جست‌وجوی استاد و پژوهشگر

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.

• 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.

Boris Čuleمشاهده پروفایل

استادیار

• Data Mining
• Machine Learning
• Artificial Intelligence
• +۶ مورد دیگر

Dr. Boris Čule is an Assistant Professor at Tilburg University's Department of Cognitive Science and Artificial Intelligence within the Tilburg School of Humanities and Digital Sciences. He holds a Ph.D. in Computer Science from the University of Antwerp, followed by post-doctoral research there. His work focuses on data mining, machine learning, and AI, particularly sequential/temporal data, including pattern mining, anomaly detection, and recommender systems. Education: Ph.D. in Computer Science, University of Antwerp Research Interests: Sequential pattern mining, session-based recommendations, time series analysis, subspace clustering, spatial pattern discovery, and applications in environmental and economic forecasting. His contributions include novel algorithms for evaluating sequential patterns, sequence classification, and anomaly detection in dynamic systems. Publications Trends: Recent work emphasizes real-world applications like UAV path planning optimization (2024), news recommendation systems (2023-2024), and employment market forecasting (2023). He explores interdisciplinary challenges such as facial action analysis in gaming (2022) and wind farm response to storms (2021). Advising/Grants: While specific grant details are not listed, his prolific publication record suggests active research projects. Courses taught include Big Data analysis and cognitive science modules.

Panagiotis Traganitisمشاهده پروفایل

استادیار

• Statistical Signal Processing
• Machine Learning
• Crowdsourcing
• +۴ مورد دیگر

Panagiotis Traganitis is an Assistant Professor in the Electrical and Computer Engineering (ECE) Department at Michigan State University (MSU), where he joined in August 2022. Previously, he was a Postdoctoral Researcher at the University of Minnesota’s Signal Processing in Networking and Communications (SPiNCOM) group under Prof. Georgios B. Giannakis. His research focuses on statistical signal processing, machine learning, crowdsourcing, weak supervision, and network science, with applications in big data analytics and distributed learning. Education: Ph.D. in Electrical Engineering, University of Minnesota (2019), Thesis: Scalable and Ensemble Learning for Big Data M.Sc. in Electrical Engineering, University of Minnesota (2015), Thesis: Large-scale Clustering using Random Sketching and Validation Diploma in Electrical & Computer Engineering, National Technical University of Athens (2013), Thesis: Reinforcement Learning Methods for Cognitive Radio Networks Research Interests: His work spans statistical learning, blind ensemble methods, weak supervision, subspace clustering, and graph-based algorithms. Current projects include blind ensemble learning, adversarial detection in crowdsourcing, and self-supervised learning. Awards & Honors: Gerondelis Foundation Graduate Scholarship (2015) Finalist, CAMSAP 2017 Student Paper Award Eurobank’s Award of Excellence in Greek Nationwide University Entrance Exams (2007) Teaching & Mentorship: He has served as a teaching assistant for courses on statistical methods and nonlinear optimization at the University of Minnesota. He is currently seeking motivated Ph.D. students to join his research group. His lab focuses on advancing robust, scalable learning algorithms with real-world applications. Labs & Collaborations: Active member of the SPiNCOM research group, collaborating on projects related to signal processing, machine learning, and network science.

Lu Liuمشاهده پروفایل

عضو هیئت علمی

• Artificial Intelligence
• Data Science
• Sustainable Systems
• +۵ مورد دیگر

Professor Lu Liu is an Honorary Professor at the University of Leicester's School of Computing and Mathematical Sciences, specializing in AI, Data Science, Sustainable Systems, and IoT. He focuses on developing trustworthy and sustainable systems using machine learning for healthcare, Net Zero, and digital manufacturing. He leads projects on self-learning digital twin models and has secured 30+ grants from UKRI/EPSRC, EU, Innovate UK, etc. His work includes carbon footprint reduction in data centers and communication systems. Education: PhD from the Surrey Space Centre (University of Surrey). Previously held roles including Head of School (2019-2023) and Research Fellow at the WRG e-Science Centre (University of Leeds). Extensive industry collaborations with BT, Rolls-Royce, and CGI. Research Interests: Machine learning for sustainable ICT systems, edge computing, medical image analysis, autonomous systems, and formal methods for safety-critical systems. His work bridges theory and practical applications in energy efficiency, healthcare, and smart manufacturing. Awards: Stanford's World Top 2% Scientists (2023), 7 Best Paper Awards, and 9 keynote invitations. Recognized with the Staff Excellence Award in Doctoral Supervision (2018). Grants & Projects: Principal Investigator for UKRI AI for Health (£750K), Leicester Innovation Accelerator (£2.5M), and Co-Investigator for AI4NetZero (£2.5M). Over 300 publications in top journals/conferences. Labs & Leadership: Co-Director of Leicester Centre for Digital Manufacturing (LCDM), Deputy REF Lead for UoA 11, and Fellow of BCS. Served as General Chair for IEEE EUC 2023, IEEE/ACM UCC 2021, and other major conferences.

Ingrid Chieh Yuمشاهده پروفایل

دانشیار

• Data Management
• Knowledge Systems
• Software Product Lines
• +۷ مورد دیگر

Ingrid Chieh Yu is an Associate Professor in the Department of Informatics at the University of Oslo, where she also serves as Head of Research and Vice Head of Department (2021-2024) and Deputy Centre Director of SIRIUS. Her academic home is within the Data and Knowledge Management (DKM) research group, with additional affiliations to the Concurrent Security and Robustness for Networked Systems (ConSeRNS) initiative. Professor Yu's research spans multiple domains in computer science, with early work focused on formal methods, software product lines, and concurrent systems. In recent years, her research has expanded significantly into explainable AI, counterfactual explanations, and machine learning interpretability. She has made substantial contributions to feature model evolution, software configuration, and model checking techniques. Her work demonstrates a consistent thread of applying formal methods to practical software engineering challenges, with a notable pivot toward AI explainability in the past five years. Her publication record shows a clear evolution from foundational work in software engineering and formal methods (2006-2015) to increasingly AI-focused research (2016-present). The most recent publications (2020-2025) predominantly address explainable AI, counterfactual explanations, and robust recourse methods, reflecting her adaptation to emerging challenges in machine learning. She maintains strong collaborative relationships with researchers including Einar Broch Johnsen, Crystal Chang Din, and Peyman Rasouli. Professor Yu teaches core computer science courses including INF2220 Algorithms and Data Structures, INF3230 Formal Modeling and Analysis of Communicating Systems, and INF5130 Selected Topics in Rewriting Logic. Her teaching reflects her research expertise, bridging theoretical foundations with practical applications. She leads significant research projects including HyVar: Scalable Hybrid Variability for Distributed Evolving Software Systems and Leveraging Energy-Aware Programming (LEAP), demonstrating her capacity to secure and manage substantial research initiatives. Her work with the SIRIUS center indicates strong industry connections and applied research focus.

• Data Science
• Machine Learning
• Recommender Systems
• +۷ مورد دیگر

András A. Benczúr is a prominent researcher affiliated with the Hungarian Academy of Sciences, specifically with SZTAKI (Institute for Computer Science and Control). He has maintained a prolific research career spanning multiple decades with over 160 publications documented in the dblp database. His work primarily focuses on data science, machine learning, and network analysis, with significant contributions to recommender systems and big data analytics. Dr. Benczúr's research interests include Data Science, Machine Learning, Recommender Systems, Network Analysis, Big Data Analytics, Graph Theory, Information Systems, Web Mining, Stream Processing, and Artificial Intelligence. His work demonstrates a consistent focus on developing theoretical foundations while addressing practical applications in various domains. He has made significant contributions to understanding information networks, developing efficient algorithms for data streams, and creating novel approaches to recommendation systems. His recent publications (2023-2025) show a continued focus on cutting-edge topics including neural network uncertainty, AI-driven communication for 6G networks, network embedding applications for public health, and theoretical evaluations of explainable AI methods. These works demonstrate his ability to bridge theoretical computer science with practical applications across multiple domains including telecommunications, public health, and blockchain technology. Dr. Benczúr has collaborated extensively with researchers across Hungary and internationally, with notable long-term collaborations with Bálint Daróczy (30 joint publications), Róbert Pálovics (27 joint publications), and Domokos Kelen (14 joint publications). His work appears in prestigious venues including IEEE Access, ICLR, WWW, RecSys, and numerous IEEE and ACM conferences. His research has practical applications in diverse areas including social network analysis, telecommunications infrastructure, public health monitoring, and blockchain technology. The Hexa-X project publications indicate his involvement in shaping the future of 6G communication standards through AI integration, while his work on network embeddings has been applied to vaccine skepticism detection, demonstrating the societal impact of his research.

Yingwu Zhuمشاهده پروفایل

دانشیار

• Distributed Systems
• Peer-to-Peer Networks
• Computer Security
• +۵ مورد دیگر

Yingwu Zhu is an Associate Professor at the Department of Computer Science and Software Engineering within Seattle University's College of Science & Engineering . With a Ph.D. in Computer Science from the University of Cincinnati and prior degrees from Huazhong University of Science & Technology, his career spans academic research in distributed systems , peer-to-peer networks , and computer security . Education : Ph.D., Computer Science, University of Cincinnati M.S., Computer Science, Huazhong University of Science & Technology B.S., Computer Science, Huazhong University of Science & Technology Research Interests Dr. Zhu's work focuses on peer-to-peer computing , with emphasis on: Semantic Search over DHT overlays Resilient Anonymous Routing using redundancy and erasure coding Content-Based Publish/Subscribe Systems with DHT integration Load Balancing in structured P2P networks Trust Management via network coding Bandwidth-Efficient Algorithms for distributed systems His research has been published in top venues like IEEE TPDS , JPDC , and HiPC , with acceptance rates as low as 14%. Key themes include scalability , fault tolerance , and proximity-aware networking . Scientific Recognition Best Paper Award , NAS'08 Top pick , NAS 2006 Academic Service He actively contributes to the field through program committee memberships at ICCCN , HiPC , IPDPS , and P2P conferences, while maintaining the Distributed Systems Laboratory (DSL) with high-end computational resources.

Matthias Weidlichمشاهده پروفایل

استاد

• Process Mining
• Databases
• Information Systems
• +۴ مورد دیگر

Matthias Weidlich is a full Professor and Chair of Databases and Information Systems in the Department of Computer Science at Humboldt University of Berlin since 2018, previously serving as a junior professor from 2015 supported by the German Research Foundation (DFG) Emmy-Noether Programme. He held research positions at Imperial College London and Technion – Israel Institute of Technology prior to his Berlin appointment. Education: PhD in Computer Science, Hasso Plattner Institute (HPI), University of Potsdam Research Interests: Weidlich's work centers on three interconnected pillars: (1) data-driven process mining techniques for optimizing operations in healthcare and logistics; (2) efficient algorithms for streaming data analysis, particularly pattern detection in IoT infrastructures; and (3) exploratory data analysis frameworks enabling reproducible scientific workflows in astrophysics and bioinformatics collaborations. Scientific Awards: Best Paper Award at ICPM 2023 Best Paper Award at ACM DEBS 2022 Best Paper Award at BPM 2017 Berlin Young Scientist Award (2016) Junior-Fellow of the German Informatics Society (GI) Professional Service: Steering Committee Member, BPM conference series Steering Committee Member, ACM DEBS conference series Co-Editor-in-Chief, Information Systems journal Associate Editor, The VLDB Journal Associate Editor, Process Science Organizer, German Informatics Society Mentoring Programme Advising and Grants: Advising: Coordinates mentorship initiatives connecting students with industry professionals through German Informatics Society programmes Grants: DFG Emmy-Noether Programme funding (2015) for junior professorship establishing his research group

• Computer Security
• Network Security
• Cybersecurity
• +۶ مورد دیگر

Francesco Tommasi is an Associate Professor at the Department of Innovation Engineering, University of Salento, where he teaches Operating Systems, System and Network Programming, and System Security courses for both undergraduate and graduate Computer Engineering programs. His academic affiliation spans multiple departments within the School of Engineering, focusing on the intersection of computer science, networking, and security. His current research interests center on computer security, with specific focus on authentication systems, web security, and mobile security. His past research includes Quality of Service for computer networks, IP mobility, satellite internetworking, and satellite distance learning technologies. Tommasi has made significant contributions to the field of cybersecurity, particularly in identifying and analyzing novel attack vectors including Browser-in-the-Middle attacks and MobileApp-in-the-Middle attacks. His recent publications (2021-2025) demonstrate a strong focus on cybersecurity challenges, particularly authentication vulnerabilities, web security threats, and mobile security issues. The research shows progression from fundamental network security concepts to more sophisticated attack methodologies targeting modern authentication systems and mobile platforms. His work bridges theoretical security concepts with practical attack demonstrations, contributing significantly to the understanding of security vulnerabilities in contemporary systems. Co-author of the first Internet Proposed Standard signed by an Italian university (RFC 2961) Co-author of attack #701 included in the CAPEC database of dangerous cyber attacks Scientific director of the Laboratory for Internetworking and Interoperability between Systems (LIIS) Co-scientific director of the Cybersecurity Research Lab Responsible for the Unisalento node of the CINI Cybersecurity National Lab Tommasi has directed numerous research projects funded by MIUR, European Space Agency, and regional authorities, including satellite distance learning initiatives, cybersecurity projects, and technology transfer initiatives. He created the Salento Satellite Campus and has been instrumental in developing satellite-based educational infrastructure. His laboratory work focuses on practical security testing and developing defensive mechanisms against emerging cyber threats. He leads the Laboratory for Internetworking and Interoperability between Systems (LIIS) and co-directs the Cybersecurity Research Lab, which focuses on practical security testing, vulnerability analysis, and developing defensive security mechanisms. These labs collaborate with national and international partners on cutting-edge cybersecurity research and implementation projects.

Rage Uday Kiranمشاهده پروفایل

دانشیار

• Data Mining
• Parallel Computation
• Air Pollution Data Analytics
• +۳ مورد دیگر

Prof. Rage Uday Kiran is an Associate Professor at the University of Aizu in Aizu wakamatsu, Fukushima, Japan, with additional visiting researcher appointments at the University of Tokyo and the National Institute of Information and Communications Technology (NICT) in Tokyo. He holds a PhD in Computer Science from the International Institute of Information Technology, Hyderabad, and has published over 50 articles in top-tier venues including EDBT, SSDBM, PAKDD, and DASFAA. Education PhD in Computer Science - IIIT-Hyderabad, India (2005-2011) MS in IT Agriculture - DA-IICT, India (2003-2005) B.Tech in Agriculture Engineering - Acharya N. G. Ranga Agricultural Engineering College, India (1999-2003) Research Focus Dr. Kiran specializes in developing novel data mining models and high-performance algorithms for temporal, spatiotemporal, and transactional databases. His primary research explores pattern mining techniques for rare item discovery, parallel computation optimizations, and real-world applications in air pollution analysis, traffic congestion prediction, agricultural ICT systems, and recommender systems. His recent work focuses on utility-based pattern discovery in spatiotemporal contexts and periodic pattern detection in temporal databases, emphasizing scalability and efficiency for large-scale datasets. Professional Experience Project Assistant Professor - University of Tokyo (2015-2020) Researcher - NICT Tokyo (2015-2020) Post Doctoral Researcher - University of Tokyo (2012-2015) Technical Contributions Dr. Kiran maintains public datasets for pattern mining research and develops open-source tools including the PAttern MIning Python Kit (PAMI-PyKit). His technical expertise spans C/C++/Java, database systems (MySQL/PostgreSQL), web technologies, and cloud platforms (Azure/AWS).

• Formal Methods
• Concurrent Systems
• Real-Time Systems
• +۷ مورد دیگر

Professor Marcin Szpyrka is a faculty member at AGH University of Science and Technology in Kraków, Poland, where he works in the Department of Applied Informatics within the Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering. He holds the position of Professor and maintains an office in building C-2, room 321. His academic work spans formal methods, concurrent systems modeling, and data mining applications. Professor Szpyrka's primary research interests focus on formal methods including the Alvis modeling language, Petri nets, and process algebras. He specializes in the modeling and verification of concurrent and real-time systems , with applications spanning industrial processes, automotive diagnostics, and cybersecurity. His work bridges theoretical computer science with practical engineering applications, particularly in embedded systems and data analysis. Analysis of Professor Szpyrka's recent publications reveals a clear research trajectory focusing on formal verification techniques applied to increasingly complex systems. His work has evolved from foundational research on the Alvis language to practical applications in automotive systems, industrial processes, and cybersecurity. The publications demonstrate consistent expertise in modeling concurrent systems while expanding into data mining and machine learning applications for anomaly detection and system diagnostics. Rector's Finance Committee Disciplinary Council for Technical Information Technology and Telecommunications Disciplinary Appeals Committee for Doctoral Students Supervised 9 PhD dissertations between 2009-2024 Developed academic resources including the aghdpl LaTeX class for thesis preparation Mentored students working on formal methods, concurrent systems, and data mining applications Professor Szpyrka leads the Formal Methods research group with projects hosted at fm.kis.agh.edu.pl, focusing on the development and application of the Alvis modeling language for system verification.

• Computer Science
• Parallel Computing
• Data Stream Processing
• +۳ مورد دیگر

Dr. Jakub Yaghob is a researcher at the Faculty of Mathematics and Physics , Charles University , specializing in computer science and parallel computing. He teaches advanced programming topics including Compiler Principles , Parallel Programming , and Cloud Computing . Research Interests : Parallel data stream processing, virtualization technologies, semantic web infrastructures, and performance optimization Teaching : Advanced C++ programming, virtualization administration, and computer systems architecture Technical Expertise : Design of parallelization frameworks, astrophysical data analysis, and hybrid CPU-GPU systems His publications focus on: Optimizing stream data processing across distributed architectures Developing domain-specific languages like Bobolang Performance evaluation in educational programming contexts Applications of parallel computing in astrophysics

• Management Information Systems
• Business Analytics
• Decision Support Systems
• +۲ مورد دیگر

Wei Huang (Wayne) is a Chair Professor and founding dean of the College of Business at Southern University of Science & Technology (SUSTech) in Shenzhen, China. He holds the prestigious National Yangtze Chair in Management Information Systems and has over 35 years of full-time teaching and research experience across multiple continents. His academic career spans renowned institutions including University of New South Wales (Sydney, Australia), National University of Singapore (NUS), Harvard University, Chinese University of Hong Kong (CUHK), and Xi'an Jiaotong University. His research interests focus on Management Information Systems , specifically on using IS/IT to support decision-making and collaboration, and business analytics. Dr. Huang has published over 200 research papers in top-tier journals including MIS Quarterly (MISQ), Journal of Management Information Systems (JMIS), Journal of the Association for Information Systems (JAIS), and IEEE Transactions, with his work being cited by leading journals such as Management Science and ISR. His recent publications demonstrate a clear trend toward integrating emerging technologies like artificial intelligence, blockchain, and IoT with traditional business analytics to create more robust decision support systems. The research spans multiple subfields including machine learning, data visualization, collaborative technologies, and cross-cultural aspects of information systems adoption, reflecting both the breadth and depth of his scholarly contributions. AIS Fellow (2020) - second professor from a Chinese university to receive this honor Sandra Slaughter Outstanding Services Award (2018) - first mainland Chinese scholar to receive this award National Yangtze Chair in Management Information Systems Outstanding SIG Award for AIS SIG-ISAP (2017) Highest Quality Rating from the British Library Outstanding Professional Service Award of ICIS Dr. Huang has secured significant research funding from diverse sources including the Australian Research Council (ARC), National Science Foundation of China, National University of Singapore, Chinese University of Hong Kong, and Harvard University. His leadership extends beyond research to academic service where he has served as PACIS Council Chair (2019-2022), founding President of AIS SIG-ISAP for over 15 years, and key founding member of China AIS Chapter. He currently serves as Vice Chairman of the Teaching Steering Committee for Management Science and Engineering Programs at Guangdong Universities, Honorary Dean of the China Guoxin Credit Big Data Research Institute, and several other high-level advisory roles with Chinese governmental bodies.

Kenton Senaمشاهده پروفایل

مدرس

• Forest Restoration Ecology
• Environmental Science
• Literature of the Environment
• +۳ مورد دیگر

Kenton Sena is a Lecturer at the University of Kentucky's Lewis Honors College, with a focus on forest restoration ecology, environmental science, and ecological literature. He received a B.A. in Biology from Asbury University, an M.S. in Forestry, and a Ph.D. in Integrated Plant and Soil Science (Forest Science emphasis) from the University of Kentucky. His research centers on forest restoration in Appalachia, particularly addressing Phytophthora cinnamomi pathogen dynamics and mine reclamation. Education: B.A. in Biology (Asbury University), M.S. in Forestry (University of Kentucky), Ph.D. in Integrated Plant and Soil Science (University of Kentucky) Sena's scholarly work spans forest ecology, environmental ethics, and invasive species management, with recent publications examining topics like White Oak population dynamics, eDNA monitoring of salamanders, and the environmental legacy in Tolkien's writings. He has contributed to journals including Forests , Science of the Total Environment , and Ecological Restoration . His awards include the 2023 Great Teacher Award. His teaching emphasizes interdisciplinary approaches to environmental challenges, bridging ecological science with literary perspectives on sustainability. While he has not explicitly mentored graduate students in the provided data, his fieldwork and publications demonstrate practical applications for ecosystem management in post-mining landscapes and urban reforestation.