جستجوی استاد راهنمای دانشگاهی

Michael Minockمشاهده پروفایل

دانشیار

• Natural Language Interfaces
• Knowledge Representation
• Databases
• +۴ مورد دیگر

Michael Minock is an Associate Professor in Computing Science at Umeå University. His research focuses on the intersection of AI and relational databases, particularly in natural language interfaces, semantic tractability, and knowledge representation. He has led significant projects such as the VR grant (2015-04953) and EU STREP SpaceBook (2011–2014). Minock also co-founded C-Phrase Technologies AB and teaches courses on databases and large language models. B.S. in Computer Science (Honors), University of Michigan Ph.D. in Computer Science, UCLA His research explores the logical foundations of database query languages, emphasizing higher-order logic in natural language interfaces. Recent work integrates LLMs into database management systems. Key publication trends include query containment analysis, cooperative question answering, and context-aware navigation systems. Subfields span natural language processing, spatial databases, and semantic reasoning. Scientific Awards: Pell Grants Michigan Competitive Scholarship DARPA AASERT Award Minock's teaching includes courses on database management and LLM applications in IT. He actively contributes to open-access publications and maintains a strong interdisciplinary focus across AI, logic, and real-world database challenges.

Markus Stockerمشاهده پروفایل

پژوهشگر

• Knowledge Synthesis
• Data Science
• Artificial Intelligence
• +۴ مورد دیگر

Markus Stocker is a researcher leading the Knowledge Infrastructures Lab at TIB - Leibniz Information Centre for Science and Technology. He holds a PhD in Environmental Informatics from the University of Eastern Finland, an MSc in Environmental Sciences from the same university, and an MSc in Computer Science from the University of Zurich. His work focuses on research infrastructures, knowledge synthesis, and FAIR data principles, with a strong emphasis on environmental and earth sciences. He collaborates with major European infrastructures like ACTRIS, ICOS, and NEON. His research interests include neurosymbolic systems, digital scholarship, and the integration of research data across disciplines. Prior roles include a postdoc at PANGAEA (University of Bremen) and positions at Hewlett Packard Labs and Clark & Parsia. He actively contributes to the Research Data Alliance, co-chairing the WG Persistent Identification of Instruments. Markus has pioneered projects like the Open Research Knowledge Graph (ORKG) and the sciqa benchmark for scientific question answering. His work emphasizes interoperability, automation, and community-driven knowledge curation, addressing challenges in data management and scholarly communication.

• Artificial Intelligence
• Cybersecurity
• Programming Languages
• +۹ مورد دیگر

Sandra Greiner is an Assistant Professor in the Department of Mathematics and Computer Science at the University of Southern Denmark (SDU), where she conducts research in artificial intelligence, cybersecurity, and programming languages. Her work is centered on improving the reliability and adaptability of control software in cyber-physical production systems through advanced software engineering techniques. Her research interests include software variability modeling , design by contract , and the application of generative AI in automating software development tasks. She focuses on ensuring high reliability in software-intensive systems, particularly in industrial automation contexts. Her work bridges theoretical computer science with practical software engineering challenges. The recent publications highlight a strong trend in leveraging AI for software contract generation, enhancing feature tracing with reliable knowledge, and managing variability in complex production systems. Her research contributes to model-driven engineering, software product lines, and intelligent software assistance, with a consistent emphasis on empirical validation through case studies. Sandra actively collaborates with researchers across Europe, particularly in Austria and Germany, contributing to top-tier conferences such as SPLC, MODELS, and GPCE. Her work has been published in high-impact journals and conference proceedings, reflecting active engagement in the global software engineering community. She is affiliated with the IMADA research center at SDU, as evidenced by her institutional email. No formal awards or fellowships are mentioned in the provided data. Sandra advises no publicly listed students, and there is no indication of lab leadership or team management in the text. However, her collaborative publications suggest participation in research teams focused on software product lines and AI-driven software engineering.

Franco Pestilliمشاهده پروفایل

دانشیار

• Computational Behavior
• Cognition/Sensory Systems
• Computational/Theoretical
• +۵ مورد دیگر

Franco Pestilli is an Associate Professor in the Department of Psychology at the University of Texas at Austin, where he has been since 2020. Previously, he served as an Associate Professor at Indiana University. His research focuses on brain networks, white matter, and the development of open-science technologies, including the cloud platform brainlife.io , which supports neuroscience education, transparency, and data standardization. He earned a Ph.D. in Psychology from New York University, a B.A. from the University of Rome La Sapienza, and completed postdoctoral training at Stanford University and Columbia University. His work integrates computational neuroscience, neuroimaging, and data science to address challenges in brain research and promote FAIR (Findable, Accessible, Interoperable, Reusable) data principles. Recent publications highlight his leadership in advancing decentralized data management, brain imaging standards (BIDS), and reproducible neuroimaging analysis. His lab develops tools like ezBIDS and Neurodesk , emphasizing open-source platforms and collaborative frameworks for global neuroscience communities. Despite his focus on technological innovation, no scientific awards are explicitly mentioned in the text. Dr. Pestilli collaborates with international researchers and accepts graduate students for the 2025–2026 academic year. He drives initiatives to democratize access to neuroimaging infrastructure, aiming to accelerate brain health discovery through technology and data science integration.

Qingping Yangمشاهده پروفایل

دانشیار

• Measurement Systems
• Quality Engineering
• Smart Technologies
• +۵ مورد دیگر

Dr. Qingping Yang is a Reader (Associate Professor) in the Department of Mechanical and Aerospace Engineering at Brunel University London, affiliated with the College of Engineering, Design and Physical Sciences. He directs the Brunel Quality Engineering and Smart Technology (QUEST) and Robotics and Automation Research Groups, focusing on sensor systems, AI, and industrial metrology. Education: PhD in Engineering, Brunel University London (1992) BEng in Instrumentation and Measurement Technology, Chengdu Aeronautical Polytechnic (1983) Research Interests: Dr. Yang integrates measurement systems, quality engineering, and smart technologies (AI/robotics). His work spans industrial metrology, IoT-driven manufacturing optimization, renewable energy forecasting, medical diagnostics, and autonomous inspection systems. Key methodologies include machine learning, Bayesian risk modeling, and non-destructive evaluation. Publication Trends: Recent articles emphasize AI applications in manufacturing (defect detection, robotic path planning), IoT-based monitoring (3D printing, building safety), and healthcare analytics (COVID-19 diagnosis). Methodological innovations include graph theory optimization, computer vision, and ontology-driven quality frameworks. Awards: Three Brunel University Performance Bonuses Marquis Who's Who in the World (1998) Marquis Who's Who in Science and Engineering (2000) Advising & Grants: Supervised 39+ PhD/MPhil students (23 as primary supervisor). Secured £2.7M as PI and £1.2M as Co-I across 18 projects (EU/UK industry/government-funded). Current PhD projects include AI for non-destructive testing and sustainable manufacturing. Leadership: Heads QUEST and Robotics research groups. Former EU research coordinator and MSc program director. Editor-in-Chief of the International Journal of Metrology and Quality Engineering .

Frank Pallasمشاهده پروفایل

استاد

• Privacy Engineering
• Policy-Aligned Systems
• Techno-Legal Implications
• +۷ مورد دیگر

Frank Pallas is a Professor at the Paris Lodron University of Salzburg, affiliated with the Faculty of Digital and Analytical Sciences. His research focuses on Privacy Engineering and Policy-Aligned Systems (PEPSys), integrating technical and legal aspects of emerging technologies. Education: Dipl.-Inform. and Dr.-Ing. in Computer Science from TU Berlin His work emphasizes techno-legal privacy engineering , policy-alignment mechanisms , and experimental assessment of privacy techniques , addressing anonymity, utility metrics, and societal impacts. Recent publications explore data anonymization, GDPR compliance, and privacy in distributed systems. Key projects include co-leading Salzburg's EXDIGIT initiative for digital sciences. Activities span organizing international workshops on privacy engineering, media contributions, and third-mission engagements like public lectures on digital privacy.

• Cybersecurity
• Artificial Intelligence
• Blockchain Technology
• +۴ مورد دیگر

Dr. Shun-Wen Hsiao is an Associate Professor in the Department of Management Information Systems at National Chengchi University (NCCU), Taiwan. He earned his PhD in Information Management from National Taiwan University and has been with NCCU since 2017, first as an Assistant Professor before his promotion to Associate Professor in 2023. His research spans: Cybersecurity : Malware analysis, threat detection, IoT security, and blockchain applications Artificial Intelligence : Neural networks for security, NLP, and recommendation systems FinTech : Blockchain frameworks, secure voting systems, and P2P lending risk models E-commerce : Livestreaming recommendation algorithms and consumer behavior analysis He has received multiple awards including: National Science Council Research Award (2023) Three consecutive Outstanding Team awards for Information Security R&D (2016-2018) Research grants from Taiwan's Ministry of Science and Technology Dr. Hsiao leads several major research projects funded by Taiwan's National Science and Technology Council, focusing on explainable AI for cybersecurity, financial technology security, and big data analytics platforms. His work frequently appears in top IEEE transactions and cybersecurity conferences.

José María Alonso Moralمشاهده پروفایل

دانشیار

• Explainable Artificial Intelligence
• Trustworthy AI
• Machine Learning
• +۳ مورد دیگر

Dr. José María Alonso Moral is an Associate Professor at the Department of Electronics and Computation of CiTIUS-USC (Research Center for Intelligent Technologies) at the University of Santiago de Compostela. With a PhD in Telecommunication Engineering from Universidad Politécnica de Madrid (2007) and a 'Doctor Europeus' distinction, he has held prestigious positions including Ramón y Cajal researcher (2018-2022), Juan de la Cierva postdoctoral researcher (2012), and served as President of the European Society for Fuzzy Logic and Technology (EUSFLAT) and Vice-Chair of IEEE Computational Intelligence Society's Explainable Fuzzy Systems Task Force. IEEE Senior Member Associate Editor: IEEE Computational Intelligence Magazine (Q1) and International Journal of Approximate Reasoning (Q2) Board Member: ACL SIGGEN (2019-2022) His research focuses on Explainable AI and Trustworthy Artificial Intelligence , particularly through interpretable fuzzy systems and natural language generation. Key contributions include the ExpliClas web service for decision tree explanations and JFML library for IEEE 1855-2016 fuzzy system modeling. Recent publications examine counterfactual explanations, fuzzy temporal constraint networks, and feature attribution faithfulness metrics. He leads projects like TRUST (Trustworthy AI) and ReproHum (NLP Evaluation Reproducibility), emphasizing human-centered AI design and cross-disciplinary applications in healthcare and education. As an educator, he developed high school workshops on explainable AI fundamentals through CiTIUS-USC's STEM outreach program. His technical work includes developing explainable classifiers for Alzheimer's disease progression and implementing fuzzy logic systems in embedded platforms like Arduino and Raspberry Pi.

Ziang Xiaoمشاهده پروفایل

استادیار

• Human-Computer Interaction
• Natural Language Processing
• Human-AI Interaction
• +۵ مورد دیگر

Ziang Xiao is an Assistant Professor in the Department of Computer Science at Johns Hopkins University's Whiting School of Engineering and a member of the Data Science and AI Institute. His research bridges human-computer interaction, natural language processing, and social psychology to understand human behavior at scale through AI systems. His educational background includes a Ph.D. in Computer Science from the University of Illinois Urbana-Champaign (co-advised by Prof. Hari Sundaram and Prof. Karrie Karahalios), where he also earned dual Bachelor's degrees in Psychology and Statistics & Computer Science under Prof. Dov Cohen. Prior to joining JHU, he was a postdoctoral researcher in the Fairness, Accountability, Transparency, and Ethics group at Microsoft Research Montréal. Xiao's research focuses on three interconnected areas: AI for Social Science (using LLMs to simulate human behavior), Human-centered Model Evaluation (developing frameworks like ECBD for evidence-centered benchmark design), and Information Seeking (studying how AI systems affect information diversity). His work emphasizes democratizing technology to operationalize human intuitions about behavior and decision-making, with notable contributions to LLM evaluation metrics and ethical AI design. Analysis of his recent publications reveals a strong trend toward human-centered AI evaluation, with increasing focus on LLM safety, value alignment, and interdisciplinary applications across social science domains. His work consistently appears in top-tier venues including CHI, ACL, and NeurIPS, with a 2024 CHI Best Paper Award for research on LLM-powered search systems. Best Paper Award at CHI 2024 for 'Generative Echo Chamber? Effect of LLM-Powered Search Systems on Diverse Information Seeking' Multiple publications in ACM/IEEE flagship conferences (CHI, ACL, NeurIPS) Research featured in Johns Hopkins news for social robot interruption handling and chatbot bias studies Xiao actively advises doctoral students and postdocs, with current advisees including Yu Lu Liu, Nikhil Sharma, and Han Jiang. His teaching includes graduate courses on Human-Computer Interaction and Advanced HCI Research Methods. He co-leads research initiatives at the intersection of AI and social science, collaborating with the Data Science and AI Institute to develop frameworks for responsible AI deployment. Current projects examine LLM vulnerabilities in GUI agents, multilingual information disparities, and adaptive decision support systems that balance automation with user control.

• Artificial Intelligence
• Evolutionary Algorithms
• Automatic Machine Learning
• +۴ مورد دیگر

Shengcai Liu is an Assistant Professor in the Department of Computer Science and Engineering at the Southern University of Science and Technology (SUSTech). Holding a PhD in Engineering, he has established himself as a researcher with over 10 publications as first or corresponding author in top artificial intelligence conferences and journals. His academic journey includes research positions at Singapore's Agency for Science, Technology and Research (A*STAR) before joining SUSTech's faculty. Education: PhD in Computer Science and Engineering, University of Science and Technology of China (2014-2020) Bachelor's in Computer Science and Engineering, University of Science and Technology of China (2010-2014) Dr. Liu's research centers on automatic algorithm design and evolutionary approaches to machine learning. He investigates how AI systems can autonomously construct efficient algorithms for complex optimization problems while maintaining robustness and reliability. His work bridges theoretical foundations with practical applications in combinatorial optimization, adversarial machine learning, and large language model development, with particular focus on making algorithm design processes more systematic and effective. His publication record demonstrates a clear trajectory from foundational work on algorithm portfolios to sophisticated integration of evolutionary methods with large language models. Recent work shows increasing focus on textual adversarial attacks, combinatorial optimization, and leveraging large language models as optimization tools themselves. This progression reflects both theoretical depth and practical relevance to current AI challenges. Professional Activities: Project Leader for Huawei-funded research on combinatorial optimization solvers (CNY 400,000) Member of IEEE, IEEE CIS, and AAAI Reviewer for top journals including TPAMI, TEVC, TCYB Conference reviewer for NeurIPS, ICML, AAAI, IJCAI Dr. Liu maintains active research collaborations through the Artificial Intelligence RAMS Technology Innovation Laboratory at SUSTech, where his team focuses on developing next-generation automatic machine learning systems with applications in optimization and security domains.

• Educational Technology
• Artificial Intelligence
• Sustainability
• +۶ مورد دیگر

Prof. Ibrahim Arpaci serves as Head of the Department of Software Engineering at Bandirma Onyedi Eylul University's Faculty of Engineering and Natural Sciences in Turkey, holding academic appointments as Professor in the Department of Software Engineering. Education: Ph.D. in Information Systems from Middle East Technical University M.Sc. in Information Systems from Middle East Technical University His research program bridges educational technology with cutting-edge computational methods, focusing on AI-driven sustainability solutions, cybersecurity applications, and innovative pedagogical frameworks. Key investigations include cryptocurrency adoption behaviors, Generation Z's interaction with AI products, and Metaverse integration in educational contexts, demonstrating strong interdisciplinary connections between computer science, environmental studies, and behavioral psychology. Methodologically, he frequently employs hybrid approaches combining structural equation modeling with deep neural networks. Analysis of his 2022-2023 publications reveals dominant trends in technology-mediated education, with 63% of works examining AI applications across STEAM learning, cloud computing adoption, and bibliometric patterns in nanotechnology education. Emerging research directions prominently feature the Metaverse for sustainable education and image processing solutions for industrial automation, consistently applying advanced machine learning techniques to domain-specific challenges. Scientific Awards: None mentioned in source text. Advising and Grants: No student supervision or grant funding details were provided in the available information. Labs and Teams: No specific research laboratories or collaborative teams were described in the source material.

Begona Rodriguez De Cespedesمشاهده پروفایل

مدرس ارشد

• Translation Training
• Professionalisation
• Impact of Automation
• +۲ مورد دیگر

Begona Rodriguez De Cespedes is a Senior Lecturer and Course Leader of the MA Translation Studies at the University of Portsmouth , affiliated with the School of Education, Languages and Linguistics and the Centre for European and International Studies Research . Her work bridges academic translation education with industry practices. Research Interests : Focuses on pedagogical challenges in translator training, professionalisation, automation's impact, enterprising education, and UK language service provision. Her studies explore employability, industry training practices, and technology integration. Recent Research Trends : Analysis of AI's role in translation, digital transformation of language service providers, and curriculum innovations for freelance readiness. Collaborative projects include the European Master’s in Translation and National Network for Translation. Scientific Awards : No specific awards listed in the provided text. PhD Supervision & Grants : Supervises PhD students and participates in knowledge exchange projects. Served as Portsmouth coordinator for the European Project OPTIMALE and EMT (European Master’s in Translation) from 2011-2021. Labs & Teams : Active in the Sociology and Social Theory Research Group and ELIA Exchange UK , collaborating with language service providers and European universities.

Sash Vaidمشاهده پروفایل

دانشیار

• Political Economy of Marketing
• Dual-use Industrial and Cyber Technologies
• Exogenous Shocks
• +۳ مورد دیگر

Dr. Sash Vaid is a tenured Associate Professor of Marketing at McMaster University's DeGroote School of Business and an Affiliate Faculty member with the School of Computational Science and Engineering. He also holds a Research Affiliate position at the University of Michigan, specializing in econometrics and quantitative marketing of dual-use industrial and cyber technologies. His educational background includes: Ph.D. from the University of Houston MBA from Duke University Graduate studies at Rice University Graduate studies at the Institute for the Study of Business Markets, Penn State University Dr. Vaid's research revolves around a 'Vaid Venn Diagram' intersecting political economy of marketing, dual-use industrial and cyber technologies, and exogenous shocks. He investigates regulatory contexts impacting firms, defense-derived technologies (AI, cybersecurity, geodata) repurposed for commercial use, and macro/micro-level shocks affecting marketing interfaces. His methodology leverages field experiments and programming languages (SQL, Python, Java, C++, CSS, R) to quantify cyber-driven consumption. Analysis of his 2020-2025 publications reveals strong interdisciplinary trends spanning marketing analytics, executive turnover dynamics, AI applications, and pandemic modeling. His work bridges business, computational science, and public health, with significant focus on B2B executive transitions, lead generation technologies, and Covid-19 infection rate estimation using AI. No scientific awards were mentioned in the provided text. Dr. Vaid advises students across undergraduate, graduate, and doctoral programs. He teaches Marketing Analytics (the first undergraduate business course integrating dual-use technology industry), Synthetic Consumer Analytics in the Master of Management in AI and Analytics program, and doctoral courses in Causal Inference. His research is supported by grants including SSHRC funding, and he co-leads development of Canada's 1st Sales & Data Science Lab. He directs the Vaid Lab for Dual-use Technologies and co-leads the Sales & Data Science Lab at the McLean Centre for Collaborative Discovery, providing students with training in high-performance computing, data visualization, deep learning hardware, and collaborative data analysis systems.

• Internet of Things (IoT)
• Edge and Fog Computing
• Network Security
• +۶ مورد دیگر

Amirhosein Taherkordi is an Associate Professor at the Department of Information Security and Communication Technology within the Faculty of Information Technology and Electrical Engineering at the Norwegian University of Science and Technology (NTNU). His academic profile shows continuous research activity with publications spanning from 2011 through 2025, indicating an established career trajectory in computer science and networking research. Dr. Taherkordi's research interests focus on addressing fundamental challenges in distributed computing environments, particularly in resource-constrained scenarios. His work spans Internet of Things (IoT) systems, edge and fog computing architectures, network security protocols, and machine learning applications for network traffic analysis. He has made significant contributions to energy-efficient data collection protocols for wireless sensor networks, privacy-preserving techniques for industrial IoT systems, and communication-efficient approaches for federated learning in vehicular networks. His research consistently bridges theoretical innovation with practical implementation, addressing real-world challenges in smart transportation, environmental monitoring, and industrial automation systems. An analysis of Dr. Taherkordi's recent publication trends (2023-2025) reveals a strong emphasis on federated learning applications for vehicular networks (FedAGL, FedAPT), energy-efficient IoT data collection strategies (eU2U, ECMSH), and the integration of transfer learning with edge computing for transportation applications (TELEGAIT, FOGFLEET). His work increasingly addresses the critical tension between computational efficiency and accuracy in distributed systems, with growing applications in environmental monitoring (PmForecast) and circular economy frameworks. The interdisciplinary nature of his research spans computer science, electrical engineering, and environmental science domains. Dr. Taherkordi maintains an active collaborative research profile, working with international colleagues across multiple institutions as evidenced by his diverse publication venues including IEEE Transactions, ACM journals, and various conference proceedings. His research program appears to be well-established with consistent funding, though specific grant details aren't provided in the available text. He likely leads or contributes significantly to research groups focused on networking, IoT, and edge computing at NTNU, mentoring students in these emerging technology domains.

Arzu Atasoyمشاهده پروفایل

دانشیار

• Writing Education
• Turkish Language Teaching
• Language Assessment
• +۳ مورد دیگر

Arzu Atasoy serves as Associate Professor in the Department of Turkish and Social Sciences Education at Gaziantep University's Faculty of Education since 2024, following progressive academic appointments including Doctor Lecturer (2020-2024) and Research Assistant positions at both Gaziantep and Hatay Mustafa Kemal Universities. Her academic credentials feature a Doctorate in Turkish Education from Hatay Mustafa Kemal University (2015-2019), Master's degree in Turkish Education from Mustafa Kemal University (2013-2015), and Bachelor's in Turkish Language Teaching from Erciyes University (2008-2011). Professor Atasoy's research centers on writing education with specialized focus on writing alienation phenomena, AI-assisted text evaluation, and sociocultural dimensions of writing development. Her methodological repertoire combines mixed-methods designs with bibliometric analyses to investigate writing instruction across Turkish educational contexts. Recent publications demonstrate accelerating integration of artificial intelligence in language assessment, particularly through ChatGPT applications for student text evaluation, alongside systematic reviews mapping writing education research evolution. Her scholarly recognition includes: TÜBİTAK 2211-E Direct Domestic PhD Scholarship TÜBİTAK 2210-A General Domestic Master's Scholarship She actively supervises master's research including completed studies on Turkish teachers' writing practices and ongoing projects analyzing AI-generated texts and developing mobile writing applications. Her extensive teaching portfolio spans undergraduate Creative Writing and Turkish Language courses through graduate-level Writing Education Research Design seminars across all academic levels.