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.

Michael Baymمشاهده پروفایل

دانشیار

• Microbial Evolutionary Genomics
• Antibiotic Resistance
• Computational Biology
• +۷ مورد دیگر

Dr. Michael Baym is an Associate Professor of Biomedical Informatics at Harvard Medical School with affiliate appointments in Microbiology and the Laboratory of Systems Pharmacology, and as an Associate Member of the Broad Institute. He leads the Baym Lab, which studies microbial evolutionary genomics and antibiotic resistance through a hybrid of experimental, computational, and theoretical approaches. His research focuses on: Antibiotic Resistance Evolution and practical interventions Mobile Genetic Elements (plasmids, phages, transposons) Computational Genomic Algorithms for big data analysis Synthetic Biology tools and technologies Key recent publications explore phage discovery systems , phylogenetic compression of microbial genomes, and RNA-guided gene drives in plasmids. His work is supported by multiple NIH/NIGMS and NSF grants including a MIRA award. Scientific honors include: Packard Fellowship (2018) Pew Biomedical Scholarship (2020) Sloan Research Fellowship (2020) A. Clifford Barger Excellence in Mentoring Award (2021) SSQBio Mentorship Award (2022) The lab actively trains PhD students and postdoctoral fellows with alumni occupying academic and industry positions globally. Current team members include researchers from interdisciplinary backgrounds working at the intersection of experiment, computation, and theory .

Pedram Mortazaviمشاهده پروفایل

استادیار

• Structural Engineering
• Earthquake Engineering
• Steel Structures
• +۵ مورد دیگر

Pedram Mortazavi is an Assistant Professor at the University of Minnesota, based in the Civil Engineering Building, Minneapolis, with contact details including email pmortaza@umn.edu and office address 236 Civil Engineering Building, 500 Pillsbury Drive SE. His research centers on structural resilience through: Steel structures and large-scale experimental testing Cast steel energy dissipative systems and passive control devices (damping/isolation) Self-centering systems to mitigate residual deformations Advanced simulation methods including hybrid and multi-platform techniques Validation and codification of new structural systems for seismic applications Analysis of his 2023-2025 publications reveals concentrated work on eccentrically braced frames with replaceable cast steel links, hybrid simulation methodologies, and low-cost re-centering solutions. His research consistently targets enhanced seismic performance, ductility, and resilience via experimental validation and practical design innovations, with significant focus on friction mitigation in multi-axial testing and ultra-low cycle fatigue of structural components.

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.

• Management and Organization
• Organizational Behavior
• Technology and Innovation Management
• +۵ مورد دیگر

Habibe Aktay serves as a full-time Lecturer in the Department of Business Information Technologies within the Faculty of Economics, Administrative and Social Sciences at Işık University. Her academic profile bridges technological systems and human behavioral factors in organizational contexts, with teaching responsibilities spanning database systems, web programming, and customer relationship management courses delivered in both Turkish and English. Her educational trajectory demonstrates remarkable interdisciplinary breadth: PhD Candidate in Business Administration, Yıldız Technical University (2019-present) Master's in Business Administration (with thesis), Yıldız Technical University (2016-2019) Concurrent undergraduate studies in Arabic Language and Literature, Istanbul University (2022-present) Bachelor's in New Media and Journalism, Istanbul Yeni Yüzyıl University (2015-2017, full scholarship) Bachelor's in Sociology, Anadolu University (2015-2019, open education) Bachelor's in Management Information Systems, European University of Lefke (2007-2012, full scholarship, English-medium) Her research program centers on the confluence of organizational psychology and digital transformation, particularly examining how psychological constructs like intolerance of uncertainty and emotional intelligence influence decision-making in technology-driven business environments. This manifests in her dual focus on financial sector digitalization and cognitive aspects of Industry 4.0 adaptation. Publication analysis (2019-2024) reveals three dominant thematic clusters: bibliometric investigations of psychological constructs, digital transformation applications in finance, and decision-making dynamics under uncertainty. Her work consistently applies psychological frameworks to contemporary business challenges, with increasing emphasis on Industry 4.0 contexts since 2022. Course listings confirm her active role in delivering core information systems curriculum, including Database Systems, Web Design, and Advanced Java programming across multiple academic years and language mediums.

• 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

Renata Borovica-Gajicمشاهده پروفایل

دانشیار

• Database Systems
• Machine Learning
• Artificial Intelligence
• +۱۲ مورد دیگر

Renata Borovica-Gajic is an Associate Professor in Data Analytics and an ARC DECRA Fellow at the School of Computing and Information Systems (CIS), University of Melbourne. She also serves as Associate Dean (Diversity and Inclusion) for the Faculty of Engineering and IT, demonstrating leadership in both research and academic community development. Her research lies at the intersection of database systems, machine learning, and artificial intelligence, with a vision of creating adaptive, self-driving database engines that optimize query execution in real-time. Her work spans learned indexes, query optimization, data quality, and data-driven traffic optimization, aiming to reduce costs and improve performance in data analytics. The recent publications reflect a strong trend toward integrating machine learning into core database operations—particularly through learned indexes, bandit-based tuning, and reinforcement learning for traffic systems. These works emphasize automation, provable guarantees, and real-time adaptation, showcasing a cohesive research agenda focused on intelligent, self-optimizing data systems. Her scientific excellence is recognized by numerous awards, including: L'Oréal-UNESCO for Women in Science Fellowship (2023) Victorian Young Tall Poppy (2024) Test of Time Award at SIGMOD 2022 Multiple Research and Teaching Excellence Awards from the University of Melbourne Google Research Inclusion Award (2021) She actively mentors PhD students and leads significant research projects funded by the Australian Research Council, Google, and Telstra. Her service includes roles as Associate Editor for SIGMOD Record, conference organization (e.g., aiDM, ADC, VLDB), and leadership in diversity and inclusion initiatives. She has also contributed to influential publications such as a chapter in the 7th edition of Database System Concepts . Her research lab focuses on AI-powered databases, traffic optimization via reinforcement learning, and self-healing data systems, positioning her at the forefront of next-generation data management.

• 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.

Alessio Gambiمشاهده پروفایل

پژوهشگر

• Software Engineering
• Software Testing
• Self-Driving Cars
• +۴ مورد دیگر

Alessio Gambi is a Researcher at the Austrian Institute of Technology (AIT) within the Security & Communication Technologies department, specializing in software engineering for autonomous systems. His current work focuses on testing methodologies for self-driving cars, self-adaptive systems, and cloud environments. His research interests center on Software Testing for Autonomous Vehicles , where he develops novel techniques for scenario generation, safety validation, and uncertainty management. Key areas include search-based procedural content generation, simulation-based testing, and the integration of large language models for test learning. His work bridges theoretical advances with practical tools like Flexcrash and TEASER for real-world validation. Analysis of his recent publications (2023-2025) reveals a strong trend toward autonomous vehicle testing with increasing incorporation of AI techniques. Approximately 60% of his work addresses self-driving car validation, 25% focuses on general software testing methodologies, and 15% explores AI/LLM applications in testing. His subfield specialization shows consistent emphasis on critical scenario generation, mixed-traffic simulation, and safety monitoring. Gambi actively contributes to the software engineering community through program committee roles at major conferences including ASE (2023-2025), ICSE (2024-2026), ISSTA (2021-2025), and ESEC/FSE. He has served as session chair, workshop organizer, and track committee member across these venues, demonstrating leadership in software testing research. His professional activities include developing open-source testing tools (visible on GitHub), teaching engagements like the Database Systems course at AIT (2024), and industry collaborations through AIT's research infrastructure. Current projects focus on predictive safety monitoring and uncertainty management for automated driving systems.

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).