جست‌وجوی استاد و پژوهشگر

• Computational Social Choice
• Voting Theory
• Multi-winner Elections
• +۵ مورد دیگر

Martin Lackner is an Associate Professor in the Databases and Artificial Intelligence department at the Faculty of Informatics, Vienna University of Technology. His research focuses on computational social choice, voting theory, and algorithmic decision making, with particular expertise in multi-winner elections, approval-based voting systems, and participatory budgeting. He maintains an active research program with continuous publications in top AI and theoretical computer science venues. Lackner's research interests center on the theoretical foundations and practical implementations of fair decision-making systems. His work bridges theoretical computer science with practical applications in democratic processes, examining how computational methods can enhance fairness, representation, and efficiency in collective decision making. He has made significant contributions to understanding the computational complexity of voting rules, developing new algorithms for preference aggregation, and establishing axiomatic properties for multiwinner election methods. His recent publications demonstrate a consistent focus on fairness in long-term decision processes, with particular attention to perpetual voting systems, participatory budgeting mechanisms, and approval-based multiwinner rules. The research shows strong theoretical grounding combined with practical implementation considerations, as evidenced by his development of the abcvoting Python package for implementing approval-based voting rules. Lackner has received continuous funding for his research through multiple projects: SuDeMa (2019–2025): Algorithms for Sustainable Group Decision Making (Austrian Science Fund) FAIR (2013–2018): Fixed-Parameter Tractability in Artificial Intelligence and Reasoning (Austrian Science Fund) HINT (2012–2017): Heterogenous Information Integration (Austrian Science Fund) SEE (2012–2016): SPARQL Evaluation and Extensions (Vienna Science and Technology Fund) As an academic advisor, Lackner has supervised doctoral and master's students including J. Maly (2020) on ranking sets of objects and B. Krenn (2019) on algorithms for implicit delegation to predict preferences. His work appears in premier venues including AAAI, IJCAI, AAMAS, and the Journal of Artificial Intelligence Research, demonstrating both theoretical depth and practical relevance to democratic processes and collective decision making.

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

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

• Algorithms and Data Structures
• Big Data
• High-Performance Computing
• +۱۲ مورد دیگر

Francesco Silvestri is an Associate Professor in Computer Engineering at the Department of Information Engineering , University of Padova . He has held previous roles as Assistant Professor (2016-2019), Post-Doc (2015-2016 at IT University of Copenhagen; 2009-2014 at University of Padova), and Part-Time Lecturer (2013-2014 at IT University of Copenhagen). He earned a Ph.D. in Computer Engineering from the University of Padova (2009). Research Focus: Algorithms and data structures for big data processing, high-performance computing, similarity search, graph mining, I/O-efficient algorithms, mobility algorithms, and differential privacy applications. Teaching: Courses include Big Data Computing and Algorithm Design (University of Padova), Advanced Algorithms Seminar (IT University of Copenhagen). Scientific Contributions: ICDT Best Paper Award (2023) for subgraph enumeration ACM SIGMOD Research Highlights (2020) for fair similarity search Developed patented MapReduce methods for triangle enumeration Collaborations and Supervision: Current PhD students: Mariafiore Tognon (2024), Linghan Zeng (2023), Fabrizio Boninsegna (2022). Collaborators include Ninh Pham (University of Auckland), Gianfranco Bilardi, and Rasmus Pagh. Personal Interests: Active in Dottor Clown Padova for hospital clowning, family life with wife Elisa Salvagnin and three children, and cycling advocacy in Padova.

Nathan Backmanمشاهده پروفایل

استاد

• Parallel Processing
• Distributed Systems
• Resource Scheduling
• +۷ مورد دیگر

Nathan Backman is a Professor of Computer Science, actively contributing to research and education in parallel and distributed computing systems. His work centers on resource scheduling for heterogeneous and non-dedicated computing environments, particularly in the context of batch and stream processing. His research led to the development of the Continuous-MapReduce (C-MR) framework, which enables continuous execution of complex MapReduce workflows for real-time stream processing applications. This innovation bridges gaps between traditional batch processing and low-latency stream processing models. Dr. Backman is deeply engaged in computer science education, teaching a broad curriculum including Introduction to Computer Programming, Algorithms, Database Management Systems, Web & Network Programming, Computer Security, Operating Systems, Artificial Intelligence, and Capstone projects. He also leads specialized courses in Competitive Programming and Networks & Distributed Systems. He is a strong advocate for extracurricular learning and regularly organizes and hosts algorithmic programming contests and cybersecurity Capture-the-Flag (attack/defense) competitions. He has served as a judge for the North Central North American region of the ACM International Collegiate Programming Contest (ICPC) and as a problem writer for the ACM ICPC North American Qualifier, demonstrating his commitment to fostering student talent in competitive computing. His areas of expertise span distributed computing, scheduling algorithms, educational outreach in computer science, and cybersecurity training through competition. While no formal awards or publications are listed in the provided text, his contributions to pedagogy and research infrastructure are significant.

Divyakant Agrawalمشاهده پروفایل

استاد

• Big Data
• Cloud Computing
• Distributed Systems
• +۵ مورد دیگر

Divyakant Agrawal is a Distinguished Professor and Chair of the Computer Science Department at the University of California, Santa Barbara (UCSB). He holds a PhD from SUNY Stony Brook and a BS from Birla Institute of Technology and Science. His research focuses on large-scale distributed systems, cloud data management, and database security, with notable contributions to geo-replicated data, privacy-preserving techniques, and social network analysis. Agrawal has led NSF-funded projects on data summarization, hardware acceleration, and wireless sensor networks. He served as VP of Data Solutions at ASK.com and as a visiting senior researcher at NEC Laboratories and the University of Hong Kong. Education: PhD (Computer Science, SUNY Stony Brook), BS (Electrical Engineering, BITS Pilani). Leadership Roles: Chair of UCSB Computer Science Department (1999–2003), Visiting Professorships at National University of Singapore and University of Hong Kong. His research spans distributed algorithms, database concurrency control, and privacy in cloud systems. Key areas include transaction processing, data stream operators, and fraud detection in advertising networks. Agrawal has authored over 300 papers and co-edited journals like the VLDB. He is a Fellow of IEEE, ACM, and AAAS, and has received awards such as the Outstanding Graduate Mentor from UCSB (2011). Professional Contributions: Served on program committees for SIGMOD, VLDB, and IEEE conferences; led editorial roles for Distributed and Parallel Databases and the VLDB Journal. His work on systems like G-Store (cloud data retrieval) and Zephyr (elastic databases) showcases innovations in scalability and autonomy.

• Data Mining
• Big Data
• Fuzzy Systems
• +۹ مورد دیگر

Carlos Jesús Fernández Basso is a Post-Doc Researcher at the Universidad de Granada in the Department of Computer Science. His work focuses on the intersection of Big Data, Data Mining, and Fuzzy Systems, with applications in energy efficiency, social network analysis, and medical diagnostics. His research interests include: Energy-efficient building management using IoT and Big Data Distributed frequent itemset mining and association rule discovery Fuzzy logic for data interpretability in complex systems Text mining and pattern detection in social media Recent publications highlight his contributions to Spark-based distributed algorithms, predictive control systems, and visualization of fuzzy association rules. He collaborates with researchers across disciplines, including applications in healthcare and cybersecurity.

• Algorithms
• Machine Learning
• Deep Learning
• +۶ مورد دیگر

David Li serves as Program Director for Data Analytics and Visualization at the Department of Graduate Computer Science and Engineering, part of the Katz School of Science and Health at Yeshiva University. His research focuses on algorithmic modeling, machine learning, and interdisciplinary applications in fields such as computational chemistry, finance, and ecology. He has published extensively on topics including MapReduce optimization, reinforcement learning frameworks, and ecological forecasting. Notable work includes the Best Regional Paper Award-winning Dual-Path Deep Learning Framework for Video Quality Assessment presented at IEEE ICCE 2025. Education: Details not provided in current text. Courses Taught: Deep Reinforcement Learning, Numerical Methods, Data Acquisition & Management, Independent Study. Grants & Collaborations: Collaborates with students on projects across algorithm design, machine learning, and interdisciplinary applications. His work bridges theoretical computer science with practical challenges in diverse domains, emphasizing scalable solutions for complex systems.

Daniel RANCمشاهده پروفایل

مدرس ارشد

• Big Data
• Cloud Computing
• IP Multimedia Subsystem
• +۵ مورد دیگر

Daniel RANC is a Senior Lecturer at Telecom SudParis, affiliated with the NeSS research group within the SAMOVAR laboratory. His academic work focuses on Big Data processing, cloud computing, and IP Multimedia Subsystem (IMS) networks, with particular expertise in data modeling, NoSQL databases, and quality of service management. His research interests span multiple domains including Big Data analytics, where he has developed preprocessing and modeling tools; cloud computing with predicate-based resource management; and telecommunications with a focus on IMS networks and QoS management. His work demonstrates strong interdisciplinary connections between data science, network engineering, and educational technology. RANC's publication record shows consistent contributions to his fields from 2007 through 2016, with a particular concentration on Big Data processing frameworks and network management systems. His research demonstrates a progression from eLearning systems interoperability toward more complex Big Data and cloud computing challenges. Daniel RANC collaborates extensively with researchers including Hadi Hashem and Brahim Raouyane, with whom he has co-authored numerous publications across multiple conferences and journals. His work appears in reputable venues including Foundations of Computing and Decision Sciences, International Journal of Computer Science and Applications, and various IEEE conferences.

Abdelmounaam Rezguiمشاهده پروفایل

استادیار

• Big Data Management
• Cloud Computing
• Data Analytics
• +۲ مورد دیگر

Abdelmounaam Rezgui is an Assistant Professor in the Department of Computer Science and Engineering at New Mexico Tech, where he directs the Cloud Computing and Big Data (C2BD) Lab. He holds a Ph.D. from Virginia Tech and specializes in developing efficient cloud computing solutions for big data applications. His research focuses on cloud computing optimization, big data management, and distributed systems design. Key areas include: Efficiency improvements for cloud platforms supporting big data workloads Fault-tolerant architectures for distributed systems Social computing and network analysis techniques Service-oriented computing paradigms Recent publications demonstrate strong emphasis on cloud infrastructure optimization, distributed computing frameworks, and AI-assisted system maintenance. Research consistently addresses practical challenges in large-scale data processing across federated environments. Awards and recognitions include: Best student paper award at IEEE ISCC 2016 Best paper award at ICICS 2016 He has advised multiple graduate students including 9 PhD candidates and 2 MS students, with research often supported through cloud computing infrastructure grants. As director of the C2BD Lab, he leads projects investigating volunteered federated clouds, hardware failure prediction systems, and efficient data processing frameworks. The lab collaborates with industry partners on practical cloud computing implementations.

Ashraf Aboulnagaمشاهده پروفایل

استاد

• Database Systems
• Cloud Computing
• Big Data
• +۳ مورد دیگر

Ashraf Aboulnaga is a Professor in the Department of Computer Science and Engineering at the University of Texas at Arlington (UTA). He holds a Ph.D. in Computer Science from the University of Wisconsin-Madison and has held prior roles as Chief Scientist at the Qatar Computing Research Institute, Associate Professor at the University of Waterloo, and Research Staff Member at IBM Almaden Research Center. His research focuses on database systems, cloud computing, and graph analytics, with recent emphasis on cloud-native database architectures and scalable graph processing. Education: Ph.D. (2002), M.S. (1999) from University of Wisconsin-Madison; B.S./M.S. (1993/1996) from Alexandria University. Affiliations: ACM Distinguished Scientist (2015), IEEE Senior Member (2009). Research interests include cloud database systems, distributed transaction processing, and knowledge graph integration. He has authored over 100 publications in top venues like SIGMOD, VLDB, and ICDE, and received Best Paper Awards at SoCC 2015 and VLDB 2011. Grants: Current funding includes NSF travel support for ICDE 2024 and a $1.4M NSF MRI grant for DiscourseLab (2025). Past grants include DOE support for soil microbiome data integration. Service: Associate Editor for VLDB Journal, PVLDB, and Distributed and Parallel Databases. Served as Program Committee member for SIGMOD, ICDE, and EDBT. Industry collaborations include advising on cloud DBMS architectures and distributed systems.

Karuna Pande Joshiمشاهده پروفایل

استاد

• AI/Data Science
• Legal Text Analytics
• Cloud Computing
• +۵ مورد دیگر

Karuna Pande Joshi is a Professor of Information Systems at the University of Maryland, Baltimore County (UMBC), and the Director of the Center for Accelerated Real-time Analytics (CARTA). She also leads the Knowledge, Analytics, Cognitive and Cloud Lab. Her research focuses on AI/Data Science, Legal Text Analytics, Cloud Computing, and Healthcare IT, with notable work on oblivious cloud systems and automating legal documents. Dr. Joshi holds a PhD and MS from UMBC, supported by IBM PhD Fellowships, and a BEng from the University of Mumbai. Her industry experience includes over 15 years as an IT manager at organizations like the International Monetary Fund, managing projects in databases, web content management, and network applications. She has secured grants from NSF, ONR, DoD, Cisco, and GE Research, including an NSF I-Corps award and TEDCO MII grant for commercializing research through a startup. Research interests span cloud usability standards (collaborating with NIST), medical device regulatory compliance, and cybersecurity frameworks. Notable projects include the ALDA cognitive assistant for legal documents and frameworks for secure cloud EHR systems using attribute-based encryption. She advises over 14 graduate students and leads interdisciplinary teams addressing challenges in healthcare IT, cybersecurity, and cloud services. Awards include IBM PhD Fellowship (twice), TEDCO MII grant, and NSF I-Corps award. Her work emphasizes practical solutions for transitioning organizations to cloud systems while addressing compliance, privacy, and security challenges.

Jean-Pierre Corriveauمشاهده پروفایل

دانشیار

• Object-Oriented Testing
• Text Understanding
• Behavior-Driven Development
• +۳ مورد دیگر

Jean-Pierre Corriveau is an Associate Professor and Graduate Director at the School of Computer Science, Carleton University. He holds a Ph.D. in Natural Language Processing from the University of Toronto (1991), an M.C.S. from the University of Ottawa (1984), and a B.Sc. from the University of Ottawa. His research focuses on Object-Oriented Testing (scenario testing, test data generation) and Text Understanding (contextualized and generative comprehension), with recent emphasis on behavior-driven development and acceptance testing of systems with complex scenarios. Dr. Corriveau has extensive industry experience, including roles at Nortel where he contributed to the TELOS project, leading to the ObjecTime startup and subsequent IBM Rational Software Architect tools. His work bridges academia and industry, addressing challenges in software engineering, cybersecurity, and natural language processing. Notable contributions include frameworks for MapReduce optimization, secure localization in wireless sensor networks, and privacy-preserving data anonymization techniques. His publications span topics like AI detection in text, federated learning for NER, intrusion detection systems, and resilient network algorithms. He has advised on graduate studies as Director and contributed to teaching strategies for millennial students through innovative virtual learning environments in platforms like Second Life. His research trends reflect a blend of theoretical advancements in computational linguistics and practical software engineering solutions, with a recurring focus on system reliability, distributed computing, and privacy-preserving technologies. Grants and collaborations are implied through his tool development and industry partnerships, though specific grant details aren’t explicitly listed. Labs/teams associated with his work include the ObjecTime initiative and Carleton’s distributed systems research groups, though no dedicated lab names are mentioned. Future directions emphasize scalable testing methodologies and ethical AI integration in text analysis.

Gösta Grahneمشاهده پروفایل

استاد

• Database Theory
• Data Mining
• Graph Databases
• +۴ مورد دیگر

Gösta Grahne is a Professor in the Department of Computer Science at Concordia University, Montreal, Canada. He holds a Ph.D. from the University of Helsinki and completed a postdoctoral fellowship at the University of Toronto. His research focuses on database theory, data mining, and systems for managing incomplete information and uncertainty. He is affiliated with the Concordia Database Systems Research Group. Education: Ph.D., University of Helsinki (1989) Postdoctoral Fellow, University of Toronto (1990–1992) Research Interests Dr. Grahne's work spans database theory , data integration , and uncertainty management . He has contributed to foundational areas such as regular path queries, XML processing, and probabilistic databases. His recent projects explore provenance tracking and formal methods for data exchange. His publications span over three decades, with notable contributions to conferences like PODS and ICDT. He maintains an active research group and collaborates internationally on theoretical and applied database challenges.

Nikola Nikolovمشاهده پروفایل

دانشیار

• Machine Learning
• Natural Language Processing
• Graph Drawing
• +۵ مورد دیگر

Nikola Nikolov is an Associate Professor in the Department of Computer Science & Information Systems at the University of Limerick. He holds memberships in the Centre for Research Training in Foundations of Data Science, the Data-Driven Computer Engineering Research Centre, and Lero – the Irish Research Centre for Software, reflecting his deep integration into Ireland's national research infrastructure. His research spans Machine Learning, Natural Language Processing, and Graph Drawing, with specialized expertise in deep learning architectures, matrix factorization techniques, and feature extraction methodologies. Current work focuses on multilingual NLP applications for social media analysis, convolutional neural network optimizations, and collaborative filtering systems, demonstrating consistent innovation across computational linguistics and graph theory domains. Analysis of his 2023-2025 publications reveals a dominant focus on combating online toxicity through advanced NLP systems for racism and hate speech detection across multiple languages, alongside significant contributions to computer vision efficiency and recommendation system architectures. His work bridges theoretical advancements with real-world applications in healthcare analytics and autonomous systems. Prof. Nikolov actively contributes to UN Sustainable Development Goals through data science applications while maintaining strong industry connections via research centres focused on software innovation and data-driven engineering solutions.

• Data Science
• Edge Computing
• IoT Security
• +۳ مورد دیگر

Dr. Yalong Wu is an Assistant Professor of Data Science at the University of Houston-Clear Lake's College of Science and Engineering. His research focuses on data-driven solutions for edge computing, IoT security, machine learning applications, and network optimization in distributed systems. Research domains include big data quality assessment, blockchain security frameworks, edge intelligence for video analytics, and neuromorphic computing. Recent publications demonstrate strong emphasis on practical implementations for cybersecurity and network efficiency. Publications show consistent focus on distributed systems challenges, with emerging work on adversarial machine learning, blockchain applications, and edge computing frameworks. Methodological approaches integrate statistical modeling, game theory, and optimization techniques.