جست‌وجوی استاد و پژوهشگر

• Distributed Systems
• Database Management
• Caching Techniques
• +۳ مورد دیگر

Shahram Ghandeharizadeh is a Professor at the University of Southern California (USC), known for pioneering work in database systems, distributed computing, and multimedia systems. He has authored over 178 publications and received the ACM Software System Award in 2008. His research focuses on caching strategies, data storage optimization, and distributed systems, with notable contributions to Cache Augmented SQL (CASQL) systems and flying light speck-based 3D displays. He collaborates extensively with industry and academia on projects like the BG social networking benchmark and the NOVA distributed database framework. His work bridges theoretical foundations with practical applications in cloud computing, real-time systems, and swarm robotics. Research interests include: - Cache optimization and write-back policies - Database architecture and consistency protocols - 3D visualization using flying light specks - Social networking benchmarking and scalability - Disaggregated cloud storage systems Key contributions include the Gamma Database Machine Project (1990), foundational work on continuous media servers (Mitra), and recent innovations in distributed systems like Gemini and Nova-LSM. He has advised on projects ranging from multimedia scheduling algorithms to fault-tolerant swarm-based displays. His work has been published in top venues including VLDB, SIGMOD, and ACM Multimedia, with a focus on practical implementations in cloud and edge computing environments.

Amanda L Kongمشاهده پروفایل

استاد

• Breast Cancer Disparities
• Breast Cancer Outcomes
• Geriatric Oncology
• +۳ مورد دیگر

Amanda L Kong is a Professor of Surgery and Section Chief of Breast Surgery in the Division of Surgical Oncology at the Medical College of Wisconsin. She is a board-certified surgical oncologist specializing in breast cancer and benign breast diseases with leadership roles including Vice Chair of the Breast Fellowship Training Committee of the Society of Surgical Oncology and State Chair for Wisconsin for the Commission on Cancer. Dr. Kong's educational background includes: Doctor of Medicine from Warren Alpert Medical School of Brown University (2002) General Surgery Residency at Mount Sinai Hospital, New York City (2003-2007) Breast Surgical Oncology Fellowship at The University of Texas MD Anderson Cancer Center (2007-2008) Master of Science in Epidemiology from the Medical College of Wisconsin (2010) Her research centers on breast cancer disparities across racial, ethnic, and age groups, hospital volume effects on treatment quality, and quality-of-life optimization for survivors. Using large administrative databases and tumor registries, she investigates care variations in high- versus low-volume centers and develops evidence-based approaches for elderly patients balancing treatment efficacy with well-being. Analysis of her 2024-2025 publications reveals consistent focus on surgical decision-making, disparities research, and health services outcomes. Key themes include sentinel lymph node biopsy utilization patterns, neoadjuvant therapy trends for HER2-positive cancer, and online information quality—highlighting her commitment to data-driven improvements in breast cancer care delivery and equity. Dr. Kong's honors include: Fellow of the American College of Surgeons Fellow of the Society of Surgical Oncology Best Doctor by M Magazine Best Doctors in America designation Multiple Medical College of Wisconsin Outstanding Medical Student Teaching Awards Excellence in Professionalism Award (2016) A dedicated mentor to students and residents, she integrates clinical practice with research through the CAPS group (formerly PCOR) to address real-world patient care challenges. Her leadership in national committees shapes surgical oncology training standards and Commission on Cancer accreditation while advancing evidence-based practice guidelines. As surgical lead for the Froedtert & MCW Inflammatory Breast Cancer Program (IBC Connect consortium), she directs a multidisciplinary team focused on rare breast cancer subtypes. Her research group leverages tumor registries and administrative databases to identify disparities and optimize care pathways across diverse patient populations.

Tamer Özsuمشاهده پروفایل

استاد

• Distributed Data Management
• Graph Databases
• RDF Systems
• +۲ مورد دیگر

Tamer Özsu is a University Professor at the David R. Cheriton School of Computer Science, University of Waterloo, where he previously served as Director (2007-2010) and currently holds the role of Associate Dean of Research. He is also a Distinguished Visiting Professor at Tsinghua University. Professor Özsu's research spans distributed data management, graph/RDF systems, and database fundamentals. His current work focuses on: Distributed graph processing algorithms SPARQL query optimization for RDF systems Streaming graph analytics Indexing techniques for modern hardware Distributed database architectures He has authored the seminal textbook Principles of Distributed Database Systems and co-edited the Encyclopedia of Database Systems . As founding Editor-in-Chief of ACM Books, he has shaped computing literature. His recent publications demonstrate continued innovation in graph partitioning, streaming graph algorithms, and scalable RDF processing. Özsu maintains active research leadership in database systems with over 30 years of influential contributions.

• Low carbon energy systems
• System flexibility
• Demand side flexibility
• +۵ مورد دیگر

Dr. Phil Grunewald is a Supernumerary Research Fellow at Oriel College and Research Director of the Energy Demand Observatory and Laboratory (EDOL) at the University of Oxford's Department of Engineering Science. He also serves as Oxford Principal Investigator for the JED-AI project. Previously, he led the Oxford Energy Network and the Flexibility theme at the Environmental Change Institute (2013-2020). Education PhD from Imperial College London on grid storage economics, supported by a UKERC scholarship. Research Focus Grunewald employs interdisciplinary approaches to analyze energy demand flexibility using smart meter data, machine learning, and socio-technical variables. Core research themes include: Low-carbon energy transitions Demand response and system flexibility Machine learning applications for household energy clustering Longitudinal analysis of crisis impacts on consumption Equity in energy system redesign Publications Recent articles (2019-2024) demonstrate strong focus on: 1) Data-driven household energy pattern analysis using machine learning, 2) Crisis resilience and emissions impacts of demand flexibility, 3) Socio-technical barriers to energy transitions, and 4) Methodological innovations in activity-based energy modeling. Recurring themes include temporal consumption variability, policy equity, and decarbonization trade-offs. Awards EPSRC Fellowship (2015-2021) Marie Curie Fellowship Fellow of the Institution of Civil Engineers (FICE) Projects & Grants Principal Investigator for: EDOL (EPSRC): Longitudinal household energy-observatory ReNEW (Oxford Martin): Energy equity and wellbeing Shift-0 (MCS Foundation): Heat-pump adoption impacts METER study : Machine learning analysis of UK household diaries/meter data Teams & Infrastructure Leads energy demand research group developing specialized tools for activity recording and meter data analysis. Maintains UK Household Energy Use database.

• 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.

• 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.

Anne Warcholdمشاهده پروفایل

پژوهشگر ارشد

• Environmental Sciences
• Sustainability Science
• Systems Analysis
• +۳ مورد دیگر

Anne Warchold is a Research Fellow at the Faculty of Science and Engineering, University of Groningen. Her work focuses on sustainability science, systems analysis, and economics, with a particular emphasis on the UN Sustainable Development Goals (SDGs) and the European bioeconomy strategy. She holds an MSc in Sustainability Management and a BSc in Business Administration & Economics, with a focus on environmental management and energy economics. Her doctoral research quantified SDG system complexities, exploring synergies, trade-offs, and interactions with drivers like the EU bioeconomy transition. Previously, Anne conducted research at the Potsdam Institute for Climate Impact Research (PIK), addressing sustainability challenges such as inequality reduction, sustainable food systems, and urban decarbonization. She leads projects like BeyondSDG (ERC-funded) and FORHIMSDG (DAAD-funded), promoting Himalayan development through SDG teaching/research. Her grants include funding from the EU Horizon 2020 and German Federal Ministry of Education and Research. Anne’s research bridges science and policy, emphasizing systems thinking to accelerate sustainable development. Her work spans interdisciplinary collaborations, methodological innovations for SDG analysis, and impactful policy-oriented outputs. Notable contributions include developing SDG interaction models, critiquing data selection practices, and proposing pathways for localized decarbonization.

• Operating systems
• Distributed systems
• Computer Science
• +۳ مورد دیگر

Professor Willy Zwaenepoel is a distinguished academic and Dean of the Faculty of Engineering at the University of Sydney. He holds Fellowships from ACM, IEEE, and ATSE. His research focuses on distributed systems, operating systems, and experimental computer science. Previously, he spent two decades at Rice University and nine years as Dean of EPFL's School of Computer and Communication Sciences before joining Sydney in 2018. Education: BS/MS, Ghent University (1979) MS/PhD, Stanford University (1980/1984) Research Interests: His work emphasizes distributed systems and operating systems, with contributions to key-value stores, transactional systems, and large-scale graph processing. Recent projects include optimizing geo-replicated systems and improving datacenter scheduling efficiency. Articles Trends: Recent publications address OS scheduling (Nest), distributed graph mining (Tesseract), and transactional systems' performance limits. He explores hardware-software co-design for multicore systems and energy-efficient data centers. Awards: Fellow of ACM (2021) Fellow of IEEE (2020) Fellow of ATSE (2019) Grants & Advising: Active grants include adaptive key-value store research (2021) and large-graph processing systems (2018). He advises PhD students and postdocs on distributed systems and storage challenges. Labs/Teams: Leads the Sydney systems research group focusing on scalable distributed systems and cloud infrastructure.

Christoforos Kozyrakisمشاهده پروفایل

استاد

• Computer Architecture
• Computer Systems
• Cloud Computing
• +۳ مورد دیگر

Christoforos Kozyrakis is a Professor of Electrical Engineering and Computer Science at Stanford University. He leads the MAST research group and serves as faculty director of the Stanford Platform Lab. His research focuses on computer architecture, systems, cloud computing, and machine learning integration. Education: B.S. from University of Crete (undated), Ph.D. in Computer Science from UC Berkeley (2002). He holds ACM and IEEE Fellowships and has received awards including the ACM SIGARCH Maurice Wilkes Award (2015) and NSF Career Award (2006). Research interests include cloud computing systems, ML-driven systems optimization, and secure hardware-software co-design. Recent work emphasizes scalable storage fabrics for ML training and secure GPU execution frameworks. Key contributions span 207+ publications, including influential papers on database systems, storage optimization, and cloud security. Current projects explore low-latency storage systems (RAIL), composite storage fabrics (Tectonic-Shift), and secure enclave technologies (ShEF). Awards: ACM SIGARCH Maurice Wilkes Award (2015), IEEE Fellow (2014), IBM Faculty Award (2006) Advising: Supervises doctoral students like Athinagoras Skiadopoulos and master's advisees including Andrew Bartolo Labs: MAST Research Group and Stanford Platform Lab

• Building Stock Modeling
• Energy Analysis
• 3D Geometric Modeling
• +۵ مورد دیگر

Steve Evans is a Senior Research Fellow at the Bartlett School of Environment, Energy & Resources at University College London (UCL). He leads the 3DStock model development within the Building Stock Lab at UCL Energy Institute and is currently heading up the construction of the National Buildings Database. His work bridges multiple disciplines through the application of GIS and geospatial analysis to energy and building stock modeling problems, with extensive experience collaborating with government departments including DESNZ and BEIS. Evans' research focuses on developing detailed 3D models of building stocks to analyze energy use and inform decarbonization strategies. His work centers around the 3DStock model, which represents every building and its energy performance at a fine level of granularity. This research has significant implications for urban planning, energy policy, and climate change mitigation efforts, particularly through his work on the National Buildings Database which is designed to trigger a 'paradigm shift' in how building data informs policy decisions. His publication record demonstrates a consistent progression from foundational 3D modeling techniques to practical policy applications, with particular emphasis on the relationship between urban form, building characteristics, and energy consumption. The research shows increasing sophistication in modeling approaches while maintaining direct relevance to real-world decarbonization challenges faced by cities and national governments. Principal Investigator for the National Buildings Database project (£2.7M) from the UK Department for Energy Security and NetZero Team Lead for the Non-domestic Building Survey (NDBS, £0.8M) Contributor to the RCUK Centre for Energy Epidemiology (CEE) (EPSRC, £5.75m) Member of the Centre for Research on Energy Demand Solutions (CREDS) (EPSRC, £19.44m) Consultant on multiple government projects including Social Housing Decarbonisation Fund and Renewable Heat Incentive evaluation Evans manages the Building Stock Lab team at UCL, which develops and maintains the 3DStock model. This lab serves as a hub for interdisciplinary research connecting energy analysis, urban planning, and geospatial technologies. The lab's work directly informs government policy through contracts with departments including DESNZ and BEIS, as well as collaborations with local authorities like the London Borough of Islington on social housing decarbonization projects.

• Building Performance
• Energy Efficiency
• Sustainable Architecture
• +۵ مورد دیگر

Professor Paul Ruyssevelt serves as Chair of Energy & Building Performance at University College London's Bartlett School of Environment, Energy & Resources, where he leads the Building Stock Lab. With over 40 years' experience spanning academia and industry, he specializes in energy performance of non-domestic buildings, urban-scale energy modeling, and addressing the energy performance gap. His work bridges practical building science with policy development through major initiatives like the National Building Database and IEA Energy in Buildings and Communities. His research focuses on building stock modeling at city and national scales, particularly for non-domestic buildings and informal settlements in the Global South. Key projects include the £2.7m National Building Database (NBD) for UK building energy analysis, GEMDev for equitable energy planning in off-grid communities, and Data-Smart Building Case Studies for operational efficiency. His publication trends reveal increasing emphasis on informal settlement energy modeling , stock-level retrofit optimization , and thermal performance in extreme climates , with recent work integrating participatory methods and geospatial data. As UK alternate delegate and Vice Chair for the IEA EBC Technology Collaboration Programme, he leads Annex 70 on Building Energy Epidemiology. His teaching includes dissertation supervision for Smart Energy and the Built Environment MSc and lectures for Sustainable Energy BASC, Energy Systems and Data Analytics MSc, and Health, Wellbeing and Sustainable Buildings MSc programs. His educational background includes a PhD in Superinsulated Homes from University of Westminster (1987), RIBA Part III Professional Practice, and BArch (2.1) from Newcastle University. Prior to UCL, he held academic positions at Oxford Brookes University and Architectural Association School of Architecture, alongside extensive industry experience including director roles at Ruyssevelt Consulting Limited and Camco International Limited.

Xiaoyi Luمشاهده پروفایل

دانشیار

• Parallel and Distributed Computing
• High-Performance Communication and I/O Technologies
• Big Data Analytics
• +۴ مورد دیگر

Dr. Xiaoyi Lu is an Associate Professor in the Department of Computer Science & Engineering at the University of California, Merced (UC Merced), where he founded and directs the Parallel and Distributed Systems Laboratory (PADSYS Lab). He is affiliated with the AgAID Institute since 2023 and has authored over 170 publications, including ten Best Paper Awards or Nominations (e.g., SC 2019, IPDPS 2024). His research outcomes like OpenDOTA and MVAPICH2-Virt are used by hundreds of organizations globally. Research Interests: He focuses on scalable parallel systems for HPC, Big Data, AI, Cloud, and Edge Computing, leveraging advanced technologies like RDMA/PMEM/NVMe/GPU/DPU. His work bridges high-performance computing with applications in precision agriculture, biostatistics, and digital twin technology. Article Trends: Recent publications emphasize DPU offloading, compression-optimized collective communication, error detection in HPC, and scalable Bayesian group testing. Topics span GPU clusters, NVMe-over-Fabrics, and adaptive networks for LLM training, reflecting his expertise in heterogeneous architectures and distributed systems. Scientific Awards: NSF CAREER Award (2024) Amazon Research Award (2023) Google Research Award (2022) Meta Faculty Research Award (2022) Multiple Best Paper Nominations Professional Activities: He serves as Associate Editor for Frontiers in High Performance Computing and organizes tracks at SCAsia and HiPC. His leadership in PADSYS Lab drives innovation in systems for social good.

Jan Fasslerمشاهده پروفایل

استاد

• Evolution
• Genetics
• Molecular Biology
• +۶ مورد دیگر

Jan Fassler is a Professor and Director of Graduate Studies at the University of Iowa Department of Biology , where she investigates molecular mechanisms of stress response and transcription regulation in fungi and their implications for animal biology and neurodegenerative diseases. Education: PhD from Purdue University Her research spans evolutionary genetics, protein aggregation/disaggregation dynamics, and antifungal drug discovery. She collaborates with the Erives, Phillips, and Weeks labs to study the absence of amyloid disaggregase Hsp104 in animals and its therapeutic potential for Alzheimer’s and Parkinson’s diseases. Recent publications focus on the MED15 gene’s role in transcriptional plasticity, polyglutamine tract variation in yeast adaptation, and protein interaction networks in the RNA polymerase II Mediator complex. Her work leverages Saccharomyces cerevisiae and Cryptococcus neoformans as models for environmental stress signaling and fungal pathogenesis. Contact Details: Email: jan-fassler@uiowa.edu Phone: 319-335-1542 Address: 202 Biology Building East, University of Iowa, Iowa City, IA 52242, United States

Sang-Hoon Kimمشاهده پروفایل

دانشیار

• Operating Systems
• Memory Systems
• Storage Systems
• +۴ مورد دیگر

Sang-Hoon Kim is an Associate Professor in the Department of Software and Computer Engineering and Department of Artificial Intelligence at Ajou University, South Korea. He leads the Systems Software Lab (Paldal Hall 1004-2) and maintains active collaborations with Virginia Tech as a Visiting Scholar since August 2024. His academic journey includes a Ph.D. in Computer Science from KAIST (2016) under advisors Seungryoul Maeng and Jin-Soo Kim, and a B.S. in Computer Science from KAIST (2002). His research spans operating systems, memory management, and storage systems with focus on mobile platforms, heterogeneous architectures, and SSD technologies. Key interests include memory fragmentation control , distributed thread execution , key-value storage optimization , and resource disaggregation . His work bridges theoretical innovation with practical system implementations, particularly for mobile and datacenter environments. Kim's publication portfolio shows consistent output in top-tier venues including USENIX FAST, VLDB, ICDCS, and ASPLOS. His research demonstrates evolution from mobile memory management (2015-2017) toward distributed systems and hardware-aware software (2019-present), with recent emphasis on resource-disaggregated environments and heterogeneous-ISA computing. The 2024 Best Paper Award at USENIX FAST highlights his impact in storage systems research. Best Paper Award at USENIX FAST'24 Multiple patents including US-9588912B2 for memory control He directs significant research projects funded by ETRI, NRF, and US ONR, including current work on memory-centric computing systems (2020-2023) and disaggregated non-volatile memory systems using RDMA (2018-2020). His Systems Software Lab maintains strong industry partnerships with Samsung Electronics and NHN, with prior projects improving Android memory management and developing SSD-based storage systems for large-scale internet services.

Pankaj Mehraمشاهده پروفایل

استاد پژوهشی

• Memory Systems
• Parallel and Distributed Computing
• Artificial Intelligence
• +۷ مورد دیگر

Pankaj Mehra is a Research Professor in the Department of Computer Science and Engineering at The Ohio State University and CEO of Elephance Memory, a company he founded that develops software for disaggregated data center memory optimization. He holds a Ph.D. in Computer Science from The University of Illinois at Urbana-Champaign and has extensive industry leadership experience including SVP/WW CTO at Fusion-io, VP/Senior Fellow roles at SanDisk and Western Digital, and VP positions at Samsung where he led SmartSSD development. His research focuses on memory-semantic fabrics (CXL, UALink), Memory Objects abstraction for data-centric programming, and AI Super Devices (AISDs) for near-data processing in AI workloads. Key interests include optimizing memory systems for Retrieval Augmented Generation (RAG), vector databases, and Approximate Nearest Neighbor Search (ANNS) algorithms. His notable scientific awards include: Samsung R&D Award (2019) for SmartSSD development CES R&D Innovation Award (2021) for SmartSSD Dr. Mehra founded HP Labs Russia and served as Chief Scientist until 2010. He is an active industry contributor to the Open Compute Project and DOE EMC3 initiatives, with scheduled presentations at the 2025 OCP Global Summit on composable memory systems and data-centric computing. His entrepreneurial ventures include Elephance Memory, IntelliFabric, Whodini, and AwarenaaS, with publications spanning 3 books and over 100 papers and patents in memory technologies and distributed systems.