez Mendezمشاهده پروفایل

پژوهشگر ارشد

• Knowledge Graphs
• Ontology Engineering
• Linked Data & Semantic Web
• +۷ مورد دیگر

Sergio Rodriguez Mendez is a Research Fellow in Knowledge Graph Engineering at the School of Computing, Australian National University. His work focuses on advancing ontology engineering, linked data, semantic web technologies, and their applications in domains like astronomy, brain-computer interfaces, and IoT. He is a member of the Australian Government Linked Data Working Group (AGLDWG) and the W3C Knowledge Graph Construction Community Group, and holds a senior role at the Software Innovation Institute (SII). His research interests include Knowledge Graphs, Ontology Engineering, Linked Data, Data Science, Machine Learning, Natural Language Processing, Brain-Computer Interfaces, IoT, Cloud/Fog/Edge Computing, and Software Engineering. He has contributed to frameworks like pathfinder for astronomical literature review and Doc-KG for document-to-KG conversion. Recent work emphasizes integrating large language models (LLMs) with knowledge graphs, such as AstroLLaVA for astronomical data unification and hybrid frameworks for entity linking. His publications span conferences like WWW, JCDL, and the ACM/IEEE Joint Conferences. He actively supervises research students and is involved in initiatives like the ASKG project for enriching scholarly knowledge graphs. His work addresses challenges in semantic data representation, automated query processing, and syntactic complexity reduction, with a focus on domain-specific applications in science and cultural heritage.

Qi Fengمشاهده پروفایل

استادیار

• Deep Learning
• Computer Graphics
• Computer Vision
• +۵ مورد دیگر

Qi Feng is an Assistant Professor at the Faculty of Science and Engineering, Waseda University, specializing in deep learning applications for computer graphics and vision, with a focus on virtual and augmented reality. He holds a Doctor of Engineering (2022), Master of Engineering (2019), and Bachelor of Engineering (2017) from Waseda University. Research Interests: His work spans 3D reconstruction, depth estimation, occlusion handling, and multimodal systems integrating eye tracking and speech recognition. Notable projects include SyncViolinist for audio-driven motion generation and the Depth360 dataset for omnidirectional imaging. Publications: Qi's research has been featured in top conferences like CHI, ISMR, ICCVW, and journals such as The Visual Computer. His articles emphasize practical solutions for immersive environments, language learning, and synchronized audio-visual editing. Contact: Email: fengqi@ruri.waseda.jp