جست‌وجوی استاد و پژوهشگر

Katia Obraczkaمشاهده پروفایل

استاد

• Computer Networks
• Distributed Systems
• IoT
• +۳ مورد دیگر

Katia Obraczka is a Professor of Computer Science and Engineering at UC Santa Cruz and the director of the Center for Information Technology Research in the Interest of Society (CITRIS) and the Banatao Institute. She leads interdisciplinary research focused on societal and economic impacts of IT, particularly in IoT, edge computing, and cyber-physical systems. Her work emphasizes technology for social good, including environmental monitoring and healthcare applications. Education: B.S. and M.S. in Electrical and Computer Engineering from the Federal University of Rio de Janeiro M.S. and Ph.D. in Computer Science from the University of Southern California Research Interests: IoT and Edge Computing – Developing energy-efficient, decentralized systems for smart agriculture and healthcare monitoring. Cyber-Physical Systems – Designing resilient networks for autonomous vehicles and industrial control. Machine Learning – Applying ML to improve network performance and user mobility modeling. Healthcare Technology – Innovations in pressure injury prevention and real-time patient monitoring. Articles Trends: Recent publications emphasize IoT multipath routing, edge intelligence architectures, and energy-harvesting smart systems. Over 80% of her 2023-2025 work addresses healthcare and environmental applications, with notable contributions to autonomous vehicle platooning and QoS-aware quantum networks. Labs & Initiatives: Directs CITRIS UCSC programs like the CITRIS Initiative for Drone Education and Research and oversees the Internet Research Group , which explores edge computing and network security. Grants & Collaborations: Active in cross-campus and industry partnerships, including drone-based wildfire monitoring and solar-powered IoT deployments with UC Berkeley and Merced.

• Cybersecurity
• Artificial Intelligence
• Computer Networks
• +۷ مورد دیگر

Pere Barlet-Ros is a Full Professor in the Department of Computer Architecture at the Universitat Politècnica de Catalunya (UPC), affiliated with the Barcelona School of Informatics (FIB) and the Barcelona Neural Networking Center (BNN-UPC). He leads research in AI-driven network technologies and is a key member of the UPC CBA and IDEAI-UPC research groups. Ph.D. in Computer Science, UPC (2008) M.Sc. in Computer Science, UPC (2003) His research focuses on applying artificial intelligence, particularly graph neural networks, to solve critical challenges in network monitoring, traffic classification, congestion control, and cybersecurity. His work bridges theoretical innovation with practical deployment, as evidenced by integration into commercial tools like Auvik TrafficInsights and Talaia Polygraph. He explores digital twins for network optimization, privacy-preserving technologies, and next-generation web tracking detection. The recent publications highlight a strong trend toward leveraging graph-based machine learning for network modeling, anomaly detection, and traffic compression. His work spans top-tier journals like IEEE/ACM Transactions on Networking and conferences such as IEEE EuroS&P and ACM SIGCOMM workshops, emphasizing robust, real-world AI/ML applications in networking. 2nd VALORTEC prize for best business plan (2014) Fiber Entrepreneurs award, best entrepreneur at FIB (2015) Pere Barlet-Ros has served as Principal Investigator on seven competitive research projects and has advised at least 14 PhD students, including Paul Almasan, Miquel Ferriol, and Ismael Castell. His lab, BNN-UPC, fosters interdisciplinary collaboration in neural networking and AI for infrastructure. His entrepreneurial experience with Talaia Networks underscores a commitment to translating research into impactful technology.

Gary Wongمشاهده پروفایل

پژوهشگر

• Computer Science
• Networking
• Cloud Computing
• +۶ مورد دیگر

Gary Wong is a Researcher at the Flux Research Group within the School of Computing at the University of Utah . His work spans network testbed design, cloud infrastructure, and protocol development. Based in Room 3492 of the MEB building, he contributes to experimental research in Salt Lake City. Research Focus: Wireless communication, cloud computing, software-defined networking, and distributed systems Institutional Affiliation: University of Utah, School of Computing His recent publications (2023-2005) highlight expertise in systems performance measurement, open wireless research platforms (POWDER), cloud infrastructure (CloudLab), and network security. The work shows a consistent emphasis on testbed federation, QoS customization, and reproducible experimental methodologies. Notable collaborations include projects like POWDER (wireless experimentation), CloudLab (scalable cloud infrastructure), and PhantomNet (mobile networking). His Erdős number is at most 5, indicating connections to foundational mathematical research through co-authorship chains.

Sara Aloufمشاهده پروفایل

پژوهشگر

• Stochastic Modeling
• Communication Networks
• Caching Systems
• +۱۱ مورد دیگر

Sara Alouf is an Associate Researcher and Vice-Head of the Network Engineering and Operations (NEO) project team at Inria Sophia Antipolis Méditerranée. She holds a Habilitation from Université Côte d'Azur (2017), a Ph.D. in Network Sciences (2002), and an M.Sc. (1999) from Université Nice Sophia Antipolis, along with an Electrical and Electronics Engineering Degree from Lebanese University (1998). Her research focuses on stochastic modeling , caching systems , and performance evaluation of communication networks , with past work in green networking , peer-to-peer storage , and delay-tolerant networks . Education: Habilitation (2017) - Université Côte d'Azur Ph.D. (2002), M.Sc. (1999) - Université Nice Sophia Antipolis Electrical Engineering (1998) - Lebanese University Her recent work involves no-regret caching algorithms , stochastic solar energy models , and count-min sketch analysis , while earlier research addressed power saving in cellular networks and WiMAX sleep mode optimization . Articles span topics like multi-level cache hierarchies , peer-to-peer storage reliability , and evolutionary network protocols . She has co-authored three patents related to congestion avoidance and RTT estimation . Scientific awards include the Best Paper Award at IFIP Wireless Days 2009 . Professional service highlights: Board of Directors - ACM SIGMETRICS (2019-2023) Editorial roles at IEEE/ACM Transactions on Networking and Elsevier journals Conference organization for ACM Sigmetrics/IFIP Performance (2016, 2024) and ITC (2015, 2023) She has taught Performance Evaluation of Networks at Université Nice Sophia Antipolis, Probabilities and Statistics (with Giovanni Neglia), and Optimization of Business Processes at Vrije Universiteit Amsterdam. Her lab (NEO) investigates network stochastic models , green IT solutions , and train control communication systems .

Amr El Abbadiمشاهده پروفایل

استاد

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

Amr El Abbadi is a Professor in the Department of Computer Science at the University of California, Santa Barbara. He earned his B.Eng. from Alexandria University, Egypt, and Ph.D. from Cornell University. His work spans databases, distributed systems, and cloud computing, focusing on fault-tolerant data management, transactional consistency, and scalability in multi-datacenter environments. Education B.Eng. in Computer Science from Alexandria University Ph.D. in Computer Science from Cornell University El Abbadi has published over 300 articles and supervised more than 30 PhD students. His research emphasizes reconciling strong consistency with scalability in cloud systems, including advancements in geo-replicated data management and transactional guarantees for complex queries. He has received prestigious accolades such as the UCSB Senate Outstanding Mentorship Award (2007) and has been recognized through fellowships by the ACM , AAAS , and IEEE . Professional Leadership Chair of the Computer Science Department at UCSB (2007–2011) Executive Committee, IEEE Technical Committee on Data Engineering (TCDE) Board Member, VLDB Endowment (2002–2008) Editorial Roles Editor, The VLDB Journal Editor, IEEE Transactions on Computers Editor, The Computer Journal Conference Leadership Program Chair, SIGSPATIAL GIS 2010 Program Chair, ACM Symposium on Cloud Computing (SoCC) 2011 Program Chair, COMAD (India) 2012 Program Chair, First ACM Conference on Social Networks (COSN)

• High Performance Computing
• Distributed Systems
• Cloud Computing
• +۲ مورد دیگر

Zhuozhao Li is an Assistant Professor in the Department of Computer Science and Engineering at Southern University of Science and Technology (SUSTech) in Shenzhen, China. He joined SUSTech in August 2021 after serving as a Postdoctoral Scholar at the University of Chicago from July 2018 to July 2021. His research focuses on high-performance computing, distributed systems, and cloud/edge computing, with applications across scientific domains. Ph.D. in Computer Science, University of Virginia, May 2018 M.S. in Computer Science, University of Southern California, May 2012 B.E. in Computer Science, Zhejiang University, July 2010 Dr. Li's research spans several critical areas in modern computing infrastructure. His primary focus is on High Performance Computing , where he develops novel approaches to optimize computational efficiency in scientific applications. In Distributed Systems , his work addresses challenges in large-scale system coordination and resource management. His contributions to Cloud/Edge Computing focus on improving virtualization techniques and resource allocation strategies. Additionally, he explores applications in the Internet of Things , examining how distributed computing paradigms can enhance IoT infrastructure. Dr. Li's publication record demonstrates a consistent focus on advancing distributed computing systems and their scientific applications. His recent work shows a clear progression from foundational research on data-parallel frameworks and job scheduling toward more applied systems for scientific computing. His contributions to projects like funcX and DLHub represent significant advancements in making distributed computing more accessible for scientific workflows. A notable trend is the increasing interdisciplinary nature of his work, with applications spanning from computational biology (SARS-CoV-2 research) to privacy-preserving spatial crowdsourcing. ACM HPDC Best Paper Nominees, 2019 IEEEMASS Service Award, 2019 Outstanding Graduate Research Assistant, University of Virginia, 2018 Dr. Li actively mentors students and researchers, currently recruiting PhD students, Master's students, postdocs, and research assistants for his research group at SUSTech. His research has been supported through various collaborative projects, including significant contributions to the U.S. Department of Energy National Virtual Biotechnology Laboratory Project about COVID-19, which was awarded the Secretary of Energy Achievement Award. His work on funcX, DLHub, and Parsl represents major software infrastructure development efforts with broad scientific impact. Dr. Li is affiliated with multiple research initiatives, including the Globus Lab (during his postdoc at University of Chicago) and various collaborative projects involving high-performance computing resources. At SUSTech, he leads a research group focused on distributed systems and high-performance computing. His work often involves collaboration with researchers across institutions, particularly in projects related to scientific computing infrastructure like funcX and DLHub.

Tianyin Xuمشاهده پروفایل

دانشیار

• Cloud Computing
• Datacenter Systems
• System Reliability
• +۴ مورد دیگر

Tianyin Xu is an Associate Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign (UIUC), with a courtesy appointment in the Department of Electrical and Computer Engineering. He leads research in cloud and datacenter systems reliability, security, and configuration management. His work bridges theoretical foundations with practical implementations that have been adopted in real-world systems. Dr. Xu earned his PhD from UC San Diego and has held positions at Microsoft Research Asia, Facebook, and has collaborated with industry partners including VMware, Intel, and IBM. His research has been published in top-tier systems conferences including OSDI, SOSP, NSDI, ASPLOS, and EuroSys. His primary research interests focus on making computer systems reliable and secure at cloud and datacenter scale. He investigates approaches to help systems defend against software bugs, configuration errors, and design flaws. His work often combines formal methods with practical systems building to create tools that detect and prevent failures in production environments. Analysis of his recent publications shows a strong focus on cloud reliability, AI for systems, memory management, and configuration validation. His work increasingly integrates AI techniques with traditional systems approaches, particularly in using large language models for configuration validation and multi-agent systems for reliability engineering. There's also a consistent thread of work on memory systems, particularly with emerging technologies like CXL. 2025 Best Paper Award at ASPLOS'25 (cxlfork) 2025 Spotlight at ICML'25 (ITBench) 2024 Jay Lepreau Best Paper Award at OSDI'24 (Anvil) 2024 C.W. Gear Outstanding Junior Faculty Award 2022 NSF CAREER Award 2021 Facebook Academic of the Month 2016 Jay Lepreau Best Paper Award at OSDI'16 (PCheck) Dr. Xu actively advises numerous PhD, Master's, and undergraduate students, with many alumni securing positions at top tech companies and academic institutions. He serves on program committees for major systems conferences and co-organizes the UIUC Systems Research Seminar. His research is supported by NSF, VMware, Facebook, IBM, Intel, Boeing, Azure, GCP, and GitHub. He is also a co-lead of Infrastructure for Trusted and Confidential Computing at IIDAI and co-editor of SIGOPS Blog.

• Computer networks and communications
• Cybersecurity
• Industry 4.0
• +۷ مورد دیگر

Guido Marchetto is a Full Professor in the Department of Control and Computer Science (DAUIN) at Politecnico di Torino , where he conducts cutting-edge research in computer networks, cybersecurity, machine learning, and Industry 4.0. He is a member of the NETGROUP - Computer Networks Group , the SmartData@PoliTO interdepartmental center, and leads research at ACS LAB and LABINF . He serves as Deputy Coordinator of the Doctoral College in Computer and Systems Engineering and is the Scientific Advisor for the European Technology Platform for High Performance Computing (ETP4HPC). PhD Students: Federico Rinaudi, Doriana Monaco, Antonino Angi Research Groups: NETGROUP, SmartData@PoliTO Labs: ACS LAB, LABINF His research focuses on intelligent network management, explainable AI for networking, reinforcement learning, federated learning, and softwarized networks. He has led multiple EU and commercial research projects such as MIRANDA and DESIRE , with applications in cybersecurity, edge computing, and digital twins. Marchetto’s recent publications reveal a strong trend toward integrating AI/ML techniques—especially reinforcement learning and language models—into network automation, traffic engineering, and IoT systems. His work bridges theoretical innovation with practical implementation in cloud, edge, and telco environments. Scientific Awards: Best Paper Award Finalist, IEEE HPSR (2014) Best Paper Award, IEEE ICC (2007) He is a Senior Member of IEEE and serves on the editorial board of IEEE Transactions on Vehicular Technology . He has also been a Contract Professor at the Turin Polytechnic University in Tashkent from 2012 to 2022. His extensive grant leadership includes EU Horizon projects and numerous commercial research contracts with a focus on network intelligence and automation. He is actively involved in doctoral education and curriculum development, contributing to multiple degree programs and supervising ongoing PhD research in AI-driven networking and cybersecurity.

E. A. Valentijnمشاهده پروفایل

استاد

• Astronomy
• Astrophysics
• Cosmology
• +۴ مورد دیگر

E. A. Valentijn is a Professor at the University of Groningen's Faculty of Science and Engineering, Department of Astronomy. He coordinates the Target project as chair of its steering committee and leads OmegaCEN, a National datacenter for wide-field astronomical imaging funded by the Netherlands Research School for Astronomy (NOVA). At OmegaCEN, he oversees approximately 17 staff and PhD researchers focused on developing advanced data processing systems for large astronomical datasets. Valentijn also serves as the National Project Manager for the Euclid mission since 2013 and holds membership in the Scientific Technical Council of SURF. Research Focus Valentijn's research integrates astronomy, astrophysics, and data science with specialized focus on: Galaxy clusters and dark matter cosmology Wide-field astronomical imaging systems Large-scale data processing methodologies Machine learning applications in astronomy Euclid mission scientific objectives His work contributes to UN Sustainable Development Goals through scientific innovation. Publications Overview His recent publications (2025) predominantly focus on Euclid mission data analysis, featuring gravitational lens detection using convolutional neural networks, galaxy cluster dynamics, and multi-wavelength catalog development. These works demonstrate strong emphasis on machine learning applications in astronomical data processing and survey science. Leadership and Initiatives Valentijn founded and directs multiple scientific organizations including OmegaCEN (2002), Astro-WISE (2001), Target (2008), and the Infoversum Foundation (2012). His team at OmegaCEN includes 17 researchers and PhD students working on astronomical data infrastructure.

• Computer Networking
• Networked Systems
• Datacenter Networks
• +۵ مورد دیگر

Vishal Shrivastav is an Assistant Professor of Electrical and Computer Engineering at Purdue University's Elmore Family School of Electrical and Computer Engineering. He is affiliated with Max W and Maileen Brown Family Hall in West Lafayette, Indiana. His research focuses on Computer Networking and Networked Systems , with a primary emphasis on Computer Engineering . His work spans areas including programmable switches, datacenter networks, and privacy-preserving reconfigurable systems. Shrivastav's recent research trends highlight advancements in ultra-low latency networking fabrics, scalable oblivious reconfiguration mechanisms, and machine learning-driven traffic analysis. His projects often involve hardware-software co-design for high-performance networked systems. He maintains an active research webpage at https://engineering.purdue.edu/~vshriva/ and collaborates with industry on real-time IoT analytics and next-generation network platforms. His office is located in BHEE 334B at the West Lafayette campus.

Christophe Bobdaمشاهده پروفایل

استاد

• System on Chip Design
• FPGA and Reconfigurable Computing
• Embedded Systems
• +۵ مورد دیگر

Christophe Bobda is a Citi Endowed Professor in Advanced Technologies and Associate Chair for Academics at the University of Florida's Department of Electrical & Computer Engineering within the Herbert Wertheim College of Engineering. His research focuses on FPGA-based systems, cybersecurity, embedded systems, and resilient architectures. He holds a Ph.D. from the University of Paderborn, Germany, and degrees from Universities in Germany and Cameroon. Research interests include System-on-Chip design, reconfigurable computing, and robotics. Notable contributions span secure cloud FPGA deployment, multi-tenant hardware security, and near-sensor processing architectures. He received the HWCOE International Educator of the Year award in 2024. Recent work emphasizes FPGA security in cloud environments, with projects like CIVIC-FPGA and ISO-TENANT addressing isolation and attack mitigation. His lab explores embedded imaging and robotics applications, leveraging low-power and high-performance FPGA solutions. NSF grants support his research in FPGA acceleration and datacenter infrastructure. Publications highlight advancements in event-based vision systems, 3D semantic modeling, and hardware trojan detection. Ongoing projects include chiplet interfaces for on-device AI and galvanic isolation for physical attack prevention. His work bridges theoretical cybersecurity with practical FPGA implementations for resilient systems.

Hyebin Songمشاهده پروفایل

استادیار

• High-dimensional Statistics
• Statistical Learning
• Statistical Computing
• +۴ مورد دیگر

Hyebin Song is an Assistant Professor of Statistics at the Pennsylvania State University since 2020. She holds a PhD in Statistics from the University of Wisconsin-Madison (2020) and a BA in Applied Statistics from Yonsei University (2012). Previously, she worked as a Statistician at the Bank of Korea. Her research focuses on developing statistical methodologies for high-dimensional and complex datasets, with applications in neuroscience, systems biology, and computational protein modeling. Key areas include semi-parametric inference, statistical learning, and computational biology. She has developed influential software tools like PUlasso for high-dimensional variable selection and contributed to advancements in Markov chain autocovariance estimation. Her work has been recognized with the ASA SLDS Student Paper Competition Award (2018). Education: PhD in Statistics (UW-Madison, 2020), BA in Applied Statistics (Yonsei University, 2012). Current affiliations include the Department of Statistics at Penn State and collaborations with computational biology labs. She actively advises PhD students in statistical methodology and applications. Research interests span high-dimensional statistics, shape-constrained inference, and statistical computing, with applications to protein structure analysis and functional genomics. Notable software includes the 'momentLS' package for Markov chain estimation and 'pudms' for deep mutational scanning analysis.

Robert René Maria Birkeمشاهده پروفایل

استادیار

• Virtual Resource Management
• Network Design
• Workload Characterization
• +۶ مورد دیگر

Robert René Maria Birke is a tenured assistant professor in the Department of Computer Science at the University of Turin, leading research in the Parallel Computing group. His expertise spans virtual resource management, network design, workload characterization, and optimization of AI/big-data applications. Previously, he served as a visiting researcher at IBM Research Zurich and Principal Scientist at ABB Corporate Research, combining industry experience with academic rigor since earning his Ph.D. from Politecnico di Torino in 2009. His educational background includes: Ph.D. in Electronics and Communications Engineering, Politecnico di Torino (2009) Dr. Birke's research centers on systems-level challenges in distributed AI, with current projects investigating federated learning architectures, confidential computing via Trusted Execution Environments, and RISC-V processor optimizations for decentralized machine learning. His work bridges theoretical foundations with practical deployments, particularly in edge computing scenarios and high-performance data synthesis applications. Recent publications reveal growing emphasis on securing generative models against forgery attacks and optimizing tabular data synthesis techniques. Analysis of his 15 most recent publications (2024-2026) shows dominant themes in confidential federated learning (33% of works), RISC-V system optimizations (27%), and generative model security/synthesis (40%). This output spans premier venues including IEEE Transactions, ACM Computing Surveys, and SIGCOMM-affiliated conferences, demonstrating consistent contributions to systems-AI intersection research. Professional recognition includes: IEEE Senior Member While the text confirms extensive collaboration through co-authorships (notably with Marco Aldinucci, Lydia Chen, and Giulio Malenza), no specific student advisees or grant details are provided. His work appears embedded within European initiatives like ICS and EUPilot projects, focusing on compute continuum challenges. Dr. Birke actively contributes to the Parallel Computing group's mission through projects including HPC4AI@UNITO (datacenter digital twins) and Cross-Facility Federated Learning frameworks. His research ecosystem involves multi-institutional teams across Italy, Switzerland, and the EU, with recent talks addressing FLaaS implementations and generative model impacts on system design.

Jaume Comellasمشاهده پروفایل

مدرس

• Optical Communications
• Telecommunications Networks
• Fiber Optics
• +۵ مورد دیگر

Jaume Comellas is a Lecturer in the Department of Signal Theory and Communications at the Universitat Politècnica de Catalunya (UPC), affiliated with the Barcelona Higher Technical School of Telecommunication Engineering. He is a core member of the Optical Communications Group (GCO) and the Advanced Broadband Communications Center (CCABA). His research focuses on optical communications, network engineering, quantum key distribution, and network security, with emphasis on optimizing elastic optical networks, quantum encryption protocols, and digital twin applications. Key projects include developing secure lightpath systems using quantum keys, improving network scalability via software-defined networking (SDN), and analyzing network traffic patterns in flex-grid and SDM-based infrastructures. He has contributed to over 255 publications, including works on optical encryption scenarios, convolutional neural networks for network blocking prediction, and distributed telemetry systems. His work integrates advanced optical technologies with machine learning, addressing challenges in network resilience, spectrum management, and quantum communication security. Collaborations span institutions like the UPC, European research networks, and industry partners. He actively participates in international conferences and serves on technical committees for conferences such as ICTON and IEEE events. Comellas leads projects funded by Horizon Europe, Spanish Ministry of Science, and industry grants, focusing on 5G/6G networks, quantum communication systems, and high-capacity optical infrastructures. His contributions bridge theoretical research with practical network solutions, enhancing both academic and industrial advancements in telecommunications.

Mohammad Alizadehمشاهده پروفایل

دانشیار

• Computer Networks
• Systems
• Machine Learning
• +۶ مورد دیگر

Mohammad Alizadeh is an Associate Professor in the Department of Electrical Engineering and Computer Science (EECS) at MIT, affiliated with the Computer Science and Artificial Intelligence Laboratory (CSAIL). He holds additional roles as Director of the 6-A MEng Thesis Program and Industry Officer for EECS, overseeing the EECS Alliance industry outreach program. His research focuses on computer networks, systems, and their applications in datacenters, cloud computing, and edge computing. Notable areas include machine learning for systems, congestion control, and network protocols. Education: PhD (Stanford University), MS in Electrical Engineering (Stanford), BS (Sharif University of Technology). Industry Experience: Former roles at Microsoft Research, Insieme Networks, and Cisco Systems. Research Interests: Alizadeh’s work emphasizes improving network performance, robustness, and management in large-scale systems. Key projects include DCTCP, CONGA, and Pensieve (adaptive video streaming with reinforcement learning). His contributions span network architecture, programmable switches, and learning-based systems. Awards: NSF CAREER Award Alfred P. Sloan Research Fellowship SIGCOMM Rising Star Award Microsoft Research Faculty Fellowship Teaching: Courses include 6.888 (Advanced Topics in Networking), 6.829 (Computer Networks), and 6.02 (Digital Communication Systems). His teaching emphasizes cutting-edge networking research and practical system design. Labs/Teams: Active member of CSAIL, leading projects in distributed systems, network protocols, and cloud computing.