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Sourav DuttaView profile

Associate Professor

• High-performance Computing
• Hardware/Software Co-design
• Machine Learning
• +3 more

Sourav Dutta is an Associate Professor in the Department of Computer Science at Ramapo College of New Jersey, part of the School of Theoretical and Applied Science (TAS). He joined the institution in 2017. His research focuses on high-performance computing, hardware/software co-design, and machine learning. Dutta holds a PhD in Computer Engineering from Southern Illinois University Carbondale. His educational background includes advanced studies in computer engineering, with a specialization in parallel computing systems. His work bridges theoretical computer science with practical applications in distributed systems, resource optimization, and intelligent algorithms. Key research areas include multithreaded systems performance analysis, collaborative recommendation systems, and bioinformatics applications using big data techniques. His publications span venues like IEEE Transactions on Parallel and Distributed Systems, IEEE Big Data, and ACM Transactions on Parallel Computing. Dutta teaches courses such as Computer Science I and II, Data Structures and Algorithms, Operating Systems, and Big Data Computing. His contributions to the field include innovations in concurrent system design, IoT resource allocation, and energy-efficient computing architectures.

• Big Data
• Machine Learning
• Distributed Systems
• +6 more

Sonia Ikken Benali is a Researcher-Lecturer at CESI (Lille, France), affiliated with the Engineering and Numerical Tools research team. Her work focuses on Big Data, Machine Learning, Distributed Systems, Business Intelligence, and Optimization Problems. She teaches courses in Data Storage/Processing, Business Intelligence, and Operations Research at the Integrated Preparatory Program and Engineering Program levels. Education : Ph.D. in Efficient Placement Design for Big Data in Clouds (Télécom SudParis - Institut Mines-Télécom - Pierre and Marie Curie University, 2018) M.Sc. in Networks and Distributed Systems (Abderrahmane Mira University, Algeria, 2011) State Engineer in Advanced Information Systems (Abderrahmane Mira University, 2010) Her research emphasizes optimizing cloud storage efficiency, cost reduction in distributed systems, and scheduling algorithms for big data workflows. Key contributions include collaborative cloud storage frameworks and Markov model-based I/O scheduling for MapReduce tasks. Lab/Team : Part of the Engineering and Numerical Tools research group at CESI, focusing on practical applications of advanced computational methods.

Shikharesh MajumdarView profile

Professor

• Parallel and Distributed Systems
• Performance Modelling
• Cloud Computing
• +5 more

Dr. Shikharesh Majumdar is the Chancellor’s Professor and Director of the Real-Time and Distributed Systems Research Centre at Carleton University's Department of Systems and Computer Engineering. He holds a Ph.D. from the University of Saskatchewan. His research focuses on parallel and distributed systems, performance modeling, cloud computing, and IoT. He teaches undergraduate and graduate courses in operating systems and distributed systems engineering. His work includes pioneering contributions to resource management on clouds and grids, energy-efficient systems, and middleware for distributed computing. He has supervised numerous graduate and undergraduate students, many of whom have received awards such as the University Senate Medal and NSERC scholarships. Dr. Majumdar has authored over 100 peer-reviewed publications and delivered keynotes at major conferences like IEEE Cloud and ACM ICDCN. His research addresses challenges in big data analytics, sensor-based systems, and edge computing. He actively contributes to industry through collaborations with organizations like TELUS and Nortel. His current projects include optimizing resource allocation for MapReduce jobs, enhancing IoT systems, and developing frameworks for complex event processing. The Real-Time and Distributed Systems Research Centre under his leadership explores cutting-edge solutions for distributed computing challenges.

• Data Analytics
• Machine Learning
• Natural Language Processing
• +6 more

Dr. Jaya Lakshmi Tangirala is a Lecturer in the College of Business, Technology and Engineering, affiliated with both the School of Engineering and Built Environment and the School of Computing and Digital Technologies. She holds a Ph.D. in Data Analytics and has over two decades of experience in computer science and engineering education. Her research focuses on Data Analytics with applications in link prediction in social networks, recommender systems, Natural Language Processing, and healthcare informatics. She teaches courses such as BSc Computer Science and MSc Software Engineering, delivering modules like Algorithms and Data Structures and Advanced Software Engineering. Her research explores cutting-edge technologies for societal impact, particularly in education, finance, and healthcare sectors. Recent work includes advancements in matrix factorization for recommendations and hypergraph-based link prediction. Over 40 publications span journals like IEEE Access, Entropy, and peer-reviewed conferences such as IEEE ASONAM and ICNSC. Dr. Tangirala’s contributions bridge theoretical insights and practical applications, emphasizing data-driven solutions. Her work on phishing URL prediction, medical mutation classification, and IoT security protocols highlights interdisciplinary innovation. She actively collaborates with industry and academia to address real-world challenges through data analytics.

Christos DoulkeridisView profile

Professor

• Parallel and Distributed Query Processing
• Large-Scale Data Management
• Spatio-Temporal Data Management
• +4 more

Christos Doulkeridis is a Professor at the Department of Digital Systems, University of Piraeus, Greece. He specializes in parallel and distributed query processing, large-scale data management, and spatio-temporal data systems. His work focuses on optimizing big data frameworks for mobility analytics and distributed knowledge discovery. He holds a PhD from Athens University of Economics and Business (2007) and has been involved in several EU-funded projects like EMERALDS, Green.DAT.AI, and MobiSpaces as Principal Investigator or Coordinator. Education: PhD in Informatics (2007), Athens University of Economics and Business M.Sc. in Information Systems (2003), Athens University of Economics and Business Diploma in Electrical and Computer Engineering (2001), National Technical University of Athens Awards: Best Paper Awards at SIGSPATIAL, SSTD, EuroVA Marie-Curie and ERCIM Fellowships SemEval 2017 Task 4 & 6 competition wins His research interests include scalable data processing frameworks, mobility data analytics, and spatio-temporal query optimization. He leads projects like MobiSpaces (Horizon Europe), aiming to create energy-efficient data spaces for mobility data. He has published over 100 papers in top venues like EDBT, SIGMOD, and ICDE, focusing on distributed systems, query processing, and machine learning applications in data management. Teaching: He teaches undergraduate and graduate courses in data structures, data analysis, big data processing, and database systems at the University of Piraeus. His courses integrate practical tools like Spark and Hadoop for real-world data challenges.

Markus HeglandView profile

Professor

• Computational Mathematics
• High-Dimensional Problems
• Ill-Posed Problems
• +4 more

Markus Hegland is a Professor and Head of the Centre for Mathematics and its Applications (CMA) at the Australian National University (ANU). He holds a PhD from ETH Zurich (1988) and has been affiliated with ANU since 1992, focusing on High-Performance Computing (HPC) and numerical analysis. As a Hans Fischer Senior Fellow at TUM-IAS, his research emphasizes high-dimensional problems, ill-posed systems, and data mining applications. His work bridges computational mathematics with practical domains like systems biology and spectral enhancement. Research interests include sparse grid techniques, regularization methods, and algorithm development for HPC. Notable contributions include the OPTICOM method for stable sparse grid solutions and convergence theory for variable Hilbert scales regularization. He has led projects on fault-tolerant HPC algorithms and collaborated with Fujitsu on HPC applications. Publications span numerical analysis, bioinformatics, and computational physics. His work on the chemical master equation and gyrokinetics showcases interdisciplinary impact. Currently, he explores resilient grid-based solvers and machine learning integration with HPC frameworks. No awards are explicitly listed, but his senior fellowship underscores recognition in his field. Grants and collaborations include ARC-funded research in bioinformatics and HPC resilience. His work on digital twins and algorithm optimization reflects broader interests in advanced computational modeling. He is actively involved in teaching and supervising in computational mathematics and data science at ANU.

Prakash RamananView profile

Professor

• Algorithms
• Database Systems
• Distributed Computing
• +3 more

Prakash Ramanan is a Professor at the School of Computing within the College of Engineering at Wichita State University. His research focuses on algorithms, distributed computing, database systems, and XML processing. He holds an office at 208 Jabara Hall on the university's main campus. His academic interests include parallel algorithms, MapReduce frameworks, computational complexity, and query processing in XML databases. His work spans theoretical algorithm analysis to practical implementations in distributed systems. Ramanan's publications emphasize algorithmic efficiency, lower bounds analysis, and optimization techniques. His recent work addresses MapReduce-based matrix multiplication and XPath query evaluation over XML streams. Earlier contributions include foundational studies on tree patterns, join operations, and parallel computing. No scientific awards or grants are explicitly listed in the provided information. He currently advises no listed students or supervisees. His research activities involve collaborations in distributed systems and database query optimization.

Long ChengView profile

Researcher

• Cloud Computing
• Big Data Analytics
• Parallel and Distributed Systems
• +3 more

Dr. Long Cheng was a former Researcher at the International Center for Computational Logic (ICCL) within the Faculty of Computer Science at TU Dresden. His research focused on cloud computing, big data analytics, and distributed systems, particularly in optimizing large-scale data processing, outer join operations, and semantic web technologies. He contributed to projects involving parallel programming models like X10, MapReduce frameworks, and efficient data compression techniques for RDF datasets. Cheng collaborated with the Knowledge-Based Systems research group, addressing challenges in scalable systems, high-throughput indexing, and parallel reasoning under non-monotonic logics. His work emphasized performance optimization in distributed environments, including skew handling, data redistribution strategies, and algorithm scalability for large datasets. Publications span topics such as outer join evaluation in cloud environments, efficient compression of semantic web data, and high-performance query execution over distributed systems. His research bridges theoretical computational logic with practical applications in big data and cloud infrastructure.

• Computer Architecture
• High-Performance Computing (HPC)
• Cloud Computing
• +4 more

Prof. Weikuan Yu is a Professor in the Department of Computer Science at Florida State University. His research focuses on computer architecture, high-performance computing (HPC), cloud computing, parallel file systems, and deep learning applications. He holds the role of Chair and can be contacted via yuw@cs.fsu.edu or (850) 644-5442. His expertise includes optimizing storage systems and I/O behaviors in scientific workflows, developing scalable distributed systems, and applying machine learning to improve computational efficiency. Notable projects include work on burst buffer systems (e.g., BurstFS, TRIO), persistent memory management (PHAST), and distributed deep learning frameworks (e.g., compression techniques for time-evolutionary data). Recent research trends emphasize enhancing HPC storage efficiency through novel file systems and I/O emulation, as well as leveraging machine learning for fault tolerance and configuration tuning. His work bridges hardware-software co-design to address challenges in exascale computing and big data analytics. Prof. Yu has contributed to multiple open-source projects and frameworks, including OpenSHMEM-based key-value stores and MapReduce optimizations. His publications highlight advancements in parallel processing, distributed algorithms, and energy-efficient memory architectures.

Sikha BaguiView profile

Professor

• Big Data Analytics
• Machine Learning
• Data Mining
• +7 more

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.

Wooyoung KimView profile

Associate Professor

• Bioinformatics
• Systems Biology
• Computational Biology
• +8 more

Wooyoung Kim is an Associate Professor in the Division of Computing and Software Systems at the University of Washington Bothell's School of Science, Technology, Engineering & Mathematics. With PhD and MS degrees in Computer Science from Georgia State University and Georgia Institute of Technology, and dual MS/BS in Mathematics from Yonsei University, he bridges computer science and bioinformatics through interdisciplinary research. Ph.D., Computer Science, Georgia State University (2012) M.S., Computer Science, Georgia Tech (2010) M.S./B.S., Mathematics, Yonsei University (Korea) His research focuses on bioinformatics and systems biology , particularly network motif analysis in biological networks. This work spans pattern recognition, machine learning, and cloud computing applications for analyzing genome sequencing data and immunotherapy research. Current projects include: iBioML: Collaborative interpretable ML platform for bioinformatics VIA-QMI: Visualized immunotherapy data analysis tool NemoSuite: Web-based network motif analytics Dr. Kim has received multiple awards including the Best Research Award (2024) and NSF RUI grant (NSF RUI 1257525) . He has developed software tools like NemoLib (C++/Java/Python libraries) and NemoMap for motif detection. Collaborators include Dr. Kristina Hillesland (UW Bothell), Dr. Stephen Smith (UW Seattle), and Dr. Serghei Mangul (USC) Active in IEEE-BIBM, ISBRA, and ACM-BCB program committees

• Parallel Computing
• Distributed Systems
• Multiprocessor Operating Systems
• +5 more

Vasilios Mamalis is a Professor at the Department of Informatics and Computer Engineering, University of West Attica, and a member of the Collaborating Scientific Staff at the Hellenic Open University's Informatics program. His academic career spans decades with significant contributions to parallel and distributed computing, wireless sensor networks, and cloud technologies. Education: Diploma in Computer Engineering and Informatics, University of Patras (1993) PhD in Computer Engineering and Informatics, University of Patras (1998) Research Interests focus on parallel algorithms, distributed systems, wireless sensor networks, cloud computing, and information retrieval. His work addresses energy efficiency in ad-hoc networks, optimization techniques, and educational technology applications. Publication Trends show extensive work on WSN clustering, cloud task scheduling, parallel simplex methods, and fog computing applications in education and urban systems. He combines metaheuristics with infrastructure optimization in large-scale networks. Scientific Contributions include editorial roles in the Journal of Balkan Libraries Union and program committee memberships in international conferences. He actively reviews for journals and conferences in computing. Teaching Expertise covers operating systems, parallel computing, distributed systems, and cloud technologies at both undergraduate and postgraduate levels. His Research Leadership involves EU/Greek-funded projects on communication protocols, parallel content-based retrieval, and wireless sensor networks.

• Bioinformatics
• DNA Sequencing
• Genomics
• +3 more

Dries Decap is a postdoctoral researcher at the Faculty of Engineering and Architecture, Ghent University, within the Department of Information Technology (INTEC). His work focuses on bioinformatics, DNA sequencing, and computational biology with applications in genomics and high-performance computing. Research Interests: Dr. Decap specializes in developing scalable bioinformatics frameworks for DNA and RNA sequencing analysis, including tools like elPrep and Halvade . His research spans variant calling, motif discovery, and parallel computing implementations. Projects: He led his doctoral project (2011-2017) on "Doctoral project Dries Decap" under supervisors Bart Dhoedt and Jan Fostier. He also contributed to projects like IAMBEE for adaptive pathway analysis and BLSSpeller for regulatory motif discovery. Collaborations: Collaborators include Kathleen Marchal, Charlotte Herzeel, and Pascal Costanza. He has worked with institutions like IMEC and Ghent University on scalable genomic data processing solutions.

• Uncertain Data Management
• MapReduce Algorithms
• Social Choice Theory
• +5 more

Anish Das Sarma is a researcher affiliated with Google, USA , specializing in uncertain data management, MapReduce algorithms, and knowledge graph systems. He earned a PhD from Stanford University in 2010 under the supervision of Jennifer Widom and Alon Halevy, with a dissertation on "Managing Uncertain Data." His career spans collaborations with leading institutions, focusing on scalable data integration, social choice theory, and machine learning applications in scholarly knowledge organization. PhD in Computer Science, Stanford University (2010) Key collaborations: Stanford, Google Research, NFDI4DataScience His research interests intersect uncertain data modeling , MapReduce optimization , and large language model applications for scientific synthesis. Recent work includes FAIR data frameworks, ontology learning, and clinical entity linking. Article trends highlight his evolution from foundational database systems (2004-2015) to modern applications of LLMs in scholarly communication (2023-2024). Key areas: scalable algorithms, research data management, and ethical AI.

• Cybersecurity
• Cloud Computing
• Formal Methods
• +3 more

Laurent BOBELIN serves as a Contractual Lecturer-Researcher at INSA Centre Val de Loire, holding dual roles as SDS Board Member and Team Leader. His research is institutionally anchored at LIFO (Laboratoire d'Informatique Fondamentale d'Orléans), a joint research unit between INSA Centre Val de Loire and the University of Orléans, focusing on fundamental computer science. His research portfolio demonstrates deep expertise in Cybersecurity and Cloud Computing , with significant extensions into Formal Methods for complex system architectures. He has pioneered applications in Health Informatics , developing the E-HandicapScale diagnostic platform for disabled patients, and recently expanded into Agricultural Technology with federated learning-based intrusion detection systems. His methodological approach consistently integrates formal verification with practical security enforcement across domains. Analysis of his publication trajectory reveals a strategic evolution from foundational cloud security architectures (2014-2016) toward interdisciplinary applications. His work increasingly bridges cybersecurity with domain-specific challenges—notably in healthcare diagnostics and agricultural IoT—while maintaining rigorous formal methods as the unifying thread. The 2023 agricultural security paper exemplifies his current focus on securing emerging technology ecosystems through distributed learning paradigms. Scientific Awards: No awards were documented in the source materials. Advising and Grants: The provided information contains no details regarding graduate students, research grants, or funded projects. Labs and Teams: As Team Leader and SDS Board Member at INSA Centre Val de Loire, BOBELIN directs research activities within his team while contributing to strategic governance. His primary research affiliation with LIFO connects him to a major French computer science laboratory specializing in algorithms, formal methods, and security, located at the University of Orléans campus (Building IIIA).