جستجوی استاد راهنمای دانشگاهی

Ugur Cetintemel is the Khosrowshahi University Professor of Computer Science at Brown University, where he has been since completing his PhD at the University of Maryland in 2001. His research focuses on data management systems, database systems, distributed systems, and stream processing, with recent work integrating AI techniques into database systems. He teaches courses such as Database Management Systems and Data Science fundamentals. Notable contributions include the Aurora and Borealis stream processing engines, S-Store for transaction processing, and DBPal for natural language interfaces. His work emphasizes scalable, efficient systems for large-scale data challenges. Education: PhD in Computer Science, University of Maryland, 2001 MS in Computer Science, Bilkent University, 1996 BS in Computer Science, Bilkent University, 1994 Research Interests: Data management, stream processing, distributed systems, predictive analytics, and AI integration with databases. Key projects include optimizing database systems for modern hardware, developing real-time stream processing frameworks, and exploring interactive data exploration techniques. Grants & Advising: Extensive contributions to grants and collaborations, though specific grant details are not listed. Supervises graduate students in areas like database systems and machine learning integration. Part of the Brown Data Management Group. Labs/Teams: Leads research within the Brown Data Management Group, focusing on advancing database systems for big data and real-time analytics.

• Data Management Systems
• Database Systems
• Distributed Systems
• +۵ مورد دیگر

Henry F. (Hank) Korth is a Professor of Computer Science and Engineering at Lehigh University, with a courtesy appointment in the Department of Decision and Technology Analytics in the College of Business. He serves as Director of the Blockchain Lab in the Center for Financial Services and Co-Director of the Computer Science and Business Program. Korth is a Fellow of the ACM and IEEE, and a recipient of the VLDB 10-Year Award and Bell Labs President's Silver Award for contributions to database technologies. PhD in Computer Science from Princeton University MA, MSE in Computer Science from Princeton University BA in Mathematics from Williams College Korth's research spans database systems, blockchain systems, distributed systems, and real-time systems. He has pioneered transaction management in parallel and distributed systems, query processing, and the impact of modern computing architectures on database performance. His recent work focuses on blockchain applications in enterprise databases, including acceleration of zero-knowledge proofs, benchmarking frameworks, central-bank digital currencies, and private-yet-provable accounting systems. His contributions are rooted in both theoretical advancements and practical implementations, such as the QTM™ aggregation engine and the DataBlitz™ main-memory storage manager. Scientific awards include: ACM Fellow IEEE Fellow 10-Year Award at the VLDB Conference Bell Labs President's Silver Award Korth actively supervises research within the Blockchain Lab and is affiliated with the Scalable Software Systems Research Group at Lehigh. His scholarly output reflects a deep engagement with blockchain benchmarking, concurrency control, verifiable databases, and the evolution of database systems in response to technological shifts.

• Database Systems
• Blockchain Systems
• Distributed Systems
• +۷ مورد دیگر

Walid G. Aref is a Professor of Computer Science at Purdue University since Fall 1999. His research focuses on database systems, spatial and spatio-temporal data management, query processing, and indexing. He has led projects funded by NSF, NIH, and industry partners. Aref is a Fellow of the IEEE and has received awards including the NSF CAREER Award (2001) and VLDB’s Ten-Year Best Paper Award (2016). He serves as Editor-in-Chief of ACM Transactions on Spatial Algorithms and Systems and has authored numerous influential papers. Education: BSc/MSc from Alexandria University (Egypt), PhD from University of Maryland (1993). Research Interests: Extending database functionality for emerging applications like spatial, graph, and sensor databases. Notable contributions include the Chameleon project, AQWA system for big spatial data, and privacy-preserving Casper framework. Selected Projects: NSF-funded work on multi-predicate spatial queries, in-memory graph relational systems, and adaptive spatio-textual processing. Systems like Tornado (spatio-textual streams) and LocationSpark (distributed spatial data) exemplify his contributions. Awards: Multiple best paper awards, IEEE Fellow, and leadership roles in ACM SIGSPATIAL.

• Databases
• Data Mining
• Spatial Databases
• +۶ مورد دیگر

Anne Kelly Knowles is the McBride Professor of History at the University of Maine, Department of History. Previously, she taught at Middlebury College (2002–2015) and the University of Wales, Aberystwyth (1993–1997). Her research focuses on historical geography, GIS applications in history, the Holocaust, and 19th-century U.S. economic landscapes. She holds a PhD in Geography from the University of Wisconsin-Madison (1993). Her work bridges historical inquiry with digital tools, notably through projects like the Holocaust Geographies Collaborative. Key contributions include Geographies of the Holocaust (2014) and Mastering Iron: The Struggle to Modernize an American Industry, 1800–1868 (2013). She has pioneered methods in Historical GIS (HGIS), emphasizing spatial analysis for understanding historical patterns and events. Awarded the Smithsonian American Ingenuity Award (2012) and the Distinguished Historical Geographer honor (2014), Knowles has led major grants including a National Endowment for the Humanities (NEH) Digital Humanities Advancement Grant for the Holocaust Ghettos Project (2018–2022). Her TED-Ed talk on reimagining Gettysburg highlights her ability to make complex spatial history accessible. Grants: NEH Digital Humanities grants (2016, 2018–2022), Guggenheim Fellowship (2017), NSF Collaborative Research (2008–2011). Labs/Teams: Co-founder of the Holocaust Geographies Collaborative; collaborator on the Holocaust Ghettos Project.

• Historical Geography
• Historical GIS
• Holocaust Studies
• +۳ مورد دیگر

Liuba Shrira is a Professor of Computer Science at Brandeis University, affiliated with the Michtom School of Computer Science and the Benjamin and Mae Volen National Center for Complex Systems. Her research focuses on distributed systems, storage systems, blockchain technology, concurrent programming, and system architectures. She holds a Ph.D., M.S., and B.S. from the Technion – Israel Institute of Technology. Her work emphasizes reliable and highly available systems, including innovations in snapshot management, transactional memory, and adversarial cross-chain commerce. She has been recognized with awards such as the ACM Distinguished Scientist (2009), Lady Davis Fellowship (2010-2011), and a Best Paper Award (2020). Her research has been supported by grants from the National Science Foundation and other institutions. Recent publications highlight advancements in optimistic concurrency control, blockchain interoperability, and modular past-state systems. Shrira has also contributed to middleware design and distributed computing frameworks, with applications in both academic and industry settings.

• Computer Operating Systems
• Computer Systems Analysis
• Distributed Systems
• +۵ مورد دیگر

Joong Chae Na is a Professor at the Department of Computer Science and Engineering, Sejong University. He holds a Ph.D. (2005), M.S. (2000), and B.S. (1998) in Computer Science from Seoul National University. His professional experience includes: Researcher at the Department of Computer Science, University of Helsinki (2006) Research Professor at the Department of Advanced Technology Fusion, Konkuk University (2007) Professor at Sejong University (2008–present) Research Interests: Computer Algorithms Theory of Computation Parallel Algorithms Bioinformatics String Algorithms Index Data Structures Publications focus on pattern matching, data structures, and bioinformatics applications with topics like order-preserving matching, B+tree variants, and GPU-accelerated haplotyping. His work spans theoretical and applied computer science. No scientific awards were explicitly mentioned in the provided text.

• Computer Algorithms
• Theory of Computation
• Parallel Algorithms
• +۴ مورد دیگر

Ron C. Chiang is an Associate Professor at the University of St. Thomas, specializing in software engineering, distributed systems, and cloud computing. His research focuses on optimizing virtualization, resource management, and high-performance architectures. He holds a Ph.D. in Computer Engineering from George Washington University and has contributed to advancements in cloud security, I/O prefetching, and precision agriculture through machine learning. Education: Ph.D. in Computer Engineering (George Washington University). Research Interests: Distributed systems, cloud computing, virtualization systems, and data-intensive applications. He has developed innovative solutions for container placement strategies, interference-aware scheduling, and energy-efficient memory management. Awards include the Best Student Paper Finalist at SC11, a 'Best of HotPower' award, and grants from IBM and Amazon. He teaches courses on cloud computing, database systems, and infrastructure as code. His work integrates theoretical computer science with practical applications, such as sustainable farming through AI and healthcare platforms in cloud environments. Grants: IBM Academic Initiative (2015–2017), Amazon AWS Educator Credits (2015–2016). He has published extensively in journals like IEEE Transactions and at conferences including SC, ICAC, and ICPA.

• Distributed Systems
• Cloud Computing
• High Performance Computer Architectures
• +۴ 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

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

Alvin Cheung is an Associate Professor in the Department of Computer Science at the University of California, Berkeley, where he is a member of the Data Systems and Foundations group and Programming Systems group. He also participates in the Sky Computing Lab and SpeciaLIzed Computing Ecosystems (SLICE) Lab, and serves as a faculty affiliate at the Berkeley Institute for Data Science. His research spans database systems, programming languages, and software engineering with applications across various domains. Professor Cheung's research focuses on creating systems that bridge the gap between data management and programming languages. His work centers on three main themes: verified lifting techniques for inferring program properties; designing new data processing and programming language techniques; and improving end-user data programming experiences through novel interfaces and code generators. His research integrates formal methods, deep learning, and program synthesis to solve practical challenges in data-intensive applications. His publications reveal a strong trend toward leveraging machine learning, particularly large language models, to enhance code generation, optimization, and understanding. Recent work increasingly focuses on verified approaches that combine formal reasoning with neural techniques, addressing challenges in database systems, compiler design, and programming language theory while maintaining correctness guarantees. ACSIC Rock Star Award (2025) AITO Dahl Nygaard Junior Prize (2024) VLDB Early Career Research Contributions Award (2023) Army Research Office Young Investigator Award (2022) Office of Naval Research Young Investigator Award (2021) Sloan Research Fellowship (2019) DOE Presidential Early Career Award for Scientists & Engineers (2019) Professor Cheung has advised numerous doctoral and master's students who have gone on to positions at leading technology companies including AWS, OpenAI, Microsoft Research, and Adobe Research. His research has been generously supported by multiple federal agencies including the National Science Foundation, Department of Energy, Office of Naval Research, Army Research Office, and Intel Corporation, reflecting the significance and impact of his work across both academic and industrial settings. He leads research efforts in the EPIC Data Lab, Sky Computing Lab, and SLICE Lab, where his team develops innovative approaches to data management, programming systems, and specialized computing ecosystems. Current projects focus on applying verified lifting techniques, developing new data processing frameworks, and creating user-friendly interfaces for data programming across diverse application domains.

• Database Management Systems
• Programming Languages
• Data Management
• +۵ مورد دیگر

John Bochanski is an Associate Professor of Computer Science & Physics at Rider University, specializing in using large astronomical surveys to study the formation and evolution of the Milky Way galaxy. He is actively involved in major astronomical projects including the Sloan Digital Sky Survey (SDSS) and the Large Synoptic Survey Telescope (LSST) Science Collaborations. His educational background includes a Ph.D. in Astronomy from the University of Washington (2008), an M.S. in Astronomy from the same institution, and a B.S. in Astronomy & Astrophysics from Villanova University, where he graduated magna cum laude. Bochanski's research focuses on galactic structure and kinematics using cool, red stars as tracers. He employs large surveys like SDSS and Gaia to study two key regimes in the Milky Way: using cool red dwarfs to examine galactic structure near the Sun, and utilizing brighter red giants to investigate the distant Milky Way halo. His work is particularly relevant to upcoming surveys with the Nancy Grace Roman Space Telescope and the Vera Rubin Observatory. Supported by the LSST Discovery Alliance in cooperation with Princeton University's Department of Astrophysical Sciences, his research spans stellar kinematics, low-mass stars, and advanced data analysis techniques. His publication record shows a consistent focus on galactic structure, brown dwarfs, and large astronomical surveys, with recent work centered on SDSS-V and Gaia data analysis. The trend in his publications demonstrates increasing involvement in large-scale collaborative projects and data-intensive astronomy. Corporation for Science Advancement - Scialog Fellow (2018) Namesake of Main–belt asteroid 141414 Bochanski (2002 AK205) (2012) MIT Spot Award (2009, 2010) Elected to Phi Beta Kappa (2002) Jason Cardelli Memorial Undergraduate Research Award (2002) National Barry M. Goldwater Scholar (2001) Elected to Phi Kappa Phi (2001) Elected to Sigma Pi Sigma Physics (2000) Bochanski has held positions at multiple prestigious institutions including Rider University, Haverford College, Pennsylvania State University, and MIT. His current research is supported by the LSST Discovery Alliance, indicating significant grant funding for his work. He has mentored numerous students through his position as Program Founder & Director of Computer Science at Rider University since 2016. His technical expertise includes working with the FIRE (Magellan FIRE) spectrograph and developing data analysis pipelines for large astronomical surveys. His work with the Sloan Digital Sky Survey and Gaia mission data places him at the forefront of modern galactic astronomy research.

• Astronomy
• Physics
• Galactic Structure
• +۴ مورد دیگر

Jennifer Mapes is an Assistant Professor in the Department of Geography at Kent State University, located in Kent, Ohio. Her research focuses on urban sustainability, global change impacts on local outcomes, and small-town redevelopment, particularly in the context of Kent’s downtown revitalization. She holds a B.A. from SUNY Geneseo (2000), an M.S. from Penn State (2005), and a Ph.D. from the University of Southern California (2009). Her research interests bridge global and local scales, emphasizing how socio-economic and environmental changes affect small cities. She is currently writing a book titled The New American Small Town , drawing on her dissertation research in Western U.S. towns. Her work integrates qualitative methods and GIS tools to explore community dynamics and sustainable development. Recent publications highlight her focus on urban sustainability, GIS applications, and small-town resilience. She has collaborated on projects like Main Street Kent’s downtown map kiosks and the Kent GIS database. Notable awards include the 2017 Volunteer of the Year recognition for community engagement. Her teaching spans Human Geography, Qualitative Methods, and Urban Sustainability. She actively contributes to interdisciplinary initiatives, such as reclaiming urban spaces for sustainability and analyzing voting patterns in small towns through big data.

• Urban Sustainability
• Small Towns in the U.S.
• Urban Redevelopment
• +۴ مورد دیگر

Arthur Lee is a Research Professor and Chair of the Department of Computer Science at SUNY Korea, a campus of Stony Brook University. He holds a BS, MS, and PhD in Computer Science from the University of Utah and Stanford University. Previously, he held academic roles at Korea University (7 years, 2 years as Chair), University of Utah (5 years), and Claremont McKenna College (12 years, 2 years as Chair). His industry experience includes roles at Sandia National Laboratories, Xerox PARC, Evans & Sutherland, and SAP Labs. Research interests focus on programming languages (object-oriented and dynamic), database systems (main-memory row/column-stores for distributed big data), and data science. He has authored over 35 peer-reviewed publications and delivered 10+ tutorials internationally. Awards include the Keck Endowed Professorship and the 2004 Outstanding Teaching Award. Teaching responsibilities at SUNY Korea include courses such as CSE 114, CSE 214, CSE 305, and advanced graduate courses (CSE 526, CSE 532). He served as Editor-in-Chief of the Journal of the Korea Computer Graphics Society and maintains membership in ACM.

• Programming Languages
• Database Systems
• Distributed Systems
• +۲ مورد دیگر

Samuel Madden is the MIT College of Computing Distinguished Professor and CS Head in the Electrical Engineering and Computer Science (EECS) Department at Massachusetts Institute of Technology. He leads the Systems that Learn Initiative and co-directs both the Data Systems and AI Lab (DSAIL) and the Data Systems Group (DSG) at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL). Professor Madden's research focuses on database systems, including main memory databases, data warehousing/analytics, querying video data, and machine learning for data systems. His recent projects include Palimpsest (a declarative system for optimizing AI workloads), BRAD (simplifying cloud data processing), self-organizing data containers, and VaaS (Video Analytics as a Service). His work bridges traditional database systems with emerging AI technologies, exploring how learned models can improve data management efficiency. His research has resulted in numerous influential systems including C-Store (column-oriented database), CarTel (mobile sensor network system), BlinkDB (approximate query processing), Silo (main memory transaction processing), and Datahub (versioned data platform). These projects have significantly impacted both academic research and industry database technologies. ACM Fellow (2020) SIGMOD Edgar H. Codd Innovations Award (2024) Multiple 'Test of Time' awards from VLDB, SIGMOD, SIGMOBILE, and SenSys MIT TR35 Outstanding Researcher Under 35 (2005) Sloan Fellowship (2007) NSF CAREER Award (2005) Professor Madden actively advises six current PhD students and has mentored numerous successful researchers who now hold positions at leading universities and tech companies. His lab maintains strong industry connections through the Data Systems and AI Lab, providing students with opportunities to work on real-world data challenges. As Chief Scientist of Cambridge Mobile Telematics and advisor to several database startups, he ensures his research remains grounded in practical applications while pushing the boundaries of database system design. His recent publications demonstrate a clear trajectory toward tighter integration between AI and database systems, with significant contributions in cloud data infrastructure, video analytics, and learned query optimization techniques that are shaping the next generation of data management systems.

• Database Systems
• Main Memory Databases
• Data Warehousing and Analytics
• +۶ مورد دیگر

Dr. Tanya Beaulieu is an Associate Professor of Management Information Systems at the University of Maine's Maine Business School. She holds a Ph.D. from Washington State University and has over 20 years of industry experience in software development and consulting. Her research focuses on online communities, crowdfunding, and technology's role in entrepreneurship. Dr. Beaulieu has been recognized with awards including the 2024 Carol B. Gilmore Memorial Service Award and the 2023 Extraordinary Service Award from SIG GTM. She serves on editorial boards for Information Systems Research and previously led Journal of the Association for Information Systems as Managing Editor. Her educational background includes a Ph.D. in Management Information Systems (2015), complemented by professional certifications in software development. She teaches courses focused on information systems and technology for business applications. Dr. Beaulieu's research spans digital transformation challenges in rural business, post-data breach customer behavior, and cryptocurrency media narratives. Her work frequently addresses intersections between technology adoption and human behavior in both academic and corporate contexts. Dr. Beaulieu has secured grants for online course development and research initiatives, demonstrating her commitment to innovative educational practices. Her invited presentations include discussions on blockchain technology and visualizing computer-mediated conversations. Her professional service includes conference organizing roles and peer review activities in top journals like MIS Quarterly and Journal of Computer Information Systems .

• Management Information Systems
• Online Communities Communication
• Crowdfunding
• +۴ مورد دیگر

Prakash Ramanan is a Professor at the School of Computing within the College of Engineering at Wichita State University. His research focuses on algorithms, distributed computing, database systems, and XML processing. He holds an office at 208 Jabara Hall on the university's main campus. His academic interests include parallel algorithms, MapReduce frameworks, computational complexity, and query processing in XML databases. His work spans theoretical algorithm analysis to practical implementations in distributed systems. Ramanan's publications emphasize algorithmic efficiency, lower bounds analysis, and optimization techniques. His recent work addresses MapReduce-based matrix multiplication and XPath query evaluation over XML streams. Earlier contributions include foundational studies on tree patterns, join operations, and parallel computing. No scientific awards or grants are explicitly listed in the provided information. He currently advises no listed students or supervisees. His research activities involve collaborations in distributed systems and database query optimization.

• Algorithms
• Database Systems
• Distributed Computing
• +۳ مورد دیگر