جست‌وجوی استاد و پژوهشگر

• Data Analytics
• Machine Learning
• Natural Language Processing
• +۶ مورد دیگر

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.

Santosh Nagarakatteمشاهده پروفایل

استاد

• Compilers
• Formal Verification
• Computer Architecture
• +۴ مورد دیگر

Santosh Nagarakatte is a Professor and Undergraduate Program Director in the Department of Computer Science at Rutgers University. His research focuses on hardware-software interfaces, compilers, formal verification, and numerical methods. He has contributed to projects like the RLIBM math library and verified eBPF ecosystems, emphasizing robustness and security in software systems. Education: PhD in Computer Science from the University of Pennsylvania. His work has been recognized with awards including the ACM Distinguished Member (2023), NSF CAREER Award (2015), and multiple distinguished paper awards at top conferences like PLDI and POPL. Research interests include compiler optimizations, verified compilers, memory safety, and high-performance computing. He has advised numerous PhD students and leads projects funded by Intel, NSF, and the eBPF Foundation. His lab focuses on practical formal methods and math library correctness.

Christos Doulkeridisمشاهده پروفایل

استاد

• Parallel and Distributed Query Processing
• Large-Scale Data Management
• Spatio-Temporal Data Management
• +۴ مورد دیگر

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 Heglandمشاهده پروفایل

استاد

• Computational Mathematics
• High-Dimensional Problems
• Ill-Posed Problems
• +۴ مورد دیگر

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 Ramananمشاهده پروفایل

استاد

• Algorithms
• Database Systems
• Distributed Computing
• +۳ مورد دیگر

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 Chengمشاهده پروفایل

پژوهشگر

• Cloud Computing
• Big Data Analytics
• Parallel and Distributed Systems
• +۳ مورد دیگر

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
• +۴ مورد دیگر

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.

• Artificial Intelligence
• Machine Learning
• Data Mining
• +۳ مورد دیگر

Jakub Neumann holds the following positions at the University of Gdańsk: Associate Professor, Division of Artificial Intelligence, Institute of Informatics, Faculty of Mathematics, Physics and Informatics Deputy Director of the Institute for Development and Promotion, Institute of Informatics Deputy Dean for Education, Faculty of Mathematics, Physics and Informatics His research interests include: Artificial Intelligence and Machine Learning (focus: imbalanced data classification using MapReduce) Autonomous Systems (path planning for autonomous surface vehicles) Educational Technology (XML-based tools for online course development) Theoretical Computer Science (tree automata and complexity) Publication trends: Neumann's work evolved from theoretical computer science (2002) to e-learning (2003-2007), autonomous systems (2012-2013), and recently big data and machine learning (2017-2018). His recent focus is on distributed computing solutions for imbalanced datasets, reflecting the growing importance of big data in AI applications.

Maurício Breternitzمشاهده پروفایل

استادیار

• Machine Learning
• High Performance Computing
• Neuromorphic Computing
• +۷ مورد دیگر

Maurício Breternitz is an Invited Assistant Professor and Principal Researcher at ISTAR-IUL, ISCTE - University Institute of Lisbon. With a PhD in Computer Engineering from Carnegie-Mellon University and extensive industrial research experience at AMD, Intel, and IBM, he focuses on bridging academia and industry for practical innovation. His academic service includes leadership roles in conferences like IISWC and editorial responsibilities at IEEE Micro. Education: Electronics Engineer (ITA, Brazil), MSc in Computer Science (UNICAMP), PhD in Computer Engineering (Carnegie-Mellon) Research: Machine Learning acceleration, Neuromorphic systems, Cloud workloads optimization, Heterogeneous computing His work spans two decades of patents (56 issued, 55 pending) and projects like the Horizon 2020 DIVIDEND CHIST-ERA and the FCT-funded AIMHealth initiative for AI-based public health solutions. Recent publications emphasize weightless neural networks, edge computing, and federated learning applications. Key contributions include: GPU acceleration for Hadoop MapReduce APU code migration techniques Microcode compression algorithms saving $18M Founding the International Workshop on Architectural Support for Binary Translation He has advised 7 Master's theses and 1 ongoing PhD project at UNICAMP, while serving on editorial boards and program committees for top-tier conferences like ISCA and CGO.

Dariusz Jankowskiمشاهده پروفایل

پژوهشگر

• Machine Learning
• Data Stream Mining
• Data Stream Classification
• +۳ مورد دیگر

Dariusz Jankowski is a researcher at the Department of Systems and Computer Networks within the Faculty of Information and Communication Technology at Wrocław University of Science and Technology. His work focuses on machine learning, data stream mining, and big data analysis with applications in medical diagnostics and industrial processes. Email: dariusz.jankowski@pwr.edu.pl Office: Room P.2.9, Building C-16, Wrocław Office Hours: Tuesday 11-13, Thursday 13-15 Research interests include machine learning, data stream classification with concept drift, and big data analysis. He has applied these techniques to medical diagnosis (migraine) and industrial applications (dental milling, cotton textile industry). Recent publications focus on decision tree algorithms for data streams, random forest optimization with MapReduce, hyperspectral image segmentation, and ensemble classifier systems. His work spans both theoretical machine learning and practical implementations across diverse domains. Current affiliations include participation in academic conferences like IDEAL, CORES, and HAIS. While no awards are explicitly mentioned, his collaborative publications suggest active involvement in research networks.

Sikha Baguiمشاهده پروفایل

استاد

• Big Data Analytics
• Machine Learning
• Data Mining
• +۷ 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.

Wooyoung Kimمشاهده پروفایل

دانشیار

• Bioinformatics
• Systems Biology
• Computational Biology
• +۸ مورد دیگر

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
• +۵ مورد دیگر

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
• +۳ مورد دیگر

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
• +۵ مورد دیگر

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.