جست‌وجوی استاد و پژوهشگر

Rashmi Vinayakمشاهده پروفایل

دانشیار

• Computer Systems
• Networked Systems
• Information Theory
• +۵ مورد دیگر

Rashmi Vinayak is an Associate Professor in the Computer Science Department at Carnegie Mellon University, with a courtesy appointment in the Electrical and Computer Engineering Department. She is a member of both the Systems group and Theory group at CMU and leads TheSys research group. She is also affiliated with the Parallel Data Lab (PDL). Her educational background includes a Ph.D. from UC Berkeley in 2016, followed by postdoctoral studies at the same institution. Rashmi's research spans the intersection of computer/networked systems and information/coding theory. Her current focus is on robustness and resource efficiency in data systems across storage, communication, and computation. Key thrusts include storage systems, caching systems, and systems for machine learning. Her work on SIEVE, a cache eviction algorithm, has been widely adopted by industry including VMware, Google, Redpanda, and numerous open source libraries. Her recent publications demonstrate a strong trend toward practical systems research with theoretical foundations, particularly in caching algorithms, storage systems, and machine learning infrastructure. Many of her papers have received best paper awards and industry adoption. Notable awards include: Sloan Research Fellowship (2023) IEEE Information Theory Society Goldsmith Lecturer (2023) NSF CAREER Award (2020) Multiple USENIX NSDI Community (Best Paper) Awards VMware Systems Research Award (2021) Facebook and Google Research Awards Rashmi has supervised numerous PhD, Master's, and undergraduate students, many of whom have gone on to prestigious positions at Harvard, Google, Meta, and other leading institutions. Her research has been generously funded by NSF, Sloan Foundation, Open Compute Project, Google, Facebook/Meta, VMware, and Amazon Web Services. She actively collaborates with industry partners including Google, Microsoft, NetApp, Facebook, Cisco, Intel and Cloudera. She leads TheSys research group which focuses on designing next-generation data systems that are robust, efficient, and performant. The group takes a multi-disciplinary approach spanning computer systems, information theory, and machine learning.

• Cloud Computing
• Operating Systems
• File/Storage Systems
• +۳ مورد دیگر

Haryadi S. Gunawi is a Professor in the Department of Computer Science at the University of Chicago where he leads the UCARE research group (UChicago systems research on Availability, Reliability, and Efficiency). His work focuses on improving the dependability of storage and cloud computing systems, with a particular emphasis on addressing performance stability, reliability, and scalability challenges in modern computing environments. Dr. Gunawi received his Ph.D. in Computer Science from the University of Wisconsin, Madison in 2009. Following his doctoral studies, he was a postdoctoral fellow at the University of California, Berkeley from 2010 to 2012 before joining the University of Chicago faculty. His research focuses on three main areas: (1) performance stability, where he builds storage and distributed systems robust to latency tails and "limping" hardware; (2) reliability and scalability, where he addresses concurrency and scalability bugs in cloud-scale distributed systems; and (3) the intersection of machine learning and systems, exploring how machine learning techniques can solve operating and storage system problems. His work often combines theoretical insights with practical system implementations that address real-world challenges in cloud and storage infrastructure. Dr. Gunawi's publication record shows a consistent focus on storage and cloud system reliability, with recent work increasingly incorporating machine learning techniques to address traditional systems challenges. His research spans the full stack from hardware interfaces to distributed system design, with a strong emphasis on practical solutions that can be deployed in production environments. His work often involves close collaboration with industry partners to ensure real-world relevance and impact. Dr. Gunawi has received numerous prestigious awards including the NSF CAREER award, NSF Computing Innovation Fellowship, Google Faculty Research Award, multiple NetApp Faculty Fellowships, and an Honorable Mention for the 2009 ACM Doctoral Dissertation Award. He has also received the Provost's Global Faculty Award and Facebook Faculty Research Award, highlighting the broad recognition of his contributions to the field. As an advisor, Dr. Gunawi has mentored several PhD students including Ruidan Li, Ray Andrew, Rani Ayu Putri, and William Nixon. His research has been supported by major grants from NSF, Google, Facebook, and NetApp, enabling his team to pursue ambitious research projects at the intersection of systems, storage, and machine learning. Dr. Gunawi leads the UCARE research group at UChicago, which focuses on improving the dependability of storage and cloud-scale distributed systems. He is also involved with the Chameleon cloud research infrastructure project and the broader Systems Group at UChicago, contributing to a vibrant research community focused on systems, programming languages, and software engineering.

Kaiyang Liuمشاهده پروفایل

استادیار

• Distributed Cloud/Edge Computing
• Data Center Networks
• Distributed Machine Learning
• +۳ مورد دیگر

Kaiyang Liu is an Assistant Professor at the Department of Computer Science, Memorial University of Newfoundland. He holds a Ph.D. from Central South University (2019) and was a Postdoctoral Fellow at the University of Victoria, Canada. His research focuses on distributed cloud/edge computing, data center networks, and distributed machine learning, emphasizing optimization for data-intensive services. He is an IEEE Senior Member and has received prestigious awards, including the NSERC Discovery Grants and IEEE TCCLD Outstanding Ph.D. Thesis Award. Education: Ph.D. in Information Science and Technology, Central South University (2014–2019) M.Sc. in Information Science and Technology, Central South University (2012–2014) B.Eng. in Information Science and Technology, Central South University (2008–2012) Research Assistant at the University of Victoria (2016–2018) Research Interests: Kaiyang’s work bridges AI and cloud computing, exploring optimization strategies for next-generation systems. Key areas include learning-based congestion control, energy-efficient resource management, and scalable distributed storage solutions. His research has been published in top-tier journals like IEEE Transactions on Parallel and Distributed Systems and conferences such as IEEE ICDCS. Awards & Grants: NSERC Discovery Grants & Discovery Launch Supplement (2024) IEEE TCCLD Outstanding Ph.D. Thesis Award (2020) CSC-UVic Fellowship (2016–2018) Teaching: He teaches courses on Computer Networks, Advanced Computer Networks, and Operating Systems at Memorial University and previously at the University of Victoria. Labs & Teams: His research group focuses on Space Edge Computing, leveraging LEO satellites for resilient, low-latency networks. Ongoing projects include optimizing distributed systems for AI workloads and satellite-based data centers.

• Cloud Computing
• Data Center Optimization
• Resource Allocation
• +۴ مورد دیگر

Bhuvan Urgaonkar is a Professor in the Department of Computer Science and Engineering at Penn State University's College of Engineering. His research centers on optimizing cloud computing systems through innovative approaches to resource allocation, cost efficiency, and energy management. Current research focuses on Burstable Instance Scaling Serverless Computing Optimization Distributed Storage Systems Multi-resource Fair Allocation Cloud Economics Recent publications highlight advancements in autoscaling techniques, serverless architecture design, and trace modeling for high-load scenarios. These works emphasize practical solutions for cost-effective resource utilization in public cloud environments. Scientific Awards: CNS: Core: Small: Consistent, Geo-Distributed Data Stores on the Public Cloud (NSF, 2022-2025) CNS Core: Small: Principled Methodologies for Automated Cost-Effective Service Blending (NSF, 2021-2024) PPoSS: Cross-Layer Design for HPC in the Cloud (NSF, 2020-2022) CSR: Burstable Instances for Cost-Efficacy (NSF, 2017-2020) CSR: Student Travel Support for SIGMETRICS (NSF, 2016-2017)

John D. Kubiatowiczمشاهده پروفایل

استاد

• Computer Architecture
• Quantum Computing
• Distributed Systems
• +۷ مورد دیگر

Professor John D. Kubiatowicz is a faculty member at the University of California at Berkeley in the Department of Electrical Engineering and Computer Sciences since 1998. He holds a PhD in Electrical Engineering and Computer Science (minor in Physics) from MIT (1998), an M.S. in EECS (1993), and a double B.S. in Electrical Engineering and Physics (1987) from MIT. His research interests span Quantum Computing Architectures Distributed Systems and Storage Network Security and Peer-to-Peer Protocols Introspective and Manycore Operating Systems Edge and Fog Computing Hardware-Assisted Security He has pioneered systems like OceanStore , a global-scale distributed file system, and Tessellation , a manycore OS with continuous adaptation. The scientific awards he has received include Presidential Early Career Award (PECASE, 2000) Scientific American 50 (2002) Diane S. McEntyre Teaching Award (2003) IEEE ICRA Best Paper (2025) George M. Sprowls Award for MIT PhD thesis (1998) Okawa Research Grant (1998) Best Paper at International Conference on Supercomputing (1993) His recent publications focus on Quantum Circuit Design and Optimization Edge/Fog Computing Architectures Secure Runtime Systems Distributed Garbage Collection Manycore OS Innovations Hardware-Assisted Security Mechanisms He leads the Quantum Architecture Research Center and co-founded the SWARM Lab at Berkeley, advancing a vision of self-adapting, secure systems from the chip level to internet scale.

• Coding Theory
• Network Coding
• Information Theory
• +۴ مورد دیگر

Chih-Chun Wang is a Professor at the Elmore Family School of Electrical and Computer Engineering , Purdue University, with additional leadership roles as Associate Head for Facilities, Planning, and Staff. He earned his Ph.D. in Information Sciences and Systems from Princeton University in 2005, following an M.S. (2002) and B.S. (1999) in Electrical Engineering from Princeton and National Taiwan University, respectively. Research Interests: His work spans Network coding (graph-theoretic capacity, wireless network coding, feedback mechanisms) Coding theory (LDPC codes, Reed-Solomon decoding, iterative algorithms) Information theory (multi-user detection, network information theory) Signal processing (turbo equalization, space-time codes) Control theory (optimal stopping theory) Scientific Contributions: He has published extensively on Age-of-Information (AoI) minimization, low-latency coding, and multi-hop relay optimization. His research trends include Integrating machine learning with network coding Wireless security for Beyond-5G systems Distributed storage networks with intelligent helper selection Delay-constrained communication protocols Awards: Recognized as an IEEE Fellow in 2024 for contributions to network coding and information theory. Teaching: He teaches undergraduate courses like ECE301: Signals and Systems and graduate courses such as ECE639: Error Control Coding , with a focus on iterative decoding, LDPC codes, and network information theory. Advising: Supervised 15+ Ph.D. students, including current advisees Pin-Wen Su (delay-oriented coding), Wonjun Lee (cyber-physical systems), and Giles Bischoff (low-latency systems). Former students hold prominent roles at institutions like Google, Meta, and Intel.

• Information Theory
• Coding Theory
• Algorithm Design
• +۳ مورد دیگر

Sidharth Jaggi is a Professor at the School of Mathematics, University of Bristol, with over 19 years of experience in Information and Data Sciences through the lens of Information Theory. His work emphasizes fundamental performance limits and algorithm design for systems under adversarial threats. Education: B.Tech, M.Phil, PhD Research interests focus on adversarial communication, information-theoretic security, coding theory, and sparse data estimation. He leads the CAN-DO-IT team (Codes, Algorithms, Networks – Design and Optimization for Information Theory), integrating theoretical tools into practical applications like secure distributed computing and robust data storage. Recent publications highlight advancements in adversarial channels , group testing , and privacy-preserving coding . Trends include covert communication under spectral constraints, causal feedback benefits, and efficient algorithms for high-dimensional problems. Current projects include "Information Theory for Interactive Distributed AI" (2024–2029), exploring interactive systems under adversarial constraints.

Vincent Rocaمشاهده پروفایل

پژوهشگر

• Privacy
• Robust Content Distribution
• Transport Protocols
• +۶ مورد دیگر

Vincent Roca is a permanent researcher at Inria , a French national research institute for digital science and technology. Since 2019, he leads the Privatics research team focusing on privacy, previously part of the Planete team (2000-2012) for Internet protocols and applications. His work spans Privacy in smartphones, IoT, and web browsers Design of the ROBERT and CLEA protocols for the French TousAntiCovid app Standardization in IETF working groups, notably on FEC and network coding Co-author of Inria's cybersecurity white book (2019) Instructor of the popular Privacy in the digital world MOOC (14,006 participants) Research Interests include privacy risks in digital technologies, robust content distribution via FLUTE/ALC transport protocols and LDPC-Staircase FEC codes, and network coding for scalable services. His articles focus on privacy in LoRaWAN, contact tracing protocols, GDPR compliance, and FEC optimization. Scientific Awards include the Third Applied Research Price from FIEEC (2014) Knight Medal of the French Order of Merit (2020) Students supervised include PhD candidates in privacy-preserving IoT, secure authentication, and alumni like Samuel Pelissier (LoRaWAN privacy) and Michael Toth (GDPR compliance). Software Contributions include openfec.org (AL-FEC codes) and FLUTE/ALC implementations .

• Fault-Tolerant Computing
• Storage Systems
• Erasure Coding
• +۳ مورد دیگر

James S. Plank is a Professor in the Department of Electrical Engineering and Computer Science at the University of Tennessee. He holds a PhD from Princeton University (1993) and has been at UT since 1993. His research focuses on fault-tolerant computing, erasure coding, distributed systems, and neuromorphic computing. He teaches programming courses from introductory to graduate levels and has won multiple teaching awards, including seven departmental awards, the College of Arts and Sciences Senior Faculty Teaching Award, and the Chancellor’s Citation for Excellence in Teaching. Plank is a member of the IEEE Computer Society and has contributed to open-source software like JGraph and Jerasure. His recent research emphasizes neuromorphic computing systems, including projects like NeuroPong and RISP Neuroprocessor. He collaborates with industry and academia on storage systems, checkpointing, and hardware-software co-design. Plank advises numerous graduate and undergraduate students, evident in his annual summer student gallery. He has secured grants such as the NSF-funded "Ground-roaming autonomous neuromorphic targeter" (2020). His lab, Neuromorphic UT, explores applications in control systems, vision, and robotics. Plank’s contributions to erasure coding and storage reliability include seminal works like the RAID-6 Liberation Code and SD codes for mixed failure modes.

Marc Sánchez Artigasمشاهده پروفایل

دانشیار

• Distributed Computing
• Cloud Computing
• Big Data
• +۶ مورد دیگر

Marc Sánchez Artigas is an Associate Professor at Rovira i Virgili University, Department of Computer Engineering and Mathematics. He holds a PhD from Pompeu Fabra University (2009) and conducted postdoctoral research at EPFL (Switzerland). His research focuses on distributed computing, cloud storage systems, and serverless architectures. He leads the CloudLab research group and coordinates major EU projects like Horizon Europe's CloudSkin and H2020's IOStack. Education: PhD in Computer Science (2009), Pompeu Fabra University MSc in Computer Engineering (2004), Universitat Rovira i Virgili BSc in Computer Engineering (2002), Universitat Rovira i Virgili Research Interests: Distributed systems, cloud computing, software-defined storage, serverless computing, and privacy-preserving storage solutions. His work emphasizes scalable architectures, data management in heterogeneous environments, and optimizing cloud storage efficiency through novel algorithms and frameworks. Awards: Best Paper (IEEE LCN 2007), Best Dataset (ACM IMC 2015), Serra-Hunter Excellence Professorship, and multiple grants from EU and Spanish funding bodies. Grants & Projects: Coordinated over €5 million in projects including H2020 CloudButton (serverless analytics), FP7 CloudSpaces (personal clouds), and national initiatives like Software-Defined Edge Clouds. Active in coordinating IPCEI-CIS for cloud infrastructure. Teaching: Courses on distributed systems, parallel architectures, and cloud computing. Taught at Universitat Rovira i Virgili and Universitat Oberta de Catalunya.

• Algebra
• Number Theory
• Coding Theory
• +۳ مورد دیگر

Assoc Prof Frederique Elise Oggier is an Associate Professor in the School of Physical and Mathematical Sciences, Division of Mathematical Sciences at Nanyang Technological University (NTU). She holds a PhD from the Swiss Federal Institute of Technology (EPFL) and has held visiting positions at Caltech and the Research Center for Information Security (Tokyo). Her research focuses on algebraic coding theory, lattice-based cryptography, and applications of number theory to secure and reliable communication systems. Education: Bachelor’s and Master’s in Mathematics from the University of Geneva PhD in Mathematics from EPFL Research Interests: Her work bridges abstract algebra with practical coding challenges, emphasizing lattice codes for wiretap channels, distributed storage systems, and security protocols. Specialized in algebraic structures like cyclic division algebras and modular lattices, her contributions advance both theoretical foundations and real-world implementations of secure communication systems. Publications: Her recent work addresses cutting-edge topics such as MDS codes, non-GRS code constructions, and lattice-based security in noisy channels. These contributions highlight her expertise in coding theory and its interdisciplinary applications. Grants/Advising: While specific grants are not detailed, her prolific publication record reflects sustained research activity. She advises on projects related to distributed storage and secure coding, though student names are not listed in the provided texts. Labs/Teams: Affiliated with NTU’s mathematical sciences division, she collaborates with global researchers on projects such as lattice coding for 5G/6G systems and cryptographic protocols leveraging algebraic number theory.

DU Mingمشاهده پروفایل

استاد مهمان

• Global Economic Governance
• International Law
• Rule of Law in China
• +۳ مورد دیگر

Professor DU Ming is a Visiting Professor in Chinese Law and Co-Director of the Global Policy Institute at Durham University. He holds an LLM from Harvard Law School and a DPhil from the University of Oxford. His research focuses on global economic governance, China's approach to international law, and the development of legal frameworks in contemporary China. He advises on complex international disputes and sits on editorial boards of leading law journals. **Education:** Tsinghua University School of Law (China) LLM, Harvard Law School (Victor and William Fung Fellow) DPhil, University of Oxford (Clarendon Scholar) **Research Interests:** His work bridges legal theory and practice, particularly in cross-border trade/investment law, corporate governance, and the ethical implications of emerging technologies. Recent publications explore federated learning governance, model licensing, and AI ethics within legal systems. **Awards:** Victor and William Fung Fellowship (Harvard) Clarendon Scholarship (Oxford) His advisory roles and legal practice in New York and Beijing inform his research on transnational legal challenges. Collaborations span technical and legal domains, addressing issues like data privacy in federated learning systems.

Lipeng Wanمشاهده پروفایل

استادیار

• Big Data Management
• High-Performance Computing
• Data-Intensive Computing
• +۳ مورد دیگر

Dr. Lipeng Wan is a tenure-track Assistant Professor of Computer Science at Georgia State University (GSU), located at 25 Park Place, room 733. He holds a B.Eng. in Communication Engineering from Nanjing University of Science and Technology (2008), an M.Eng. in Information and Communication Engineering from Southeast University (2011), and a Ph.D. in Computer Science from the University of Tennessee, Knoxville (2016). Prior to joining GSU, he served as a Computer Scientist at Oak Ridge National Laboratory (ORNL), first as a postdoctoral researcher (2016–2018) and later as a full-time research staff member (2018–202?). His research focuses on big data management and analytics , high-performance and data-intensive computing , and resilience and performance optimization for distributed systems . Key interests include scientific data workflows, I/O innovations for exascale systems, and error-controlled data compression frameworks like MGARD and HPDR. Dr. Wan’s recent work emphasizes adaptive data transmission (e.g., JANUS), load balancing in cloud environments (SciLance), and optimizing file access patterns on HPC systems. His publications address challenges in exascale computing, including I/O performance, geographically distributed data management, and feature-preserving compression for climate simulations. He leads research at GSU in collaboration with national labs like ORNL, focusing on advancing scalable data management techniques for high-performance computing applications.

• Cloud Computing
• Computer Networking
• Machine Learning
• +۱ مورد دیگر

Xiaoqian (Tiffany) Zhang is an Assistant Professor in the Department of Computer Science at the University of Nebraska at Omaha (UNO) College of Information Science & Technology. Her work focuses on cloud computing, computer networking, and cybersecurity. Education: Ph.D. in Computer Science (University of Massachusetts Boston, 2023), MS in Mathematics (New York University, 2014), BA in Mathematics and Economics (Skidmore College, 2012) Her research explores cloud computing and networking with emphasis on machine learning and cybersecurity . Recent work addresses challenges in disaggregated storage systems , edge computing , and augmented reality . She received the DOE NNSA Joule Award (2022) for her scholarship. Her publications span topics including network congestion control , distributed file systems , and audio noise filtering . She teaches courses in cloud computing and computer networking .

Han Mao Kiahمشاهده پروفایل

استادیار

• Coding Theory
• Combinatorics
• Information Theory
• +۱ مورد دیگر

Han Mao Kiah is an Assistant Professor at Nanyang Technological University , specializing in Coding Theory and Combinatorics . He earned his Ph.D. in Mathematics at NTU under Yeow Meng Chee and held a postdoctoral position at the Coordinated Science Lab, University of Illinois at Urbana-Champagne with Olgica Milenkovic . Current Role: Assistant Professor, NTU, Department of Mathematics Education: Ph.D. in Mathematics (NTU), Postdoc (University of Illinois) His research focuses on Coding Theory for applications in DNA-based data storage , Reed-Solomon codes , and combinatorial designs . Recent work includes Private Information Retrieval , Sequence Reconstruction , and Error Correction in distributed systems. Key trends in his publications involve Reed-Solomon codes , Private Information Retrieval , and DNA sequence profiling with a focus on Efficient Algorithms and Constrained Coding for error control in emerging storage systems. ISITA Early Career Researcher Paper Award (2022, Researcher: D. T. Dao) Memorable Paper Award Finalist (2021, Student: J. Chrisnata) Best Student Paper Award (2020, Student: J. Chrisnata) Student Paper Award Finalist (2012)