جست‌وجوی استاد و پژوهشگر

Jian Huangمشاهده پروفایل

دانشیار

• Computer Architecture
• Memory and Storage Systems
• AI Infrastructure
• +۵ مورد دیگر

Jian Huang is an Associate Professor of Electrical and Computer Engineering at the University of Illinois at Urbana-Champaign (UIUC), holding a 100% appointment since August 2024. He is also an affiliated Associate Professor in Computer Science and serves as the Y. T. Lo Faculty Fellow in ECE. Huang leads the Systems Platform Research Group and received his Ph.D. in Computer Science from Georgia Institute of Technology in August 2017, where his dissertation earned the College of Computing Dissertation Award. His research focuses on Computer Architecture, Memory and Storage Systems, AI Infrastructure, Distributed Systems, and Systems Security . Huang has pioneered work in sustainable AI infrastructures, modular data centers, neural processing unit virtualization, and ransomware-aware storage systems. His research has been featured in top-tier conferences including ISCA, MICRO, ASPLOS, OSDI, SOSP, and USENIX ATC, with over 50 publications and multiple patents issued worldwide. Huang's research has significant industry impact, with some work transferred into products and featured in popular media outlets including ACM CACM, TechXplore, and Science Daily. His work on non-volatile memory and storage systems has received extensive coverage, with some projects generating over 50 media reports. Among his notable achievements are the ACM SIGMICRO Early Career Award, NSF CAREER Award, IEEE Micro Top Picks (three times), USENIX Best Paper Award, and the MICRO Best Paper Runner Up in 2024. He has secured over $4.01M in grants ($11.12M with collaborators) from NSF, DARPA, Army Research Office, and industry partners. ACM SIGMICRO Early Career Award NSF CAREER Award IEEE Micro Top Picks (three times) USENIX Best Paper Award MICRO Best Paper Runner Up (2024) Google Faculty Research Award NetApp Faculty Fellowship Award Huang actively contributes to the academic community as program co-chair of NVMW'23 and organizer of HotInfra'23. He currently advises 12 Ph.D. students, 1 master student, and multiple undergraduates. His teaching innovations include developing ECE522: Emerging Memory/Storage System and revising ECE511: Computer Architecture with cutting-edge content.

Charalampos Papamanthouمشاهده پروفایل

دانشیار

• Computer Security
• Applied Cryptography
• Verifiable Computations
• +۵ مورد دیگر

Charalampos Papamanthou is an Associate Professor of Computer Science at Yale University, where he also serves as Co-director of the Yale Applied Cryptography Laboratory and a member of the Yale Institute for Foundations of Data Science. He holds affiliations with the Yale Center for Algorithms, Data, and Market Design. Additionally, he is Chief Scientist at Lagrange Labs. His research focuses on computer security and applied cryptography, particularly verifiable and privacy-preserving computations, leakage-abuse attacks on searchable encryption, and scalable blockchains/cryptocurrencies. He has advised numerous students and postdocs, and his work is supported by NSF, Protocol Labs, and JP Morgan. Research Interests: His primary areas include cryptographic protocols, privacy-preserving systems, blockchain infrastructure, secure cloud computing, and distributed consensus mechanisms. He has pioneered advancements in zero-knowledge proofs, private information retrieval, and dynamic searchable encryption. Awards: He has received prestigious awards such as the CCS Test-of-Time Award (2022), JP Morgan Faculty Research Award (2022), and NSF CAREER Award (2017). His contributions span over 140 publications in top venues like CRYPTO, CCS, and SODA. Teaching: He has taught advanced courses in cryptography, algorithms, and computer systems security at Yale and previously at the University of Maryland and Brown University. Recently, he chairs Yale’s PhD admissions in Computer Science. Labs & Teams: Leads the Yale Applied Cryptography Lab, focusing on real-world applications of cryptographic research. Collaborates with industry partners like Lagrange Labs to develop privacy-preserving technologies.

Gustavo Alonsoمشاهده پروفایل

استاد

• Databases
• Distributed Systems
• Cloud Computing Architecture
• +۴ مورد دیگر

Gustavo Alonso is Full Professor at the Department of Computer Science (D-INFK) of ETH Zurich and Head of the Institute for Computing Platforms . He has been at ETH since 1995, first as a post-doc, then as Assistant Professor from April 1998, and promoted to Full Professor in October 2001. Within the Systems Group he leads the Information and Communication Systems Research Group . Education: 1989 – Telecommunications Engineering (undergraduate), Madrid Technical University (UPM-ETSIT), Spain 1992 – M.S. Computer Science, University of California, Santa Barbara (UCSB) 1994 – Ph.D. Computer Science, University of California, Santa Barbara (UCSB) Research Interests: His work spans databases, distributed systems, cloud-computing architecture, FPGAs, hardware acceleration for data science, parallel and reconfigurable computing . The group investigates how modern heterogeneous hardware—from GPUs to SmartNICs—can be integrated into data-processing systems to achieve orders-of-magnitude performance gains, energy savings, and new functionality such as in-network computation and serverless acceleration. Scientific Awards & Honors: Fellow of the ACM (Association for Computing Machinery) Fellow of the IEEE (Institute of Electrical and Electronics Engineers) Distinguished Alumnus, Department of Computer Science, UC Santa Barbara Four Test-of-Time / Most Influential Paper Awards across databases, programming languages, cloud computing, and software engineering Labs & Projects: He directs the Information and Communication Systems Research Group within the Systems Group ( systems.ethz.ch ). The lab develops open-source platforms such as Coyote v2 for FPGA abstractions, Shuhai for HBM benchmarking, and MicroRec for micro-second recommendation serving, while collaborating with industry on SmartNICs, serverless analytics, and cloud-scale data analytics.

• Quantum-safe cryptography
• Lattice-Based Cryptography
• Post-Quantum Cryptography
• +۴ مورد دیگر

Ron Steinfeld is an Associate Professor in the Department of Software Systems & Cybersecurity at Monash University, Australia. He holds editorial roles in Designs Codes and Cryptography (since 2017) and has served on technical committees for top-tier conferences like ASIACRYPT, CRYPTO, and EUROCRYPT. His research focuses on quantum-safe cryptography, lattice-based cryptography, and blockchain security, with over 80 refereed publications and AUD$4M+ in research funding. Education: BSc Mathematics and Physics (Monash University, 1998) BE (Hons., First Class) Electrical and Computer Systems (Monash University, 2000) PhD Computer Science (Monash University, 2003) Research Interests: Design and analysis of cryptographic algorithms, quantum-safe protocols, lattice-based security foundations, and blockchain applications. His work underpins NIST standard algorithms like Kyber and Dilithium through structured lattice problem research. Recent Articles Trends: Focus on privacy-preserving blockchain protocols, post-quantum signature schemes, encrypted data search, and cryptographic applications in adversarial AI. Recent work includes fair Bitcoin watchtower schemes and genomic database privacy solutions. Awards: ASIACRYPT 2015 Best Paper Award Advising & Grants: Supervises PhD projects on quantum-safe cryptography and blockchain security. Leads initiatives like the AUD$4M ARC-funded Quantum Information Technology project. Collaborates with industry partners including CSIRO/Data61. Labs/Teams: Key contributor to Monash’s cybersecurity and cryptography research groups, involving interdisciplinary projects on materials discovery ( Æinstein initiative) and post-quantum blockchain security.

Gauri Joshiمشاهده پروفایل

دانشیار

• Distributed Machine Learning
• Federated Learning
• Stochastic Modeling
• +۷ مورد دیگر

Gauri Joshi is an Associate Professor in the Electrical and Computer Engineering (ECE) department at Carnegie Mellon University, with affiliate appointments in the Machine Learning Department and Robotics Institute. Her work focuses on system-aware algorithms for distributed machine learning, combining optimization, probability, and coding theory to address communication and computational constraints in edge networks. MIT Ph.D. in EECS (2016) IIT Bombay B.Tech/M.Tech in Electrical Engineering (2010) Research themes include federated learning with communication efficiency, erasure coding for non-linear computations, and reinforcement learning for heterogeneous queueing systems. She leads the Optimization, Probability and Learning (OPAL) lab , affiliated with the Parallel Data Lab (PDL), CyLab, and FLAME Center. Recent publications highlight advances in: federated fine-tuning with low-rank adaptation, robust PCA for model aggregation, privacy-preserving prediction mechanisms, and adaptive reinforcement learning for job dispatching systems. Her group has received 15+ paper awards across SIGMETRICS, MobiHoc, and NeurIPS workshops. Scientific recognition includes: IEEE Goldsmith Lecturer (2025), MIT Technology Review '35 Innovators Under 35' (2022), ONR Young Investigator Award (2023), NSF CAREER (2021), and ACM SIGMETRICS Best Paper (2020). She has advised 20+ graduate students, many now at tech giants like Google, Apple, and Meta. Service contributions span program co-chair roles (MLSys 2025), associate editorships (IEEE/ACM Transactions on Networking), and workshop organization (ICML, NeurIPS). Her NSF AI-EDGE Institute leadership (2021-present) drives next-generation intelligent edge networks for robotics and aerospace applications.

Khuzaima Daudjeeمشاهده پروفایل

استاد

• Distributed Systems
• Database Management
• Cloud Computing
• +۳ مورد دیگر

Khuzaima Daudjee is a Professor and David R. Cheriton Faculty Fellow in the Cheriton School of Computer Science at the University of Waterloo. His research focuses on systems-oriented problems at the intersection of systems and data management, particularly building large-scale systems, storage infrastructure in the cloud, and modern hardware applications. He leads projects in distributed database systems, elastic scaling, and resource optimization. His recent work includes Caerus (geo-replicated transactions), Tiresias (predictive storage), and MorphoSys (automatic physical design metamorphosis). Daudjee has chaired major conferences including ICDE 2026 and serves on editorial boards for VLDB, SIGMOD, and IEEE TKDE journals. His awards include ACM Distinguished Scientist and multiple best paper awards. Educational initiatives include developing distributed systems teaching materials and supervising graduate students across database and distributed systems domains. His industry collaborations involve cloud infrastructure optimization and scalable data processing frameworks.

I-Hong Houمشاهده پروفایل

استاد

• Wireless networks
• Cloud/Edge computing
• Machine learning
• +۳ مورد دیگر

I-Hong Hou is a Professor in the Department of Electrical and Computer Engineering at Texas A&M University, part of the College of Engineering. He holds a B.S. in Electrical Engineering from National Taiwan University (2004), and M.S./Ph.D. in Computer Science from the University of Illinois, Urbana-Champaign (2008/2011). His research focuses on wireless networks, cloud/edge computing, and machine learning with notable contributions to real-time systems and network optimization. Education : B.S., Electrical Engineering, National Taiwan University, 2004 M.S., Computer Science, University of Illinois at Urbana-Champaign, 2008 Ph.D., Computer Science, University of Illinois at Urbana-Champaign, 2011 Research Highlights : Hou’s work emphasizes Age of Information (AoI) , distributed learning, and scheduling algorithms for edge computing. He has pioneered frameworks integrating machine learning with network protocols, such as deep reinforcement learning for restless bandits and second-order optimization for wireless systems. His methods address real-time communication challenges in multi-hop networks and dynamic environments. Awards : Best Paper Awards at ACM MobiHoc (2017, 2020) Best Student Paper, WiOpt 2017 C.W. Gear Outstanding Graduate Student Award, UIUC Advising & Grants : Advised PhD student Siqi Fan (graduated 2024). His research has been supported by grants exploring edge-cloud reconfiguration, real-time video delivery, and neural Whittle index networks. Recent work includes optimizing freshness of information in multi-user systems and developing threshold-optimal policies for complex decision-making. Labs/Teams : Leads the Computer Engineering and Systems Group (CESG) at Texas A&M, collaborating on projects blending networking, machine learning, and distributed systems.

Timothy Roscoeمشاهده پروفایل

استاد

• Operating Systems
• Networking
• Distributed Systems
• +۳ مورد دیگر

Prof. Dr. Timothy Roscoe is a Full Professor in the Department of Computer Science at ETH Zurich. His research focuses on operating systems, networking, and distributed systems. He was inducted as an ACM Fellow in 2014 for contributions to these fields. Previously, he worked at Intel Research Berkeley, UC Berkeley (Adjunct Professor), and Sprint Labs. His work includes foundational contributions to systems like PlanetLab, P2 Declarative Networking, and the Nemesis OS. Roscoe holds a PhD from the University of Cambridge and has led projects in cloud computing, wide-area network measurement, and secure systems design. His current research emphasizes hardware-software co-design, formal verification of systems, and scalable memory architectures. Education: PhD in Computer Science, University of Cambridge (1995) Key Roles: Principal Researcher at Intel Berkeley Lab, Visiting Researcher at National ICT Australia Research Highlights: Contributions to distributed systems (PlanetLab), declarative networking (P2), and secure microkernels (seL4 integration). Current projects address challenges in disaggregated memory systems, coherent interconnects, and trustworthy embedded systems. Awards: ACM Fellow (2014), numerous grants and industry collaborations. Labs/Teams: Systems Group at ETH Zurich, focusing on OS design, hardware architectures, and formal methods.

• 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.

Mo Addaمشاهده پروفایل

مدرس ارشد

• Network Security
• Distributed Systems
• Wireless Networks
• +۵ مورد دیگر

Dr. Mo Adda is a Principal Lecturer at the University of Portsmouth's School of Computing, part of the Faculty of Technology. He holds a PhD in Distributed Systems and Parallel Processing from the University of Surrey. His research focuses on network security, distributed systems, wireless networks, and cybercrime prevention. He leads projects in the Centre for Cybercrime and Economic Crime, exploring fault management in networks, blockchain applications, and IoT forensics. With 16 supervised theses, he advises on topics like energy-efficient cloud systems and machine learning for environmental modeling. His work bridges academia and industry, addressing challenges in software-defined networks, traffic control, and secure data sharing in social networks. Education: PhD in Distributed Systems (University of Surrey) Affiliations: Centre of Excellence in Defence, Risk & Resilience; Portsmouth Centre for Advanced Materials and Manufacturing Research Interests Dr. Adda's research spans: Network security and fault detection mechanisms Blockchain applications in IoT and forensics Energy-efficient cloud data center optimization Machine learning for climate modeling Self-organizing network protocols Grants & Collaborations His projects include collaborations with industry partners on secure data leakage detection in cloud systems and resilient wireless protocols for harsh environments. He has pioneered fault classification systems using clustering algorithms and fuzzy logic. Labs & Teams He contributes to the Centre for Cybercrime and Economic Crime, focusing on digital forensics and network intrusion analysis. His team develops frameworks for privacy management in social networks and proactive routing in software-defined networks.

Daniel Abadiمشاهده پروفایل

استاد

• Database System Architecture
• Distributed Database Systems
• Scalable Transaction Processing
• +۳ مورد دیگر

Daniel Abadi is the Darnell-Kanal Professor of Computer Science at the University of Maryland, College Park, with an appointment in the University of Maryland Institute for Advanced Computer Studies. He leads the Data Systems Lab at Maryland (DSLAM) and has made significant contributions to database system architecture and implementation, particularly in scalable and distributed systems. Prof. Abadi's research focuses on database system architecture, especially at the intersection with scalable and distributed systems. He is best-known for the development of the storage and query execution engines of the C-Store (column-oriented database) prototype, which was commercialized by Vertica and eventually acquired by Hewlett-Packard, and for his HadoopDB research on fault tolerant scalable analytical database systems which was commercialized by Hadapt and acquired by Teradata. His current work includes deterministic distributed systems like Calvin and SLOG, which provide strictly serializable, low-latency, geographically replicated database transactions. Analysis of his recent publications reveals a strong focus on modern database challenges including transaction processing, distributed systems architecture, data mesh concepts, schema evolution, and IoT data management. His work consistently addresses the tension between consistency, availability, and performance in distributed database systems, with recent emphasis on moving beyond traditional two-phase commit protocols and exploring novel approaches to data architecture like data mesh. ACM Fellow Sloan Research Fellowship Churchill Scholarship NSF CAREER Award VLDB Best Paper Award Two VLDB Test of Time Awards (for C-Store and HadoopDB) 2008 SIGMOD Jim Gray Doctoral Dissertation Award 2013-2014 Yale Provost's Teaching Prize 2013 VLDB Early Career Researcher Award PhD dissertation advisor for Alexander Thomson and Jose Falerio, whose dissertations won SIGMOD Jim Gray Doctoral Dissertation Awards in 2015 and 2020 respectively Prof. Abadi has advised multiple PhD students, including Alexander Thomson and Jose Falerio, both of whom received the prestigious SIGMOD Jim Gray Doctoral Dissertation Award. His research has been supported by numerous grants including NSF funding for projects like SLOG. He maintains an active presence in the database community through his widely-read blog DBMS Musings and through service on program committees for major conferences including SIGMOD, VLDB, and CIDR. He leads the DSLAM research group at the University of Maryland, which focuses on cutting-edge database system research with strong industry connections and practical impact.

Patrick McDanielمشاهده پروفایل

استاد

• Security of Machine Learning/AI Systems
• Adversarial Machine Learning
• Systems Security
• +۴ مورد دیگر

Patrick McDaniel is a Professor in the School of Computer, Data & Information Sciences at the University of Wisconsin-Madison. He is a Fellow of IEEE, ACM, and AAAS, and leads the MadS&P (UW-Madison Security and Privacy) group. His research spans computer and network security, adversarial machine learning, and technical public policy. Academic Affiliation : Tsun-Ming Shih Professor of Computer Sciences Key Research Areas : Mobile/IoT security, election systems security, AI policy Research Trends : His recent publications and funded projects focus on adversarial machine learning, SDN security, and robustness of AI systems. Keywords include cybersecurity, machine learning, network security, and privacy. Grants and Leadership : He has secured over $5M in NSF funding for end-to-end trustworthiness of ML systems and leads collaborative projects with Army Research and UW-Madison. Awards : Multiple best/most influential paper awards (ICSE 2015, PLDI 2014, ACSAC 2024, EthiCS 2023) Advising : Mentored 13+ PhD students and postdocs now at top institutions like Google, Purdue, and University of Toronto

Michele Zorziمشاهده پروفایل

استاد

• Wireless Communications
• Networking
• mmWave Communications
• +۵ مورد دیگر

Michele Zorzi is a Professor of Telecommunications at the School of Engineering, University of Padova, Italy, where he has held a faculty position since 2003. He leads the SIGNET (Signal processing and Networking) Research Group, focusing on cutting-edge wireless networking challenges including mmWave communications and underwater networks. His extensive publication record exceeds 600 papers in top-tier journals and conferences, reflecting significant contributions to the field through both theoretical and experimental work. He received his Laurea Degree (1990) and Ph.D. (1994) in Electrical Engineering from the University of Padova. Prior academic appointments include Politecnico di Milano (1993-1996), University of California San Diego (1995-1998), and University of Ferrara (1998-2003), where he progressed from Associate Professor to full Professor. His educational trajectory demonstrates deep roots in Italian academia with international exposure. Professor Zorzi's research spans wireless communications and networking, with current emphases on mmWave networking for vehicular systems, underwater acoustic/optical communications, non-terrestrial networks, and AI-driven networking solutions. His group conducts experimental validations including at-sea trials for underwater systems and testbeds for vehicular networks. Key projects include PRATA for predictive QoS in autonomous driving and IoT-based environmental monitoring of the Venice Lagoon, demonstrating practical applications of theoretical work. Analysis of his 2022-2025 publications reveals strong trends in applying artificial intelligence to networking challenges across diverse environments. There is significant emphasis on vehicular networks (predictive QoS, teleoperated driving), underwater systems (acoustic/optical communications, AUV swarms), and satellite networks (Starlink integration, NTN security). Experimental validation in real-world scenarios like the Venice Lagoon monitoring project and underwater sea trials characterizes his applied research approach. His scientific accolades include: IEEE Fellow (2007) IEEE Communications Society Best Tutorial Paper Award (2008, 2019) Stephen O. Rice Best Paper Award (2018) Multiple best paper awards at IEEE conferences (2005-2020) As principal investigator for numerous European and US research projects plus 20+ industry-funded initiatives, Professor Zorzi has mentored over 35 PhD students and post-docs. Graduates now hold prominent positions at institutions including Stanford, UCSD, CTTC, and Huawei. His SIGNET group maintains active international collaborations and contributes to open-source networking tools via GitHub, demonstrating commitment to community engagement. The SIGNET Research Group, housed within the Department of Information Engineering, operates specialized experimental facilities for mmWave and underwater communications. Current initiatives include AI-based predictive QoS frameworks for vehicular networks, underwater optical communication systems using ultraviolet light, and large-scale IoT deployments for environmental monitoring. The group's GitHub presence indicates strong open-science practices, while recent sea trials confirm hands-on experimental capabilities beyond theoretical work.

• Soft Robotics
• Tactile Sensing
• Artificial Skin
• +۴ مورد دیگر

Perla Maiolino serves as an Associate Professor in Engineering Science at the University of Oxford and Principal Investigator of the Soft Robotics Lab (SRL) within the Oxford Robotics Institute. Her academic foundation includes BEng, MEng, and PhD degrees in Robotics and Automation from the University of Genoa, where she pioneered CySkin technology for distributed tactile sensing in robots—later exhibited at the Science Museum in London. She expanded her expertise during a 2017-2018 postdoctoral fellowship at Cambridge University's Biologically Inspired Robotics Lab, focusing on soft robotics and tactile perception. Dr. Maiolino's research centers on developing artificial skin systems, soft robotic actuators, and distributed sensing architectures. Her work bridges biological inspiration with engineering innovation to create robots capable of safe human interaction and dexterous manipulation in unstructured environments. Key contributions include compliant beaded-string jamming mechanisms for anthropomorphic fingers, monolithic 3D-printed soft pneumatic arms (JAMMit!), and distributed time-of-flight sensor networks for robotic self-awareness. Recent publications (2024-2025) reveal a strong convergence of tactile sensing with machine learning, featuring optical flow for gesture recognition, diffusion models for artificial skin simulation, and zero-shot sim-to-real transfer techniques. Her team has made significant advances in multi-modal sensing integration, variable stiffness actuation, and scene flow estimation for robots operating in dynamic surroundings. Scientific Awards No specific awards were documented in the provided institutional materials. Advising and Grants While her leadership of the Soft Robotics Lab implies active student supervision and grant management, detailed information about advisees or funded projects was not included in the source documentation. Labs and Teams As Principal Investigator of the Soft Robotics Lab at Oxford Robotics Institute, Dr. Maiolino directs research on tactile perception systems, soft actuation mechanisms, and sensor-integrated robotic structures. The lab's work focuses on applications requiring safe physical interaction, including healthcare robotics and human-robot collaboration scenarios, with emphasis on multi-material 3D printing and embedded sensing technologies.

Daniel Abadiمشاهده پروفایل

استاد

• Database system architecture
• Distributed database systems
• Column-oriented databases
• +۵ مورد دیگر

Daniel Abadi is the Darnell-Kanal Professor of Computer Science at the University of Maryland, College Park. He leads the Data Systems Lab at Maryland (DSLAM) and is widely recognized for his groundbreaking contributions to database system architecture and implementation. Previously, he was a faculty member at Yale University where he received the Provost's Teaching Prize. Abadi's research primarily focuses on database system architecture, particularly at the intersection with scalable and distributed systems. He is best known for developing the storage and query execution engines of the C-Store prototype (a column-oriented database system commercialized by Vertica and later acquired by Hewlett-Packard), HadoopDB research (commercialized by Hadapt and acquired by Teradata), and deterministic distributed transactional systems like Calvin (currently being commercialized by Fauna). His work bridges theoretical innovation with practical industrial impact. Analysis of his recent publications reveals a consistent trajectory toward solving fundamental challenges in distributed database systems. His research has evolved from foundational work on column-stores and hybrid database architectures to cutting-edge innovations in geo-replicated transactions, concurrency control mechanisms, and the integration of machine learning with database systems. The trend shows increasing focus on practical implementations that address real-world scalability and performance challenges in large-scale data processing environments. ACM Fellow Churchill Scholarship recipient NSF CAREER Award winner Sloan Research Fellowship recipient VLDB Best Paper Award winner Two VLDB Test of Time Awards (for C-Store and HadoopDB) 2008 SIGMOD Jim Gray Doctoral Dissertation Award 2013-2014 Yale Provost's Teaching Prize 2013 VLDB Early Career Researcher Award Professor Abadi has successfully mentored several PhD students, most notably Alexander Thomson and Jose Falerio, both of whom won the prestigious SIGMOD Jim Gray Doctoral Dissertation Award for their work under his supervision. His research has been generously supported by multiple NSF grants including BIGDATA awards and other funding mechanisms that have enabled significant advances in database technology. He actively collaborates with industry partners, with several of his research projects leading directly to commercial products. At the University of Maryland, Abadi directs the Data Systems Lab at Maryland (DSLAM), which focuses on developing innovative database technologies that address contemporary challenges in data management. The lab's research spans distributed transaction processing, database architecture, and the integration of database systems with emerging computing paradigms. Notable projects include SLOG (Serializable, Low-latency, Geo-replicated Transactions), which eliminates traditional tradeoffs in distributed database design, and ongoing work in deterministic database systems that provide strong consistency guarantees without sacrificing performance.