جست‌وجوی استاد و پژوهشگر

• Compiler Optimization
• High Performance Computing
• Sparse Matrix Computations
• +۴ مورد دیگر

P. Sadayappan is a Professor at the School of Computing, University of Utah, specializing in high performance computing, compiler optimization, and scalable machine learning. His research focuses on developing efficient computational methods for scientific applications, particularly in the areas of sparse/dense matrix and tensor computations. His research interests include: Compiler Optimization for High Performance Computing Optimization of Sparse/Dense Matrix/Tensor Computations Scalable Machine Learning Algorithm-Architecture Co-Design Optimization Sadayappan's recent publications demonstrate a strong focus on tensor computations, GPU acceleration, and compiler optimizations for machine learning workloads. His work spans from fundamental compiler theory to practical implementations that improve performance across various architectures. A significant trend in his recent work involves the development of frameworks for efficient tensor operations, sparse matrix computations, and domain-specific code generation, with particular emphasis on performance portability across heterogeneous computing platforms. His notable scientific achievement includes receiving the ACM SIGPLAN Most Influential PLDI Paper Award in 2018 for his work on polyhedral compilation. Sadayappan has been principal investigator or co-investigator on numerous significant research grants, including: NSF award #2217154 (2022-2027): A Comprehensive Framework for Efficient, Scalable, and Performance-Portable Tensor Applications NSF award #2112606 (2021-2026): AI Institute for Intelligent CyberInfrastructure with Computational Learning in the Environment (ICICLE) NIH SBIR-Phase 2 (2023-2025): Enabling next generation machine learning for large scale image analysis NSF award #2009007 (2020-2024): Data Locality Optimization for Sparse Matrix/Tensor Computations DARPA SBIR-Phase 2 (2017-2022): Performance Portable Framework for Developing Graph Applications He teaches CS 4230/6230 (Parallel and High-Performance Computing) at the University of Utah and collaborates extensively with researchers across multiple institutions on projects involving computational chemistry, physics simulations, graph analytics, and machine learning.

Ning Ningمشاهده پروفایل

استادیار

• Stochastic Processes
• Markov Chains
• Time Series Analysis
• +۴ مورد دیگر

Ning Ning is an Assistant Professor in the Department of Statistics at Texas A&M University, affiliated with the Institute of Data Science and the Institute for Quantum Science and Engineering. She co-founded the Stochastic Processes Seminar in the Department of Mathematics. Her research focuses on stochastic processes, Markov chains, machine learning, and quantum computing. Prior to her academic career, she worked as a software developer and database architect in Los Angeles for four years, specializing in data analysis and parallel computing. Education: B.S. in Math from Shandong University (China Math Base), M.S. in Math from the University of Southern California, and Ph.D. in Statistics and Applied Probability from UCSB. She held postdoctoral positions at the University of Washington (Applied Math) and the University of Michigan (Statistics). Research interests include stochastic algorithms, Monte Carlo methods, hidden Markov models, and quantum counterparts of classical methods. She is an Associate Editor for Bayesian Analysis (since 2025), Journal of Computational and Graphical Statistics (since 2024), and Statistics and Computing (since 2023). Recent talks and contributions include invited sessions at the Bayes Comp 2025 conference and the KAUST Stochastic Numerics Workshop. Her work spans theoretical advancements and computational innovations in statistical methods, with applications to large-scale data analysis and machine learning.

• Cryptography
• Machine Learning
• Database Systems
• +۳ مورد دیگر

Michael Brand is an Adjunct Professor at RMIT University's School of Computing Technologies. His research focuses on cryptography, privacy-preserving technologies, machine learning, and algorithm design. Key areas include zero-knowledge proofs, secure financial systems like FinTracer, and computational theory. His work bridges theoretical foundations with practical applications in data security and real-time processing systems. Research interests span advanced cryptographic protocols, privacy-enhancing machine learning techniques, and distributed computing paradigms. Notable contributions include innovations in blockchain tracing mechanisms and foundational work in statistical estimation theory. He actively publishes in top-tier venues, addressing challenges in secure computation, network analysis, and database systems. Collaborations emphasize interdisciplinary solutions to modern computational and security challenges.

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.

Konstantinos Stefanidisمشاهده پروفایل

استاد

• Data Science
• Big Data Management
• Recommender Systems
• +۵ مورد دیگر

Professor Konstantinos Stefanidis holds a full professorship in Data Science at Tampere University's Faculty of Information Technology and Communication Sciences (ITC). He leads the Data Science Research Centre and Recommender Systems Research Group, focusing on advancing fairness and transparency in AI-driven systems. His work bridges computational techniques with societal impact, emphasizing ethical considerations in big data analytics. Education: PhD in Computer Science (2009) from University of Ioannina, Greece Postdoctoral research at institutions including Hong Kong, Norway, and Greece Research Interests: His primary areas include: Recommender Systems with fairness guarantees Entity Resolution in big data Algorithmic transparency and ethical AI Sequential group recommendations Health informatics applications He has published over 100 papers in top-tier venues like ICDE, EDBT, and VLDB. Recent Contributions: Recent work emphasizes: Counterfactual explanations for group recommendations Fairness-aware entity resolution Health domain recommendations Explainable AI frameworks Awards & Recognition: Best Paper Awards at ACM SAC (2022), PIC (2021), and QAUCA (2019) Recipient of Academy Research Fellowships General co-chair for major conferences (EDBT/ICDT 2026, ADBIS 2025) Advising & Collaboration: Mentors over 10 PhD/MSc students. Active in international collaborations including EU-funded projects. Leads research teams addressing global challenges like sustainable AI and healthcare innovation. Labs & Teams: Coordinates the Recommender Systems Group and Data Science Subunit at Tampere. Collaborates with industry partners and academic institutions globally to advance socially responsible technologies.

Nikos Ntarmosمشاهده پروفایل

مدرس ارشد

• Distributed Systems
• Big Data Management
• NoSQL Databases
• +۳ مورد دیگر

Nikos Ntarmos is a Senior Lecturer at the University of Glasgow's School of Computing Science, with affiliations to the IDEAS research lab. He holds a PhD in Computing Engineering & Informatics from the University of Patras and specializes in distributed systems and large-scale data management. Research focuses on: Distributed data storage and indexing for NoSQL systems Efficient query processing for graph databases Scalable algorithms for big data analytics Recommendation systems and collaborative filtering Publications demonstrate evolving interests from distributed hash tables to contemporary graph processing and recommendation systems. Recent work increasingly addresses practical applications in education technology alongside core database research.

• 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.

Christos Nomikosمشاهده پروفایل

دانشیار

• Algorithm Design and Analysis
• Approximation Algorithms
• Computational Complexity
• +۳ مورد دیگر

Christos Nomikos is an Associate Professor at the Department of Computer Science and Engineering, University of Ioannina. He holds a PhD from the National Technical University of Athens (NTUA) and has been a faculty member since 2003. His roles include teaching and research supervision in computer science disciplines. Education: Diploma in Electrical Engineering, NTUA, 1992 PhD in Computer Science, NTUA, 1997 Research Interests: Christos Nomikos focuses on theoretical computer science and logic, including algorithm design and analysis, computational complexity, formal languages, logic programming, and graph theory. His work explores foundational aspects of programming languages and their semantics, as well as applications in knowledge representation and automated reasoning. Teaching: In the 2024-25 academic year, he teaches Discrete Mathematics II , Principles of Programming Languages , Computational Complexity , and Approximation Algorithms . He emphasizes structured learning approaches and maintains a dedicated ecourse page for students. Advising & Grants: He supervises undergraduate thesis projects in computer science, with a focus on theoretical topics like Turing machine transformations and Boolean grammar algorithms. No explicit grant information is provided in the texts. His advising process involves rigorous academic evaluation and prefers candidates with strong performance in core computer science courses. Labs/Teams: While no specific lab or team affiliations are mentioned, his research collaborations include work with international conferences and journals, indicating active participation in academic networks.

Syam Menonمشاهده پروفایل

استاد

• Privacy
• Data Mining
• Software Design Optimization
• +۳ مورد دیگر

Syam Menon is a Professor in the Information Systems department at the School of Management , University of Texas at Dallas. He teaches courses such as MIS 6316 Data Communications, MIS 6324 Business Intelligence Software and Techniques, and MIS 6334 Advanced Business Intelligence. PhD , University of Chicago, 1997 MBA , University of Chicago, 1996 BTech , University of Kerala, India, 1989 His research focuses on Privacy , Data Mining , and Software Design Optimization . His work addresses network congestion in peer-to-peer systems, optimization of cellular networks, and privacy-preserving techniques in shared databases. He has published extensively in journals like Information Systems Research , IEEE Transactions on Communications , and Management Science . His recent publications highlight trends in Telecommunications and Computer Science , particularly in Network Optimization , Data Privacy , and Algorithm Design . He is affiliated with professional organizations such as INFORMS , IEEE , ACM , and Association for Information Systems . Contact Syam Menon via syam@utdallas.edu or at (972) 883-4779.

Dylan Keonمشاهده پروفایل

استادیار

• Spatial Analysis
• Spatio-Temporal Data
• Climate Data
• +۵ مورد دیگر

Dylan Keon is an Assistant Professor (Senior Research) at Oregon State University's School of Electrical Engineering and Computer Science. He serves as the Associate Director of the Northwest Alliance for Computational Science & Engineering (NACSE). His research focuses on spatial analysis, spatio-temporal data systems, climate data production, and decision support systems for environmental and disaster management applications. He holds a Ph.D. in Computational Geography and M.S. in Plant Ecology and GIS/Statistics from Oregon State University, along with a B.S. in Botany from Western Michigan University. Dr. Keon has over 20 years of experience in developing geospatial tools for environmental monitoring, including projects funded by USDA, NSF, and other agencies. His work spans topics such as precision agriculture, climate modeling, tsunami engineering, and aquatic pathogen tracking. He currently leads operations on multiple USDA-funded initiatives and has contributed to critical infrastructure resilience frameworks for coastal regions. His research trends emphasize interdisciplinary collaboration, leveraging computational methods to address complex environmental challenges. Recent efforts include advancing solar radiation models, improving tsunami inundation simulations, and creating databases for tracking aquatic pathogens. Keon’s contributions bridge academic research with real-world applications, particularly in disaster preparedness and sustainable environmental practices.

Ali Hursonمشاهده پروفایل

استاد

• High-performance computing
• Pervasive computing
• Mobile databases
• +۱۱ مورد دیگر

Dr. Ali Hurson is a Professor in the Department of Electrical and Computer Engineering at Missouri University of Science and Technology. His research spans high-performance computing, pervasive computing, mobile databases, personalized education, intelligent transportation systems, and cyber-physical systems. PhD, Computer Science, University of Central Florida MS, Computer Science, University of Iowa BS, Physics, University of Tehran Dr. Hurson’s research focuses on mobile data access systems, cybersecurity for critical infrastructure, and educational technologies. His work addresses challenges in data dissemination, power management, and fault propagation in heterogeneous systems. His recent publications emphasize agent-based modeling for cyber attacks, predictive analytics in education, and fault tolerance in cyber-physical systems. Trends include integration of machine learning, security frameworks, and sustainable computing. Editor-in-Chief, Advances in Computers Editor-in-Chief, Journal of Sustainable Computing and Communication Dr. Hurson has secured over $3 million in grants from NSF, DOE, DOT, and industry partners. He has held academic roles at Penn State University and the University of Oklahoma, transitioning to Missouri S&T in 2007.

Prashant Pandeyمشاهده پروفایل

استادیار

• Large-Scale Data Systems
• High-Performance Computing
• Computational Biology
• +۳ مورد دیگر

Prashant Pandey is an Assistant Professor at Northeastern University's Khoury College of Computer Sciences, co-directing the Data Lab and affiliated with Berkeley Lab's Performance and Algorithms Research Group. He holds a PhD in Computer Science from Stony Brook University (2018), with prior postdoc research at UC Berkeley, LBL, and Carnegie Mellon University, and a year as a Research Scientist at VMware Research. His research focuses on scalable data systems with theoretical guarantees, spanning high-performance computing, computational biology, stream processing, and storage. Key projects include designing adaptive filters, GPU-accelerated data structures, and graph containers for skewed dynamic data. Recent talks include sessions on hash tables, B-trees, and graph frameworks at conferences in Bangalore, Las Vegas, and Santiago. Teaching includes courses like Advanced Database Systems and Data Structures for Scalable Computing. His work bridges systems and algorithms, emphasizing practical applications in bioinformatics and exascale computing. Awards and grants are not explicitly listed, but his contributions include foundational papers on filters, hash tables, and genomic data indexing. Ongoing efforts involve the Data Lab's exploration of high-performance filters for GPUs and collaborative projects like the BP-tree and Vector Quotient Filters. Students under his supervision focus on database systems, learned indexes, and external-memory joins.

Ting Leiمشاهده پروفایل

دانشیار

• Geographic Information Science
• Transportation Geography
• Remote Sensing
• +۳ مورد دیگر

Ting Lei is an Associate Professor in the Department of Geography at the University of Kansas, located in Malott Hall #1021. His primary research interests focus on Geographic Information Science (GIS), including algorithmic development, geospatial computational methods, network analysis, location theory, and web GIS. He also explores remote sensing applications and advancements in GIS technology such as data structures, databases, computational geometry, and open-source software. His teaching covers GIS principles, transportation geography, and geo-computational methods. Dr. Lei's publications emphasize spatial data conflation, transportation network vulnerability, and optimization models for facility location. Recent works include studies on optimal spatial data matching, hub center interdiction problems, and unified location-allocation approaches integrating GIS and distributed computing. His research addresses real-world challenges in urban planning, water resources management, and celestial imaging analysis. Key research trends in his articles include computational GIS advancements, transportation infrastructure resilience, and interdisciplinary applications of geospatial technologies. No specific scientific awards are listed, though his extensive publication record highlights scholarly contribution. Advising and grants details are not provided in the text, but his active research in multiple geospatial domains indicates engagement with academic and applied projects.

Qin Zhangمشاهده پروفایل

استاد

• Algorithms for Big Data
• Theoretical Foundations of Machine Learning
• Quantum Information and Computing
• +۴ مورد دیگر

Roles and Affiliations: Professor of Computer Science at Indiana University Bloomington, Adjunct Professor in Mathematics. Previously at IBM Almaden Research Center and Aarhus University's Center for Massive Data Algorithmics. PhD from HKUST in Computer Science and Engineering. Education: PhD in Computer Science, HKUST, advised by Mordecai Golin and Ke Yi Research Interests: Focuses on algorithms for big data, including communication-efficient distributed computation, streaming/sketching algorithms, and quantum data management. Explores theoretical foundations of machine learning, particularly distributed learning. Leads projects funded by NSF grants on parallel reinforcement learning, distributed graph algorithms, and noisy data processing. Publications and Awards: Over 100 papers in top venues like FOCS, SIGMOD, and NeurIPS. Won the Best Paper Award at SPAA 2017 for distributed clustering work. Invited to special journal issues for top conferences. Teaching and Grants: Teaches advanced courses on sublinear algorithms and algorithm design. PI on NSF grants totaling over $2M, including collaborative projects on distributed computing and epidemiological modeling. Advises PhD students in areas like quantum algorithms and reinforcement learning. Service: Served on program committees for SIGMOD, NeurIPS, ICML, and PODS. Organizes theory seminars and Midwest Theory Day workshops.

• Distributed Systems
• Data Management
• Dependability
• +۴ مورد دیگر

Rui Oliveira is an Associate Professor at Universidade do Minho in Portugal, affiliated with the Distributed Systems Group. His research focuses on dependable distributed systems, fault-tolerant database systems, and large-scale distributed systems. He teaches Distributed Systems across undergraduate, master's, and doctoral programs. Research Interests His work spans epidemic communication protocols, data management in cloud environments, and high-performance transactional middleware. Recent contributions include frameworks for privacy-preserving machine learning (SOTERIA), distributed tracing (CAT), and fault-diagnosis tools for I/O behavior analysis. Key Contributions Developed the Loom disaggregated database system, the TADA distributed agreement toolkit, and the DATAFLASKS epidemic storage substrate. His work emphasizes practical implementations of scalable and resilient distributed systems. Labs/Teams Affiliated with HASLab (Hardware and Software Systems Laboratory), focusing on distributed systems research. Collaborates with industry on cloud computing and cybersecurity challenges.