جست‌وجوی استاد و پژوهشگر

Helena Mihaljevićمشاهده پروفایل

استاد

• Statistical Data Analysis
• Data Mining
• Machine Learning
• +۶ مورد دیگر

Professor Helena Mihaljević serves as ECDF-Professor of Data Science at HTW Berlin (University of Applied Sciences), appointed in July 2018. She holds her position within the School of Computing, Communication and Business, Department of Computer Science, Communication and Business, with her office located at Wilhelminenhofstraße 75A, Building C, Room 649 in Berlin. Her research focuses on applied data science with emphasis on statistical data analysis, machine learning, natural language processing, and algorithmic transparency . She specializes in data science applications for text corpora and urban data, applied machine learning, and data algorithms with privacy considerations. Her work bridges technical expertise with social impact, particularly in analyzing the gender gap in STEM fields through publication pattern analysis. Professor Mihaljević leads research activities in the interdisciplinary 'Gender Gap in Science' project supported by the International Science Council, developing algorithms to analyze women's representation in research publications, collaboration patterns, and access to prestigious journals. She also collaborates with Berlin's public transport provider BVG, which co-finances her professorship. Supervised 1 doctoral candidate Member of 3 professional organizations Conducted 14 research projects Delivered 53 lectures/events Her research is organized within the Sustainable Smart Cities cluster. Office hours are Thursdays 2-3 PM by appointment via Moodle scheduler. She also serves as Examination Advisor, Study Advisor, BAföG Representative, and Examination Board Member for the Computer Science and Business - Women's Studies Programme.

Wolfgang Wörndlمشاهده پروفایل

استاد

• Interactive Recommender Systems
• Mobile Computing
• Tourism Recommender Systems
• +۵ مورد دیگر

Dr. Wolfgang Wörndl is a Professor at the Technical University of Munich (TUM), affiliated with the School of Computation, Information and Technology (CIT) and the Chair of Connected Mobility. His research focuses on interactive recommender systems in mobile scenarios, particularly in tourism applications. Wörndl's research interests span interactive recommender systems, mobile computing, tourism recommender systems, sustainable city trips, and user interaction design. His work addresses challenges in personalization, sustainability, and user experience in mobile recommendation contexts. He has made significant contributions to understanding how to design recommender systems that balance user preferences with environmental considerations in tourism contexts, with recent publications demonstrating a strong focus on sustainable tourism through integration of CO2 emissions, popularity metrics, and seasonal variations into recommendation algorithms. Wörndl actively contributes to the academic community as Workshop & Tutorial Co-Chair for ACM UMAP 2025, Co-Chair for the ACM Europe School on Recommender Systems 2025, and Co-Organizer for multiple workshops including the Workshop on Recommenders in Tourism (RecTour) at ACM RecSys 2025. Best/Outstanding Reviewer at ACM RecSys multiple times As an educator, Wörndl teaches courses including Connected Mobility Basics, Edge Computing and the Internet of Things, and Seminar Human-Centered Recommender Systems. He mentors students through thesis projects focused on user interaction with responsible recommender systems, hybrid user interfaces with agentic AI for travel destination recommendation, and privacy in group recommender systems. His research bridges technical innovation with practical user experience considerations, with several implemented systems including the Green Destination Recommender web application that encourages responsible city trip recommendations through thoughtful interface design.

• Optical Wireless Communication
• Sensor Networks
• Machine Learning
• +۵ مورد دیگر

Raul Zamorano is a research-active academic at Northumbria University , Newcastle, United Kingdom. His work is centered on the intersection of optical wireless communication , sensor networks , and machine learning applications in both healthcare and agriculture. He is a key contributor to interdisciplinary projects that integrate 5G technologies , visible light communication (VLC) , and AI-driven diagnostics . Education: Raul holds a BA degree, as listed in his qualifications on the Northumbria staff profile. Research Interests: Optical wireless communication systems Hybrid RF-optical sensor networks AI applications in smart agriculture and medical diagnostics 5G-enabled digital twins Energy harvesting for IoT devices His recent work demonstrates a strong focus on real-world applications , such as remote COVID-19 detection, crop stress monitoring, and underground mining safety systems. These projects highlight his ability to translate complex theoretical models into practical, scalable technologies. Scientific Contributions: While no formal awards are listed, his prolific publication record in top-tier journals and conferences reflects significant academic impact. His collaborative work spans multiple sectors, including telecommunications, healthcare, agriculture, and environmental monitoring. Collaboration & Labs: Raul is actively involved in cross-disciplinary research teams, often working with co-authors from institutions across Europe and beyond. His affiliations suggest participation in both university-led and externally funded research initiatives, though specific lab or grant details are not disclosed in the provided text.

Theodoridis Ioannisمشاهده پروفایل

استاد

• Data Science
• Databases
• Big Data Management
• +۳ مورد دیگر

Professor Theodoridis Ioannis is a distinguished faculty member in the Department of Informatics at the University of Piraeus, where he serves as Director of the Data Science Laboratory within the School of Information and Communication Technologies. With a career spanning over two decades, he has established himself as a leading expert in data management and analysis. His research interests focus on Data Science, particularly in databases, big data management, data mining, and geoinformatics. Professor Theodoridis has made significant contributions to spatial database systems, time series analysis, and distributed data processing. His work bridges theoretical foundations with practical applications in areas such as smart cities, mobility analytics, and scientific data management. His publication record demonstrates consistent research productivity with over 100 peer-reviewed articles in top-tier venues, accumulating more than 10,000 citations. His research output shows a clear evolution from foundational database techniques toward contemporary challenges in big data analytics, machine learning integration, and privacy-preserving methods. Member of editorial board of ACM Computing Surveys (since 2016) Reviewer for numerous international journals and conferences Active participant in data management conference committees Professor Theodoridis has secured significant research funding through Horizon 2020 projects, serving as project coordinator and research team leader since 2001. His work demonstrates strong industry and academic collaboration, with applications spanning multiple domains. He has also co-authored three influential monographs in his field. He leads the Data Science Laboratory, which serves as a hub for interdisciplinary research at the intersection of database systems, machine learning, and domain-specific applications. The laboratory fosters collaboration between computer scientists, domain experts, and industry partners to address real-world data challenges.

Giri Kumar Tayiمشاهده پروفایل

استاد

• Operations Management
• Information Systems
• Operations Research
• +۱۰ مورد دیگر

Giri Kumar Tayi is a Professor of Management Science and Information Systems at the State University of New York at Albany. His expertise spans interdisciplinary research areas including Operations Management, Information Systems, and Operations Research with a focus on E-Commerce, Supply Chain Optimization, Data Quality, Digital Government, and Software Process Diversity. He holds a Ph.D. from Carnegie Mellon University. Dr. Tayi's research focuses on strategic challenges in digital ecosystems, such as omni-channel retailing dynamics, information security governance, and sustainable consumption practices. He has pioneered studies in consumer behavior in online platforms, supply chain transparency through certification systems, and the application of ontologies in data architecture design for ethical consumption. With over 60 refereed journal articles and 20+ conference proceedings, his work appears in top-tier journals like Operations Research , Management Science , and Information Systems Research . He has held editorial roles at 10+ journals including Information Systems Research and IEEE Intelligent Systems . His research has been supported by NSF and industry collaborations. Key contributions include frameworks for data quality management in clinical trials, ontological modeling for certification systems, and strategic analyses of digital platforms. His current projects explore blockchain applications in supply chain governance and AI-driven decision support systems for sustainable operations.

Ken M. L. Yiuمشاهده پروفایل

استاد

• Databases
• Query Processing
• Spatiotemporal Data
• +۷ مورد دیگر

Ken M. L. Yiu is a Professor in the Department of Computing at Hong Kong Polytechnic University , Faculty of Engineering. He received his PhD and Bachelor's degree from the University of Hong Kong in 2006 and 2002, respectively, and was previously affiliated with Aalborg University (2006–2009). He is a leading researcher in databases, with a focus on spatiotemporal data, query processing, and multidimensional data management. PhD, University of Hong Kong (2006) Bachelor of Computer Engineering, University of Hong Kong (2002) His research interests lie at the intersection of database systems and spatial analytics. He investigates efficient indexing, query optimization, and privacy-preserving techniques for large-scale spatial and temporal datasets. His recent work explores learned index structures, GPU-accelerated query processing, and high-dimensional data retrieval. He has made significant contributions to spatial query processing, trajectory analytics, and location-based services. The trends in his recent publications (2021–2025) reflect a strong focus on high-performance database systems, including GPU acceleration (GHive), perfect hashing on GPUs (GPH), and learned cardinality estimation. His work increasingly integrates machine learning with traditional database techniques, as seen in AlayaDB for LLM inference and learning-based query optimization. He also continues to advance core database problems such as spatial indexing, trajectory analysis, and similarity search. SSTD 2025 10-Year Impact Award Ken Yiu has successfully led multiple competitive research projects funded by the Hong Kong GRF, including grants on learned index structures (2024–2026), smart memory for vector data mining (2021–2023), and efficient spatial data management (2017–2019). He has supervised numerous PhD and MPhil students, many of whom now hold academic positions (e.g., Bo Tang at SUSTech, Yu Li at HDU) or work in top tech companies (e.g., Huawei, Alibaba). His professional service is extensive, including roles as PI for major grants, area chair (ICDE 2024), and program committee member for top conferences like SIGMOD, VLDB, and ICDE. He is actively involved in research groups and projects related to database systems, particularly in spatiotemporal data management and efficient query processing. His lab collaborates closely with students and co-supervisors like Bo Tang on topics such as trajectory mining, spatial indexing, and learned databases. The research group maintains strong ties with international institutions and contributes to major open problems in database performance and scalability.

Ismail Sengor Altingovdeمشاهده پروفایل

دانشیار

• Information Retrieval
• Web Search and Mining
• Big Data
• +۳ مورد دیگر

Dr. Ismail Sengor Altingovde serves as an Associate Professor in the Department of Computer Engineering within the College of Engineering at Middle East Technical University (METU) in Ankara, Turkey. Previously, he completed his B.S. and M.S. degrees at Bilkent University, followed by a Ph.D. in Computer Engineering from Bilkent in 2009. His academic journey includes post-doctoral research at Bilkent University (2009-2011) and L3S Research Center in Hannover, Germany (2011-2012) before joining METU. His educational background includes: B.S. in Computer Engineering, Bilkent University (1999) M.S. in Computer Engineering, Bilkent University (2001) Ph.D. in Computer Engineering, Bilkent University (2009) - Thesis: "Improving The Efficiency of Search Engines: Strategies for Focused Crawling, Searching, and Index Pruning" Dr. Altingovde's research focuses on Information Retrieval , particularly Web Search and Mining, Big Data analysis, and Database Management Systems. His work addresses critical challenges in search result diversification, query performance prediction, and scalable indexing techniques. He has pioneered approaches in handling zero-result queries, integrating social signals into search, and developing neural information retrieval models. His research bridges theoretical algorithm design with practical implementations for real-world search engines. His publication record demonstrates a strong trajectory in search technologies, with recent work emphasizing neural information retrieval, social media integration in search, and cross-lingual search systems. The research shows consistent focus on improving search relevance while addressing scalability challenges in modern web-scale systems. His work spans both theoretical contributions and practical implementations with industry relevance. Among his notable recognitions: Distinguished Young Scientist 2016 award (GEBIP) from Turkish Academy of Sciences (TUBA) 2013 Yahoo! Faculty Research and Engagement Program (FREP) award (selected from 27 researchers across 24 countries) Dr. Altingovde actively contributes to the research community through professional service including co-chairing ECIR 2017 short paper track and serving on program committees for CIKM, SIGIR, and Web Science conferences. He has secured multiple research grants from TÜBITAK (Scientific and Technological Research Council of Turkey) including projects on query result caching, semantic relationships for search scalability, and domain-specific search engines. His work connects academic research with practical applications through collaborations with industry partners like Yahoo! Research. He leads research within METU's Computer Engineering Department, contributing to projects like LivingKnowledge (focusing on fact, opinions and bias in time) and previously participating in European projects like MUSCLE (Multimedia Understanding through Semantics, Computation, and Learning). His laboratory work emphasizes experimental validation of search algorithms with real-world datasets and practical implementations.

Pradeep K. Atreyمشاهده پروفایل

دانشیار

• Security and Privacy
• Cloud-based Multimedia Analytics
• Disinformation Detection
• +۴ مورد دیگر

Pradeep K. Atrey is an Associate Professor in the Department of Computer Science at the University at Albany, State University of New York. He directs the computer science undergraduate program and co-founded the Albany Lab for Privacy and Security (ALPS). His research focuses on security and privacy in multimedia systems, including cloud-based analytics, disinformation detection, applied cryptography, and social media analysis. Dr. Atrey has published over 130 articles in top-tier venues like ACM and IEEE journals/conferences, with funding from NSERC (Canada), DFAIT, and the Saudi Arabian government. He serves on editorial boards of journals such as ACM TOMM and IEEE Transactions on Multimedia. Education: Ph.D. in Computer Science (National University of Singapore), Postdoctoral Researcher (University of Ottawa). Research highlights include pioneering work on secure GPS trajectory processing (GeoSecure series), anti-deepfake frameworks, and privacy-aware social network architectures (SecureCSocial). Awards include the ACM TOMM Associate Editor of the Year (2015), Erica and Arnold Rogers Award (2014), and multiple recognition for editorial excellence. Grants and collaborations involve federal agencies and international partnerships. His work bridges theoretical security concepts with practical applications in cloud computing, multimedia forensics, and ethical AI. ALPS lab conducts cutting-edge research in privacy-preserving technologies, including secure computation protocols for sensitive data.

Jinbo Biمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine learning
• Data mining
• +۷ مورد دیگر

Jinbo Bi is the Frederick H. Leonhardt Professor of Computer Science and Associate Head in the Department of Computer Science & Engineering at the University of Connecticut's School of Engineering. He holds additional affiliations with the Department of Community Medicine and Health Care at UConn Health Center, Department of Mathematics, Department of Statistics in the College of Liberal Arts and Sciences, and the Department of Biomedical Engineering. Bi earned his Ph.D. from Rensselaer Polytechnic Institute and M.Sc. from Beijing Institute of Technology. His research spans artificial intelligence, machine learning, data mining, pattern recognition, computer vision, bioinformatics, medical informatics, and drug discovery. He directs two laboratories: the Laboratory of Machine Learning & Health Informatics and the Laboratory of Artificial Intelligence & Drug Discovery. His recent publications demonstrate a strong focus on applying machine learning to healthcare challenges, particularly in mental health monitoring through smartphone data, depression prediction, substance use disorder analysis, and drug discovery. His work combines theoretical advances in AI with practical healthcare applications, showing particular strength in multi-view learning, graph neural networks, and optimization techniques. Bi has received recognition including a best paper award at BIBM 2014 workshop and two highly accessed articles in BMC Genomics and BMC Genetics. His research is supported by numerous grants from NSF, NIH, VA, and industry partners including Travelers Insurance and Pfizer. As an educator, Bi teaches advanced courses in machine learning and reinforcement learning. His laboratory currently has multiple postdoctoral fellowship openings, reflecting his active research program and mentorship of numerous students who appear as co-authors on his publications.

• Artificial Intelligence
• Explainable AI
• Recurrent Neural Networks
• +۵ مورد دیگر

Isel Grau Garcia is an Assistant Professor at Eindhoven University of Technology (TU/e), affiliated with the Information Systems group within the Department of Industrial Engineering and Innovation Sciences. Her research focuses on explainable AI, recurrent neural networks, time-series analysis, and bias mitigation in healthcare and business applications. She holds a Ph.D. in Computer Science from Vrije Universiteit Brussel (VUB) and has co-authored over 75 peer-reviewed publications. Key roles include co-promotor for three PhD students and involvement in the ENFIELD project on trustworthy AI. Education: Ph.D. in Artificial Intelligence, Vrije Universiteit Brussel (2020) M.Sc. in Artificial Intelligence, Central University of Las Villas (2014) B.Sc. in Computer Science, Central University of Las Villas (2011) Research Interests: Her work emphasizes interpretability of machine learning models, semi-supervised classification, and interdisciplinary applications in healthcare. Recent projects include developing Fuzzy Cognitive Map models without training data and quantifying trust in clinical decision support systems. Key Contributions: Her articles explore topics like bias detection via SHAP and FCMs, sparsity-optimized feature importance, and recurrent neural networks for temporal dynamics. These contributions align with UN SDG goals for innovation and health. Awards: Best Demo Award at BNAIC/BENELearn 2021 Industria Students Award – Best Talent (2024) Advising & Grants: Supervises three PhD students and collaborates on the ENFIELD project (€ funding). Active in organizing workshops and reviewing for top venues like ECAI and IEEE Transactions on Fuzzy Systems. Labs/Teams: Engaged in interdisciplinary collaborations with institutions like Warsaw University of Technology and the University of Lisbon, focusing on AI ethics and trustworthy decision systems.

Wen Huaمشاهده پروفایل

پژوهشگر ارشد

• Data Science
• Machine Learning
• Data Mining
• +۵ مورد دیگر

Dr. Wen Hua is an Honorary Senior Research Fellow at the University of Queensland's School of Electrical Engineering & Computer Science. His research focuses on Data Science, Machine Learning, and Knowledge Graphs, with significant contributions to privacy-preserving techniques, neural network architectures, and trajectory analysis. He has co-authored over 80 peer-reviewed publications, including influential works on entity linking, meta-learning, and spatiotemporal data privacy. His work bridges theoretical advancements with practical applications in areas like recommendation systems, sentiment analysis, and information resilience. Key research interests include: Machine Learning and Deep Learning Privacy-Preserving Data Publishing Knowledge Graph Construction Text Mining and Natural Language Processing Spatiotemporal Trajectory Analysis Information Retrieval Systems Recent publications highlight trends in: Neural architectures for meta-learning and transformer-based systems Efficient algorithms for relation extraction and entity disambiguation Temporal and spatial data anonymization techniques Integration of knowledge graphs with text corpus analysis

• Sustainability
• Supply Chain Management
• Operations Research
• +۵ مورد دیگر

Zahra Mohammadnazari is an Assistant Lecturer in Business Processes and Operations Management at the CBL School of Management, Coventry University, and concurrently a Doctorate Student at the Research Centre for Business in Society. Her research focuses on sustainability, supply chain optimization, waste management, and blockchain applications in logistics. She holds a Master of Research from Kingston University (2022), an MSc in Industrial Engineering from the University of Tehran (2021), and is pursuing her Doctorate in Block chain enabled life cycle assessment in copper-flower supply chains at Coventry University (2022–2025). Her academic contributions span innovative approaches to municipal solid waste management, sustainable supply chain design, and disaster logistics optimization. Collaborations highlight cross-disciplinary work in environmental science, healthcare logistics, and data-driven decision-making. Zahra’s research methodologies often integrate machine learning, operations research, and multi-criteria decision analysis to address complex socio-technical systems challenges. Notable research outputs include a 2024 case study on England’s municipal waste management systems and a 2023 co-edited book on Industry 5.0’s gig economy. Her work has been cited 4 times (Scopus, 2023) and frequently addresses real-world applications in food security, disaster recovery, and sustainable manufacturing.

Xiaoming Fuمشاهده پروفایل

استاد

• Mobile Computing
• Social Computing
• Cloud Computing
• +۷ مورد دیگر

Xiaoming Fu is a Full Professor of Computer Science at the University of Göttingen, Germany, and Founding Director of the Sino-German Institute of Social Computing. He earned a Ph.D. from Tsinghua University (2000) and has held academic positions at TU Berlin (2000-2002) and the University of Göttingen since 2002. His research spans mobile computing, social computing, cloud computing, big data analysis, and networked systems, with a focus on (mobile) social networks, Internet of Things, and software-defined networking. Education : Ph.D. (2000) from Tsinghua University, Beijing, China. Key Positions : Founding Director, Sino-German Institute of Social Computing (2015-present); University Professor and Head of Computer Networks Group (2007-present); Assistant Professor (2002-2007); DAAD Visiting Professor at Columbia University (2008-2009); Fulbright Scholar at UCLA (2009). Editorial Leadership : Editor-in-Chief of the Journal of Social Computing and ACM Transactions on Social Computing; served on editorial boards of IEEE Network, IEEE Communications Magazine, and others. Research Leadership : Principal Investigator for EU FP6-ENABLE, FP7-GreenICN, H2020 COSAFE, and MobileCloud projects; coordinator of EU H2020 ICN2020 and CleanSky projects. Leadership in Professional Organizations : Vice Chair of IEEE Communications Society Technical Committee on Computer Communications (2010-2012); Chair of IEEE Communications Society and Internet Society Internet Technical Committee (2011-2013); founding co-chair of IEEE Vehicular Technology Society Germany Chapter. Honors and Awards : IEEE Fellow (2022), ACM Distinguished Member (2021), Member of acatech (2024), Fellow of the Institute of Engineering and Technology (2017), and more.

Richard E. Millerمشاهده پروفایل

استاد

• Writing
• Higher Education Reform
• Literacy Studies
• +۳ مورد دیگر

Richard E. Miller is a Professor of English at Rutgers University since 1993. His office is located at 43 (Historic) Mine Street, Room #1, College Ave Campus, with Zoom-based office hours by appointment. He specializes in writing pedagogy, higher education reform, and cultural studies. His research explores the intersection of technology, literacy, and institutional practices, emphasizing creative approaches to writing instruction and systemic educational reform. Miller has authored influential books including *As if Learning Mattered: Reforming Higher Education* (1998) and *Writing at the End of the World* (2005), which critically examine educational structures and societal challenges. His co-authored *Habits of the Creative Mind* series (2015, 2020, 2024) redefines composition pedagogy through frameworks encouraging curiosity and resilience. He also addresses privacy erosion in the digital age in *On the End of Privacy* (2019). His scholarship spans apocalyptic cultural narratives, queer writing assessment, and the role of humanities in crises. While no specific grants or awards are listed, his work demonstrates sustained engagement with educational equity and critical thinking. Miller has contributed to debates on writing program administration, digital literacy, and the future of privacy. His research maintains a balance between theoretical rigor and practical pedagogical innovation, influencing both academic discourse and classroom practices.

Thorvald Hæremمشاهده پروفایل

استاد

• Organizational Psychology
• Behavioral Decision Making
• Organizational Routines
• +۳ مورد دیگر

Thorvald Hærem is a Professor of Organizational Psychology at BI Norwegian Business School and serves as Head of the Department of Leadership and Organizational Behaviour. He holds a Ph.D. from Copenhagen Business School. His research focuses on behavioral decision making, organizational routines, technology’s role in organizations, and expertise development. He teaches graduate courses in Judgment and Decision Making, Creativity, and Organizational Science. Key research areas include decision-making under uncertainty, the impact of emotions on judgment, and the dynamics of organizational routines. His work explores how cognitive styles (analytic vs. intuitive) influence performance in complex tasks and how communication settings affect team coordination in crisis scenarios. Recent studies address topics such as overconfidence and status dynamics (2025), the effects of open-office noise on information processing (2023), and the replication of persuasion mechanisms in uncertain contexts (2024). His research often bridges experimental psychology and organizational behavior, emphasizing practical applications in security management and multiteam systems. Thorvald has published in top journals like Journal of Applied Psychology , Organization Science , and Journal of Behavioral Decision Making . His contributions span theoretical innovations (e.g., routine dynamics, pattern mining) and applied domains (e.g., hybrid threat preparedness, military coordination).