جست‌وجوی استاد و پژوهشگر

• Distributed Systems
• Database Management Systems
• Operating Systems
• +۷ مورد دیگر

Natacha Crooks Dr. Natacha Crooks is an Assistant Professor in the Department of Electrical Engineering and Computer Science (EECS) at UC Berkeley. Her research focuses on distributed systems, databases, and security, with a particular emphasis on consistency models, BFT protocols, and cloud computing. She is a founding member of the SkyLab and a core contributor to the Data Systems and Foundations Group at Berkeley. She holds a Ph.D. in Distributed Systems from the University of Texas at Austin (2019) and a BA in Computer Science and Law from the University of Cambridge (2012). Affiliations & Roles: Assistant Professor, UC Berkeley EECS Visiting Researcher at Azure Research, Security & Privacy Founding Member of SkyLab Member of Berkeley Center for Decentralized Intelligence Former Scientific Advisor at Improbable and Astronomer Research Interests: Her work bridges distributed systems and database research, addressing challenges in transactional consistency, fault-tolerant protocols, and cloud resource optimization. Key themes include: Designing scalable BFT consensus algorithms Secure multi-cloud storage solutions Optimizing distributed transaction processing Privacy-preserving distributed systems Awards & Recognition: Sloan Research Fellow (2025) NSF CAREER Award ACM SIGOPS Dennis M. Ritchie Doctoral Dissertation Award (2020) IEEE CS TCDE Early Career Award (2024) Teaching: Fall 2025: CS 294-282 (Research Culture and Community Norms), Wheeler 130. Industrial Collaboration: Prior roles include visiting researcher at Cornell University, Microsoft Research (DMX group), and Imperial College London. Industrial partnerships include work with Materialize, Astronomer, and Improbable.

Daniel B. Szyldمشاهده پروفایل

استاد

• Numerical Analysis
• Scientific Computing
• Numerical Linear Algebra
• +۱۲ مورد دیگر

Daniel B. Szyld is a Professor in the Department of Mathematics at Temple University's College of Science and Technology. He is co-Director of the High-Performance Computing for Scientific Applications Professional Science Master’s program and a member of the Center for Computational Mathematics and Modeling. He holds leadership roles as President of the International Linear Algebra Society (ILAS, 2020–2026) and as a Board of Trustees member at ICERM (2024–2028), and previously served as Vice-President of SIAM (2014–2015). His research interests include Numerical Analysis , Scientific Computing , Numerical Linear Algebra , Iterative Methods , Preconditioning , Domain Decomposition , and High-Performance Computing . His work often focuses on Krylov subspace methods like GMRES, block solvers, and asynchronous algorithms, with applications in large-scale scientific simulations. The 15 most recent publications reflect a strong focus on enhancing the stability, convergence, and performance of iterative solvers, especially GMRES variants and domain decomposition methods. Topics include random sketching, deflation, weighted norms, multisketching in QR factorization, and asynchronous Schwarz methods. These works appear in top journals such as SIAM Journal on Matrix Analysis and Applications , Numerische Mathematik , and Electronic Transactions on Numerical Analysis , often in collaboration with leading researchers in the field. Scientific Awards and Recognitions: Commemorative medal, Charles University of Prague, 1997 Featured in Hall of Fame by Henk van der Vorst, SARA, 2010 Dean's Distinguished Award for Excellence in Research, Temple University, 2011 Fellow, American Mathematical Society, 2017 Fellow, Society for Industrial and Applied Mathematics, 2017 Achievement in Mathematics Award, Temple University, 2018 Faculty Senate Outstanding Service Award, Temple University, 2021 Daniel B. Szyld has served on the editorial boards of numerous prestigious journals, including Mathematics of Computation , Linear Algebra and its Applications , Numerical Linear Algebra with Applications , and was Co-Editor-in-Chief of Electronic Transactions on Numerical Analysis (2005–2013) and Editor-in-Chief of SIAM Journal on Matrix Analysis and Applications (2015–2020). His research has been supported by the National Science Foundation and the Department of Energy. He has advised students and postdocs, though specific names are not listed in the provided text. He is also involved in professional service through societies such as SIAM, AMS, ILAS, and NAM, and advocates for equity and ethical engagement in mathematics. Labs and Research Groups: He is a member of the Center for Computational Mathematics and Modeling at Temple University and co-Director of the High-Performance Computing for Scientific Applications Professional Science Master’s program, indicating active leadership in computational research and training.

Tim Baarslagمشاهده پروفایل

استاد

• Automated Negotiation
• Multi-Agent Systems
• Artificial Intelligence
• +۴ مورد دیگر

Tim Baarslag is a Senior Researcher and group leader of the Intelligent and Autonomous Systems group at CWI (The Dutch research institute for Mathematics and Computer Science). He holds the title of Professor of Mathematics of Cooperative AI at Eindhoven University of Technology and serves as a Visiting Associate Professor at Nagoya University of Technology, Visiting Fellow at the University of Southampton, and Visiting Scholar at MIT. His research focuses on automated negotiation systems for collaborative decision-making in smart energy trading, IoT, autonomous vehicles, and digital privacy. Education : MSc (cum laude) and BSc (cum laude) from Utrecht University; PhD (cum laude) from Delft University of Technology Tim pioneered the COMBINE project (NWO Vidi grant) for coordinating multi-deal negotiations and developed the widely-used Genius negotiation environment. His work appears in prestigious venues like Science Magazine , Artificial Intelligence , and MIT Technology Review . He also leads the International Automated Negotiating Agent Competition and contributes to policy through memberships in The Young Academy and Netherlands Academy of Engineering . Recent research trends emphasize multi-deal negotiation protocols (2024), preference uncertainty modeling in privacy negotiations (2022), and scalable algorithms for handling outcome spaces as large as 10²⁵⁰ possibilities. His 2023 work on search algorithms for large negotiation domains has applications in energy trading and supply chain management. Scientific Awards : Cor Baayen Young Researcher Award (2017), Springer Theses Award (2016), multiple Best Paper Awards (AAMAS 2022, WI-IAT 2015, IJCAI 2014), and recognitions as Science Talent (2018), Academic Pioneer (2020), and Young Talent (2019) As a grant recipient , Tim leads NWO Vidi project COMBINE and previously held a Veni grant for preference uncertainty research. He mentors through organizing competitions, serving on conference PCs (AAAI, IJCAI), and reviewing in top journals like Artificial Intelligence . His work bridges theory and practice through the Genius framework and real-world implementations in smart grid and vehicular platooning.

Melissa C. Smithمشاهده پروفایل

استاد

• Machine Learning
• Deep Learning
• Artificial Intelligence
• +۵ مورد دیگر

Melissa C. Smith is a Professor of Electrical and Computer Engineering and Associate Dean for Graduate Studies at Clemson University. She holds a Ph.D. from the University of Tennessee and degrees from Florida State University. Her research focuses on machine learning, reconfigurable computing, and high-performance systems, with applications in embedded systems and interdisciplinary scientific advancements. Before joining Clemson in 2006, she was a research associate at Oak Ridge National Laboratory (ORNL), contributing to projects like the Spallation Neutron Source and PHENIX experiments. Education: Ph.D., Electrical and Computer Engineering, University of Tennessee M.S., Electrical Engineering, Florida State University B.S., Electrical Engineering, Florida State University Research Interests: Machine Learning and AI High-Performance and Reconfigurable Computing System Performance Modeling Embedded Systems Articles Summary: Her recent work spans machine learning applications, GPU/FPGA architectures, speech enhancement, and medical systems. Key themes include optimizing heterogeneous computing for real-time and scientific workloads, and advancing interdisciplinary solutions through architecture-application co-design. Lab & Collaborations: Leads the Future Computing Technologies Lab and collaborates with ORNL and national labs on projects like GEMmaker and HPC-enabled medical systems.

Salim El Rouayhebمشاهده پروفایل

دانشیار

• Information Theory
• Coding Theory
• Information-Theoretic Security
• +۴ مورد دیگر

Salim El Rouayheb is an Associate Professor in the Department of Electrical and Computer Engineering at Rutgers University. He leads the Coding and Securing Information (CSI) Lab, which focuses on information-theoretic security and privacy in distributed systems. His research spans multiple areas including secure machine learning, private information retrieval, and data synchronization. Dr. El Rouayheb received his Ph.D. in Electrical Engineering from Texas A&M University in 2009. Prior to joining Rutgers, he was an Assistant Professor at the Illinois Institute of Technology (2013-2017), a Research Scholar at Princeton University (2012-2013), and a Postdoctoral Researcher at UC Berkeley (2010-2011). His research interests focus on information-theoretic security in distributed systems, private information retrieval and search, secure machine learning algorithms, and data synchronization in distributed systems. He has made significant contributions to developing frameworks that provide information-theoretic privacy guarantees in various contexts including federated learning, genomic data analysis, and decentralized networks. His work often bridges theoretical foundations with practical applications, particularly in the areas of secure distributed computing and privacy-preserving algorithms. His recent publications demonstrate a strong trend toward applying information-theoretic principles to address privacy and security challenges in machine learning systems, particularly in federated and decentralized settings. Many of his papers explore random walk approaches for decentralized learning, secure matrix multiplication techniques, and privacy mechanisms that can be toggled "on and off" based on correlation patterns in data. His work spans both theoretical contributions in information theory and practical implementations for real-world systems. Dr. El Rouayheb has received several prestigious awards including the NSF CAREER Award (2016), Google Faculty Research Award (2018), and the Rutgers University Walter Tyson Junior Faculty Chair (2019). He has successfully secured multiple research grants including NSF SaTC, NSF CAREER, Google Faculty Research Awards, and Army Research Lab funding. His lab, the Coding and Securing Information (CSI) Lab, currently includes postdoc Xingran Chen, PhD student Zonghong Liu, and undergraduate researchers. The CSI Lab maintains an active research agenda with regular publications in top-tier venues and hosts the Shannon Channel, a series of online talks related to information theory. Dr. El Rouayheb is also involved in organizing workshops on coding theory and information security.

Dahlia Malkhiمشاهده پروفایل

استاد

• Distributed Systems
• Blockchain Technology
• Financial Technology
• +۳ مورد دیگر

Professor Dahlia Malkhi is a leading academic and researcher in distributed systems and blockchain technology. She currently holds a faculty position at the University of California, Santa Barbara (UCSB), where she heads the Foundations of Financial Technology (FfTech) research lab. Her work focuses on reliability, security, and consensus mechanisms in distributed systems, with a recent emphasis on blockchain innovations like HotStuff, which underpins Diem, Aptos, and other blockchains. She has held influential roles at industry leaders such as Chainlink Labs, Diem Association, VMware, and Microsoft Research. Education: Ph.D. in Computer Science from The Hebrew University of Jerusalem. Past roles include CTO of Diem Association (2019–2022), Principal Researcher at VMware (2014–2019), and Partner Principal Researcher at Microsoft Research (2004–2014). Research Interests: Blockchain consensus algorithms (e.g., HotStuff, Flexible Paxos), Byzantine Fault Tolerance (BFT), secure multi-party computation (FairPlay), and distributed database systems (CorfuDB). Her work bridges academic theory with industrial applications, emphasizing practical scalability and security. Awards: ACM Fellow (2011), IEEE TCDP Outstanding Technical Achievement Award (2021), IBM Faculty Award (2003/2004). She has also held leadership roles in conferences like Usenix ATC and program chairs for multiple distributed systems events. Advising & Grants: Advises projects at Space Computer, Lyquor Labs, and Chainlink Labs. Her research labs and collaborations include work on BBCA-Chain, Lumiere, and BFTBrain, advancing consensus mechanisms in decentralized systems. Labs/Teams: Leads UCSB’s FfTech lab, co-founded VMware Research, and contributed to foundational blockchain projects like DiemBFT and Espresso Systems. Her work impacts technologies such as NSX-T control planes and distributed financial infrastructure.

Xiaofan Yuمشاهده پروفایل

استادیار

• Embedded Systems
• Edge AI
• Neuromorphic Computing
• +۵ مورد دیگر

Xiaofan Yu is an Assistant Professor in the Department of Electrical Engineering at the University of California, Merced. He holds a Ph.D. (2025), M.S. (2020), and B.S. (2018) from the University of California, San Diego and Peking University, respectively. His research focuses on embedded systems , edge AI , and neuromorphic computing , with applications in IoT, federated learning, and hyperdimensional computing. ML&Systems Rising Star (2024) CPS Rising Star (2023) EECS Rising Star (2022) His work addresses on-device AI for real-world IoT deployments, reliability-driven sensor networks , and next-generation edge intelligence . Recent publications highlight advancements in federated learning (TIOT 2025), multimodal sensor interaction (IMWUT 2025), and noise-resilient sensor systems (Sensors 2024). Key subfields include hyperdimensional computing , asynchronous distributed training , and resource-efficient edge models . Dr. Yu actively mentors students across institutions and programs, including the Early Research Scholarship Program (UCSD) and ENLACE Summer Research Program. He has advised projects on smart elderly monitoring , LLM-based sensor reasoning , and hyperdimensional algorithm optimization . Collaborations span UCSD, TUM, and Stanford, with industry partnerships in IoT design automation (RelIoT simulator) and biomedical applications (bladder fullness restoration system).

Lin Zhongمشاهده پروفایل

استاد

• Computer Science
• Quantum Computing
• Operating Systems
• +۳ مورد دیگر

Lin Zhong is the Joseph C. Tsai Professor of Computer Science at Yale University, leading the Efficient Computing Lab. He holds a Ph.D. from Princeton University and M.S./B.S. degrees from Tsinghua University. Previously, he served at Rice University from 2005 to 2019. His research focuses on optimizing computing efficiency, quantum error correction, operating systems, and mobile systems. Education: Ph.D., Princeton University M.S., Tsinghua University B.S., Tsinghua University Research Interests: His work spans quantum computing (e.g., decoding algorithms for surface codes), operating systems (safety, correctness, and lightweight kernels), and mobile/networking systems (massive MIMO, energy-efficient designs). Recent trends include integrating large language models (LLMs) into robotics and securing cloud-based AI workflows. Awards: NSF CAREER Award ACM SIGMOBILE RockStar (2014) and Test of Time (2022) Fellowships from IEEE and ACM Best Paper Awards at ACM MobileHCI, IEEE PerCom, ACM MobiSys, and more Lab & Teams: His Efficient Computing Lab explores systems for quantum error correction (e.g., FPGA-based decoders), secure embedded systems, and LLM-driven robotics. Projects include TimelyLLM (real-time LLM serving) and Blindfold (confidential memory management).

Sergey Gorbunovمشاهده پروفایل

دانشیار

• Cryptography
• Network Security
• Blockchain Technology
• +۴ مورد دیگر

Sergey Gorbunov is an Associate Professor in the Department of Computer Science at the University of Waterloo . He holds a Ph.D. from MIT (2015), an M.Sc. and H.B.Sc. from the University of Toronto (2012 and 2011, respectively). His research focuses on Cryptography, Network Security, Blockchain Technology, Secure Protocols, and Privacy-Preserving Systems . He explores advanced cryptographic techniques for decentralized systems, privacy-enhancing technologies, and secure communication protocols. His work includes pioneering contributions to blockchain security (e.g., mitigating front-running attacks, enhancing transaction privacy) and foundational cryptographic tools like homomorphic encryption and multi-signature schemes. Recent publications emphasize resilient consensus mechanisms, anonymous payment channels, and efficient cryptographic primitives for distributed systems. Notable projects include Astrape (anonymous payment channels), Algorand Agreement (fast Byzantine consensus), and StealthDB (encrypted SQL databases). His research bridges theoretical cryptography with practical applications in secure computing and decentralized technologies.

Nabil Imamمشاهده پروفایل

استادیار

• Computational Neuroscience
• Parallel Computing
• Probabilistic Methods
• +۳ مورد دیگر

Nabil Imam is an Assistant Professor at the School of Computational Science and Engineering within the College of Computing at Georgia Institute of Technology. He holds a Ph.D. in electrical engineering and neuroscience from Cornell University, advised by Rajit Manohar and Barbara Finlay. Prior to academia, he conducted research at IBM and Intel Labs, focusing on neuromorphic engineering and AI. His current research integrates computational neuroscience, probability theory, and control systems to model biological computation, with an emphasis on process algebras for asynchronous circuits and systems. Education: Ph.D. in Electrical Engineering and Neuroscience, Cornell University (Advisors: Rajit Manohar, Barbara Finlay) Research interests include computational neuroscience, parallel computing, probabilistic methods, and neuromorphic systems. His work bridges biological neural mechanisms with technological applications, such as neuromorphic olfactory circuits and cortical development models. Notable contributions include neuromorphic chips featured in Science and Nature . His publications highlight interdisciplinary trends in neural coding, neuromorphic hardware, and evolutionary neuroscience. Recent work explores dual computational systems in mammalian brain evolution and self-organizing cortical structures. Earlier projects include scalable spiking-neuron integrated circuits (Science, 2014) and neurosynaptic cores with event-driven architectures (Best Paper Award, 2012). Awards: Best Paper Award at IEEE International Symposium on Asynchronous Circuits and Systems (2012) Teaching includes CSE 8803: Computational Methods for Complex Systems. His lab investigates process algebra frameworks for asynchronous systems and biological computation principles. Collaborations span industry (IBM, Intel) and academic institutions. Future directions emphasize theoretical neuroscience and neuromorphic technology applications.

• Networking
• Communications
• Machine Learning
• +۷ مورد دیگر

Christopher G. Brinton is the Elmore Associate Professor of Electrical and Computer Engineering at Purdue University, where he leads the ION research lab. He is affiliated with the Department of Electrical and Computer Engineering in the College of Engineering at Purdue University's West Lafayette campus. Dr. Brinton received his PhD from Princeton University, where he was previously the Associate Director of the EDGE Lab and a Lecturer of Electrical Engineering. His research focuses on the intersection of networking, communications, and machine learning, with particular emphasis on Fog computing systems, the Internet of Things (IoT), NextG Wireless, and social learning networks. His research integrates foundational techniques including convex and non-convex optimization, machine learning, and signal processing to address challenges in networked intelligent systems. The ION lab under his leadership develops both theoretical frameworks and practical implementations for next-generation networking solutions, with strong industry collaborations including Qualcomm, Nokia, Intel, Cisco, Dell, and Ericsson. Recent publications reveal a strong trend toward federated learning, decentralized algorithms, and edge intelligence, with significant contributions to model partitioning, communication-efficient learning, and robust network architectures. His work increasingly bridges traditional communication theory with modern machine learning techniques to solve emerging challenges in distributed networked systems. NSF CAREER Award ONR Young Investigator Program (YIP) Award DARPA Young Faculty Award (YFA) AFOSR Young Investigator Program (YIP) Award Intel Rising Star Faculty Award (RSA) Dr. Brinton teaches several courses including ECE 647: Performance Modeling of Computer Communication Networks, ECE 301: Signals and Systems, and ECE 547: Introduction to Computer Communication Networks. He has co-authored the book 'The Power of Networks: Six Principles That Connect Our Lives' and taught three Massive Open Online Courses (MOOCs) with over 400,000 cumulative students. While not currently actively recruiting students, he remains open to connecting with highly motivated individuals. Dr. Brinton leads the ION (Intelligent Optimization and Networking) research lab, which focuses on creating theoretical foundations and practical implementations for next-generation networked systems. The lab has recently published significant work on 6G taxonomy in collaboration with major industry partners and continues to push boundaries in distributed learning and network optimization.

Duncan Wilsonمشاهده پروفایل

استاد

• Internet of Things (IoT)
• Artificial Intelligence (AI)
• Smart Cities
• +۷ مورد دیگر

Duncan Wilson is a Professor of Connected Environments at the Bartlett Centre for Advanced Spatial Analysis (CASA) at University College London. His work bridges academia and industry, focusing on IoT, AI, and spatial analysis to enhance understanding of built and natural environments. Current role: Professor of Connected Environments at UCL Education: PhD in Artificial Intelligence and Machine Vision (UCL, 1997), BEng (Hons) in Electrical Engineering (Loughborough University, 1993) Research interests include: Cognitive computing at the network edge Extraordinary sensory systems for data capture Spatial reasoning and digital twins IoT for healthcare and biodiversity Edge AI and TinyML Recent articles span digital twin development , IoT for biodiversity monitoring , and smart healthcare infrastructure . He has received recognition for collaborative R&D approaches during his directorship at Intel's Sustainable Connected Cities institute. Teaching: Leads MSc Connected Environments and modules on IoT ethics, AI on microcontrollers, and sensor network deployment Projects: IoT Living Lab at UCL, Project Hercules for eye clinic analytics, and Shazam for Bats environmental monitoring Professional activities: Former Director of Intel Collaborative Research Institute (2012-2018), ex-member of Smart London Board (2017-2022)

David Wentzlaffمشاهده پروفایل

استاد

• Computer Architecture
• Green Computing
• Cloud Computing
• +۶ مورد دیگر

David Wentzlaff is a Professor of Electrical and Computer Engineering at Princeton University, with associated faculty roles in Computer Science and the High Meadows Environmental Institute (HMEI). He leads research in computing architecture, green computing, and sustainable system design. As Director of Undergraduate Studies, he shapes educational programs in his field. Education: Ph.D., Electrical Engineering, MIT (2012) M.S., Electrical Engineering and Computer Science, MIT (2002) B.S., Electrical Engineering, University of Illinois at Urbana-Champaign (2000) Research Focus: Future Computing Systems: Designing manycore architectures, cloud computing infrastructure, and chiplet-based systems for exascale computing. Sustainability: Developing energy-efficient hardware, recyclable computing systems, and eco-friendly decommissioning strategies. Hardware-Software Co-Design: Exploring FPGA integration, in-memory computing, and parallel processing frameworks. Advising & Grants: Advises 8 current graduate students, focusing on topics like chiplet design, neural acceleration, and sustainable computing. Recipient of NSF grants for projects like OpenPiton (open-source manycore research platform) and CAREER awards for energy-efficient architectures. Labs & Collaborations: Leads the Wentzlaff Research Group at Princeton. Develops open-source frameworks like PRGA (FPGA prototyping) and OpenPiton (manycore processor).

Bridget Goosbyمشاهده پروفایل

استاد

• Health Disparities
• Race and Ethnicity
• Biosociology
• +۷ مورد دیگر

Bridget Goosby is a Professor of Sociology at the University of Texas at Austin, affiliated with the College of Liberal Arts and serving as co-director of the LifeHD Health Disparities Research Lab with Dr. Jacob Cheadle. Her work bridges biosociology and health disparities research, focusing on how social marginalization and racism manifest in biological pathways affecting health outcomes. Education : Ph.D. from Pennsylvania State University Contact : bgoosby@prc.utexas.edu Her research integrates biometric and mixed-methods approaches to examine: Dynamic biosocial pathways linking racism to health risks Sympathetic arousal responses in natural settings Epigenetic profiles associated with socioeconomic and racial disparities Recent projects include a Spencer Foundation-funded national study on Black women faculty's health and professional navigation. Prior research has been supported by the National Institute of Child Health and Human Development (NICHD). Scientific recognition includes lifetime membership in AAAS for her contributions to understanding discrimination's biosocial health impacts. Her work appears in journals like Proceedings of the National Academy of Sciences and Annual Review of Sociology . She teaches undergraduate courses on Race, Ethnicity, and Health (UGS 302) across multiple semesters, emphasizing experiential learning through synchronous and asynchronous formats.

• Distributed Systems
• Machine Learning
• Big Data
• +۳ مورد دیگر

Jim Dowling is a distributed systems researcher at KTH Royal Institute of Technology, focusing on large-scale distributed systems, machine learning, and big data. His work emphasizes improving system dependability, performance, security, and scalability through middleware, peer-to-peer systems, and cloud-native solutions. He leads courses such as Advanced Course in Large Scale Machine Learning and Deep Learning and Scalable Machine Learning and Deep Learning , demonstrating his commitment to education in AI and distributed computing. His research spans topics like feature stores, Kubernetes integration, and AI-driven environmental analytics (e.g., ANIARA project for edge infrastructure automation and ExtremeEarth for Copernicus data analysis). He has contributed to scalable ML pipelines, cloud storage systems (HopsFS-S3), and hyperparameter optimization tools like Maggy. Key projects include the Hopsworks platform for machine learning operations and the development of cloud-native tools for big data analytics. His work bridges theoretical distributed systems research with practical applications in AI, healthcare, and environmental science. He has advised on numerous collaborative initiatives but no formal students are listed. His grants and lab activities are centered around Hopsworks and the ANIARA project, reflecting his focus on scalable, self-managing systems.