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Resit SendagView profile

Professor

• Computer Architecture
• Processor Architecture
• Multicore Processors
• +10 more

Resit Sendag is a Professor and Director of Graduate Studies in the Department of Electrical, Computer and Biomedical Engineering at the University of Rhode Island. He serves as Director of both the URI Computer Architecture Laboratory and the URI Generative AI Development Group, leading cutting-edge research in computer architecture and high-performance computing. His academic credentials include: Ph.D. in Computer Engineering from the University of Minnesota (2003) B.Sc. in Electrical Engineering from Hacettepe University, Ankara (1994) Professor Sendag specializes in computer architecture with research interests spanning processor design, memory systems, parallel computing, and hardware acceleration. His work focuses on improving computational performance through innovative techniques in cache management, prefetching, branch prediction, and specialized hardware implementations using FPGAs and GPUs. Recent research has expanded into applying these architectural principles to solve complex optimization problems like vehicle routing. His publication record demonstrates a consistent evolution from fundamental computer architecture research toward practical applications of architectural techniques. The most recent work shows strong emphasis on implementing genetic algorithms for vehicle routing problems using specialized hardware platforms (FPGAs and GPUs), while maintaining his foundational research on memory access optimization through sophisticated prefetching techniques. Professor Sendag has secured research funding from the Office of Naval Research through collaborative projects with the University of Connecticut focused on advanced manufacturing, shipbuilding processes, and material tracking systems. He actively mentors graduate students, with current advisees working on challenging computer architecture projects. His former students have achieved notable success at leading technology institutions including ETH-Zurich, Intel, NVIDIA, AMD, and various research laboratories. Professor Sendag leads key research initiatives including the URI Computer Architecture Laboratory, the Generative AI Development Group, and the PatternFinder project (an NSF-funded open-source tool for program behavior analysis).

José Carlos Cabaleiro DomínguezView profile

Professor

• High performance computing
• Parallel systems architecture
• Parallel algorithms
• +6 more

Dr. José Carlos Cabaleiro Domínguez is a Full Professor in the Department of Electronics and Computing at the University of Santiago de Compostela's Faculty of Computing, Spain. He has been a member of CiTIUS (Centro singular de investigación en tecnoloxías da información e comunicación) since 2010 and was promoted to Full Professor in 2022 after serving as an Associate Professor since 1994. His academic journey began with a BS and PhD in Physics from the University of Santiago de Compostela in 1989 and 1994 respectively, with initial teaching experience at the University of A Coruña from 1990-1994. His research focuses on high performance computing, particularly in parallel systems architecture, development of parallel algorithms for irregular problems with sparse matrices, performance prediction and improvement of parallel applications, memory hierarchy optimization, and applications for grid and cloud computing. He has developed significant expertise in 3D point cloud processing from remote sensors like LiDAR, with applications in urban infrastructure analysis, powerline detection, and route planning. Analysis of his recent publications reveals a strong emphasis on optimizing resource allocation for big data frameworks, developing deep learning applications for point cloud classification, and creating efficient algorithms for powerline detection in LiDAR surveys. His work bridges theoretical computer science with practical applications in geospatial analysis and infrastructure monitoring. His research has been published in top-tier journals including IEEE Transactions, ISPRS Journal of Photogrammetry and Remote Sensing, and Future Generation Computer Systems, reflecting his significant contributions to the field of high performance computing and its applications. Dr. Cabaleiro actively collaborates with researchers across multiple institutions, as evidenced by his extensive publication record with co-authors from various universities and research centers. His work demonstrates a consistent trajectory of advancing parallel computing techniques while applying them to increasingly complex real-world problems involving large-scale geospatial data.

Nina KazaninaView profile

Professor

• Neuroscience of Language
• Neural Mechanisms of Linguistic Computation
• Cognitive Neuroscience
• +3 more

Prof. Nina Kazanina is a faculty member in the Department of Basic Neurosciences at the University of Geneva’s Faculty of Medicine. She co-directs the NCCR Evolving Language and previously served as an Associate Professor in Psychology and Cognitive Neuroscience of Language at the University of Bristol’s School of Psychological Science. Research focuses on the neural mechanisms underlying language, memory, and cognitive domains Lab employs EEG, MEG, fMRI, and behavioral methods Translational work targets language assessment in neurodegeneration and stroke Recent work explores grammatical abstraction in language models, neural oscillations in syntax, and reward-modulated memory. Her lab investigates how the brain encodes linguistic categories and hierarchical structures, bridging neuroscience and theoretical linguistics. Key translational applications include cognitive assessment across the lifespan. Lab members include postdocs (Theo Desbordes, Berk Gercek, Mamady Nabe, Itsaso Olasagasti) and PhD student Katarina Labancova. Contact: nina.kazanina@unige.ch

Katharine HsuView profile

Professor

• Immunology
• Cancer Biology
• Bone Marrow Transplantation
• +3 more

Katharine Hsu is a Professor at Weill Cornell Medical College and a Member of Immunology & Microbial Pathogenesis at the Weill Cornell Graduate School of Medical Sciences. She directs the Tri-Institutional MD-PhD Program and conducts translational research at Memorial Sloan Kettering Cancer Center (MSKCC), focusing on natural killer (NK) cells in cancer and viral immunity. Education: MD-PhD from Cornell University Medical College, undergraduate at Stanford University Clinical Expertise: Bone marrow transplantation, leukemia, lymphoma treatment Her research explores: Genetic and molecular mechanisms of NK cell activation Role of KIR-HLA interactions in transplant outcomes CMV/HIV immune evasion strategies Metabolic regulation of NK function Development of NK-based therapies for neuroblastoma and leukemia Key publications (2023-2021) reveal trends in Single-cell NK repertoire profiling CMV-induced immune adaptations IRF4-regulated nutrient uptake HLA-KIR genotype correlations Transplantation immunogenetics Scientific distinctions include Induction into American Society for Clinical Investigation (2012) Leukemia & Lymphoma Society Scholar (2016) Weill Cornell Alumni Award of Distinction (2019) Membership in Association of American Physicians As director of the Tri-Institutional MD-PhD Program, she emphasizes diversity in biomedical training while leading a translational research team at MSKCC that bridges clinical care and NK cell biology studies.

Bradford L. ChamberlainView profile

Academic

• Parallel programming languages
• Compilers for high-performance computing
• Scalable parallel algorithms
• +3 more

Bradford L. Chamberlain is a Distinguished Technologist at Hewlett Packard Enterprise and an Affiliate Professor in the Paul G. Allen School of Computer Science & Engineering at the University of Washington. He is the founding technical lead of the open-source Chapel programming language, which targets productive parallel computing from laptops to supercomputers. Education: Ph.D. in Computer Science & Engineering, University of Washington, 2001 M.S. in Computer Science & Engineering, University of Washington, 1995 B.S. (with honors) in Computer Science, Stanford University, 1992 Research interests center on user productivity for high-performance computing, spanning programming languages, compilers, algorithms, and applications. His work emphasizes scalable parallel computation and the design of language abstractions—such as regions, distributions, and iterators—that enable both high productivity and high performance. Over the past two decades he has led the Chapel project from a small research effort to a thriving open-source language with nearly twenty full-time developers at HPE and numerous external collaborators. His recent publications explore locality-aware compiler optimizations, fabric-attached memory, and the expression of sophisticated parallel patterns like stencil tiling and data re-allocation. Teaching & service: He created and regularly teaches UW’s graduate parallel computation course (CSEP 524), has advised graduate students, and serves on departmental committees, fostering tight ties between the Allen School and HPE/Cray.

Jinhui WangView profile

Professor

• Artificial Intelligence
• Very-large-scale Integration (VLSI) Circuits
• Three-dimensional Integrated Circuit (3D IC) Design
• +6 more

Jinhui Wang is a Professor in the Department of Electrical and Computer Engineering at the University of South Alabama's College of Engineering. His research focuses on cutting-edge technologies in Artificial Intelligence , VLSI Circuits , and Neuromorphic Computing . Education: Postdoctoral work in VLSI Design at University of Rochester, NY, USA; Ph.D. and B.S. in Electrical Engineering from Beijing University of Technology and Hebei University, China. His work addresses 3D IC Design , Emerging Memory Systems , and Cooling Techniques for Electronic Devices , with recent publications emphasizing privacy-preserving AI hardware and intelligent memory architectures for mobile and embedded systems. Collaborative projects span applications in Wireless Sensor Networks , IoT , and UAV Electronic Subsystems .

David LowenthalView profile

Professor

• Parallel and distributed computing
• Operating systems
• Run-time systems

David Lowenthal is a Professor at the University of Arizona specializing in parallel and distributed computing, operating systems, and run-time systems, with his office located in GS 705. His research directly addresses critical challenges in modern high-performance computing infrastructure. His educational background includes: Ph.D. in Computer Science from the University of Arizona (1996) Professor Lowenthal's research centers on optimizing large-scale computing systems through innovative scheduling algorithms, power management techniques, and network optimization strategies. Key contributions include mitigating inter-job interference in cluster environments, developing quality-of-service mechanisms for MPI applications, and pioneering coscheduling approaches like the Jigsaw scheduler. His work bridges theoretical computer science with practical system implementation to enhance resource utilization under power constraints. Analysis of his 15 most recent publications (2016-2023) reveals three dominant research thrusts: intelligent job scheduling (particularly coscheduling and backfilling), power-aware computing for energy-constrained HPC systems, and network performance optimization in dragonfly/fat-tree topologies. These publications consistently demonstrate collaborative NSF-funded research with emphasis on real-world system implementation and performance evaluation. No scientific awards were documented in the provided text. While no student advisees were listed, his research program has secured substantial NSF funding through collaborative grants including "Collaborative Research: SHF: Medium: Co-Optimizing Computation and Data Transformations for Sparse Tensors" (2022) and "Collaborative Research: OAC Core: Improving Utilization of High-Performance Computing Systems via Intelligent Co-scheduling" (2021), indicating active grant management and interdisciplinary collaboration. No specific laboratories or research teams were mentioned in the source material.

George D KonidarisView profile

Associate Professor

• Artificial Intelligence
• Machine Learning
• Reinforcement Learning
• +2 more

George D Konidaris serves as Associate Professor of Computer Science at Brown University, where his research bridges artificial intelligence, machine learning, and robotics with emphasis on autonomous decision-making systems. His work focuses on developing algorithms that enable robots and AI agents to learn hierarchical structures, discover reusable skills, and operate effectively in complex environments. Education: 2010: PhD, University of Massachusetts, Amherst 2003: MS, University of Edinburgh 2001: BS, University of the Witwatersrand 2000: BS, University of the Witwatersrand His research spans reinforcement learning , robotic motion planning , and hierarchical abstraction , with significant contributions to skill discovery, temporal abstraction, and model-based methods. Current work integrates visuo-haptic perception for manipulation tasks and explores language-guided robotics using large language models. His approach emphasizes creating systems that learn compact world representations for efficient long-horizon planning in partially observable environments. Analysis of his 2025 publications reveals strong trends in model-based reinforcement learning with focus on memory mechanisms, uncertainty quantification, and hierarchical skill composition. Key themes include temporal abstraction for planning efficiency, visuo-haptic fusion for robotic manipulation, and language grounding for task specification. His work increasingly connects cognitive science concepts like theory of mind with AI capabilities. Teaching responsibilities include CSCI 1410 (Artificial Intelligence) and CSCI 2951X (Reintegrating AI), where he bridges theoretical foundations with practical robotics applications.

Nancy LamView profile

Associate Professor

• Organizational Behavior
• Employee Voice and Silence
• Power Dynamics
• +3 more

Nancy Lam is an Associate Professor of Management in the School of Economics and Business Administration (SEBA) at Saint Mary’s College of California , where she also serves as Department Chair of the Organizations and Responsible Business program and Director of the Executive Doctorate of Business Administration. She holds the endowed Guyette Professorship of Responsible Business and previously taught at Keck Graduate Institute (2009–2010). Education PhD in Organizational Behavior, New York University, 2009 MPhil in Management, New York University, 2006 BA, University of California, Berkeley Research Focus Dr. Lam’s scholarship centers on how ideas, information and innovation move through organizational hierarchies . One stream dissects voice and silence as multi-level, multi-actor processes, revealing when employees choose to speak up or stay quiet and how their messages are amplified or stifled by managers. A complementary line explores power’s influence on perception and evaluation , uncovering how authority shapes interpretation of events and communication. She also investigates creative pedagogy , leveraging virtual worlds and experiential projects to enhance business student engagement. Across more than 15 peer-reviewed articles and conference presentations since 2008, her work integrates organizational behavior, behavioral economics, and educational innovation , consistently highlighting the intersection of individual agency and structural constraints. Honors & Awards Outstanding Paper (Management), Global Conference on Business & Finance (2018) Outstanding Service Award, SEBA (2018) Outstanding Paper, International Conference on Business & Innovation (2017) Outstanding Research Award, SEBA (2016) Guyette Responsible Leadership Endowed Professorship (2014–present) SEBA Outstanding Teaching Award (2014) SMC Alumni-Faculty Fellowship Grant (2016) SEBA Teaching Innovation Grant (2012–2013) Best Paper (Management), American Business Research Conference (2011) Outstanding Empirical Paper Award, Eastern Academy of Management (2008) Showcase Symposium Chair, Academy of Management (2008) Teaching & Program Leadership Dr. Lam directs the Executive DBA program and chairs the Organizations & Responsible Business area, mentoring doctoral students and designing cutting-edge curricula. She teaches courses on Creativity & Innovation, Organizational Behavior, Managing Contemporary Organizations , and a popular Jan-Term course on Creativity , consistently integrating experiential and technology-enhanced methods. Labs & Teams While no formal lab is listed, she leads the Executive Doctorate of Business Administration cohort and collaborates with interdisciplinary faculty and doctoral researchers under the umbrella of SEBA’s Organizations and Responsible Business program.

Raphael De VittorisView profile

Associate Professor

• Crisis Management
• Risk Management
• Organizational Behavior
• +4 more

Raphael De Vittoris is Associate Professor in Management Sciences at Clermont School of Business, Université Clermont Auvergne. With over 15 years of crisis & risk management experience in multinational corporations, he focuses on crisis management learning, cognitive biases in decision-making, and organizational resilience. His research bridges practical crisis cell insights with academic theory, examining psychological contracts in consumer-brand relationships and serious games' role in crisis preparedness. Education PhD in Management Sciences (2020), Université Le Havre Normandie Master's in Extreme Environment Physiology Master's in Business Administration Master's in Occupational Health & Safety Research Themes His work explores antifragility (Taleb's theory application), cognitive bias impacts on crisis decisions, and psychological contract violations during brand scandals. He analyzes serious games' effectiveness in crisis training sequences and organizational responses to global crises like pandemics and industrial disasters. Advising & Methodology While not explicitly listing advisees, his research involves qualitative crisis simulations with multinational corporations. He employs autoethnographic methods and serious game frameworks for organizational learning.

Rakesh VenkatView profile

Assistant Professor

• Theoretical Computer Science
• Approximation Algorithms
• Hardness of Approximation
• +1 more

Rakesh Venkat is an Assistant Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology Hyderabad. His research focuses on Theoretical Computer Science, including approximation algorithms, hardness of approximation, and communication complexity. Education : Ph.D., Tata Institute of Fundamental Research (TIFR), Mumbai. Research Trends : His work addresses fundamental challenges in algorithm design, such as optimizing cache misses, improving clustering algorithms, analyzing graph expansion, and exploring embedding techniques. Publications span top-tier conferences like APPROX, FSTTCS, ICALP, and ITCS, with collaborations at institutions including HUJI, TIFR, and IIT-Bombay. Teaching : Courses taught include Approximation Algorithms, Advanced Data Structures, Discrete Mathematics, and Spectral Graph Theory.

• Computer Architecture

Mainak Chaudhuri serves as a Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology Kanpur, where he specializes in computer architecture research and teaching. Doctor of Philosophy, Cornell University, 2004 Master of Science, Cornell University, 2001 Bachelor of Technology, Indian Institute of Technology Kharagpur, 1999 Professor Chaudhuri's research focuses exclusively on computer architecture, with particular expertise in cache management systems, memory hierarchy optimization, and processor design. His work addresses critical challenges in modern computing systems where efficient memory access patterns significantly impact overall system performance across various computing platforms. His recent publications demonstrate consistent contributions to cache management research, particularly in last-level cache algorithms for both traditional processors and graphics processing units. The publications show an evolving research trajectory from general cache algorithms toward specialized implementations for graphics workloads, indicating strategic expansion of his research domain while maintaining core expertise in memory systems. Best paper award in the 11th IEEE International Symposium on High-Performance Computer Architecture, February 2005 While specific details about graduate student supervision aren't provided in the available documentation, his publication record suggests active research mentorship within computer architecture. The absence of explicit grant information in the source material prevents detailed commentary on funded research projects, though his award-winning work indicates successful research proposal development. Though not explicitly mentioned in the source documents, his specialization in computer architecture suggests potential involvement with computer systems laboratories at IIT Kanpur where architectural simulations and hardware implementations would be conducted, supporting both his research and educational mission in advanced computing systems.

Swarnendu BiswasView profile

Assistant Professor

• Programming Languages
• Compilers
• Runtime Systems
• +7 more

Swarnendu Biswas is an Assistant Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology Kanpur. He teaches courses including Programming for Performance (CS 610), Analysis of Concurrent Programs (CS 636), and Compiler Design (CS 335), demonstrating his expertise across multiple areas of computer systems. His research interests center on Programming Languages, Compilers, Runtime Systems, and Parallel Software Systems. He leads the PROSPAR (Programming Languages and PARallel Systems) research group, which focuses on developing techniques to build efficient and correct parallel software through program analysis, compiler optimizations, and runtime systems. His recent publications reveal a strong trend in addressing fundamental challenges in parallel computing, with work spanning cache coherence, false sharing detection, data race analysis for GPUs, verification of neural networks, and thermal-aware management of heterogeneous systems. His research bridges theory and practice with significant contributions to both hardware and software aspects of parallel systems. His scientific achievements have been recognized through multiple prestigious awards: Google India Research Award 2021 Google Explore CSR 2022 Research Grant from Intel Corporation SERB Start-up Research Grant 2019 Google Cloud Platform Research Credits (2019, 2020) IITK Initiation Grant 2019 As an advisor, he mentors several PhD and MTech students working on cutting-edge research in parallel systems. His PROSPAR group has secured significant funding from industry and government sources, supporting innovative research in programming languages and parallel systems. The group actively collaborates with industry partners including Google and Intel, addressing real-world challenges in parallel computing. He leads the PROSPAR research group at IIT Kanpur, which brings together faculty, PhD students, and MTech researchers to tackle challenging problems at the intersection of programming languages, compilers, and parallel systems. The group maintains strong industry connections and focuses on practical solutions that can be deployed in real systems.

• Artificial Intelligence
• Systems Engineering
• Cyber-Physical Production Systems
• +5 more

Dr. Saber Darmoul is an Associate Professor specializing in Systems Engineering and Multidisciplinary Design. His research bridges artificial intelligence with industrial applications, focusing on cyber-physical production systems, operational resilience, and smart transportation. He actively explores knowledge representation, reinforcement learning, and agent-based modeling to address complex system challenges. Key Research Interests: Operational resilience, AI in manufacturing, multi-agent systems, smart mobility Technical Expertise: Ontology modeling, simulation platforms, immune-inspired control architectures His recent publications (2025-2019) demonstrate a consistent focus on integrating artificial immune systems into control architectures for transportation and manufacturing. Notable trends include: Development of knowledge-based systems for dynamic reconfiguration Application of multi-criteria decision frameworks in production environments Advancing predictive maintenance strategies through distributed systems Exploring 6G system-of-systems engineering While no explicit awards or student advisement information appears in available records, his 15 most recent publications reflect sustained academic productivity and evolving focus from foundational control systems (2017-2019) to advanced applications in Industry 4.0 (2020-2025).

• Database Systems
• In-Database Machine Learning
• GPU Acceleration for Databases
• +3 more

Maximilian Schüle serves as Assistant Professor in the Department of Data Engineering at the University of Bamberg's Faculty of Information Systems and Applied Computer Sciences since October 2022. Previously, he held research positions at Technical University of Munich (2017-2022). His research bridges database systems and machine learning through compiler-based approaches. His research focuses on in-database machine learning , GPU-accelerated query processing , and recursive SQL extensions . Key contributions include: Developing MLIR-based compilers for automatic differentiation in SQL (DuoLingo-AutoDiff) Creating GPU code generators for database kernels using NVRTC Designing higher-order lambda functions for expressive query languages Implementing end-to-end neural network training within database engines His recent publications (2023-2025) demonstrate consistent output in top venues including ICDE, VLDB workshops, and BTW conferences, with growing emphasis on hardware-aware optimization and compiler techniques for analytical workloads. He currently leads a DFG-funded project on elastic memory hierarchies for memory-intensive applications (2025-2028), supporting multiple PhD researchers. His supervision emphasizes open-source contributions to database systems like Umbra and practical implementation skills alongside theoretical foundations. As an active member of the database community, he serves as workshop chair for BTW 2025 and regularly reviews for ACM TODS, VLDB Journal, and Information Systems. His work on public transport analytics demonstrates real-world impact through collaborations with urban mobility initiatives in Bamberg.