جست‌وجوی استاد و پژوهشگر

Anne C. Elsterمشاهده پروفایل

استاد

• High-Performance Computing
• Machine Learning
• GPU Acceleration
• +۴ مورد دیگر

Anne C. Elster is a Professor and Director of the Heterogeneous and Parallel Computing Lab (HPC-Lab) at NTNU's Department of Computer Science, with additional roles as HPC Leader at the Center for Geophysical Forecasting and Senior Research Fellow at the Oden Institute. She holds board positions at NTNU and its Faculty of Information Technology. Her research spans: High-Performance Computing : GPU acceleration, auto-tuning, and heterogeneous systems Machine Learning : Applied to optimization and computational geosciences Parallel Algorithms : For scientific computing and real-time simulations Her recent publications (2021-2024) focus on GPU auto-tuning, quantum-HPC integration, distributed systems, and ML-driven geophysical data analysis, with strong emphasis on performance optimization across architectures. Awards and honors: IEEE Computer Society Distinguished Contributor (2021) IEEE Distinguished Speaker (2019-2022) IEEE Senior Member (2000) She has supervised 100+ master's students, 15+ PhDs, and secured major grants including EU H2020 projects. Current Post Docs focus on HPC acceleration and AI applications. Her HPC-Lab collaborates with CERN, Equinor, and international universities, specializing in GPU-accelerated scientific computing and tools for performance portability.

Rajiv Guptaمشاهده پروفایل

استاد

• Parallel Computing
• Compilers
• Computer Architectures
• +۴ مورد دیگر

Rajiv Gupta is a Distinguished Professor and the Amrik Singh Poonian Professor of Computer Science at the University of California, Riverside (UCR), where he serves as Associate Dean for Academic Personnel in the Bourns College of Engineering (BCOE). He is a member of the RIPLE research group and has co-authored 327 papers with an h-index of 69 and over 16,600 citations. His extensive service includes chairing major conferences such as FCRC 2015, PPoPP 2020, ASPLOS 2011, and PLDI 2008. Professor Gupta's research focuses on Programming, Compiler, Runtime & Architectural Support for Parallel & Distributed Heterogeneous Systems and Software Tools for Monitoring and Managing Runtime Behavior . His work spans graph analytics with scalability and performance, understanding and managing the dynamic behavior of parallel programs, software speculation for irregular parallelism, dynamic program analysis for secure and reliable computing, and compiler optimizations with architectural support. His research has significant applications in high-performance computing, GPU programming, and distributed systems. Analysis of his recent publications reveals a strong focus on graph processing systems, with particular emphasis on evolving and streaming graph analytics. His work addresses critical challenges in memory management for large-scale graph processing, hardware acceleration for graph algorithms, and optimization techniques for concurrent and distributed graph computations. The research demonstrates a progression from foundational compiler and architecture work to increasingly sophisticated systems for handling modern data-intensive computing challenges. Fellow of the ACM (2009) Fellow of the IEEE (2008) Fellow of the AAAS (2011) NSF Presidential Young Investigator Award (1991) UCR Doctoral Dissertation Advisor/Mentor Award (2012) Multiple best paper awards across major conferences Two students won ACM SIGPLAN Outstanding Doctoral Dissertation Award Five advisees received NSF CAREER Award Professor Gupta has supervised 42 PhD students to completion and currently advises several doctoral candidates. His advising success is reflected in his students' achievements, including multiple award-winning dissertations and significant career accomplishments in academia and industry. His research has been supported by numerous grants from NSF, DARPA, and industry partners, enabling sustained investigation into parallel computing systems. The RIPLE research group under his leadership has produced influential work that bridges theoretical foundations with practical system implementations. As the leader of the RIPLE research group at UC Riverside, Professor Gupta oversees a vibrant team focused on innovative approaches to parallel and distributed computing. The group maintains strong collaborations with industry partners and other academic institutions, contributing to the development of next-generation computing systems. Current projects include GRASP (Graph Analytics with Scalability & Performance) and research on understanding and managing the dynamic behavior of parallel programs, reflecting the group's continued focus on cutting-edge computing challenges.

Kunle Olukotunمشاهده پروفایل

استاد

• Computer Architecture
• Parallel Programming
• Chip Multiprocessors
• +۴ مورد دیگر

Kunle Olukotun is a Professor of Electrical Engineering and Computer Science at Stanford University's School of Engineering, where he has been faculty since 1991. He directs the Stanford Pervasive Parallelism Lab (PPL) and co-leads the Transactional Coherence and Consistency (TCC) project. His research focuses on computer architecture, parallel programming environments, and scalable parallel systems. Key areas include chip multiprocessors (CMPs), transactional memory systems, domain-specific languages (DSLs) for heterogeneous computing, and hardware-software co-design for machine learning workloads. His work bridges theoretical foundations with practical systems implementation. Notable contributions include the Stanford Hydra research project (one of the first chip multiprocessors with thread-level speculation), founding Afara Websystems (acquired by Sun Microsystems), and developing the Niagara processor architecture. His DSL frameworks like Green-Marl and Spatial enable efficient graph analysis and hardware acceleration. His publications reveal strong trends in parallel systems evolution: from foundational CMP research (2000s) to transactional memory (2004-2010), then DSLs for heterogeneous computing (2010-2015), and currently foundation model systems (2023-2025). Subfield analysis shows consistent focus on hardware-software co-design, sparse computation, and compiler techniques across decades. ACM Fellow (2006) for contributions to multiprocessors on a chip and multi-threaded processor design Best Paper Award at IEEE International Symposium on Workload Characteristics (IISWC '10) for EigenBench Olukotun actively mentors researchers through the Stanford Pervasive Parallelism Lab (PPL), which seeks to proliferate parallelism across application domains. His projects have secured significant industry partnerships, including the acquisition of his startup Afara Websystems by Sun Microsystems. Current research focuses on compiler frameworks for foundation model systems and hardware acceleration for sparse machine learning workloads, supported by collaborations with major tech companies. He leads the Stanford Pervasive Parallelism Lab (PPL), which develops compiler and runtime systems for heterogeneous architectures. The lab's work spans DSLs, hardware acceleration, and parallel programming models, with strong industry ties to companies like NVIDIA and Google. Current initiatives include the Mosaic compiler framework and Stardust architecture for sparse tensor computation.

Karim Tamssaouetمشاهده پروفایل

دانشیار

• Scheduling
• Inventory-Transportation Integration
• Operations Research
• +۳ مورد دیگر

Karim Tamssaouet is an Associate Professor at BI Norwegian Business School's Department of Accounting, Auditing and Business Economics. His academic trajectory includes positions as Assistant Professor at BI (2020-2024), Postdoctoral Researcher at École des Mines de Saint-Étienne (2019-2020), and R&D Engineer at STMicroelectronics (2016-2019). He holds a PhD from École des Mines de Saint-Étienne, with additional graduate degrees from Université Paris Dauphine and Ecole Nationale Polytechnique. His research specializes in optimization methodologies for industrial challenges, with core interests in: Scheduling algorithms for job-shop and flexible manufacturing systems Integrated inventory-transportation modeling Metaheuristic design (scatter search, neighborhood structures) Dynamic lot-sizing with logistics constraints Bayesian network learning for decision optimization Publication analysis reveals consistent focus on operational efficiency in manufacturing and supply chains, with recent expansions into machine learning applications for optimization. Methodological contributions include novel neighborhood frameworks for scheduling problems and multi-objective approaches for semiconductor production. He currently teaches Business Optimization and Supply Chain Analytics, bridging theoretical operations research with business applications. No research lab affiliations, scientific awards, or supervised students are documented.

Alexander Bolotovمشاهده پروفایل

استاد

• Automated Reasoning
• Graph-Based Reasoning
• Formal Methods
• +۱۳ مورد دیگر

Alexander Bolotov is a Professor at the University of Westminster's School of Computer Science and Engineering, where he serves as the Software Systems Engineering Research Group Leader and the Doctoral Researchers Development Programme Coordinator. He teaches Level 4 Mathematics for Computing and Level 5 Software Engineering Principles and Practice modules. His research spans Automated Reasoning, Graph-Based Reasoning, Specification and Verification of Complex Reactive, Concurrent and Distributed Systems, Formal Methods in Software Engineering, Logic, Temporal Logic, Proof Theory, On-Line Knowledge Repositories, and Ontologies. His expertise includes Formal Methods, Mathematical Modelling, Software Architecture, Program Reasoning and Logic Engineering. His recent publications demonstrate a strong focus on branching-time temporal logics, formal verification methods, ontology-based e-learning platforms, and applications in digital health evidence generation. His work bridges theoretical computer science with practical applications in healthcare, finance, and educational technology. Bolotov has successfully secured multiple research grants including Innovate UK projects RADIANT Implementation and RADIANT: Regulatory Science Empowering Innovation in Transformative Medical Software and AI, as well as funding from the Alan Turing Institute for developing frameworks for literature evaluation. He actively supervises doctoral students working on topics including ontology-driven frameworks for system integration, rich web applications, cloud computing load balancing, wireless sensor networks, and semantic selection of internet sources. His research group is involved with the Distributed and Intelligent Systems, Software Systems Engineering, and Centre for Parallel Computing.

Rodrigo Santamaría Vicenteمشاهده پروفایل

دانشیار

• Bioinformatics
• Information Visualization
• Microarray Data Analysis
• +۴ مورد دیگر

Rodrigo Santamaría Vicente is an Associate Professor in the Department of Computer Science and Automation within the Faculty of Sciences at the University of Salamanca. His academic career centers on bridging computer science with biological research through advanced data analysis and visualization techniques. He serves as director of the Diploma of Specialization in Bioinformatics and Computational Genomics, demonstrating leadership in specialized academic programming. Dr. Santamaría earned his PhD in Computer Science from the University of Salamanca in 2009 with his thesis "Visual analysis of gene expression data by means of biclustering," supervised by Dr. Luis Antonio Miguel Quintales and Dr. Roberto Therón Sánchez. His academic journey began with an M.S. degree in Computer Science from the same institution. His research primarily focuses on Bioinformatics and Information Visualization, with particular emphasis on integrating diverse data sources, analysis algorithms, and visual representations to enhance understanding of complex biological problems. His work spans multiple subdomains including gene expression analysis, biclustering algorithms, phylogenetic tree visualization, and collaborative tagging systems. Santamaría's research group affiliations include both the BIOINFORMÁTICA research group and the CaUSAL (Cultura Académica, Patrimonio y Memoria Social) research group at the Institute of Functional Biology and Genomics. His publication record reveals a consistent trajectory of scholarly output since 2006, with recent work showing increased focus on biological applications of computational methods. His most significant contributions appear in the integration of visualization techniques with bioinformatics analysis, particularly through tools like BicOverlapper and Voronto that facilitate interactive exploration of complex biological data. The evolution of his work demonstrates progression from foundational algorithm development to increasingly sophisticated applications in systems biology. Author of the book "How Machines Organize: An Introduction to Distributed Systems" Director of the Diploma of Specialization in Bioinformatics and Computational Genomics Active contributor to R programming ecosystem with the biclust package As an educator, Santamaría teaches across multiple programs including the Master's Degree in Computer Engineering and previously taught Bioinformatics for the Biotechnology degree program. His teaching portfolio spans Bioinformatics (from both computer science and biology perspectives), Distributed Systems, and Service-Oriented Architectures. He maintains an active research laboratory focused on developing visualization tools for biological data analysis, with current projects emphasizing integration of gene expression data with biological ontologies and advanced biclustering techniques.