جست‌وجوی استاد و پژوهشگر

• Database Systems
• Distributed Computing
• Machine Learning
• +۳ مورد دیگر

Carsten Binnig is a researcher affiliated with TU Darmstadt, Germany, focusing on database systems, distributed computing, and machine learning integration. His work spans high-performance data management, query optimization, and hardware-software co-design for modern networks. He holds a PhD from Heidelberg University (2008) and has contributed extensively to conferences like SIGMOD, VLDB, and CIDR. His research emphasizes scalable OLTP/OLAP systems, secure cloud DBMSs, and leveraging GPUs/accelerators for database operations. Notable projects include GOLAP (GPU-accelerated OLAP), ScaleStore (storage engine using NVMe/RDMA), and DBMS Annihilator (workload generator). He co-leads initiatives on testing database systems (DBTest workshop) and benchmarks for shared databases.

• Parallel Computing
• Distributed Systems
• Intelligent Storage Systems
• +۵ مورد دیگر

Cheng Li is an Associate Professor at the School of Computer Science, University of Science and Technology of China , and Director of the Information Computing Platform at the Institute of Artificial Intelligence, Hefei Comprehensive National Science Center. His research focuses on parallel and distributed intelligent computing systems, with an emphasis on optimizing computational efficiency through data-centric approaches. Education: PhD in Computer Science from Max Planck Institute for Software Systems (MPI-SWS) and Saarland University (2016) Bachelor's degree in Computer Science from Nankai University (2009) Research interests include distributed systems, machine learning optimization, database systems, and high-performance computing. Notable contributions include innovations in distributed training of deep neural networks, geo-replicated databases, and efficient graph processing. His work has been published in top-tier conferences like NSDI, AAAI, VLDB, and SOSP. Professional Activities : PC member at OSDI 2025, EuroSys 2024/2025, FAST 2024, and SOSP 2021. Teaching includes Systems Seminar and Compiler Principles at USTC since 2017. Grants : Secured $500k/year funding from Chinese government agencies and collaborations with Huawei/Alibaba. Active in cloud-native systems and AI infrastructure research.

Max Hewittمشاهده پروفایل

دانشیار

• Forecasting and valuation implications of financial statement information
• Implications of financial statement presentation
• Investors’ reaction to disaggregated financial statement information
• +۳ مورد دیگر

Max Hewitt is an Associate Professor of Accounting at the Dhaliwal-Reidy School of Accountancy within the University of Arizona . With a Ph.D. from the University of Washington and over twenty years of teaching experience, he has instructed accounting courses at prestigious institutions including Indiana University, University of Washington, University of New South Wales, and Macquarie University. His research focuses on information processing biases in financial analysts, investors, and managers, published in top-tier journals like Contemporary Accounting Research and The Accounting Review . Max’s work examines forecasting and valuation implications of financial statement information, investor reactions to disaggregated data, and earnings management dynamics. His publications highlight his expertise in earnings persistence, accruals analysis, and investor behavior. Faculty Service Award (2025) Multiple Teaching Awards (2006–2024) from Eller College, Kelley School, and Foster School Eller Fellowship (2020–present) A Chartered Accountant with Master of Commerce and Teaching degrees, he previously worked as a financial advisor at PricewaterhouseCoopers. His teaching spans undergraduate, MBA, and doctoral levels, including courses like ACCT 310 (Cost Accounting) and ACCT 696A (Accounting Research).

Vladimir Zadorozhnyمشاهده پروفایل

استاد

• Networked information systems
• Complex adaptive systems
• Heterogeneous data fusion
• +۷ مورد دیگر

Professor Vladimir Zadorozhny is a faculty member at the University of Pittsburgh's School of Computing and Information, holding a PhD from the Institute for Problems of Informatics, Russian Academy of Sciences. His roles include Adjunct Professor at the University of Agder, Norway, and Core Faculty in Pitt's Biomedical Informatics Training Program. He leads projects in data science, medical informatics, and social inequality analysis, funded by NSF, NIH, and others. His research focuses on networked information systems, data fusion for medical and societal challenges, and scalable architectures. Education: PhD, Russian Academy of Sciences (Moscow) Research emphasizes historical data integration (e.g., CHIA project), medical decision-making (PREDICT initiative), and causal reasoning systems (CaReLearner). Awards include the Fulbright Scholarship and Leiv Eiriksson Fellowship. His work spans 100+ peer-reviewed articles, with recent contributions in Tsetlin Machine applications, fake news detection, and IoT credibility frameworks. Labs include the Center for Artificial Intelligence Research (CAIR) and ADMT Lab. Grants include NIH-funded cardiac arrest recovery prediction and NSF-funded horizontal inequality analysis. Collaborations span biomedical, engineering, and social science domains.

Gwendoline De Oliveira Nevesمشاهده پروفایل

استاد

• Human Geography
• Urban Geography
• Urban Morphology
• +۶ مورد دیگر

Gwendoline De Oliveira Neves is a Professor in the Department of Geography, History and Philosophy at Universidad Pablo de Olavide, specializing in Human Geography with particular expertise in urban morphology and metropolitan dynamics in Andalusia. Her academic journey includes a PhD from the Institut de Géographie Alpine at the University of Grenoble (France) and postdoctoral research through a prestigious Marie Curie scholarship in collaboration with the University of Seville and the Andalusian Regional Government's Ministry of Environment. Her research interests span urban geography, metropolitan planning, spatial analysis of socio-territorial dynamics, and the political geography of urban spaces. She has extensively studied Andalusian metropolitan areas, examining how migration patterns, socioeconomic factors, and urban morphology intersect with political trends, particularly the rise of far-right politics in urban contexts. Dr. De Oliveira Neves' publication record demonstrates consistent scholarly output focused on Spanish metropolitan areas, with particular attention to urban morphology, territorial organization, and social sustainability. Her work combines quantitative spatial analysis with qualitative insights to understand complex urban phenomena. Marie Curie scholarship recipient PhD from Institut de Géographie Alpine, University of Grenoble Active contributor to Spanish geography journals and international publications Her academic activity includes supervision of graduate students and participation in research projects examining metropolitan dynamics, though specific details about current advisees aren't provided in the available information. She maintains active research collaborations with institutions across Spain and has contributed to significant projects like the DINAMET project on socioterritorial dynamics in Spanish metropolitan areas.

Simon Peterمشاهده پروفایل

دانشیار

• Operating Systems
• Networking
• Data Center Systems
• +۵ مورد دیگر

Simon Peter is an Associate Professor in the Paul G. Allen School of Computer Science and Engineering at the University of Washington since 2022. His research focuses on operating systems, networking, and energy-efficient data center systems. Previously, he held positions as an Assistant Professor at UT Austin (2016–2022) and earned his Ph.D. (Dr. sc. ETH) from ETH Zurich in 2012, with a Dipl.-Inf. from Carl-von-Ossietzky University of Oldenburg in 2006. Education: Ph.D. in Computer Science, ETH Zurich, 2012 Dipl.-Inf. (M.Sc.), Computer Science, Oldenburg University, 2006 Research Interests: Design of low-latency, scalable, and energy-efficient data center systems Virtual machine and OS design (QoS in memory management, zero-copy I/O) Programmable NICs/switches (RDMA offloading, in-network ML preprocessing) Power control planes and disaggregated storage systems Teaching: Recent courses include CSE481A Operating Systems Capstone and Data Center Systems Extensive teaching history at UW and UT Austin in operating systems and data center topics Awards & Recognition: Not explicitly listed in provided materials Advising & Grants: Actively recruiting Ph.D. students via UW’s program No grant details provided in current text Labs & Teams: Part of UW’s Distributed Systems Lab Collaborates with industry on data center innovations Personal: Music enthusiast with background in violin, electronic music production, and DJing Outdoor activities include skiing and hiking Former Demoscene participant in Germany

• Computer Science
• Memory Analysis
• NUMA Architectures
• +۷ مورد دیگر

Andreas Andronikakis is a Researcher at the University of Manchester, focusing on Computer Science and Memory Analysis. His work explores NUMA Architectures, Java Applications, and Performance Analysis, with significant contributions to memory behavior in managed systems and scalable architectures. Research trends include optimizing memory management for NUMA systems, analyzing Java benchmarks, and advancing hardware/software integration in virtual machines. His publications address topics like graph databases, disaggregated data centers, and runtime environments. Collaborative efforts span institutions, with active participation in the research community. No scientific awards or student advising details are explicitly mentioned in available texts.

Stephen Stubbenمشاهده پروفایل

استاد

• Financial Accounting
• Earnings Management
• Accounting Standards
• +۳ مورد دیگر

Stephen Stubben is a Professor of Accounting and Senior Associate Dean for Research at the Eccles School of Business, University of Utah. He holds the Francis Armstrong Madsen Endowed Professorship. His roles include overseeing faculty and research initiatives, directing the Accounting PhD Program (2014–2019), and serving on editorial boards for five accounting journals. Stubben’s research focuses on financial accounting standards, earnings management, and corporate finance, with publications in top journals like The Accounting Review and Journal of Accounting Research . He has received the Wildman Award for impactful contributions to accounting practice. Education: PhD in Accounting from Stanford University (2006). Research Interests: Financial reporting quality, valuation in acquisitions, equity vs. liability classification of employee stock options, analyst behavior, and board responsiveness to shareholders. His work often bridges theoretical frameworks with practical implications for accounting standards and corporate governance. Professional Service: President of the Financial Accounting and Reporting Section of the American Accounting Association, member of FASB conference planning committees, and frequent keynote speaker. He advises PhD students and new faculty nationally, chairs dissertation committees, and teaches advanced accounting courses. Awards: Wildman Award (annual top contribution to accounting practice), peer review awards from leading journals, and teaching excellence awards across undergraduate, masters, and PhD levels. Service & Leadership: Coordinates the Accounting PhD Program, advises on strategic planning for the Eccles School, and participates in international conferences shaping accounting standards. His editorial roles ensure rigorous peer review processes in the field.

Ana Nunes Alonsoمشاهده پروفایل

استادیار

• Dependable Data Management
• Distributed Systems
• Fault Tolerance
• +۲ مورد دیگر

Ana Nunes Alonso is a researcher at the High Assurance Software Lab (HASLab) and an Invited Assistant Professor at the University of Minho, where she lectures in courses on Fault Tolerance, Distributed Systems and Operating Systems. She has been affiliated with INESC TEC since February 1, 2012, contributing to multiple National and European research and innovation projects in collaboration with industry in the Software Engineering, Insurance and Energy domains. Her research interests focus on dependable data management and data processing systems, with particular expertise in various fault tolerance mechanisms. She has published extensively on distributed agreement protocols, privacy-preserving machine learning applications, and database system architectures. Her recent publications demonstrate a strong focus on practical implementations of theoretical concepts, particularly in distributed systems and database technologies. Her work on TADA (Toolkit for Approximate Distributed Agreement) addresses challenges in distributed consensus, while her research on TiQuE and LOOM explores innovative approaches to database system design and transaction processing. Ana has supervised multiple master's thesis students on topics including distributed query processing, consensus algorithms, blockchain permissions, and SQL interpretation. Her work bridges academic research with practical industry applications, particularly in the insurance and energy sectors where data reliability and security are critical. She is affiliated with the High-Assurance Software Lab (HASLab) at INESC TEC, a research center focused on developing trustworthy software systems through rigorous engineering approaches and formal methods.

José Orlando Pereiraمشاهده پروفایل

دانشیار

• Dependable Distributed Systems
• Data Management
• Database Replication
• +۷ مورد دیگر

José Orlando Pereira is an Associate Professor at the Department of Informatics, University of Minho, and Research Coordinator at INESC TEC (INESC Technology and Science). His primary research interests lie in dependable distributed systems, with a focus on data management, group communication protocols, and tools for distributed system evaluation. He leads the High-Assurance Software Laboratory (HASLab) , part of INESC TEC and the University of Minho. Roles: Academic supervisor, conference committee member (e.g., SRDS 2025 General Co-Chair), and grant recipient (e.g., ADAPQO funding from CMU Portugal 2024). Key Projects: Includes CYBERACTIONING (cybersecurity education), EUMaster4HPC (European HPC master's program), and AIDA (adaptive assurance platform). Research Themes: Database replication, georeplicated systems, consensus protocols, edge computing, and fault injection tools like LAZYFS. His work emphasizes practical applications, such as improving transactional performance in hybrid workloads (TiQuE) and designing scalable NoSQL middleware (CloudMdsQL). He has authored over 200 publications, including seminal contributions to gossip protocols (HyParView) and consensus algorithms. Awards: Honorable Mention at ACM SIGMOD 2025 for CRDV research. Active in organizing conferences such as SRDS, DSN, and EuroSys. Teaching: Offers advanced courses on distributed systems, database administration, and large-scale data replication at both undergraduate and graduate levels.

Carsten Binnigمشاهده پروفایل

استاد

• Database Systems
• Distributed Computing
• AI Systems
• +۲ مورد دیگر

Carsten Binnig is a Professor at Technische Universität Darmstadt, leading research in Data and AI Systems. His work focuses on next-generation data management architectures leveraging modern hardware capabilities like high-speed RDMA networks. Primary research areas include: Distributed database systems for cloud and edge environments AI-driven optimization of query processing Hardware-accelerated data management Stream processing architectures Secure computation in databases Recent publications examine learned cost estimators, RDMA synchronization challenges, and benchmarking frameworks for distributed systems. His group develops novel approaches to optimize database kernels and enhance cloud DBMS security. He contributes to major database conferences including SIGMOD and VLDB, and investigates emerging challenges in LLM-based data engineering.

• Data Management Systems
• Database Systems
• Operating Systems
• +۴ مورد دیگر

Prof. Jana Giceva Makreshanska is a Professor of Database Systems at the Technical University of Munich (TUM), affiliated with the TUM School of Computation, Information and Technology and the Department of Computer Science. Her research focuses on systems support for data management, leveraging modern hardware architectures including operating systems, compilers, and computer architecture. She holds a PhD from ETH Zurich (2017) and previously served as faculty at Imperial College London. Notable awards include the ERC Starting Grant (2024), VMware Early Career Faculty Award (2019), and ETH Medal (2018). Education: PhD in Computer Science, ETH Zurich (2017) Research roles at Microsoft Research and Oracle Labs Research Interests: Her work addresses holistic system design challenges in data management, including optimizing query processing for modern hardware, compiler integration, and emerging architectures like chiplets. She explores efficient data processing techniques for big data and transactional systems. Recent Contributions: Her recent work includes innovations in disaggregated systems (HotOS 2023), chiplet-aware scheduling (2025), and universal graph data structures (VLDB 2022). Her publications span topics like NVMe array optimization, FPGA-based partitioning, and hybrid OLTP/OLAP systems. Awards: ERC Starting Grant (2024) Best Paper Runner-Up at VLDB 2022 ACM SIGMOD Highlights Award (2023) Teaching & Service: Current courses include 'Cloud-Based Data Processing' and 'Data Processing on Modern Hardware'. She chairs diversity initiatives at SoCC 2023, serves on program committees for OSDI, VLDB, and SIGMOD, and leads gender equality efforts at TUM. Labs/Teams: Maintains the Lehrstuhl III (I3) database systems group at TUM, collaborating on projects like LakeVilla and ARCAS.

Joeri Rogeljمشاهده پروفایل

استاد

• Climate Science
• Climate Policy
• Carbon Budgets
• +۷ مورد دیگر

Prof. Joeri Rogelj is a Professor of Climate Science & Policy at Imperial College London's Centre for Environmental Policy and Director of Research at the Grantham Institute. He holds a PhD from ETH Zurich and has held roles at institutions including IIASA and PIK. His work focuses on climate change mitigation pathways, carbon budgets, and the interplay between climate policy and sustainable development. He is a leading IPCC author, contributing to the 1.5°C Special Report and Sixth Assessment Report. Recognized with awards like the Early Career Scientist Award, Rogelj advises bodies such as the UN Secretary-General’s Climate Science Advisory Group and the European Scientific Advisory Board on Climate Change. Educations: PhD (Climate Science, ETH Zurich, 2013), MSc in Engineering (KU Leuven, 2003), Postgraduate in Cultures & Development Studies (KU Leuven, 2005). Early career included engineering roles in Rwanda’s hydropower sector and leadership of the Rwanda Hydropower Atlas study. Research interests center on transforming societies toward sustainability through interdisciplinary approaches linking Earth systems to policy. Key themes include net-zero targets, climate justice, and the feasibility of global warming limits. His work underpins UN climate negotiations and informs policy frameworks like the Paris Agreement. Scientific awards highlight his contributions to climate science and international collaboration. He serves on editorial boards for Science and Environmental Research Letters , further amplifying his influence on climate science communication and policy. Advising and grants: Major contributions to UN Environment Programme Emissions Gap Reports and IPCC assessments. Projects include analysis of corporate emissions targets and climate litigation support for Children vs Climate Crisis . Active in developing frameworks for fair climate contributions and equitable decarbonization pathways. Labs/Teams: Part of the Grantham Institute and Centre for Environmental Policy research groups, collaborating globally with institutions like IIASA and the UNFCCC Secretariat.

Haoran Qiuمشاهده پروفایل

پژوهشگر

• AI/ML Systems
• ML for Systems/AIOps
• Distributed Systems
• +۳ مورد دیگر

Haoran Qiu is a Systems Researcher at Microsoft Azure Research – Systems (AzRS), focusing on AI-driven cloud systems efficiency. He holds a Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign (UIUC), advised by Prof. Ravishankar K. Iyer, and a B.Eng. in Computer Science from the University of Hong Kong (HKU). His research interests span AI/ML systems, cloud computing, distributed systems, and sustainability. He has interned at Google, Microsoft Research, and IBM Research. Notable awards include the 2023 ML Systems Rising Stars by MLCommons and the Mavis Future Faculty Fellowship. Education: Ph.D., Computer Science, UIUC (2024) B.Eng., Computer Science, HKU (2019) Visiting Student, University of Wisconsin-Madison (2018) Research Interests: Designing learning-based solutions for cloud systems Energy-efficient LLM serving and resource management Resilience and sustainability in hyperscale datacenters AIOps and ML-driven systems management Recent Contributions Include: Advances in thermal-aware scheduling for LLMs (TAPAS) Green computing strategies balancing SLOs and carbon emissions Queue management for large language models (QLM) Awards: Best Paper Finalist (L4DC 2024) Best Presentation Award (COMPSYS 2022) Multiple Fellowships and Scholarships Labs/Teams: Active in Microsoft Azure Research, contributing to cutting-edge ML-centric cloud systems projects. Collaborates widely with industry (Google, IBM) and academic institutions.

Hang Liuمشاهده پروفایل

استادیار

• High-Performance Computing
• Graph Analytics
• Machine Learning
• +۳ مورد دیگر

Dr. Hang Liu is an Assistant Professor in the Department of Electrical and Computer Engineering at Rutgers University. He previously held faculty positions at Stevens Institute of Technology (2019-2022) and University of Massachusetts Lowell (2017-2019). His research focuses on high-performance computing systems leveraging GPUs, FPGAs, and SSDs for graph analytics, machine learning, and numerical methods. He holds a Ph.D. from George Washington University (2017) and a B.E. from Huazhong University of Science & Technology (2011). Education: Ph.D. Electrical and Computer Engineering, George Washington University, 2017 B.E. Software Engineering, Huazhong University of Science and Technology, 2011 Research Interests: Dr. Liu's work spans high-performance computing (HPC), graph algorithms, machine learning acceleration, and numerical methods optimization. He develops novel architectures and algorithms for efficient processing of large-scale graphs and ML models on heterogeneous hardware, with applications in scientific computing and data analytics. His systems research emphasizes algorithm-hardware co-design for FPGAs and GPUs to achieve energy-efficient performance. Awards: Notable achievements include the IEEE CS TCHPC Early Career Award (2022), NSF CAREER Award (2021), and multiple best paper awards. He leads projects funded by NSF and industry partnerships, including AWS and Lawrence Berkeley National Lab. Advising & Grants: Advisor for multiple award-winning senior design projects and research grants. His work on AWS GraphChallenge and Berkeley Lab SRP Fellowships demonstrates sustained industry-academia collaboration. Labs/Teams: Active in the Rutgers High-Performance Systems (RHPS) lab, focusing on scalable graph analytics and ML infrastructure. Collaborates with interdisciplinary teams on AI-driven scientific discovery initiatives like DeepSpeed4Science.