جست‌وجوی استاد و پژوهشگر

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.

Mahmoud Parsianمشاهده پروفایل

مدرس

• Distributed Computing
• Big Data Analytics
• Genomics Data Processing
• +۳ 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.

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.

Ben Reedمشاهده پروفایل

استادیار

• Distributed Systems
• Operating Systems
• Cloud Computing
• +۳ مورد دیگر

Benjamin Reed is an Assistant Professor in Computer Engineering at San José State University (SJSU) since 2018, currently on sabbatical during Fall 2024 and Spring 2025. His research spans distributed systems, cloud computing, operating systems, big data, and network storage. Research concentrations include: Distributed coordination systems (ZooKeeper) Lock-free transaction processing MapReduce optimization techniques Disconnected network services Educational tools for systems programming Recent publications focus on distributed storage consistency, educational technology for programming courses, and security protocols for disconnected networks. Earlier industry work includes contributions to ZooKeeper coordination system development at Yahoo! Research and distributed storage systems at Facebook. Significant projects: Directs the SJSU Systems Group researching wildfire storage systems and disconnected data distribution Leads the SJSU ICPC programming competition team Co-founded TECHcellence program creating CS pathways for underrepresented students Developed CodeVal educational tool for programming assignments

Omar Abdul Wahabمشاهده پروفایل

استادیار

• Cybersecurity
• Artificial Intelligence
• Internet of Things
• +۳ مورد دیگر

Omar Abdul Wahab is an Assistant Professor at the Department of Computer Engineering and Software Engineering, Polytechnique Montréal. He holds a PhD in Information Systems Engineering from Concordia University (2017) and completed postdoctoral research at École de technologie supérieure (2017-2018) in collaboration with Rogers and Ericsson. PhD: Concordia University, Montréal, Canada Master's: Lebanese American University, Beirut, Lebanon Bachelor's: Lebanese University, Tripoli, Lebanon His research focuses on Cybersecurity , Artificial Intelligence , and Internet of Things , with expertise in cloud computing security , federated learning , and game-theoretic security models . He has developed frameworks for threat intelligence coalitions , IoT intrusion detection , and trust-aware client selection in distributed systems. His recent publications emphasize federated learning security , IoT anomaly detection , and trust modeling in collaborative environments. Current supervised students include PhD candidates Sarhad Arisdakessian and Osama Wehbi, and Master's student Ranim Rahali.

Maolin Tangمشاهده پروفایل

مدرس ارشد

• Cloud Computing
• Big Data
• Energy Efficiency
• +۵ مورد دیگر

Dr. Maolin Tang is a Senior Lecturer in the School of Computer Science at Queensland University of Technology (QUT). He holds a PhD in Engineering from Edith Cowan University, a Master of Engineering in Computer Science from Chongqing University, and a Bachelor of Engineering in Computer Software from Huazhong University of Science and Technology. His research focuses on Big Data Cloud Computing, Energy-Efficient Data Centres, QoS-Aware Web Service Composition, and SaaS Optimization in the Cloud. Dr. Tang’s work addresses challenges such as MapReduce computation in dynamic cloud environments, energy efficiency in data centres, and composite service optimization. He has supervised numerous PhD students and published extensively in top-tier journals like IEEE Transactions on Industrial Electronics and Expert Systems with Applications. His research combines evolutionary computation techniques, genetic algorithms, and optimization strategies to solve complex computational problems. Education : PhD (Engineering), Edith Cowan University Master of Engineering (Computer Science), Chongqing University Bachelor of Engineering (Computer Software), Huazhong University of Science and Technology Dr. Tang’s research interests emphasize energy-efficient data centre management, cloud resource provisioning, and the application of computational intelligence to real-world challenges. His work on MapReduce in cloud computing aims to improve scalability and QoS guarantees, while his energy-efficient frameworks reduce operational costs and environmental impact. He is actively involved in supervising PhD students and collaborating on projects funded by initiatives like CRC for Smart Services. His publications span topics such as virtual machine placement optimization, SaaS deployment, and algorithmic approaches to dynamic resource management. Dr. Tang teaches advanced courses in algorithms and computational complexity at both undergraduate and postgraduate levels.

Kewei Shaمشاهده پروفایل

دانشیار

• Data Analytics
• Edge Computing
• Security and Privacy
• +۳ مورد دیگر

Dr. Kewei Sha is an Associate Professor at the University of North Texas (UNT) in the College of Information. Previously, he held positions as Associate Professor and Department Chair at Oklahoma City University (OCU) and the University of Houston-Clear Lake (UHCL). He earned a Ph.D. and M.S. from Wayne State University and a B.S. from East China University of Science and Technology. His research focuses on Data Analytics, Edge Computing, Security and Privacy, Blockchain, and IoT, with over $5M secured in research grants from NSF, NASA, and other institutions. He has published over 70 peer-reviewed papers and serves as an Associate Editor for IEEE IoT Journal, Smart Health, and Computing. Dr. Sha has received awards including the UHCL President’s Outstanding Research Award and IEEE Outstanding Leadership Award, and holds senior membership in ACM and IEEE. He chairs conferences like IEEE MOST 2024 and ACM/IEEE SEC 2023. His recent work includes securing NSF funding for edge-based multi-robot systems and NSF Noyce Scholarships for STEM teacher training. Key projects involve EdgeBrain for edge video analytics and ElasticEdge for live video processing. He has pioneered frameworks for data governance, secure cloud storage, and disaster rescue UAV systems. Dr. Sha also develops educational tools like AR therapy systems for autistic children and network forensics labs for teaching institutions.

Milana Grbićمشاهده پروفایل

استادیار

• Bioinformatics
• Computational Biology
• Machine Learning
• +۵ مورد دیگر

Milana Grbić is an Assistant Professor at the Department of Computer and Information Sciences within the Faculty of Science and Mathematics at the University of Banja Luka, Bosnia and Herzegovina. Her academic career spans over a decade, having progressed from Assistant (2013-2017) to Senior Assistant (2017-2020) before becoming Associate Professor in 2020. She teaches diverse courses including Programming Basics, Object-Oriented Programming, Compiler Construction, Bioinformatics, and Introduction to Data Exploration. Dr. Grbić earned her Doctor of Science in Computer Science from the University of Belgrade in 2020, following a Master's degree in Mathematics from the same institution in 2016 and her undergraduate studies in Mathematics and Computer Science from the University of Banja Luka in 2012. Her educational background provides a strong foundation for her interdisciplinary research. Her research focuses on the intersection of computer science, mathematics, and biology, with particular emphasis on bioinformatics, computational biology, and graph theory applications. She develops algorithms for analyzing protein-protein interaction networks, solves complex optimization problems including Roman domination in graphs, and applies machine learning techniques to biological data. Her work bridges theoretical computer science with practical biological applications, creating computational tools that advance our understanding of complex biological systems. Her publication record demonstrates consistent productivity with significant contributions across multiple domains. Her recent work shows a clear trend toward increasingly sophisticated applications of computational methods in biological contexts, particularly in the analysis of protein interaction networks and intrinsically disordered proteins. She has published in high-impact journals across computer science, mathematics, and bioinformatics domains, with several papers appearing in journals with impact factors above 4.0. Mathematical Institute of the Serbian Academy of Sciences and Arts (SASA) Belgrade award for best doctoral thesis in computer science (2020) Active reviewer for Journal of Big Data (2022) and Engineering Applications of Artificial Intelligence (2023) Member of International Society for Biocuration (since 2016) and Serbian Society for Bioinformatics and Computational Biology (since 2018) Dr. Grbić has successfully mentored numerous students through diploma thesis projects and actively participates in international research collaborations. She serves as principal investigator for multiple national research projects including "Analysis of Biological Networks Using Machine Learning Methods" (2024-2025) and has coordinated support for COST Action CA21160 "Non-globular proteins in the era of Machine Learning" in Bosnia and Herzegovina. Her research team collaborates extensively with international partners through various COST Actions focusing on non-globular proteins and computational biology. Her laboratory work centers on developing computational methods for biological network analysis, with particular focus on protein-protein interaction networks and intrinsically disordered proteins. She leads a research team that combines expertise in computer science, mathematics, and biology to tackle complex problems in bioinformatics, participating regularly in international workshops and training schools across Europe.

Greg Speegleمشاهده پروفایل

استاد

• Databases
• Big Data Analysis
• Big Data Query Processing
• +۳ مورد دیگر

Dr. Greg Speegle serves as a Professor in the Department of Computer Science at Baylor University, where he teaches core courses including CSI 3335 (Introduction to Database Design) and CSI 5335 (Advanced Database) within the Online Master's in Computer Science program. His expertise anchors the program's database curriculum and contributes significantly to its academic rigor. His educational foundation includes a Ph.D. in Computer Science from The University of Texas at Austin (1990) and a Bachelor of Science from Baylor University (1984), establishing deep institutional and academic roots. Dr. Speegle's research centers on database systems and big data analysis, with specialized focus on automatic integration of big data tools and query processing optimization. He leads two major initiatives: CT³ (Central Texas Computational Thinking, Coding and Tinkering), which trains public-school teachers to become certified computer science educators, and the development of a general parallelization theory to advance automated computation beyond MapReduce limitations. This dual focus bridges theoretical computer science with practical educational and industrial applications. Analysis of his 2018-2025 publications reveals dominant research trajectories in distributed systems (particularly automated code parallelization), deep learning applications for biometric identification in agriculture, and innovative online regression techniques for streaming data. His work consistently addresses scalability challenges in data processing while expanding computational thinking education through teacher training initiatives. While specific grant details remain undisclosed, his CT³ project demonstrates active engagement in K-12 computer science education reform. No information is available regarding student advising or laboratory facilities in the provided materials.

Mohsen Lesaniمشاهده پروفایل

دانشیار

• Reliability and Security of Software Systems
• Concurrent and Distributed Systems
• Secure Replicated Systems
• +۴ مورد دیگر

Mohsen Lesani is an Associate Professor at the Computer Science and Engineering Department of University of California, Santa Cruz . He obtained his PhD from UCLA , MS in Artificial Intelligence from Sharif University of Technology , and BS in Software Engineering from University of Tehran . His research focuses on reliability and security of software systems , particularly concurrent and distributed systems , with recent work on secure replicated systems and distributed machine learning . NSF CAREER Award (2020) DARPA Young Faculty Award (2022) SIGPLAN Research Highlight (2019) Distinguished Paper Award at OOPSLA 2018 Best Paper Award at ISSRE 2015 His recent publications tackle challenges in automated synthesis of distributed protocols , heterogeneous replication , secure blockchain transactions , and verified RDMA-based data types . He advises PhD students Xiao Li , Eric Chan , Javad Saber-Latibari , and Tejas Mane in the Safe and Secure Software (S3) lab . He has taught courses on Distributed Systems , Parallel Programming , and Compiler Design .