جست‌وجوی استاد و پژوهشگر

Drago Žagarمشاهده پروفایل

استاد

• Communication Networks and Protocols
• Codes and Coding
• Quality of Service in Networks
• +۷ مورد دیگر

Drago Žagar is a Full Professor at the Faculty of Electrical Engineering, Computer Science and Information Technologies (FERIT), University of Osijek, where he also serves as Head of Department. He has been permanently appointed since 2015 and has a long-standing affiliation with the institution since 1990, with brief periods at other units within the same university. His educational background includes a B.Eng. (1990), M.Sc. (1995), and Ph.D. (2002), all from the Faculty of Electrical Engineering and Computing in Zagreb. His research spans key areas in modern networking and information systems, including communication networks and protocols , quality of service , cybersecurity , and IoT applications in agriculture and logistics . His recent work emphasizes energy-efficient monitoring, anomaly detection using machine learning, smart labels for supply chains, and adaptive video streaming. The 15 most recent publications reflect a strong trend in applying sensor networks and data analytics to real-world problems in agriculture, network security, and multimedia systems. The interdisciplinary nature of his work bridges computer networks, industrial informatics, and smart technologies. He has led significant projects such as Smart Label for Measuring and Monitoring Product Storage , IoT Ecosystems for Agriculture , and Boosting Telecommunications Engineering Education under Erasmus+. He also managed national and EU-funded initiatives focused on broadband access in rural areas and IPv6 security. He has mentored numerous students and authored key educational materials, including laboratory manuals in computer networks and electronics. His teaching includes courses on Computer and Mobile Networks and Computer Systems Security . He is actively engaged in research, with publications in journals such as Sensors , Electronics , Journal of Industrial Information Integration , and Telecommunications Systems . His work is indexed in Google Scholar, Scopus, Web of Science, and CRORIS.

Michal Slupczynskiمشاهده پروفایل

پژوهشگر

• Human-Computer Interaction (HCI)
• DevOps, MLOps, LLMOps
• Workflow Automation
• +۱۵ مورد دیگر

Michal Slupczynski is a PhD student and research assistant at the Chair for Computer Science 5 (Informatik 5) within the Department of Computer Science, Faculty of Mathematics, Computer Science and Natural Sciences at RWTH Aachen University. He earned his Master’s degree in Software Systems Engineering from the same institution in 2020 and has been actively contributing to research and teaching since December 2020. His research interests include human-computer interaction (UI/UX, personal fabrication), DevOps/MLOps/LLMOps, workflow automation, decentralized P2P communities, augmented and virtual reality (especially collaborative environments), interactive storytelling, agile project management, digital transformation, gamification, and games. He applies these interests in the context of educational technology, psychomotor skill acquisition, and learning analytics. His recent publications reflect a strong focus on sensor-based motion analysis, AI-driven feedback systems, immersive learning environments, and explainable AI. He has contributed to advancements in sports motion analysis using the Kabsch algorithm, cloud-based feedback for psychomotor training, citation recommendation chatbots, and MLOps pipelines for educational data. Michal is actively involved in teaching, offering courses such as Datenbanken und Informationssysteme and seminars on Social Computing, Web Science, and High-Tech Entrepreneurship. He has supervised numerous thesis projects on topics ranging from LLMs in maintenance and legacy system modernization to blockchain escrow systems and gamification in education. He is engaged in key research projects including MILKI-PSY (Multimodal Immersive Learning with AI for Psychomotor Skills), TrainSpot (Train-the-Trainer HotSpot), Blockchain4DatenMarktplatz.NRW, and las2peer, highlighting his work in decentralized systems and technology-enhanced learning. He has also served as an editor for workshop proceedings and presented at international conferences such as EC-TEL and CollabTech.

• Epistemology
• Research Methodology
• Social Media Analytics
• +۵ مورد دیگر

Cláudia Freire is a Postdoctoral Researcher at the Centre for Research in Communication and Culture (CECC) within the Faculty of Human Sciences at the Catholic University of Portugal. Her work intersects epistemology, digital methodologies, and sustainable development goals, with a focus on social media analytics and civil diplomacy. Her academic credentials include: Ph.D. in Communication Sciences, University of São Paulo (2015) Master's in Communication Sciences, University of São Paulo (2009) Bachelor's in Arts and Humanities, University of Minas Gerais (1999) Bachelor's in Communication Sciences (Marketing), Catholic University of Minas Gerais (1997) Freire's research centers on epistemological foundations of digital communication, specializing in social media monitoring techniques, algorithmic analysis of television fiction audiences, and data visualization methodologies. She critically examines how platform algorithms shape online discourse while advancing civil diplomacy frameworks for digital inclusion. Her work integrates UN Sustainable Development Goals through communication science applications. Analysis of her 2014-2023 publications reveals consistent evolution from foundational epistemology studies toward applied streaming platform analytics. Key trends include methodological innovation in social media monitoring, algorithmic influence mapping in television fiction fandoms, and visual representation of audience data – demonstrating increasing technical sophistication while maintaining critical theoretical grounding. Freire actively contributes to academic governance through thesis examinations and journal peer review. Recent activities include: Examination of doctoral theses on algorithmic communication gatekeepers (2014) Peer review for MATRIZes and Conexión journals (2023) Collaboration with Observatório Ibero-Americano da Ficção Televisiva (OBITEL) Her current postdoctoral project investigates diachronic methodological shifts in television drama research through CECC's interdisciplinary framework, leveraging OBITEL's international network for comparative analysis of Ibero-American media studies.

H. Trautmannمشاهده پروفایل

استاد

• Data Science
• Machine Learning
• Evolutionary Optimization
• +۶ مورد دیگر

H. Trautmann is a Professor of Machine Learning and Optimization at the Paderborn University and holds a Guest Professor position in Data Science: Statistics and Optimization at the University of Twente . Affiliated with the European Research Center for Information Systems (ERCIS) , they lead the ERCIS Competence Center for Social Media Analytics and co-head the ERCIS Research Cluster on Data Science and AI . Research focuses on Data Science , Evolutionary Optimization , Algorithm Selection , and Social Media Analytics , integrating computational methods with social interaction analysis. Expertise spans Machine Learning , Multiobjective Optimization , Data Stream Mining , and Continuous Optimization .

Gianluca Martinoمشاهده پروفایل

پژوهشگر

• Computer Engineering
• Embedded Systems
• Machine Learning
• +۳ مورد دیگر

Gianluca Martino is a Research Assistant at the Technische Universität Hamburg , affiliated with the Institute of Embedded Systems and the Computer Engineering group. His work focuses on hardware acceleration, formal verification, and machine learning applications in digital systems. Role: Research Assistant Institution: Technische Universität Hamburg Department: Computer Engineering Research Interests: Gianluca's research spans hardware acceleration for machine learning, FPGA-based implementations, anomaly detection in critical systems, and formal verification of reactive systems. He explores the intersection of digital design and artificial intelligence. Publications Trends: Recent work emphasizes real-time machine learning on FPGAs, anomaly detection for particle accelerators, and formal verification of hardware using temporal logic. Key areas include embedded systems, neural network deployment, and high-integrity digital design. Contact: Email: gianluca.martino@tuhh.de

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.