جستجوی استاد راهنمای دانشگاهی

• Database Management Systems
• Self-Driving Database Architectures
• Transaction Processing Systems
• +۴ مورد دیگر

Andy Pavlo is an Associate Professor with Indefinite Tenure in the Computer Science Department at Carnegie Mellon University's School of Computer Science. He is an active member of the CMU Database Group and the Parallel Data Laboratory, where he leads research in database management systems with a focus on self-driving architectures, transaction processing, and large-scale analytics. His work bridges academic research and industry applications through projects like NoisePage, OtterTune (which he co-founded and served as CEO before it ceased operations), and Peloton. Dr. Pavlo's research interests span database management systems with particular emphasis on autonomous database architectures that can self-tune and optimize without human intervention. His work explores transaction processing systems that can handle high-throughput workloads while maintaining consistency, and large-scale data analytics techniques that efficiently process massive datasets. He has made significant contributions to query optimization, database extensibility, and automatic database tuning using machine learning techniques. His recent work on database extensibility revealed critical issues in PostgreSQL's extension ecosystem, showing that approximately 16% of extensions are incompatible with at least one other extension due to API violations and memory errors. His research output demonstrates a consistent focus on practical database systems challenges, with recent publications examining database extensibility, user-defined function optimization, and the cyclical nature of database research. The articles show a strong trend toward making database systems more autonomous, with increasing integration of machine learning techniques for automatic tuning and optimization. His work often combines deep theoretical analysis with practical implementation in open-source systems. Dijkstra Award 2024 for contributions to database systems research Dr. Pavlo actively mentors graduate students, with current advisees including Wan Shen Lim, William Zhang, and Sam Arch (co-advised with Todd Mowry). His former students have gone on to successful careers in both industry and academia. He has secured significant research funding through CMU's affiliate program with major database companies including ClickHouse, DataStax, dbt, Firebolt, MotherDuck, RelationalAI, SingleStore, Spiral, PingCAP/TiDB, Yellowbrick, and Yugabyte. His research is supported by these industry partnerships and likely includes NSF funding given his active participation in the database research community. At CMU, Dr. Pavlo leads the Database Group and organizes several seminar series including "SQL or Death," "Database Building Blocks," and "ML⇄DB Technical Talks." These seminars bring together researchers and practitioners to discuss cutting-edge developments in database systems. He also runs a summer research internship program that has attracted students for multiple consecutive years, indicating a strong research group with ongoing projects and funding.

• Self-Driving Database Management Systems
• Machine Learning for Databases
• GPU-Accelerated Data Analytics
• +۴ مورد دیگر

Lin Ma is currently an Assistant Professor at the University of Michigan, Ann Arbor in the Department of Electrical Engineering and Computer Science (College of Engineering). Previously, they served as a Post Doctoral Fellow at Carnegie Mellon University (2021-2022) and as a Software Engineer at Databricks, Inc. (2022-2023). Research Interests focus on the intersection of database systems and machine learning, particularly in developing self-driving database management systems . Key areas include workload forecasting , automated index optimization , query execution acceleration , and machine learning integration for database automation. Their work explores GPU-accelerated analytics, memory optimization, and transactional consistency models. Academic Contributions span 15+ publications in top venues like VKDB , SIGMOD , and CIDR , including recent 2025 papers on Vortex (GPU memory optimization) and Scompression (workload compression). Earlier work introduced QueryBot 5000 , a workload forecasting framework, and explored anti-caching for storage optimization in OLTP systems. Teaching includes courses like EECS 584: Advanced Database Management Systems and EECS 484: Database Management Systems at the University of Michigan (2023-2025), and 15-445/645 Database Systems at Carnegie Mellon University. Service involves program committee roles for SIGMOD (2023-2025), VLDB (2022-2025), and CIDR (2024-2025). They also served on admissions and search committees at both institutions. Advising includes supervising PhD and MS students: Siyuan (Doug) Dong , Zhongwei Xu , and Haotian (Jack) Gong (co-advised with Barzan Mozafari), among others.

Luca Demetrioمشاهده پروفایل

استادیار

• Adversarial Machine Learning
• Malware Detection
• Cybersecurity
• +۳ مورد دیگر

Luca Demetrio is an Assistant Professor at the University of Genoa, Italy, specializing in adversarial machine learning and cybersecurity. Previously, he was a Post-doctoral Researcher at the PRA Lab within the Department of Electrical and Electronic Engineering at the University of Cagliari. He holds bachelor's (2015), master's (2017), and Ph.D. (2021) degrees from the University of Genova, with his doctoral thesis focusing on formalizing evasion attacks against security detectors. His research emphasizes enhancing the robustness of machine learning models against adversarial attacks, particularly targeting malware detectors, SQL injection defenses, and Windows security systems. He leads the development of SecML Malware, a Python library for generating adversarial Windows malware, and contributes to the SecML framework. His work has been published in top-tier journals like ACM TOPS and IEEE TIFS. Key research interests include adversarial example generation, malware analysis, and cybersecurity defense mechanisms. He has explored query-efficient attacks on phishing detectors, certified adversarial robustness via randomized smoothing, and robust synthetic data-driven threat detection. His recent studies (2023–2025) address challenges in hardening machine learning models against evasion attacks, adversarial SQL injection countermeasures, and securing autonomous driving systems from adversarial reinforcement learning attacks.

• Database Management Systems
• Transaction Processing
• In-Memory Databases
• +۶ مورد دیگر

Andrew Pavlo is a Professor in the Computer Science Department at Carnegie Mellon University's School of Computer Science. His research focuses on database management systems, particularly in the areas of transaction processing, in-memory databases, and self-driving database systems. He leads a productive research group that has published extensively in top database venues including VLDB, SIGMOD, and CIDR. Pavlo's research interests span database management systems, transaction processing, in-memory databases, non-volatile memory databases, and self-driving database systems. His work often bridges theoretical database concepts with practical system implementation, focusing on performance optimization, query processing, and system architecture. Recent work has explored machine learning applications for database tuning, novel storage techniques, and innovative approaches to transaction processing. An analysis of his recent publications reveals a strong focus on self-driving database systems, with significant work on the Database Gym framework for training machine learning models to optimize database performance. His research also examines columnar storage formats, transaction scheduling, and novel approaches to user-defined function optimization. The work demonstrates a consistent trajectory toward making database systems more autonomous and efficient through a combination of systems techniques and machine learning. Pavlo has been instrumental in mentoring numerous PhD students who have become active contributors to the database research community. His research has been supported by significant grants that have enabled the development of innovative database technologies and frameworks. His research group operates within CMU's vibrant database ecosystem, collaborating with other researchers on projects related to database systems, storage engines, and query processing frameworks. The group maintains close connections with industry partners to ensure practical relevance of their research contributions.

Mendel Rosenblumمشاهده پروفایل

استاد

• System Software
• Distributed Systems
• Computer Architecture
• +۴ مورد دیگر

Mendel Rosenblum is the Cheriton Family Professor and holds dual appointments as Professor in the Departments of Computer Science and Electrical Engineering at Stanford University. He is a co-founder of VMware Inc. and served as its Chief Scientist for its first decade, playing a pivotal role in designing foundational virtualization technologies. Rosenblum's research focuses on system software, distributed systems, and computer architecture, with notable contributions to virtualization, data center networks, and operating systems. He leads the Platform Lab at Stanford, exploring next-generation data center technologies and high-performance computing systems. Administrative Role: Faculty Director of Stanford Computer Forum (2012–present) Education: PhD (UC Berkeley, 1992), MS (UC Berkeley, 1989), BA (University of Virginia, 1984) His research interests span disk storage management, computer simulation, scalable operating systems, and security. Recent work emphasizes deployable consensus algorithms, programmable smartNICs, and self-programming networks. Rosenblum has authored over 80 publications and holds multiple patents in virtualization and system software. Awards & Recognition: ACM System Software Award (2009) IEEE Computer Entrepreneur Award (2011) ACM Thacker Breakthrough in Computing Award (2018) Member, National Academy of Engineering (2013) He advises PhD and Master's students, including current advisees Sina Jandaghi Semnani and Zixi Liu. Rosenblum teaches advanced courses on web applications, distributed systems, and independent research projects.

Bryan S. Kimمشاهده پروفایل

استادیار

• Data Storage Systems
• Computer Architecture
• Operating Systems
• +۴ مورد دیگر

Bryan S. Kim is an Assistant Professor in the Department of Electrical Engineering and Computer Science at Syracuse University. His research focuses on computer systems, particularly data storage systems, emphasizing performance, reliability, and scalability in the context of heterogeneous hardware. He holds a Ph.D. and M.S. in Computer Science and Engineering from Seoul National University and a B.S. in Electrical Engineering and Computer Science from UC Berkeley. Prior to academia, he worked as a postdoctoral researcher at Seoul National University and as a manager at SK Telecom. Key research interests include SSD reliability, storage system design, and overcoming hardware limitations through innovative architectures. Notable recent work includes projects on capacity-variant storage systems, CXL-enabled SSDs, and RAID adaptations for heterogeneous SSDs. He has been awarded two NSF grants: the DESC proposal (CHIPLETS360) in 2025 and a CAREER award for bridging memory/storage gaps in 2025. Education: Ph.D. in Computer Science and Engineering, Seoul National University M.S. in Computer Science and Engineering, Seoul National University B.S. in Electrical Engineering and Computer Science, UC Berkeley Recent Awards: NSF DESC Proposal Award (2025) NSF CAREER Proposal Award (2025) Teaching: CSE486: Design of Operating Systems CIS341: Computer Organization & Programming Systems CIS700: Storage Systems for Big Data His students include Shao-Peng Yang, Xiangqun Zhang, and Omkar Desai. He advises on projects related to storage systems, and his work has been published in top-tier conferences like FAST, ATC, EuroSys, and OSDI.

• Database Systems
• Machine Learning in Databases
• Self-Driving Database Management Systems
• +۳ مورد دیگر

Lin Ma is an Assistant Professor in the Department of Electrical Engineering and Computer Science at the University of Michigan, Ann Arbor, since August 2023. His research focuses on advancing database systems through machine learning integration, with a particular emphasis on self-driving DBMS, query optimization, and GPU acceleration. He holds a PhD from Carnegie Mellon University, where he also served as a postdoctoral researcher, and previously worked as a Software Engineer at Databricks. Research Interests: Lin’s work bridges database management systems and machine learning, aiming to create autonomous systems capable of self-optimization. Key areas include workload forecasting, behavior modeling for self-driving DBMS, and leveraging GPU capabilities for large-scale analytics. His contributions have been recognized through publications in top venues like VLDB, SIGMOD, and CIDR. Service: He actively serves on program committees for major conferences including SIGMOD, VLDB, and CIDR, and has held roles such as Web/Information Chair for SIGMOD (2023). His contributions extend to academic service, including admissions and faculty search committees at CMU and UMich. Labs & Projects: Lin leads research initiatives in database systems, including the QueryBot5000 framework for workload forecasting, and collaborates on projects like Vortex and Database Gyms to advance GPU-accelerated analytics and self-driving system design.

• Orthopaedic Surgery
• Space Medicine
• Digital Health Innovation
• +۴ مورد دیگر

Dr. Aenor Sawyer is an Associate Professor in the Department of Orthopaedic Surgery at the University of California, San Francisco (UCSF) School of Medicine. She leads the Skeletal Health Service and serves as Director of Innovator Enrichment and Manager in Strategic Alliances at UCSF Innovation Ventures while directing the UC Space Health Program. Education: MD from University of California, Davis (1993), Orthopaedic Surgery residency at Stanford University, Pediatric Orthopaedic and Sports Medicine fellowships at Boston Children's Hospital Research Focus: Combines orthopaedics, space health innovation, and digital medicine through AI-driven diagnostics, remote monitoring, and skeletal health optimization across lifespans Her scholarly output spans 2025's npj Microgravity work on AI-based bone density assessment to 2021 rural SARS-CoV-2 transmission studies, with key contributions in: Spaceflight-associated neuro-ocular syndrome monitoring Digital health applications in orthopaedics Bone health in extreme environments Leadership roles include: Co-Founder/Director, UCSF Center for Advanced 3D+ Technologies Co-Founder, UCSF Center for Digital Health Innovation Chair, Murdoch Children's Research Institute Health Technology Advisory Board

• Database Systems
• Data Management
• Query Optimization
• +۳ مورد دیگر

Jan Kossmann is a researcher affiliated with the Hasso Plattner Institute at the University of Potsdam, Germany. His work focuses on database systems, query optimization, and autonomous database management. He earned his PhD in 2023 with a thesis on unsupervised database optimization, including efficient index selection and data dependency-driven query optimization. His research explores topics like workload-aware indexing, reinforcement learning applications in database systems, and the development of frameworks for evaluating autonomous database technologies. Key collaborations include projects with Rainer Schlosser, Thorsten Papenbrock, and others, emphasizing experimental evaluations and system design. Notable contributions include the SWIRL reinforcement learning-based index selection framework and the 'Magic Mirror' tool for comparing index selection approaches. His publications span venues like VLDB, EDBT, and ICDE, reflecting his deep engagement with database theory and practical system implementation.

Yong Chenمشاهده پروفایل

استاد

• Data-Intensive Computing
• Parallel and Distributed Computing
• High-Performance Computing
• +۳ مورد دیگر

Dr. Yong Chen is a Professor and Interim Department Chair in the Computer Science Department at Texas Tech University (TTU), where he founded the Data-Intensive Scalable Computing Laboratory (DISCL). He also serves as Co-Director of the NSF Cloud and Autonomic Computing Center (CAC@TTU), focusing on data-intensive computing, high-performance computing (HPC), cloud systems, and parallel/distributed architectures. His research bridges hardware-software co-design for scientific and enterprise applications. Ph.D., Computer Science, Illinois Institute of Technology (2009) M.S., Computer Science, University of Science and Technology of China (2003) B.E., Computer Engineering, University of Science and Technology of China (2000) Dr. Chen's research spans data-intensive computing, HPC, cloud systems, and parallel architectures. He develops scalable solutions for scientific discovery and enterprise computing, emphasizing systems software, storage optimization, and hardware-software co-design. His work addresses challenges in metadata management, 3D-stacked memory, and efficient resource allocation in distributed environments. Recent publications include studies on 3D-stacked memory optimization (IEEE TC), metadata indexing (SC), and parallel file system reliability (ICS). His work is characterized by interdisciplinary collaboration and practical applications in HPC domains. NSF-TCPP Early Adopter Status Award Best Paper Award (IPDPS'21) Outstanding Teaching Assistant, IIT (2006) Dr. Chen advises students through graduate and undergraduate research assistantships at DISCL and CAC@TTU, offering financial support for qualified candidates. He has contributed to major conferences as Program Co-Chair (ICPP) and Committee Member (IPDPS, CCGrid, ISC, HPCAsia). Labs/Teams: Data-Intensive Scalable Computing Laboratory (DISCL), NSF Cloud and Autonomic Computing Center (CAC@TTU).

Andrew Pavloمشاهده پروفایل

دانشیار

• Databases
• Distributed Systems
• Data-Intensive Computing
• +۳ مورد دیگر

Andrew Pavlo is an Associate Professor of Databaseology in the Computer Science Department at Carnegie Mellon University , part of the School of Computer Science . His research focuses on database systems, particularly self-driving architectures, transaction processing, and large-scale analytics. He is a member of the CMU Database Group and Parallel Data Laboratory. His awards include the NSF CAREER (2019), Sloan Fellowship (2018), and ACM SIGMOD Jim Gray Dissertation Award (2014). He co-founded OtterTune, a database tuning startup, though it later ceased operations. Current research interests emphasize autonomous database systems, query optimization, and distributed computing. Recent publications (2024) highlight work on self-driving DBMS, null representation in columnar formats, and UDF optimization techniques. Awards: NSF CAREER Award (2019) Sloan Fellowship (2018) ACM SIGMOD Jim Gray Dissertation Award (2014) Advising: Mentors students in database systems, including Sam Arch, Wan Shen Lim, and William Zhang. Labs/Teams: Leads the Database Group and collaborates with the Parallel Data Laboratory.

Alina Bialkowskiمشاهده پروفایل

مدرس ارشد

• Medical Imaging
• Biomedical Engineering
• Computer Vision
• +۶ مورد دیگر

Dr Alina Bialkowski is a Senior Lecturer at the School of Electrical Engineering and Computer Science , part of the Faculty of Engineering, Architecture and Information Technology at The University of Queensland. Her research focuses on interpretable machine learning and computer vision to enhance AI transparency and solve real-world challenges. Prior to joining UQ in late 2017, she held postdoctoral positions at University College London (2015–2017), where she studied human perception in driving, and Disney Research Pittsburgh (2014), analyzing team sports using spatiotemporal data. Dr Bialkowski earned her PhD and Bachelor of Engineering (Electrical Engineering) from Queensland University of Technology, Australia. Her doctoral research centered on group behavior analysis from visual and spatiotemporal data, with applications in sports analytics and intelligent surveillance systems. Her research interests span medical imaging (especially electromagnetic imaging of strokes), human attention modeling in driving, intelligent transport systems , surveillance systems , and sports analytics . She emphasizes explainable AI to bridge the gap between technical systems and human understanding, employing methods like feature visualization and attribution. Her work also explores sensors for non-invasive imaging and machine learning frameworks to ensure ethical AI. Dr Bialkowski has received significant recognition, including the Best Paper Prize at the 2017 IEEE Winter Conference on Applications of Computer Vision (WACV) . Her research has led to 6 international patents with collaborators such as Disney Research, Toyota Motor Europe, and The University of Queensland, focusing on electromagnetic imaging and AI-driven solutions. She actively contributes to interdisciplinary projects like The Lanyard Project (traffic sensor fusion) and co-authored a SmartSat CRC-funded research report on machine learning for satellites. Dr Bialkowski is available for supervision and advocates for AI systems that integrate human-centric principles into their design and evaluation.

Tong Zhangمشاهده پروفایل

استاد

• Computer Systems
• Memory and Data Storage
• Computer Architecture
• +۴ مورد دیگر

Tong Zhang is a Professor in the Electrical, Computer and Systems Engineering Department at Rensselaer Polytechnic Institute (RPI). He joined RPI in 2002 as an assistant professor, advancing to associate professor in 2008 and full professor in 2013. His research focuses on computer systems, particularly memory and data storage across software and hardware stacks, with interdisciplinary applications in computer architecture, VLSI signal processing, and error correction coding. He holds a B.S. and M.S. from Xian Jiaotong University (China) and a Ph.D. from the University of Minnesota. His work emphasizes energy-efficient storage solutions, transparent compression, and hardware-software co-design. Notable contributions include innovations in SSD arrays, computational storage drives, and database systems. He is an IEEE Fellow and maintains affiliations with RPI's Computer Science programs. Research interests span memory systems optimization, storage architectures, and emerging technologies like CXL-based AI acceleration. His publications highlight advancements in reducing energy consumption, improving data deduplication, and enhancing B+-tree performance on modern storage hardware. Awards: IEEE Fellow (2023) Grants/Advising: Extensive grant-funded research in storage systems; no student advisees explicitly listed. Labs/Teams: Engaged in interdisciplinary collaborations within RPI's computational storage and memory research groups.

Margo I. Seltzerمشاهده پروفایل

استاد

• Systems
• Data Provenance
• File Systems
• +۵ مورد دیگر

Margo I. Seltzer is a distinguished academic and researcher in computer science, serving as Co-Head of the Department of Computer Science at the University of British Columbia and Director of the Berkman Klein Center for Internet & Society at Harvard University. She holds prestigious positions such as Canada 150 Research Chair in Computer Systems and Cheriton Family Chair in Computer Science. Her work spans systems research, data provenance, file systems, databases, and healthcare technology. Seltzer has made significant contributions to the field through her research, including co-founding Sleepycat Software (developers of Berkeley DB), which earned the ACM SIGMOD Systems Award in 2020. She is an ACM Fellow and Sloan Foundation Fellow, among other accolades. Her research interests emphasize interpretable machine learning, graph analytics, and reproducibility in computational experiments. Education: A.B. in Applied Mathematics from Harvard/Radcliffe College; Ph.D. in Computer Science from UC Berkeley. Research Interests: Her work focuses on systems, provenance, and healthcare applications. Key areas include program synthesis, graph partitioning, differential privacy, and anomaly detection in healthcare. She has pioneered projects like CamFlow for provenance-aware security and contributed to frameworks like Tribuo for interpretable machine learning. Publications: Her recent work includes advancements in graph partitioning (CUTTANA), memory translation (Velosiraptor), and interpretable models for medical data (HyperBrain). These reflect her interdisciplinary approach, blending systems research with healthcare and AI. Awards: Beyond the ACM and Sloan honors, she has been recognized for teaching excellence, mentoring, and contributions to open-source software. She serves on advisory boards for the Canadian COVID Alert App and the US National Academies' CSTB. Advising & Grants: While currently focused on postdoctoral researchers, Seltzer has mentored numerous students, earning awards for her mentorship. Her grants include funding from the Swedish Research Council and collaborations with industry partners like Intel. Labs/Teams: Active in the Systopia lab at UBC, focusing on systems research, and involved in interdisciplinary initiatives like the Self-Driving Labs project for automated experimentation.

Julie Bernhardtمشاهده پروفایل

استاد

• Stroke Rehabilitation
• Neurorehabilitation
• Clinical Trials
• +۳ مورد دیگر

Professor Julie Bernhardt co-leads The Florey's Stroke theme and directs the AVERT Early Intervention Research Program at The Florey Institute of Neuroscience and Mental Health in Melbourne, Australia. As Australia's foremost stroke rehabilitation clinical trialist, she heads the NHMRC Centre of Research Excellence in Stroke Rehabilitation and Recovery and chairs the International Stroke Recovery and Rehabilitation Roundtable, driving global innovation in post-stroke care. Her research pioneers transformative approaches to stroke recovery through early mobility interventions, hospital environment redesign, and patient-centered rehabilitation. Key focus areas include optimizing dose-response relationships in mobility training, integrating virtual reality for therapeutic environments, and addressing disparities in rehabilitation access. She champions co-design methodologies involving stroke survivors to ensure research directly impacts clinical practice and policy. Analysis of her recent publications reveals a strategic shift toward interdisciplinary collaboration, blending clinical neuroscience with architectural design and digital health solutions. Her work increasingly emphasizes real-world implementation of evidence-based practices across diverse healthcare systems, with growing attention to young stroke survivors' unique needs and telehealth scalability post-pandemic. Her notable awards include: Honorary Life Membership, Stroke Foundation Australia (2018) Australian Financial Review/Westpac 100 Women of Influence (Global category, 2016) Austin Medical Research Foundation Distinguished Scientist (2016) American Heart Association Stroke Rehabilitation Award (2016) Chief Executive Women Scholarship MIT Sloan (2016) Viscount Linley Award, Stroke Association (2016) Male or Female Change Champion of the Year (2015) Professor Bernhardt leads the AVERT Early Rehabilitation Research Group, which operates as a nexus for international collaboration involving 40+ institutions. The group pioneers novel trial methodologies like the multi-arm adaptive AVERT DOSE study while developing practical tools for rehabilitation environments through partnerships with architects and designers. Current initiatives focus on translating research into hospital design standards and community-based rehabilitation frameworks.