جستجوی استاد راهنمای دانشگاهی

Daniel Abadi is the Darnell-Kanal Professor of Computer Science at the University of Maryland, College Park. He leads the Data Systems Lab at Maryland (DSLAM) and is widely recognized for his groundbreaking contributions to database system architecture and implementation. Previously, he was a faculty member at Yale University where he received the Provost's Teaching Prize. Abadi's research primarily focuses on database system architecture, particularly at the intersection with scalable and distributed systems. He is best known for developing the storage and query execution engines of the C-Store prototype (a column-oriented database system commercialized by Vertica and later acquired by Hewlett-Packard), HadoopDB research (commercialized by Hadapt and acquired by Teradata), and deterministic distributed transactional systems like Calvin (currently being commercialized by Fauna). His work bridges theoretical innovation with practical industrial impact. Analysis of his recent publications reveals a consistent trajectory toward solving fundamental challenges in distributed database systems. His research has evolved from foundational work on column-stores and hybrid database architectures to cutting-edge innovations in geo-replicated transactions, concurrency control mechanisms, and the integration of machine learning with database systems. The trend shows increasing focus on practical implementations that address real-world scalability and performance challenges in large-scale data processing environments. ACM Fellow Churchill Scholarship recipient NSF CAREER Award winner Sloan Research Fellowship recipient VLDB Best Paper Award winner Two VLDB Test of Time Awards (for C-Store and HadoopDB) 2008 SIGMOD Jim Gray Doctoral Dissertation Award 2013-2014 Yale Provost's Teaching Prize 2013 VLDB Early Career Researcher Award Professor Abadi has successfully mentored several PhD students, most notably Alexander Thomson and Jose Falerio, both of whom won the prestigious SIGMOD Jim Gray Doctoral Dissertation Award for their work under his supervision. His research has been generously supported by multiple NSF grants including BIGDATA awards and other funding mechanisms that have enabled significant advances in database technology. He actively collaborates with industry partners, with several of his research projects leading directly to commercial products. At the University of Maryland, Abadi directs the Data Systems Lab at Maryland (DSLAM), which focuses on developing innovative database technologies that address contemporary challenges in data management. The lab's research spans distributed transaction processing, database architecture, and the integration of database systems with emerging computing paradigms. Notable projects include SLOG (Serializable, Low-latency, Geo-replicated Transactions), which eliminates traditional tradeoffs in distributed database design, and ongoing work in deterministic database systems that provide strong consistency guarantees without sacrificing performance.

• Database system architecture
• Distributed database systems
• Column-oriented databases
• +۵ مورد دیگر

Amin Mesmoudi serves as Associate Professor in Data Engineering at the University of Poitiers' IUT (Institut Universitaire de Technologie), with dual laboratory affiliations at LIAS-ENSIP (Poitiers campus) and LIAS-ISAE-ENSMA (Chasseneuil campus). His research bridges theoretical database systems with practical large-scale data engineering challenges, particularly in semantic web technologies and machine learning applications. The laboratory maintains physical presences at both ENSIP's Bâtiment B25 in Poitiers and ISAE-ENSMA's Téléport 2 facility in Chasseneuil, facilitating cross-institutional collaboration. Mesmoudi's research program centers on scalable data management systems, with three interconnected pillars: (1) RDF and graph-based query optimization techniques for billion-triple datasets, (2) machine learning integration for spatial query performance and anomaly detection, and (3) explainability frameworks for complex black-box models. His work demonstrates consistent evolution from foundational database systems (2011-2016) toward contemporary AI-driven data engineering, particularly evident in his 2023-2025 publications on temporal dependency preservation and co-selection explainability. The Data Engineering team within LIAS laboratory provides the primary research context for these investigations. Publication analysis reveals strong methodological continuity in addressing scalability bottlenecks across database paradigms. Early work focused on SQL-on-MapReduce benchmarking for astronomy databases (2015-2016), transitioning to specialized RDF processing frameworks (2019-2021), and culminating in current hybrid approaches combining temporal modeling with machine learning (2023-2025). Key technical themes include fragmentation strategies for distributed data, optimizer feedback mechanisms, and graph-based query acceleration - all targeting real-world performance constraints in big data environments. As a core member of LIAS laboratory's Data Engineering team, Mesmoudi contributes to France's national research infrastructure in computer science and automation systems. The laboratory's dual-university structure enables unique cross-pollination between University of Poitiers' academic programs and ISAE-ENSMA's engineering specialization, with Mesmoudi's work exemplifying this synergy through applications spanning astronomy databases to wireless sensor networks.

• Data Engineering
• Machine Learning
• Anomaly Detection
• +۵ مورد دیگر

Peter McBrien is an Associate Professor in the Department of Computing at Imperial College London, affiliated with the Faculty of Engineering. He holds dual affiliations with the Distributed Software Engineering group. His research focuses on conceptual modeling, ontology engineering, database systems, and temporal data management. He has been active in advancing techniques for data visualization, semantic web technologies, and relational database integration. Key research areas include: Ontology extraction from relational databases Type inference in transactional systems Schema transformation between heterogeneous models Peer-to-peer data integration protocols Temporal database systems His publication trends emphasize integration of heterogeneous data sources through formal methods, with notable contributions to OWL ontology implementation, hypergraph data models, and benchmarking big data query languages. Recent work focuses on Spark-based semantic reasoning and distributed knowledge exchange systems. Peter McBrien's research has been supported through Imperial College's infrastructure, with ongoing contributions to the AutoMed data integration framework and RoDEx protocols for unreliable networks. His work bridges theoretical foundations of data management with practical implementations in distributed systems.

• Information Systems
• Artificial Intelligence
• Data Visualization
• +۴ مورد دیگر

Dr. Sikha S Bagui is a Distinguished University Professor in the Department of Computer Science at the Hal Marcus College of Science and Engineering, University of West Florida . She served as the former Chair of Computer Science and was the Founding Director of the Center for Cybersecurity . Her research spans database design, Big Data analytics, machine learning, and cybersecurity . Research Focus: Machine Learning, Data Mining, Network Traffic Analysis, Graph Databases, and Resampling Techniques for Imbalanced Data Awards: Askew Fellow (2018–2021), multiple Excellence in Teaching and Distinguished Research Awards (2001–2024) Contributions: Authored books on databases/SQL (translated internationally), developed the UWF-ZeekData datasets for cybersecurity research, and served as Associate Editor for multiple journals Tools & Frameworks: Active in Hadoop, Spark, Memgraph, and MITRE ATT&CK-based threat modeling Publication Trends: Recent work focuses on MITRE ATT&CK datasets , graph-based cybersecurity , resampling rare attacks , and educational impacts in computing . Her research bridges theoretical and applied domains, including clinical decision support systems and K-12 computer science education. Scientific Awards: Askew Fellow (Reubin O’D. Askew Institute for Multidisciplinary Studies, 2018–2021) Excellence in Teaching and Advising Award (UWF, 2012) Distinguished Research and Creative Activities Award (UWF, 2007, 2012) Excellence in Undergraduate Teaching and Advising Award (UWF, 2001–2006) Leadership & Service: Directed the Center for Cybersecurity, contributed to journals as Associate Editor, and engaged in initiatives like NCWIT Aspirations in Computing and the Association for Women in Computing.

• Big Data Analytics
• Machine Learning
• Data Mining
• +۷ مورد دیگر

Todor Ivanov is a Senior Researcher at the Database and Information Systems (DBIS) Group, Goethe University Frankfurt. His research focuses on Big Data systems, distributed computing, and performance optimization. He holds a Doctorate in Natural Sciences (Computer Science) from Goethe University (2019), a Master's in Distributed Software Systems from TU Darmstadt (2014), and a Bachelor's in Computational Engineering from TU Darmstadt (2009). His work includes designing and evaluating benchmarks for Big Data platforms (e.g., CoreBigBench, ABench), analyzing SQL-on-Hadoop performance, and exploring Spark Structured Streaming. He has published extensively at venues like DBTest, ICPE, and WBDB, contributing to advancements in distributed system benchmarking and cloud resource management. Teaching activities include leading hands-on Big Data labs and workshops since 2014, emphasizing practical training in Hadoop, Spark, and data-intensive applications.

• Big Data
• Distributed Systems
• Benchmarking
• +۴ مورد دیگر

Mohamed Y. Eltabakh is an Associate Professor in the Computer Science Department at Worcester Polytechnic Institute (WPI), with affiliations in the Data Science and Bioinformatics & Computational Biology programs. He holds a Ph.D. from Purdue University (2010) and has postdoctoral experience at IBM Almaden Research Center (2010-2011). His research focuses on database systems, big data analytics, query optimization, and scientific data management. He has led projects like InsightNotes (annotation management), Redoop (MapReduce optimizations), and CoHadoop (Hadoop data placement). Prof. Eltabakh teaches courses in databases, algorithms, and Hadoop infrastructure at both undergraduate and graduate levels. He has secured NSF funding ($189,952) for large-scale data analytics research and contributed to over 30 peer-reviewed publications in top-tier conferences/journals (e.g., SIGMOD, ICDE, VLDB). His work integrates with WPI’s interdisciplinary programs, including the Healthcare Delivery Institute and Data Science graduate program. Education: Ph.D. Computer Science, Purdue University, 2010 M.S. Computer Science, Purdue University, 2005 M.S. Computer Science, Alexandria University, 2001 B.S. Computer Science, Alexandria University, 1999 Research Interests: Database Management Systems Big Data Analytics & Indexing Scientific Data Curation Hadoop Framework Enhancements Metadata Management Large-Scale Query Optimization Recent Articles Trends: Focusing on scalable indexing solutions for time series data (ChainLink, TARDIS), correlation-driven query optimization in big data environments, and efficient graph processing algorithms (Bermuda). His work bridges theoretical database systems with practical industrial challenges like Teradata’s query execution optimizations and annotation management in relational databases. Awards & Grants: NSF CRI-1305258 Award: Compute Infrastructure for Large-Scale Data Analytics ($189,952, 2013–2015) Advising & Grants: Advised 8 graduate students (MSc/PhD) since 2012. Collaborated with industry partners like Teradata Labs and Teradata Incorporation for applied research projects. Labs & Teams: Member of the Database Systems Research Group (DSRG), Bioinformatics & Computational Biology program (BCB), and WPI’s Data Science program leadership (Associate Director).

• Database Management Systems
• Big Data Analytics
• Query Processing and Optimization
• +۵ مورد دیگر

Olamilekan Shobayo is a dedicated lecturer at the School of Computing and Digital Technologies within the College of Business, Technology and Engineering. With over seven years of teaching experience, he specializes in data science, big data technologies, and healthcare analytics. He teaches modules including database management, big data analysis, and project management, equipping students with skills in R, Python, SQL, Hadoop, and Tableau. His students have secured roles as data analysts, DB administrators, and business analysts in the UK and internationally. His research focuses on AI applications in healthcare, particularly in emergency medicine, and includes developing a CNN model for classifying pediatric wrist fractures using infrared thermal imaging and a random forest algorithm for stroke prediction. He also explores clustering techniques in retail analytics and sentiment analysis for financial markets. Shobayo actively reviews for journals like Electronics , Algorithms , and Diagnostics , contributing to advancing AI research and clinical practice. Key Research Areas: Medical image analysis, stroke prediction, retail customer segmentation, and AI in emergency care. Teaching Roles: Courses include MSc Computing, Big Data Analytics, and Degree Apprenticeships. Shobayo’s work bridges AI development and clinical needs, with publications in peer-reviewed journals and conferences. He also mentors through The Brilliant Club, designing STEM courses for young learners.

• Healthcare Data Analytics
• Machine Learning
• Big Data Technologies
• +۴ مورد دیگر

YongChul Kwon is a researcher affiliated with the University of Washington , focusing on distributed computing, database systems, and big data optimization. His work addresses critical challenges in MapReduce frameworks, including skew management, fault tolerance, and performance efficiency. Key research contributions include SkewTune for dynamic skew mitigation in MapReduce, SnipSuggest for SQL autocompletion, and comparative analyses of Hadoop's scalability. He has published in venues like VLDB , SIGMOD , and IEEE Data Engineering Bulletin , often collaborating with Magdalena Balazinska and Bill Howe. Recent work (2013–2016) explores iterative MapReduce frameworks, Hadoop's workload evolution, and parallel processing of scientific user-defined functions. Earlier contributions (2008–2010) include fault-tolerant stream processing, clustering algorithms for astrophysics, and collaborative query management systems. His research bridges distributed systems, database engineering, and scientific computing, with implications for cloud infrastructure and large-scale data analytics.

• Distributed Computing
• Database Systems
• Big Data
• +۷ مورد دیگر

Federica Mandreoli is a Full Professor at the Department of Physical, Computer and Mathematical Sciences, University of Modena and Reggio Emilia. She specializes in databases, big data analytics, and information management, with a focus on non-traditional data structures and advanced data processing techniques. Teaches Databases , Big Data Analytics , and Information Management courses Office: Via Campi, 213/A (former Mathematics campus) Available for student reception on Tuesdays (2:30-4:00 pm) or by appointment Her research spans database design, NoSQL systems, full-text processing, graph analytics, and web ranking models like PageRank. She emphasizes practical applications through lab work and projects, integrating theoretical knowledge with hands-on implementation skills in languages like Python and SQL. Notable course components include: Big Data architectures (Hadoop, Spark) Text analytics with NLTK and Spacy Graph databases (Neo4j) and query languages (Cypher) Information retrieval models (Boolean, vector, probabilistic) Contact: federica.mandreoli@unimore.it | Office code: MO-18-02-005 | ORCID: 0000-0002-8043-8787

• Databases
• Big Data Analytics
• Information Management
• +۴ مورد دیگر

Chandan Reddy is a Professor in the Department of Computer Science at Virginia Tech. He holds a Ph.D. from Cornell University and an M.S. from Michigan State University. His research focuses on Machine Learning, Natural Language Processing, and their applications in Healthcare, Software, Transportation, and E-commerce. His work has been funded by NSF, NIH, DOE, DOT, and industries, resulting in over 200 peer-reviewed publications. Notable awards include the Best Application Paper at SIGKDD 2010 and the Franz Edelman Award finalist in 2011. Education: Ph.D., Computer Science, Cornell University M.S., Computer Science, Michigan State University Research Interests: Machine Learning and NLP applied to healthcare analytics, big data systems, and complex data challenges. His work emphasizes scalable algorithms, fairness in AI, and generative models. Recent projects include scientific equation discovery via LLMs and bias mitigation in language models. Publications Trends: Recent work explores LLM-driven reasoning, hyperbolic neural networks, and healthcare informatics. Key areas include data privacy (e.g., synthetic medical data), interpretable models (e.g., time-series clustering), and graph-based methods for product search. Awards: Best Application Paper Award at ACM SIGKDD 2010 Best Poster Award at IEEE VAST 2014 Best Student Paper Award at IEEE ICDM 2016 INFORMS Franz Edelman Award Competition Finalist 2011 Advising & Grants: Advises graduate students on machine learning research, with grants supporting interdisciplinary projects in healthcare, transportation, and AI ethics. Leads the GraphZoo toolkit for hyperbolic GNNs and collaborates on large-scale medical data initiatives. Labs/Teams: Directs research in Virginia Tech’s Data Analytics Lab, focusing on big data platforms, healthcare analytics, and explainable AI systems. Active in developing open-source tools like GraphZoo for graph neural networks.

• Machine Learning
• Natural Language Processing
• Healthcare Analytics
• +۵ مورد دیگر

Minyi Guo is a Chair Professor and Head of the Department of Computer Science and Engineering at Shanghai Jiao Tong University (SJTU), China. Previously, he served as Professor and Department Chair at the School of Computer Science and Engineering, University of Aizu, Japan. Dr. Guo received his BSc and ME degrees from Nanjing University, China in 1982 and 1986, and his PhD from University of Tsukuba, Japan in 1998. Dr. Guo's educational background includes: BSc in Computer Science, Nanjing University, China (1982) ME in Computer Science, Nanjing University, China (1986) PhD in Computer Science, University of Tsukuba, Japan (1998) Dr. Guo's research spans multiple areas in computer science, with a primary focus on parallel/distributed computing , compiler optimizations , cloud computing , database systems , and big data . He has published over 400 papers including approximately 150 in major journals and 250 in international conferences, with more than 60 papers in IEEE/ACM transactions and over 100 papers in prestigious conferences. Dr. Guo has also authored 7 books (4 in English, 3 in Chinese) and received 5 best/highlight paper awards from international conferences. Dr. Guo's publication record demonstrates strong contributions across multiple domains of computer systems research. His recent work shows particular emphasis on big data processing, edge computing, graph neural networks, and data center optimization. The publications reveal a consistent trajectory of impactful research in parallel and distributed systems, with increasing focus on AI/ML applications and blockchain technologies in more recent years. Dr. Guo has received numerous prestigious awards and honors: State Technological Invention Award of China (second class award, 2019) Shanghai Technological Invention Award (first class award, 2018) IEEE Technical Committee on Scalable Computing Award for Excellence in Scalable Computing (2018) Ministry of Education Natural Science Award (first class award, 2017) IEEE Fellow (2017) Chief Scientist of National Basic Research Project (973 Program, 2014) Recruitment Program of Global Experts (2010) Excellent Academic Leaders of Shanghai (2010) National Science Fund for Distinguished Young Scholars (2007) As an academic leader, Dr. Guo has served as Department Head for ten years, managing a department with over 100 faculty members and 1000+ students. Under his leadership, the department was promoted to the top tier in China and ranked among the top 40 in the world. He has secured significant research funding, including serving as Chief Scientist of the prestigious 973 Program in 2014 and receiving the National Science Fund for Distinguished Young Scholars in 2007. Dr. Guo has also been selected for the Recruitment Program of Global Experts in China (2010). Dr. Guo actively contributes to the academic community as an associate editor of IEEE Transactions on Parallel and Distributed Systems, IEEE Transactions on Cloud Computing, and Journal of Parallel and Distributed Computing. He has served as General/Program Chair for IEEE conferences and delivered keynote speeches at well-established conferences. His research group has developed practical technologies with industry impact, including 28 licensed patents, some of which have been transferred to companies like Alibaba.

• Database systems
• Cloud computing
• Parallelizing compilers
• +۴ مورد دیگر

Mahmoud Parsian is a Lecturer in the Department of Information Systems & Analytics at Santa Clara University's Leavey School of Business. He holds a Ph.D. and M.S. in Computer Science from Iowa State University. As a software architect, he leads Illumina's Big Data team, focusing on large-scale genome analytics using Java, Spark, and distributed computing. His expertise includes distributed algorithms, genomics data processing, and open-source tools. Research Interests: Parsian specializes in big data systems, scalable distributed computing, and software architecture for genomics. His work emphasizes practical solutions for handling large datasets in bioinformatics and enterprise environments. Publications: He has authored five technical books, including Data Algorithms with Spark (2022) and JDBC Recipes (2005), which provide actionable solutions for developers working with databases and distributed systems. Awards: Ask Jeeves Bright Star Award Octopus Award for successive release of the Octopus software Cisco Systems Leadership Award Individual Achievement Award Professional Contributions: Parsian's work bridges academia and industry, combining teaching with real-world software development. He actively contributes to open-source projects and maintains a focus on practical, scalable solutions for complex data challenges.

• Distributed Computing
• Big Data Analytics
• Genomics Data Processing
• +۳ مورد دیگر

Dr. Sikha Bagui is a Distinguished University Professor in the Department of Computer Science at the University of West Florida , within the Hal Marcus College of Science and Engineering . She previously served as Chair of the department and Founding Director of the Center for Cybersecurity. Her research spans Big Data Analytics, Machine Learning, Data Mining, and Database Design, with extensive publications and funded projects from NSF and NSA. Ed.D. in Curriculum & Instruction: Math & Stat / Science / Computer Science, University of West Florida M.B.A., University of Toledo B.S., Cuttington University (Liberia) Dr. Bagui's research centers on data-intensive computing , focusing on scalable algorithms for Big Data analytics, optimization in distributed environments (Hadoop, Spark, Hive), and applications in cybersecurity such as intrusion detection and phishing classification. She is particularly known for her work in data preprocessing, association rule mining, and improving classifier performance on imbalanced datasets. Her recent publications reveal a strong trend toward cybersecurity applications of machine learning , leveraging frameworks like MapReduce and Spark for scalable solutions. Topics include network traffic classification, load balancing in FP-Growth, and resampling techniques for intrusion detection. Her work bridges theoretical algorithm development with practical implementation in real-world Big Data systems. Distinguished University Professor Askew Fellow NSF CSForALL Grant ($300,000) NSA NCAE Grant ($375,511) Dr. Bagui has successfully led multiple federally funded research projects and mentored numerous students through research and academic programs. While specific advisees are not listed, her leadership in research groups and outreach initiatives like Women in Computing demonstrates strong mentorship. She has also authored influential textbooks used internationally. Her lab and research efforts are aligned with the UWF Smart Home Research , AI Research Group , and High Performance Computing Research , contributing to interdisciplinary innovation.

• Big Data Analytics
• Machine Learning
• Data Mining
• +۷ مورد دیگر

Dr. Xiaoyi Lu is an Associate Professor in the Department of Computer Science & Engineering at the University of California, Merced (UC Merced), where he founded and directs the Parallel and Distributed Systems Laboratory (PADSYS Lab). He is affiliated with the AgAID Institute since 2023 and has authored over 170 publications, including ten Best Paper Awards or Nominations (e.g., SC 2019, IPDPS 2024). His research outcomes like OpenDOTA and MVAPICH2-Virt are used by hundreds of organizations globally. Research Interests: He focuses on scalable parallel systems for HPC, Big Data, AI, Cloud, and Edge Computing, leveraging advanced technologies like RDMA/PMEM/NVMe/GPU/DPU. His work bridges high-performance computing with applications in precision agriculture, biostatistics, and digital twin technology. Article Trends: Recent publications emphasize DPU offloading, compression-optimized collective communication, error detection in HPC, and scalable Bayesian group testing. Topics span GPU clusters, NVMe-over-Fabrics, and adaptive networks for LLM training, reflecting his expertise in heterogeneous architectures and distributed systems. Scientific Awards: NSF CAREER Award (2024) Amazon Research Award (2023) Google Research Award (2022) Meta Faculty Research Award (2022) Multiple Best Paper Nominations Professional Activities: He serves as Associate Editor for Frontiers in High Performance Computing and organizes tracks at SCAsia and HiPC. His leadership in PADSYS Lab drives innovation in systems for social good.

• Parallel and Distributed Computing
• High-Performance Communication and I/O Technologies
• Big Data Analytics
• +۴ مورد دیگر