جست‌وجوی استاد و پژوهشگر

Kim Eun-kyungمشاهده پروفایل

استاد

• Population-based patient care research
• Drug safety and patient empowerment
• Access to care and medications in underserved and special populations
• +۲ مورد دیگر

Professor Kim Eun-kyung is a full-time Professor at the College of Pharmacy , Seoul National University , South Korea. She directs patient-oriented research at the intersection of pharmacy, public health and clinical epidemiology, leading projects that range from nationwide pharmaco-epidemiologic cohort analyses to qualitative investigations of pharmacist-led care models. Education Ph.D. & Pharm.D., University of Florida, USA Research Interests Professor Kim’s research program is built on five mutually reinforcing pillars: Population-based patient care research – leveraging large national databases to understand medication utilization and outcomes in real-world settings. Drug safety and patient empowerment – identifying adverse drug reactions and developing interventions that enable consumers and clinicians to report and prevent harm. Access to care and medications in underserved and special populations – examining barriers faced by the elderly, rural residents, patients with rare diseases, and those undergoing high-cost procedures such as hematopoietic stem-cell transplantation. Disease prevention and appropriateness of therapy – evaluating preventive pharmacotherapy (e.g., anti-osteoporosis drugs, vitamin D) and ensuring evidence-based prescribing. Quality improvement in health-care delivery – designing multidisciplinary models that integrate pharmacists into care teams to optimize medication therapy and reduce drug-related problems. Publication Trends Across 2017–2020, Professor Kim has published extensively in International Journal of Environmental Research and Public Health , Clinical Nutrition , Medicina , BMC Public Health , Blood Research , Neuroepidemiology and other peer-reviewed journals. Her work characteristically combines rigorous secondary-data analyses of Korean or US national health surveys with systematic reviews/meta-analyses focusing on drug safety endpoints. Themes include neurological adverse events (trimetazidine-induced parkinsonism), hematologic toxicities (linezolid-induced thrombocytopenia), nutritional and metabolic complications in critical care, and access/utilization of medications in vulnerable populations. Scientific Awards & Honors (None listed in the provided texts) Grants & Research Support While specific grant identifiers are not detailed in the supplied pages, the breadth of large-scale database studies and multi-institutional collaborative projects implies sustained funding from national agencies such as the Korea Health Industry Development Institute (KHIDI) or the Ministry of Health & Welfare. Laboratory & Team Professor Kim leads an active research group located in Yeongeon Campus 17-201. The team integrates graduate students, post-doctoral researchers, pharmacists and biostatisticians working together on pharmacoepidemiology, patient-reported outcomes and health-services research.

Jennifer Widomمشاهده پروفایل

استاد

• Nontraditional Data Management
• Data Streams
• Crowdsourcing Quality Management
• +۳ مورد دیگر

Jennifer Widom is the Frederick Emmons Terman Dean of Stanford University's School of Engineering and holds the Fletcher Jones Professorship in Computer Science and Electrical Engineering. She previously served as Chair of the Computer Science Department (2009–2014) and Senior Associate Dean (2014–2016). Widom earned her Ph.D. in Computer Science from Cornell University (1987) and completed her undergraduate degree in Music at Indiana University (1982). She joined Stanford in 1993 after research at IBM Almaden. Education: Ph.D., Computer Science, Cornell University, 1987 MS, Computer Science, Cornell University, 1985 MS, Computer Science, Indiana University, 1983 BS, Music, Indiana University Jacobs School of Music, 1982 Research Interests: Widom's work focuses on nontraditional data management, including data streams, uncertain databases, crowdsourcing, and query processing systems like STREAM and Deco . She has pioneered methods for managing and querying uncertain data, optimizing graph algorithms, and integrating human computation into data systems. Key Contributions: Developed the STREAM system for real-time data stream management Advanced techniques for crowdsourcing quality management Contributed to foundational work in uncertain databases and provenance tracking Awards & Recognition: ACM Fellow (2005) Member, National Academy of Engineering (2005) Edgar F. Codd Innovations Award (2007) ACM-W Athena Lecturer (2015) EPFL-WISH Erna Hamburger Prize (2018) Teaching & Leadership: Widom teaches courses on data analytics and database systems, advising students like Arnav Joshi. She has led major initiatives in computational education and institutional leadership at Stanford.

Victor Vianuمشاهده پروفایل

استاد

• database theory
• verification
• computational logic
• +۳ مورد دیگر

Victor Vianu is a Professor in the Department of Computer Science and Engineering at the University of California, San Diego, within the Jacobs School of Engineering. His work focuses on the intersection of database theory and verification techniques, particularly in the context of data-driven business processes and workflows. Research Interests Professor Vianu's primary research interests span database theory, verification of database-driven systems, and computational logic. His current work focuses on automatic verification of interactive data-driven web services and business processes, exploring how to provide customized workflow views for different stakeholders in organizational settings. His research addresses significant technical challenges at the intersection of data management and process modeling, requiring novel approaches that go beyond traditional relational algebra to handle both data and process aspects simultaneously. His work on data-driven business processes investigates how to specify, analyze, and synthesize views of workflows that expose only information relevant to specific user roles. This research has important applications in e-commerce, digital government, healthcare, and scientific infrastructure, where different stakeholders require varying levels of workflow abstraction and detail. Research Contributions and Trends Professor Vianu's recent publications demonstrate a consistent focus on the integration of data management and workflow processes. His work has evolved from foundational database theory to increasingly practical applications in business process management. A key trend in his research is the development of formal frameworks for workflow views that maintain consistency while providing appropriate abstractions for different user roles. His publications reveal a progression from theoretical foundations to more applied aspects of workflow verification and integration, often in collaboration with researchers from INRIA and other institutions. Advising and Research Support Professor Vianu leads the UCSD Database Laboratory, which conducts research on database systems and theory. He currently advises graduate student Marysia Tran and has likely mentored numerous other students throughout his career. His research is supported by the National Science Foundation under grant "Views of Data-Driven Business Processes: Foundations and Applications" (NSF Project III 1815247). This project brings together techniques from logic, automata theory, complexity theory, algorithms, and automatic verification to address challenges in workflow management. Research Environment Professor Vianu is an active member of the UCSD Database Laboratory, which maintains a regular research seminar series. He has collaborated extensively with researchers including Alin Deutsch (UC San Diego), Serge Abiteboul (INRIA and ENS-Paris), Pierre Bourhis (Univ. of Lille and CNRS), and Adrien Koutsos (ENS Cachan). His foundational work includes co-authoring the influential textbook "Foundations of Databases" with S. Abiteboul and R. Hull, which remains a standard reference in database theory.

• Face Recognition
• Developmental Prosopagnosia
• Neurodiversity in Women
• +۳ مورد دیگر

Dr. Anna Bobak is a Senior Lecturer in Psychology at the University of Stirling, UK. She holds a PhD from Bournemouth University (2016) and joined Stirling as a Research Assistant on an EPSRC project under Peter Hancock before transitioning to her current role. Her primary research focuses on individual differences in unfamiliar face recognition, particularly developmental prosopagnosia, and the reliability of face-processing assessments. She also investigates neurodiversity in women, emphasizing lived experiences of autism and ADHD, including camouflaging behaviors and societal awareness. Research Interests: Face Recognition: Examines perceptual strategies, diagnostic criteria (e.g., Balanced Integration Score), and technological applications in forensic contexts. Neurodiversity: Explores gender-specific manifestations of autism and ADHD, societal perceptions, and support mechanisms. Cognitive Methodology: Advances psychometric rigor in face-processing studies and critiques measurement validity. Her work bridges theoretical research and real-world applications, such as evaluating automated face recognition technology’s biases and collaborating on initiatives like #ScienceForUkraine to aid displaced academics. She is affiliated with the Cognition in Complex Environments research group and contributes to global security and resilience themes at Stirling.

He Bingshengمشاهده پروفایل

استاد

• Big Data Systems
• Cloud Computing
• High Performance Computing
• +۴ مورد دیگر

Professor He Bingsheng is a faculty member at the Department of Computer Science, School of Computing, National University of Singapore (NUS), where he also serves as Vice-Dean, Research. He holds a Ph.D. in Computer Science from the Hong Kong University of Science & Technology (2008) and dual bachelor’s degrees in Computer Science & Engineering and Business Administration from Shanghai Jiao Tong University (2003). Education: Ph.D. (HKUST, 2008), B.E./B.B.A. (SJTU, 2003) Prior roles: Microsoft Research Asia (2008-2010), Nanyang Technological University (NTU), Singapore His research focuses on Big Data management systems , particularly on cloud computing and emerging hardware architectures (GPU, FPGA, NVM). He has led projects like ThunderGP, a novel FPGA-accelerated graph processing framework achieving 419x speedup for Covid-19 prevalence estimation. His work spans parallel/distributed systems and graph algorithms , with publications in ACM SIGMOD, VLDB, SC, and IEEE Transactions. The selected articles highlight his contributions to GPU/FPGA optimization , LLM applications , and graph analytics . He has received multiple best paper/demo awards, including IEEE/ACM ICCAD (2017), IEEE IC2E (2016), and ACM SIGMOD (2008). As a PC chair and editorial board member for journals like IEEE TPDS and TCC, he actively shapes academic discourse. PhD Students: Chen Xinyu, Tan Hongshi Courses Taught: CS4225/5425 Big Data Systems for Data Science

• Database Systems
• Graph Databases
• Data Management
• +۷ مورد دیگر

Amol Deshpande is a Professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley, College of Engineering. With over 160 publications spanning from 2000 to 2025, his research has significantly impacted the database systems community. His work bridges theoretical foundations with practical systems, evidenced by numerous publications in top-tier venues including SIGMOD, VLDB, and ICDE. Professor Deshpande's research focuses on database systems, with particular expertise in graph databases, data management, probabilistic databases, query optimization, and data provenance. His work addresses fundamental challenges in managing complex data, including efficient graph analytics, dataset versioning, streaming data processing, and privacy-preserving data management. Recent research directions include entity-relationship abstractions beyond traditional relations, standalone catalog engines for large data systems, and graph theoretical approaches to dataset versioning. His publication trends show a consistent focus on evolving database technologies, with early work on probabilistic databases and query optimization, transitioning to graph analytics and data provenance, and more recently addressing modern challenges in data cataloging, privacy-first data management, and serverless stream processing. His research spans both theoretical contributions (e.g., approximation algorithms for stochastic optimization) and practical systems building (e.g., RStore, TreeCat). Professor Deshpande has mentored numerous PhD students who have become active researchers in the database community, including Hui Miao, Souvik Bhattacherjee, and Konstantinos Xirogiannopoulos. His collaborative work spans across institutions, with frequent collaborations with researchers from MIT, University of Maryland, and other leading institutions. His research has been supported by major funding agencies and has influenced both academic research and industry practices in data management. The evolution of his work reflects the changing landscape of data management, from traditional relational systems to modern graph and streaming data challenges.

Albert Atseriasمشاهده پروفایل

استاد

• Computational Complexity
• Logic in Computer Science
• Finite Model Theory
• +۷ مورد دیگر

Albert Atserias is a Professor in the Department of Computer Science at the Universitat Politècnica de Catalunya (UPC), affiliated with the Faculty of Informatics of Barcelona (FIB) and the ALBCOM research group (Algorithms, Bioinformatics, Complexity, and Formal Methods). He is also associated with the Institut de Matemàtiques de la UPC-BarcelonaTech. His research is central to theoretical computer science, with a strong emphasis on logic and complexity. Atserias's research interests span Computational Complexity, Logic in Computer Science, Finite Model Theory, Proof Complexity, and Constraint Satisfaction Problems . His work explores the fundamental limits of computation, the expressive power of logical languages over finite structures, and the complexity of proving mathematical statements. He investigates the algebraic and combinatorial properties of proof systems, the limits of efficient algorithms for constraint solving, and the theoretical foundations of databases. His research often bridges logic, algebra, and combinatorics to provide deep insights into computational phenomena. The trends in his recent publications show a sustained focus on the logical and algebraic underpinnings of computational problems. Key themes include the consistency and complexity of database queries , the power and limitations of proof systems (like resolution and sum-of-squares), and the expressive power of homomorphism counts in graph theory. His work on the hardness of automating resolution and the development of circular proof systems are particularly significant contributions to proof complexity. The 2024 PODS Best Paper Award for work on relational consistency underscores the impact and timeliness of his research. Among his notable scientific awards are the prestigious ICREA Acadèmia , the PODS 2024 Best Paper Award , the Premi Extraordinari de Doctorat (Extraordinary Doctoral Prize), and the Kleene Award for Best Student Paper . These accolades reflect both the excellence of his early work and his continued leadership in the field. Atserias has been a principal investigator on numerous competitive research projects, including funding from the European Research Council (ERC) and the Spanish Ministry of Science. He has advised doctoral students, such as Toni Hakoniemi, whose thesis on proof complexity he supervised. His extensive collaborative network includes leading researchers like Phokion Kolaitis, Anuj Dawar, and Victor Dalmau. He has also served on the scientific committees of major conferences, contributing to the academic community. He is a core member of the ALBCOM research group , a leading team at UPC focused on theoretical aspects of computer science, which provides a vibrant environment for research in algorithms, complexity, and formal methods. His work is also connected to the broader Institut de Matemàtiques de la UPC, fostering interdisciplinary collaboration between computer science and mathematics.

• Statistical Network Analysis
• Machine Learning
• Data Privacy
• +۷ مورد دیگر

Tianxi Li is an Assistant Professor in the Department of Statistics at the University of Minnesota, Twin Cities, within the College of Science and Engineering. Their research integrates statistical methodology with applications in network science, data privacy, and biomedical data analysis. Their research interests lie at the intersection of statistics and network science, focusing on statistical modeling of complex networks , data privacy , network security , and biomedical applications such as neuroimaging and genomics. They develop adaptive and scalable methods for network estimation, community detection, and differential correlation analysis. The recent publications demonstrate a consistent focus on advancing statistical tools for network-structured data, with increasing applications in neuroscience and cancer genomics. The work spans theoretical development (e.g., network growth models) and practical applications (e.g., glioblastoma gene modules), reflecting a balance between methodology and real-world impact. Tianxi Li leads an active research program funded by the National Science Foundation, indicating recognition and support for their innovative work. Principal Investigator, Statistical tools for network security protection: from data privacy to threat detection , NSF (2024–2025) They advise graduate students in statistics and data science, though specific advisees are not listed. Their collaborative network includes researchers in biostatistics, computer science, and machine learning, as evidenced by co-authorships and interdisciplinary projects. Li's work contributes to the UN Sustainable Development Goals, particularly through advancements in data-driven solutions for secure and ethical data analysis.

• Software Engineering
• Formal Methods
• Model-Driven Engineering
• +۴ مورد دیگر

Pierre KELSEN is a Full Professor in the Department of Computer Science at the University of Luxembourg's Faculty of Science, Technology and Medicine (FSTM). His research focuses on Software Engineering, Formal Methods, Model-Driven Engineering, and Algorithmic Graph Theory. He leads the LASSY Laboratory for Advanced Software Systems, emphasizing model decomposition, regulatory compliance, and formal verification techniques. Education: PhD in Computer Science (1993, University of Illinois at Urbana-Champaign), M.Sc. (1989, UIUC), and Diploma (1986, University of Karlsruhe). Postdoctoral work at the University of British Columbia and Max-Planck-Institut für Informatik. Research Interests: - Development of formal modeling languages (e.g., VCL, F-Alloy) - Model transformation and validation frameworks - Algorithms for compliance and complexity challenges - Visual and modular design methodologies Funding: - ASINE (FNR Pearl, 2013–present): Architecture-based service innovation - MaRCo (FNR Core, 2010–2013): Business-centric regulatory compliance Publications span model-driven engineering, formal methods, and algorithmic foundations, with recent work exploring AI integration in domain modeling and compliance analysis. Labs/Teams: LASSY Laboratory, collaborating on tools like Lightning and Democles for executable modeling frameworks.

Melih Sefa YAVUZمشاهده پروفایل

استادیار

• Finance
• Banking
• Digital Finance
• +۱۴ مورد دیگر

Melih Sefa YAVUZ is an Assistant Professor in the Department of Finance and Banking at Istanbul Beykent University, Faculty of Economics and Administrative Sciences. He has been actively contributing to academic and administrative duties, including serving as Deputy Director of the Institute and working in the Strategy Development and Planning Department. His teaching responsibilities include Investment Analysis and Portfolio Management, Digital Finance, and Financial Statement Analysis, all delivered in Turkish. His research interests span a wide range of topics in finance and economics, including digital finance, blockchain technology, ESG performance, firm performance, digital literacy, financial decision-making, and macroeconomic influences on equity markets. These interests are reflected in his extensive publication record in both national and international peer-reviewed journals. His recent scholarly work focuses on the impact of ESG performance on financial outcomes, behavioral aspects of financial decisions, digital transformation in finance, and the interplay between blockchain initiatives and stock prices. He frequently collaborates with scholars such as Gozde Bozkurt, Hasan Sadik Tatli, and Mehmetcan Suyadal, producing empirical studies grounded in Turkish and international financial markets. Dr. YAVUZ has published in journals such as the Journal of Entrepreneurship, Management and Innovation, EMAJ: Emerging Markets Journal, and Turkish Studies - Economics, Finance, Politics. His work appears in databases including ESCI, TR INDEX, EBSCO, and JournalPark. He has also contributed to scientific books on sustainable finance, digital transformation, and public procurement. Scientific Awards: No awards mentioned in the provided text. He advises and collaborates with various researchers and co-authors, though no formal PhD or Master’s students are listed. He does not appear to have led any externally funded grants explicitly mentioned in the text. He is involved in academic administration and curriculum development, particularly in digital finance and investment-related courses. There is no mention of specific labs or research centers, but his work suggests engagement with digital finance and financial market research teams.

Estara Arrantمشاهده پروفایل

پژوهشگر ارشد

• Semitic Linguistics
• Medieval Jewish Studies
• Medieval Islamic Studies
• +۱۲ مورد دیگر

Dr Estara Arrant is a Cambridge University Library Leverhulme Early Career Research Fellow and Trinity Hall Postdoctoral Research Associate. She merges Semitic linguistics with data science to study medieval Jewish and Islamic textual cultures. PhD (University of Cambridge, 2021): Computational analysis of Cairo Genizah Torah codices MPhil (University of Oxford, 2018): Linguistic structure of Qur’ānic creation narratives BA: History & Modern Hebrew Her groundbreaking work combines machine learning and bioinformatics algorithms to analyze textual evolution. Current projects include: Developing the first NLP model for medieval Judaeo-Arabic Creating digital tools for computerized stemmatology She actively teaches Python and R as an Cambridge Digital Humanities associate, and her research has been published by Open Book Publishers and Brill .

Rachel Pottingerمشاهده پروفایل

استاد

• Data Management
• Database Systems
• Data Integration
• +۷ مورد دیگر

Rachel Pottinger is a Professor in the Department of Computer Science at the University of British Columbia within the Faculty of Science. She has been at UBC since 2004, progressing from Assistant Professor to Associate Professor in 2012 and to full Professor in 2021. She is affiliated with research centers including CAIDA (Centre for Artificial Intelligence Decision-making and Action) and DFP (Designing for People), and is part of ICICS (Institute for Computing, Information and Cognitive Systems). Her research focuses on data management, particularly semantic data integration, metadata management, and making data more accessible and understandable to users. She leads the Data Management and Mining Lab and has supervised numerous doctoral and master's students. Her work addresses three main areas: helping people understand and explore their data, managing data not well supported by databases, and coordinating data across multiple databases. Her recent publications demonstrate strong trends in database usability, data provenance visualization, query recommendation systems, and building information modeling integration. Her work bridges theoretical database concepts with practical human-centered applications, particularly in making complex data systems more accessible to non-expert users. UBC Computer Science Department Faculty Teaching Award 2013 Computer Science Department Teaching Award 2010 CS Department Teaching Award Denice Denton Emerging Leader Award 2007 Pottinger has supervised numerous PhD and Master's students, with research focusing on data provenance, database usability, and data coordination. She has been involved in significant research projects related to data lakes, open data navigation, and query recommendation systems. Her current research explores table annotation and discovery in data lakes, query refinement for aggregation queries, and query prediction based on past user behavior. She is actively involved in the academic community, serving as Secretary-Treasurer for SIGMOD, on the VLDB Journal editorial board, and as a member of the Computing Research Association's Board of Directors. She previously served as General Co-Chair of SIGMOD 2020 and as Associate Head for the Undergraduate Program of the Department of Computer Science from 2018-2020.

Phil Bernsteinمشاهده پروفایل

استاد

• Database Systems
• Transaction Processing
• Data Integration
• +۳ مورد دیگر

Phil Bernstein is a Distinguished Scientist in the Data Systems Group at Microsoft Research Redmond and an Affiliate Professor at the University of Washington where he occasionally teaches CSEP 545 Transaction Processing. With over four decades of pioneering work in database systems, he has made significant contributions across transaction processing, data integration, and distributed systems. His research interests focus on database systems, transaction processing, and data integration, with recent work on approximate nearest neighbor search over vector databases, improving database servers using disaggregated cloud resources, and the Orleans distributed systems programming framework. Bernstein's work on Orleans (2012-2019) resulted in an open-source framework widely used inside and outside Microsoft, with components addressing indexing, geo-distribution, and transactions. Bernstein has received numerous prestigious awards including being named a Fellow of the ACM and AAAS, receiving the SIGMOD Edgar F. Codd Innovations Award, and election to the National Academy of Engineering and Washington State Academy of Sciences. Fellow of the ACM Fellow of the AAAS SIGMOD Edgar F. Codd Innovations Award Member of the National Academy of Engineering Member of the Washington State Academy of Sciences As an active researcher and academic, Bernstein serves on numerous conference program committees including SIGMOD 2024 (keynotes), VLDB 2024 (Industry), and has held editorial positions for Information Systems and Springer Data-Centric Systems and Applications. His influential books, Principles of Transaction Processing (2009) and Concurrency Control and Recovery in Database Systems, remain foundational texts in the field.

Ali Ghodsiمشاهده پروفایل

استاد

• Machine Learning
• Deep Learning
• Natural Language Processing
• +۳ مورد دیگر

Ali Ghodsi is a Professor at the University of Waterloo and Director of the Data Science Lab, with affiliations at the Vector Institute. His research spans machine learning, deep learning, and artificial intelligence, with applications in natural language processing, bioinformatics, and computer vision. His group develops theoretical frameworks and algorithms for analyzing large-scale datasets, focusing on neural network architectures, knowledge distillation, and model efficiency. Current projects include deep learning for identity control, computational antibody design, and generative AI/large language models. Ghodsi has authored influential tutorials on diffusion models, graph neural networks, and large language models. Notable research contributions include computational methods for de novo peptide sequencing from mass spectrometry data, green simulation-assisted reinforcement learning, and efficient natural language processing models. His lab maintains collaborations with industry partners including Google, Amazon, and Roche.

Pingfu Fuمشاهده پروفایل

استاد

• Survival analysis
• Clinical trials
• Cancer research
• +۴ مورد دیگر

Pingfu Fu, PhD, is a Professor in the Department of Population and Quantitative Health Sciences at Case Western Reserve University's School of Medicine. He is also a member of the Developmental Therapeutics Program at the Case Comprehensive Cancer Center. His expertise spans biostatistics, mathematics, and computer science, with a focus on cancer research and HIV/AIDS. Dr. Fu advises researchers on study design and statistical methodology for clinical and pre-clinical studies. He teaches courses in survival data analysis and clinical trials, and was recognized as 'Professor of the Year' in 2010 by the Department of Epidemiology and Biostatistics. Education: PhD in Biostatistics (Case Western Reserve University, 2001), MS in Statistics (Case Western Reserve University, 1996), MS in Mathematics (Xiangtan University, 1988), and BS in Mathematics (Jiangxi Normal University, 1984). His research interests include survival analysis, tree-based methods, clinical trials, and statistical applications in medical research. He has co-authored numerous peer-reviewed articles, focusing on cancer disparities, radiomics, and computational pathology. Professional memberships include the American Statistical Association, American Mathematical Society, and American Cancer Society. Dr. Fu holds editorial roles at Reviews on Recent Clinical Trials , Journal of Clinical Oncology , and Journal of the National Cancer Center . His work has addressed mathematical challenges in stochastic processes and resolved statistical issues in study design and tree-based models. Notable contributions include developing risk prediction models for cancer outcomes and advancing interdisciplinary collaborations across oncology, biostatistics, and computer science. His lab focuses on integrating computational methods with clinical data to improve patient outcomes.