جست‌وجوی استاد و پژوهشگر

Christoph Sorgeمشاهده پروفایل

استاد

• Legal Informatics
• Data Protection Law
• IT Security
• +۱۲ مورد دیگر

Christoph Sorge is a full Professor and Head of the Chair of Legal Informatics at Saarland University, where he leads interdisciplinary research at the intersection of law and computer science. His work focuses on data protection, IT security, and the legal implications of emerging technologies such as AI, blockchain, and wearables. He is actively involved in teaching courses like 'Recht der Cybersicherheit' and 'IT-Forensik für Juristen'. The Chair of Legal Informatics is engaged in numerous high-impact research projects funded by BMBF, DFG, and BMWK, including D'Accord , EDWI , K3I-Cycling , PAIRS , ScaleTrust , and research on botnet crime . These projects explore adaptive privacy systems, data spaces, AI governance, and cybersecurity compliance, often in collaboration with legal and technical partners. His research interests span data protection law , cybersecurity , AI and law , digital ecosystems , and applied cryptography . He has contributed to the legal classification of Bitcoin and digital signatures, and his team develops tools for transparent and compliant data processing. The recent publications reflect a strong trend toward interdisciplinary compliance solutions , combining legal analysis with technical design. Themes include GDPR-compliant privacy dashboards, secure data exchange in crises, ethical AI, and legal frameworks for data trusteeship. The work consistently emphasizes user rights , transparency , and technical feasibility in legal contexts. Notable scientific contributions include project-based research rather than individual awards, though the impact is evident in policy-relevant outputs and collaborations with public institutions like the Saarland State Chancellery. Prof. Sorge supervises research staff on topics such as algorithmic summaries of court rulings, legal issues of cryptography, wearable data protection, and cloud security. While formal PhD students are not listed, his team includes researchers like Aljoscha Dietrich and Bianca Steffes. The Chair also contributes to eGovernment education through the eGov Campus project, developing data protection training for administrative staff. Lab and team activities are centered around the Chair of Legal Informatics , which functions as a research hub for legal-technical innovation, hosting internal projects and participating in national and international collaborations.

Euan Lindsayمشاهده پروفایل

استاد

• Engineering Education
• Problem-Based Learning
• Artificial Intelligence in Education
• +۴ مورد دیگر

Euan Lindsay serves as Professor in the Department of Sustainability and Planning within Aalborg University's Technical Faculty of IT and Design. He maintains dual institutional affiliations through the Aalborg Center for Problem Based Learning in Engineering Science and Sustainability and the AI for the People Institute for Advanced Study in PBL. His work bridges sustainability planning with cutting-edge educational technology applications. Research interests center on Engineering Education and Problem-Based Learning methodologies, with significant recent focus on Generative AI applications in educational contexts. Current projects explore digital twins for feedback systems, large language model implementations for course evaluations, and microcredential frameworks supporting sustainable engineering education. His scholarship demonstrates consistent evolution from traditional engineering pedagogy toward AI-integrated learning ecosystems. Analysis of recent publications reveals three dominant trajectories: (1) Ethical implementation of generative AI in educational feedback systems, (2) Institutional change management for academic innovation, and (3) Microcredential development for sustainable engineering competencies. The Digital Twins project funded by The Villum Foundation represents his most significant current research initiative. Lindsay actively contributes to educational innovation through ongoing projects including the Digital Twins for Abundant Feedback initiative (2024-2025). His publication record shows sustained output with 85+ publications through 2025, demonstrating continuous scholarly engagement. While no formal awards are documented in the source material, his leadership in major research projects indicates significant professional recognition within engineering education circles. As principal investigator on multiple large-scale projects, Lindsay directs research teams developing novel AI applications for educational feedback systems. His work through the Aalborg Center for Problem Based Learning establishes him as a key figure in sustainability-focused engineering education reform, particularly in Scandinavian and European contexts.

Bonita Sharifمشاهده پروفایل

دانشیار

• Software Engineering
• Eye Tracking
• Empirical Studies
• +۶ مورد دیگر

Bonita Sharif is an Associate Professor in the School of Computing at the University of Nebraska-Lincoln (UNL), where she has directed the Software Engineering Research and Empirical Studies Lab since 2018. Her research bridges software engineering, eye tracking, and human factors, with over 80 refereed publications and leadership roles in conferences like VISSOFT and ETRA. Ph.D., Computer Science, Kent State University (2010) MS, Computer Science, Kent State University (2003) BS, Computer Science, Cyprus College (2000) Her research interests focus on using eye tracking to understand code comprehension, software traceability, and emotional awareness in development. She has pioneered methodologies for integrating eye movement data into software engineering tasks and developed the iTrace infrastructure for analyzing developer behavior. Recent publications (2023-2025) explore code readability rules , UML diagram layouts , and developer decision-making via eye tracking. She has also contributed to tools like iTrace-Toolkit for analyzing biometric data and EMIP Toolkit for post-processing eye movement datasets. Awards include the NSF CAREER and CRI awards. She has served as Associate Editor for IEEE Transactions on Software Engineering and ACM Transactions on Computer Education , and chaired conferences like VISSOFT 2016 and ETRA 2018/2019 . Mentoring involves tailored support for graduate and undergraduate students, integrating them into all research phases. She has supervised 19 graduate students and 39 undergraduates, with 43 co-authored papers.

Jaspal Subhlokمشاهده پروفایل

استاد

• Educational Technologies
• AI in Education
• Parallel and Distributed Computing
• +۳ مورد دیگر

Jaspal Subhlok is a Professor of Computer Science and Electrical and Computer Engineering at the University of Houston. He served as Chair of the Department of Computer Science from 2010 to 2019. His research focuses on educational technologies, AI-driven learning tools (e.g., the Videopoints project ), and high-performance computing (HPC). He is also the co-founder and former CEO of Videopoints LLC (2016-2022). Subhlok's educational background includes a B.A. in Computer Science from the Indian Institute of Technology, Kharagpur, and a PhD from Rice University. His work emphasizes integrating technology into education, such as indexed captioned searchable videos and AI-generated lecture summaries. He has received multiple awards for teaching, research, and service from the University of Houston. His research team has developed tools like the High Performance Systems Lab (HPSL) and the Lecture Video Visual Objects (LVVO) Dataset . He has advised numerous PhD and MS students, focusing on topics ranging from parallel computing to AI in education. Subhlok also co-hosted the radio show Border Crossings from 2002 to 2008, blending his academic work with public engagement.

Amy S. Burnsideمشاهده پروفایل

عضو هیئت علمی

• Flow Cytometry
• Immunology
• Biomedical Sciences
• +۵ مورد دیگر

Amy S. Burnside is a Research Assistant Professor and Adjunct Faculty member in the Department of Veterinary and Animal Sciences at the University of Massachusetts Amherst, where she serves as the Flow Cytometry Core Facility Manager. She is based in the Integrated Sciences Building and plays a key role in supporting advanced cellular analysis research across the Five College consortium and surrounding regions. Education: B.A. in Biochemistry, Smith College Ph.D. in Animal Biotechnology and Biomedical Sciences, University of Massachusetts Amherst Postdoctoral Training, University of Massachusetts Amherst Dr. Burnside's research expertise centers on flow cytometry and its applications in biomedical and veterinary sciences. Her work enables high-parameter cellular analysis and sorting using state-of-the-art instruments, including the BD LSRII, Dual Fortessa, and FACSAria II systems. She supports a wide range of researchers by providing training, technical guidance, and access to cutting-edge cytometry platforms, contributing significantly to translational and basic research in immunology, cell biology, and biotechnology. No recent publications are listed in the provided text, so no article trends can be summarized. There are no scientific awards mentioned in the available information. Dr. Burnside advises no students listed in the provided materials. However, she plays a critical role in research mentoring through her facility management, training users, and supporting grant-funded projects that involve flow cytometry. Her work indirectly supports numerous research grants across disciplines by enabling essential experimental methodologies. She leads operations at the Flow Cytometry Core Facility located in Room 068 of the Integrated Sciences Building. The facility houses multiple advanced flow cytometers and serves academic and non-academic researchers in the Five College area. It is sponsored by faculty across disciplines and maintained by the Department of Veterinary and Animal Sciences, reflecting its interdisciplinary importance.

Jakub Klikowskiمشاهده پروفایل

استادیار

• Machine Learning
• Data Stream Classification
• Imbalanced Data
• +۱۲ مورد دیگر

Jakub Klikowski is an Assistant Professor in the Department of Computer Systems and Networks at the Faculty of Electronics, Wrocław University of Science and Technology. He has been employed since October 2018 and earned his PhD in engineering and technical sciences in March 2022 for his dissertation on ensemble methods for imbalanced data stream classification. He is actively involved in research and teaching, contributing to multiple research teams including the Machine Learning Team and Advanced Data Analysis Methods Team. Research Interests: Classification of imbalanced and streaming data Ensemble learning and one-class classification Natural language processing (NLP) Concept drift detection and adaptive learning Graph neural networks and spam/disinformation detection Multi-criteria optimization and evolutionary algorithms His research is primarily conducted within the IDSTREAM and GEOM projects, focusing on developing robust classifiers for challenging data environments. His recent work emphasizes hybrid preprocessing, ensemble weighting, and drift detection mechanisms. Publication Trends: His publications from 2019 to 2022 reveal a consistent focus on improving classification performance in imbalanced data streams using ensemble techniques, preprocessing strategies, and drift detection. Key themes include weighted bagging, Hellinger distance, centroid analysis, and genetic optimization, all applied to real-world decision-making tasks. Scientific Awards: Rector of Wrocław University of Science and Technology Award (2020) Distinction in the Secundus program for young scientists (2023) Honorable Mention for doctoral dissertation (2022) Advising and Grants: Dr. Klikowski supervises diploma theses on topics such as NLP, text summarization, hate speech detection, and graph neural networks. He is involved in research projects including IDSTREAM and GEOM, which support his work in data stream classification and optimization. He collaborates with leading researchers like Prof. Michał Woźniak and Prof. Robert Burduk. Labs and Teams: He is an active member of the Machine Learning Team, Advanced Data Analysis Methods Team, and other research groups at the Department of Computer Systems and Networks. These teams foster interdisciplinary research in AI, data science, and network optimization.

Andreas Håheimمشاهده پروفایل

مدرس ارشد

• Social Work
• Social Sciences
• Welfare Studies
• +۲ مورد دیگر

Andreas Håheim is a Senior Lecturer in Social Work at the University of Stavanger, affiliated with the Faculty of Social Sciences and the Department of Social Sciences. His academic focus lies in the field of social work and broader social sciences, with likely engagement in areas such as social policy, community development, and welfare studies. While specific research projects or publications are not detailed in the available text, his position suggests active involvement in teaching and potentially practice-based research within social services and societal well-being. There is no available information on recent publications or research trends to summarize at this time. Andreas Håheim has not been mentioned as receiving any scientific awards or honors in the provided content. No information is available regarding student supervision, grants, or funded research projects. Similarly, there are no details about leadership in labs or research teams.

• Multimedia Data Management
• Collaborative Filtering
• Similarity Search
• +۵ مورد دیگر

Ilaria Bartolini is a Full Professor at the University of Bologna's Department of Computer Science and Engineering, specializing in multimedia data management and preference learning. She coordinates the MultiMedia Database Group and leads the PhD Programme in Computer Science and Engineering. Her research focuses on similarity search, collaborative filtering, and applications in cultural heritage and health domains. Education: Laurea in Computer Science (1997), University of Bologna PhD in Electronic and Computer Science Engineering (2002), University of Bologna Research Interests: Her work spans multimedia systems, data curation, and AI-driven solutions for healthcare and cultural heritage. She collaborates with institutions like HKUST, NJIT, and the European Processor Initiative, leading projects such as DETECt (EU H2020) and S-PIC4CHU (PRIN 2022). Grants & Projects: EU H2020 DETECt (2018–present): Transcultural crime narrative analysis PRIN 2022 S-PIC4CHU (2024–present): Data science integrity GenData 2020, WISDOM, COOPERARE (Italian national projects) Teaching: Leads courses on web applications, multimedia data management, and digital humanities at the undergraduate and graduate levels. Her educational framework emphasizes active e-learning through tools like ALDO. Labs & Teams: Directs the MultiMedia Database Group, fostering interdisciplinary research in data science and AI applications.

• Data Mining
• Computational Creativity
• Artificial Intelligence
• +۵ مورد دیگر

Professor Hannu TT Toivonen is a Full Professor at the University of Helsinki's Department of Computer Science, Faculty of Science. He has held leadership roles including Vice-Dean of Academic Affairs (2018-21), Head of the Department of Computer Science (2007-09), and founding director of the Data Science MSc program (2016-18). His research focuses on data mining, computational creativity, and AI applications in fields like gene mapping and natural language generation. He has secured over €7M in external funding, including EU projects, and holds 10 patents. Education: PhD in Computer Science from University of Helsinki (1996) Industrial Experience: 6 years at Nokia Research Centre Research interests span AI ethics, machine learning, and interdisciplinary applications. He has published 200+ refereed papers with 25,000+ citations (h-index 57). Recent work includes computational creativity exhibitions and AI ethics discussions in Finnish media. Awards: Knight of the White Rose of Finland (2019), TKO-äly Honorary Membership (2010), etc. Grants: Principal Investigator in EU projects like Embeddia (2019-22) and NewsEye (2018-22). Led the Helsinki Doctoral Programme in Computer Science (2007-11) and currently serves on editorial boards of top journals and conferences including IEEE ICDM and ICCC.

• Artificial Intelligence in Medicine
• Machine Learning
• Health Informatics
• +۴ مورد دیگر

Dr Zina Ibrahim is a Senior Lecturer in Artificial Intelligence in Medicine at King's College London, affiliated with the Department of Biostatistics & Health Informatics within the Institute of Psychiatry, Psychology & Neuroscience. She joined King's College in 2011 as a Postdoctoral Researcher and transitioned to her current position in 2016. Her academic background includes a BSc in Software Engineering (2003), an MSc (2004), and a PhD (2010) in Artificial Intelligence from the University of Windsor, Canada. Her research focuses on theoretical foundations of knowledge representation and machine learning applied to biomedical knowledge discovery and medical decision support systems. Key areas include neurosymbolic AI, trustworthiness in medical AI, and EHR analysis. Notable projects include the myHealthE remote monitoring system and the SemEHR semantic search framework. Recent publications emphasize medical AI applications such as time-series imputation, clinical text summarization, and adverse drug event detection. Her work bridges technical AI advancements with clinical implementation challenges in NHS settings. Key Achievements: Winner of the Government's AI Fairness Innovation Challenge (2024) Teaching: Leads courses on Machine Learning for Health and Bioinformatics, and Health Informatics fundamentals. Lab Affiliation: Member of the Precision Health Informatics Group and actively involved in interdisciplinary collaborations.

Alexander C. Nwalaمشاهده پروفایل

استادیار

• Social media analysis
• Computational social science
• Web science
• +۷ مورد دیگر

Dr. Alexander C. Nwala is an Assistant Professor of Data Science at William & Mary, where he also directs the News Web and Social Media (NEWS) research lab. He holds a PhD in Computer Science from Old Dominion University and was previously a postdoctoral researcher at the Observatory on Social Media (OSoMe) at Indiana University, Bloomington. Research Interests: Social media and network analysis Computational social science Web and data science Web archiving Local news dynamics Natural language processing (NLP) His research focuses on understanding online behavior, disinformation, and the role of local news on social media. He develops tools to analyze and characterize social media accounts, detect coordination, and preserve digital content. Recent Publication Trends: His recent work centers on modeling social media account behavior using language frameworks (e.g., BLOC), analyzing manipulation tactics like deletion, and improving web archiving practices through quality assessment of social media seeds. These efforts integrate NLP, data science, and web science to address challenges in digital information integrity and preservation. Scientific Contributions and Tools: BLOC : A language framework for modeling social media account behavior agwagram : Tool for studying authentic and inauthentic Twitter behaviors Local Memory Project : Service for discovering local media Coordiscope : Detects coordination between social media accounts StoryGraph : Analyzes news cycle similarity across sources Sumgram : Summarizes text collections using conjoined n-grams Teaching and Mentoring: Dr. Nwala teaches courses such as Ethics in Data Science and Web Science. He advises graduate students including Gangani Ariyarathne, Isuru Ariyarathne, and Ian MacDonald. He has taught students at high school, undergraduate, and graduate levels and collaborates across disciplines with computer scientists, journalists, archivists, and legal scholars. Research Lab: The NEWS Lab at William & Mary investigates the web as an information ecosystem, with a focus on local news and disinformation vectors on social media platforms.

Oscar Chaparroمشاهده پروفایل

دانشیار

• Software Engineering
• Software Maintenance & Evolution
• Program Comprehension
• +۱۳ مورد دیگر

Oscar Chaparro is an Associate Professor of Computer Science at William & Mary, where he leads the Software Evolution and Analysis (SEA) Lab. He holds tenure effective Fall 2025 and is currently on sabbatical during the 2025–2026 academic year. Education: Ph.D. in Software Engineering, The University of Texas at Dallas, advised by Dr. Andrian Marcus B.Eng. and M.Eng. in Systems Engineering and Computing, Universidad Nacional de Colombia, Bogotá, Colombia Oscar Chaparro's research focuses on improving software maintenance and evolution through empirical and automated methods. His work spans automated bug report management, verification-guided refactoring, informed decision-making for software changes, and software licensing and supply chain management. He integrates techniques from program analysis, software repository mining (MSR), natural language processing (NLP), machine learning, and computer vision to analyze software artifacts such as bug reports, source code, and developer discussions. His research aims to enhance developer productivity, software quality, and legal compliance in modern software development. His recent publications (2023–2025) demonstrate a strong trend in addressing real-world software engineering challenges, particularly in bug localization, duplicate detection, interactive reporting tools, and the emerging domain of quantum and AI-related software issues. His work often involves collaboration with industry and interdisciplinary perspectives, including legal aspects of software. Scientific Awards and Recognitions: Distinguished Reviewer Award at ICPC'25 Student awards: Charles Center Summer Research Grant (Sam), 2025 International Student Achievement Award (Antu) Oscar actively mentors students, including Antu Saha, Nathan Cooper, and Trevor Stalnaker, who frequently co-author his publications. He has received research funding and institutional support, enabling student-led projects and tool development. He is involved in organizing events such as the NLBSE Tool Competition. Labs and Teams: He leads the Software Evolution and Analysis (SEA) Lab, which conducts cutting-edge research in software engineering, focusing on practical tools and empirical studies to support developers throughout the software lifecycle.

Christian Winklerمشاهده پروفایل

مدرس ارشد

• Computational Linguistics
• Natural Language Processing
• Requirements Engineering
• +۷ مورد دیگر

Christian Winkler is a Senior Lecturer at the Institute for Romance Studies and the Institute for Applied Informatics at the University of Klagenfurt (Alpen-Adria-Universität Klagenfurt). He has been actively involved in interdisciplinary research and teaching since 1994, bridging the fields of linguistics and computer science. His academic affiliations include the Faculty of Cultural Sciences (KUWI) and the Faculty of Technical Sciences (TEWI), where he contributes to programs in Romance studies, Anglistics, and computer linguistics. Education: Studied Romanistics, Anglistics, and Linguistics at the University of Klagenfurt. Languages: Fluent in English, Italian, French, Spanish, and Portuguese. Winkler's research is centered on the intersection of natural language and information systems. His primary interests include computational linguistics, natural language processing (NLP), requirements engineering, syntactic theory (especially infinitive constructions in European languages), and the application of linguistic principles to software engineering. He has made significant contributions to the development of linguistic tools for conceptual modeling and text analysis, particularly through the long-running NIBA (Natürlichsprachliche Informationsbedarfsanalyse) project. The 15 most recent publications reflect a strong trend in applying linguistic analysis to software and information systems. Key themes include natural language-based requirements engineering, text summarization, semantic tagging, ontology integration, and enterprise search optimization. These works are predominantly published in the ICSSEA and NLDB conference series, indicating a sustained focus on the practical application of linguistics in computing contexts. Subfields span AI-driven text evaluation, verb phrase similarity, collaborative knowledge repositories, and mobile instructional applications. Scientific Awards: No awards or fellowships are mentioned in the provided text. Christian Winkler has played a significant role in academic advising and research collaboration, particularly through his long-term involvement in major research projects. The NIBA project, ongoing since 1995 and funded by the Klaus Tschira Stiftung and the Austrian National Bank, represents a major collaborative effort in linguistically based requirements engineering. He has also contributed to numerous grants and initiatives in natural language processing and software engineering. While no formal advisees are listed, his co-authorship with students and researchers suggests mentoring and collaborative supervision. Labs and Research Teams: NIBA Project Team: Core member since 1995, focusing on natural language analysis for information systems. ICSSEA Conference Series: Regular participant and contributor, indicating integration into a broader European research network in software and systems engineering. Natural Language and Information Systems (NLIS) Community: Active in program committees, showing leadership in the academic community.

• Large Language Models
• Multilingual Machine Translation
• Vision-Language Models
• +۳ مورد دیگر

Xiaocheng Feng is a prominent researcher in computational linguistics and large language models (LLMs), with extensive contributions to multilingual systems, vision-language integration, and knowledge alignment. His work spans from 2016 to 2025, focusing on advanced topics like Mitigating hallucinations in multimodal models Efficient knowledge editing across multiple models Length-controlled text generation for black-box LLMs Improving contextual faithfulness via retrieval heads Cross-lingual connection mechanisms for fine-tuning His recent publications address critical challenges in LLMs, including knowledge misalignment, entity-level unlearning, and Pareto optimization in multilingual translation. He has explored probability density estimation for text control, adaptive context modeling in visual storytelling, and cross-lingual annotation projection for low-resource languages. Collaborations with researchers like Bing Qin, Lei Huang, and Weitao Ma highlight his central role in advancing NLP methodologies.

• Spatial Databases
• Big Spatial Data
• Location-Based Services
• +۶ مورد دیگر

Mohamed F. Mokbel is a Professor at the University of Minnesota with a distinguished career in spatial databases and big spatial data management. His research spans over two decades with more than 260 publications in top-tier venues including ICDE, SIGMOD, VLDB, and GIS conferences. He has established himself as a leading researcher in trajectory data management, mobility data science, and scalable spatial processing systems. Dr. Mokbel's research interests focus on the intersection of spatial databases, big data, and machine learning. He has pioneered work in trajectory data management systems, developing frameworks like ST-Hadoop for processing spatio-temporal data at scale. His recent research explores the application of large language models to trajectory analysis, spatial data cleaning systems with spatial awareness, and innovative approaches to mobility data science. His work addresses fundamental challenges in handling massive spatial datasets while maintaining efficiency and accuracy. His publication record reveals significant trends toward integrating machine learning with spatial data management, particularly in trajectory imputation using BERT models (KAMEL system) and applying NLP techniques to trajectory analysis. Recent work shows increasing focus on urban mobility applications, privacy considerations in location data, and the development of specialized frameworks for processing different types of spatial data at scale. Dr. Mokbel has mentored numerous students who have become productive researchers in their own right, with many publications featuring students as first authors. His collaborative network is extensive, with frequent co-authorship with researchers like Walid G. Aref, Ahmed Eldawy, and Amr Magdy, indicating strong research group leadership. He has contributed significantly to the spatial database community through systems like RASED for monitoring OpenStreetMap updates, KAMEL for trajectory imputation, and Sparcle for spatial data cleaning. His work bridges theoretical database concepts with practical applications in transportation, urban planning, and location-based services.