جست‌وجوی استاد و پژوهشگر

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.

Michael Ferdmanمشاهده پروفایل

دانشیار

• Computer Architecture
• Operating Systems
• Machine Learning
• +۷ مورد دیگر

Michael Ferdman is an Associate Professor in the Department of Computer Science at Stony Brook University, where he leads research in computer architecture and systems. His office is located in Room 343 at Stony Brook, NY 11794-2424, and he can be contacted via phone (631-632-8449) or email. Ferdman directs the Computer Architecture and Systems Laboratory (compas.cs.stonybrook.edu), focusing on next-generation server infrastructure. Ferdman's research spans the entire computing stack with emphasis on: FPGA integration for server environments (Intel HARP, Microsoft Catapult) Machine learning accelerators for convolutional neural networks Server systems optimization in the post-Moore era Network processing and software-defined networking Programming models for emerging memory technologies (HBM, 3D XPoint) Reconfigurable hardware and high-level synthesis His work addresses both performance and security challenges in modern computing infrastructure. Analysis of his 15 most recent publications (2022-2025) reveals consistent focus on: Hardware acceleration techniques (FPGAs, specialized processors) Memory hierarchy optimization and cache management Security vulnerabilities in web applications and systems Post-Moore computing architectures Parallel processing and distributed systems His research shows strong emphasis on practical implementations bridging hardware and software layers. Awards recognizing his contributions include: Graduate Teaching Award (2014) Best Paper Award at ASPLOS XVII Best Paper Finalist at HPCA XVII Three IEEE Micro Top Picks selections (2009, 2012) He teaches advanced courses including CSE 502, CSE 602, and CSE 506 at Stony Brook University.

Bruce MacDowell Maggsمشاهده پروفایل

استاد

• Computer Networks
• Distributed Systems
• Algorithms
• +۷ مورد دیگر

Bruce MacDowell Maggs is a Professor in the Department of Computer Science at Duke University and serves as Vice President of Research at Akamai Technologies. His career bridges academic research and industrial innovation in computer science, particularly in distributed systems and networking. Research Interests: His work spans computer networks , distributed systems , parallel algorithms , content delivery , and fault-tolerant computing . He has made significant contributions to network routing, load balancing, scalability of web applications, and energy efficiency in large-scale systems. His research often combines theoretical rigor with practical system design. The recent publications highlight a consistent focus on scalability , network performance , and security in internet-scale applications. Key themes include query caching , traffic modeling , resilient routing , and energy optimization , reflecting his deep involvement in the infrastructure of modern web services. No scientific awards are mentioned in the provided text. Advising and Teaching: He has advised numerous Ph.D. students, many of whom are now faculty or researchers at top institutions. His former students include Ramesh Sitaraman, Anja Feldmann, and Andrea Richa. He currently advises Anat Talmy at Duke. He has taught a wide range of courses at Duke, Carnegie Mellon, and MIT, including Computer Networks, Operating Systems, Algorithms, and Discrete Mathematics. Labs and Teams: While not explicitly named, his research is closely tied to systems and networking groups at Duke and his industrial work at Akamai, a leader in content delivery networks. His collaborations with Tom Leighton and others at Akamai suggest leadership in research teams developing foundational internet technologies.

• Battery simulation
• Cyber physical system
• Design simulation
• +۱۲ مورد دیگر

Sara Vinco is an Associate Professor at the Department of Control and Computer Engineering (DAUIN), Politecnico di Torino, Italy. She specializes in battery simulation, digital twins, and energy-efficient design automation for heterogeneous embedded systems, aligning with Industrial and Information Engineering (Area 0009) and ERC sectors including Computer Architecture and Machine Learning . Her research focuses on advancing cyber-physical systems through simulation frameworks like SystemC-AMS, enabling holistic modeling of analog, digital, and thermal domains. Key projects include data-driven digital twins for EV batteries and low-area digital circuits in industrial/medical applications, supported by commercial contracts such as C-based virtual prototyping. Her recent publications (2022-2023) emphasize machine learning for battery SOH/SOC estimation , energy monitoring in production lines , and multi-domain fault modeling . These works span journals like IEEE Transactions and conferences including DATE and ISLPED. Awarded the FFABR 2017 grant and IEEE FDL Best Paper Award 2011 , she also chairs editorial boards for IEEE Transactions on CAD and DATE Conference. She supervises PhD students Giovanni Pollo (Digital Circuits) and Khaled Alamin (EV Battery Twins), reflecting her leadership in smart systems design.

• Atmospheric Science
• Climate Dynamics
• Meteorology
• +۳ مورد دیگر

Univ.-Prof. Aiko Voigt is a Professor and Head of the Department of Meteorology and Geophysics at the University of Vienna. Her research focuses on climate dynamics, cloud physics, and atmospheric processes. She leads the Environment and Climate Research Hub and teaches advanced courses like 'Climate Modelling Lab' and 'Cloud Physics.' Her work explores cloud-radiative interactions, climate change impacts, and extreme weather dynamics. Recent studies analyze energy imbalances, high-cloud feedbacks, and tropical precipitation patterns. Voigt's contributions bridge climate modeling with observational data, emphasizing high-resolution simulations and interdisciplinary approaches. Teaching includes courses such as 'Climate System of the Earth,' 'Scientific Communication,' and 'Introduction to Computational Meteorology.' Her research spans from present-day climate to Snowball Earth scenarios, addressing both modern and paleoclimatic challenges. Publications highlight advancements in radiative transfer algorithms, cyclone dynamics under warming, and uncertainties in climate model predictions. Her work underscores the critical role of clouds in amplifying climate sensitivity and reshaping atmospheric circulation patterns.

• Internet of Things (IoT)
• Edge Computing
• Cloud-Edge-IO Continuum
• +۴ مورد دیگر

Carlos Enrique Palau is a prominent researcher in the field of Internet of Things (IoT), edge computing, and cyber-physical systems. His work focuses on interoperability, security, and scalability in distributed systems, particularly in industrial and smart city applications. He has contributed to frameworks for cloud-edge continuum integration, blockchain-based IoT solutions, and federated computing architectures. Key areas of research include: IoT interoperability and semantic frameworks Edge computing and distributed workload management Cybersecurity for IoT and critical infrastructure Smart port logistics and real-time data analytics Cognitive services in legacy port management systems His recent work explores: Data-as-a-Product frameworks for Industry 4.0/5.0 Autonomous workload scheduling in energy-efficient edge-cloud systems Deception mechanisms for IoT security Self-* capabilities in cloud-edge nodes Palau has collaborated extensively with institutions like Universitat Politècnica de València and international partners in projects funded by EU initiatives. His research addresses practical challenges in industrial IoT deployments, smart city infrastructure, and emergency management systems.

Richard Allenمشاهده پروفایل

استاد

• Seismology
• Earthquake Early Warning Systems
• Structural Health Monitoring
• +۹ مورد دیگر

Richard Allen is a Professor and the Class of 1954 Endowed Chair at the University of California, Berkeley, serving as Director of the Berkeley Seismological Laboratory. His work focuses on seismology, earthquake early warning systems, and seismic hazard mitigation. Research interests include earthquake rupture mechanisms, regional seismic structure and dynamics, mantle upwelling processes, fault interaction analysis, stress modeling in seismology, and machine learning applications in seismic data analysis. He pioneered smartphone-based seismic networks like MyShake and advanced technologies such as distributed acoustic sensing (DAS) for offshore monitoring. His recent publications highlight trends in earthquake early warning algorithms (EPIC, bEPIC), real-time ground-motion modeling, ShakeAlert system performance, and integration of multimodal data (e.g., social media, LLMs, DAS) for hazard mitigation. Collaborative efforts include global smartphone networks and cloud computing for seismic datasets. Allen leads the Berkeley Seismological Laboratory, driving innovations in seismic monitoring, structural health assessment, and public alerting systems to enhance disaster resilience.

Rute C. Sofiaمشاهده پروفایل

دانشیار

• Internet of Things
• Edge computing
• Network architectures and protocols
• +۱۰ مورد دیگر

Rute C. Sofia is Industrial IoT Head at fortiss – the Bavarian research institute for software-intensive systems – and Invited Associate Professor at Universidade Lusófona de Humanidades e Tecnologias (ULHT). She is also an Associate Researcher at ISTAR, Instituto Universitário de Lisboa (Iscte-IUL). Previously she co-founded and served as Scientific Director of COPELABS/ULHT (2013-2017) and was Senior Researcher there from 2010-2019. Education Ph.D. in Computer Science, University of Lisbon, 2004 Visiting Scholar, Northwestern University (ICAIR) & University of Pennsylvania, 2000–2003 M.Sc. in Computer Science, University of Lisbon, 1999 B.Eng. in Computer Engineering, University of Coimbra, 1995 Research Interests Her work spans network architectures and protocols , Internet of Things (IoT) , edge and in-network computing , deterministic wired/wireless industrial networks , and network mining . A current focus is on resilient, AI-driven orchestration across the IoT–Edge–Cloud continuum for 6G and Industrial IoT systems. Scientific Awards & Recognition ACM Europe Councilor (2021–2025) ACM Senior Member & IEEE Senior Member IEEE ComSoc N2Women Awards co-chair (2020–2021) Highly Cited Paper Award, Applied Sciences MDPI (2023) Labs, Teams & Grants She currently leads the Industrial IoT competence field at fortiss, coordinating projects such as SemComIIoT (semantic communications for IIoT) and the open-source ns-3 DetNetWiFi framework. Earlier she co-founded the Portuguese startup Senception Lda (2013-2019) and the research unit COPELABS , driving EU H2020 initiatives like UMOBILE and shaping national strategies for cyber-physical systems.

Lisa Willsمشاهده پروفایل

استادیار

• Computer Architecture
• Hardware Acceleration
• Hardware-Software Co-Design
• +۵ مورد دیگر

Lisa Wills serves as Assistant Professor of Computer Science at Duke University's Trinity College of Arts & Sciences and holds a joint appointment in Electrical and Computer Engineering at the Pratt School of Engineering since 2019. Her research bridges computer architecture and domain-specific applications, with a focus on hardware acceleration for computationally intensive fields. Dr. Wills earned her Ph.D. from Columbia University in 2014. Her academic journey reflects a deep commitment to advancing hardware-software co-design methodologies for real-world computational challenges. Her research centers on developing efficient hardware accelerators for big data analytics, particularly in genomics, graph processing, and database systems. She pioneers frameworks that simplify accelerator deployment while tackling critical bottlenecks in genomic data analysis, protein structure prediction, and privacy-preserving computing. Current work focuses on hardware-aware machine learning systems and energy-efficient architectures for emerging AI applications. Analysis of her publication record reveals a clear trajectory: from foundational work in database processing units (2014-2016) to specialized genomic accelerators (2019-2021), then evolving toward ML-enhanced design automation (2022-2023) and cutting-edge architectural abstractions (2024-2025). Her research consistently targets the intersection of hardware efficiency and domain-specific computational demands, with increasing emphasis on AI/ML workloads. Google ML and Systems Junior Faculty Award (2025) Dr. Wills actively mentors doctoral students including Chris Kjellqvist (lead architect of Beethoven accelerator framework), Mason Ma (PyTFHE FHE framework), and Mansi Choudhary (COCOSSim accelerator simulator). Her research is supported by significant grants including the NSF AI Institute: Athena ($20M, 2021-2027), Meta-funded ProSE accelerator project (2023-2026), and NSF CAREER award (2021-2026), totaling over $25M in active funding. She directs the APEX Lab (Application-driven Programmable Efficient Accelerated Systems), which develops open-source frameworks like Beethoven for FPGA/ASIC accelerator deployment and focuses on lowering barriers for non-hardware researchers to leverage custom acceleration in genomics, AI, and big data applications.

• Energy Efficiency in Communication Networks
• Quality of Service (QoS) and Quality of Experience (QoE)
• Network and Service Management
• +۵ مورد دیگر

Dr. Frank Loh is a researcher at the Department of Computer Science III, University of Würzburg, specializing in energy efficiency, network performance, and Quality of Experience (QoE) in communication networks. His work focuses on optimizing LoRaWAN deployments, serverless computing, and edge-cloud environments, with an emphasis on reducing message collisions and improving resource utilization. He actively contributes to methodologies for gateway placement, traffic modeling, and energy consumption metrics. Research Areas Energy Efficiency in Communication Networks Quality of Service (QoS) and Quality of Experience (QoE) LoRaWAN Network Planning Edge and Serverless Computing Network Resource Analysis Recent Publications 2025: Energy modeling for 6G base stations 2025: Server cluster resilience via Markov models 2024: Serverless computing in edge-cloud environments 2024: LoRaWAN channel access optimization

Jiang Hongboمشاهده پروفایل

استاد

• Distributed systems
• Mobile computing
• Smart sensing
• +۵ مورد دیگر

Hongbo Jiang is a Distinguished Professor and Vice Dean of the College of Computer Science and Electronic Engineering at Hunan University, China. He holds concurrent roles as Director of the Trusted Systems and Networking Key Laboratory of Hunan Province and Director of the Hunan International Technical Cooperation Base for High-Performance Computing and Distributed Systems. His academic journey includes tenures as a Professor at Huazhong University of Science and Technology and a Hong Kong Scholar Research Fellow at The Chinese University of Hong Kong. Education: PhD in Computer Science (Case Western Reserve University, 2008), B.S./M.S. in Mathematics (Huazhong University of Science and Technology, 2002). Research Interests: Distributed systems, mobile computing, smart sensing, wireless networks, IoT, and edge computing. Ongoing projects include mobile/wireless applications, data science in IoT, and edge computing platforms. His work emphasizes practical implementations such as DriverSonar for driving safety and SmileAuth for biometric authentication. Key Achievements: Elected Member of Academia Europaea (2022), Fellow of AAIA, IET, and BCS. Notable awards include the Wu Wenjun Science and Technology Award (2020) and multiple best paper recognitions. Over 100+ publications in top venues like ACM MobiCom, IEEE/ACM Transactions. Professional Contributions: Editorial roles across 8+ journals including IEEE Transactions on Mobile Computing and ACM Transactions on Sensor Networks. Conference leadership includes co-founding ACM TURC and EAI ICECI. Active in technical committees for INFOCOM, MOBIHOC, and ICDCS. Labs/Teams: Leads research groups focused on networking, IoT, and edge computing. Current openings for PhD/MSc students and PostDoc researchers with strong mathematical and systems backgrounds.

Alexandros Daglisمشاهده پروفایل

دانشیار

• Rack-Scale Computing
• Network-Compute Integration
• Low-Latency Datacenter Architecture
• +۵ مورد دیگر

Alexandros Daglis is an Associate Professor of Computer Science at the Georgia Institute of Technology, with an adjunct appointment in the School of Electrical and Computer Engineering. His research focuses on blurring boundaries between network and compute for high-performance, scalable microsecond-scale services in datacenters, particularly through network endpoints and memory-centric computing. Primary Affiliation: Georgia Tech College of Computing, School of Computer Science Adjunct Affiliation: School of Electrical and Computer Engineering Key research areas include: Rack-scale computing and network-compute co-design CXL-based memory systems Low-latency datacenter architectures Transactional memory and concurrency control Edge-cloud continuum and geo-distributed infrastructures He has received prestigious awards including the NSF CAREER Award, Google Faculty Research Award, and Georgia Tech's Outstanding Junior Faculty Teaching Award. His students include Marina Vemmou (network-compute co-design), Albert Cho (memory system design), and Peidi Song (microsecond-scale scheduling). Grants: NSF, IARPA, Intel, Samsung Teaching: High Performance Computer Architecture, Systems and Networks, Datacenter Design

Flavio Espositoمشاهده پروفایل

دانشیار

• Cyber-physical Systems
• Networked Systems
• Network Virtualization
• +۵ مورد دیگر

Flavio Esposito is an Associate Professor in the Computer Science Department at Saint Louis University's School of Engineering. He also serves as a Research Institute Fellow and CS Graduate Coordinator. His office is located in ISE 234D at 3450 Lindell Blvd, St. Louis, MO. Dr. Esposito's research focuses on cyber-physical systems and networked systems, including network virtualization, network management, Software-Defined Networks (SDN), network architectures, and wireless networks. He has a strong interest in interdisciplinary applications of these technologies to medicine and agriculture. His work bridges theoretical networking concepts with practical implementations. His publications span key areas in networking research, with recent work focusing on congestion control algorithms, virtual network embedding, recursive network architectures, and edge computing applications. The research trends show a progression from foundational networking protocols toward more sophisticated applications integrating machine learning, edge computing, and cyber-physical systems, with increasing emphasis on real-world applications in diverse domains. Outstanding Graduate Mentoring Faculty Award from the School of Engineering (2021) Finalist for the Undergraduate Mentoring Award in the College of Arts and Sciences Multiple NSF research awards including US Ignite, ICE-T, CNS Core, CC* Integration, CPS:TTP, and ModernCARE projects COMCAST Innovation Fund Award (January 2020) International Center for Responsible Gaming (ICRG) Award ($150K) Dr. Esposito actively mentors PhD and MS students, with numerous current and past students who have gone on to positions at major tech companies, universities, and research institutions. He has been a Principal Investigator on multiple significant research grants totaling millions of dollars. He co-founded Spaghetti Code Labs with former PhD student Alessandro Sangiorgi, whose cybersecurity educational app WeeNet has achieved 5.7M+ downloads. He leads several research labs and teams focused on cyber-physical systems, with current openings for PhD students, visiting researchers, and postdocs working on networks, learning, edge computing, and applications to medicine and agriculture. His teams have developed numerous software systems including Software Mutant, Neighborhood Method Prototype, VINEA, ProtoRINA, and BUtorrent.

• High-energy transient phenomena
• Fast radio bursts
• Tidal disruption events
• +۱۱ مورد دیگر

Wenbin Lu is an Assistant Professor in the Department of Astronomy at the University of California Berkeley, where he conducts theoretical research on high-energy transient phenomena. He is also affiliated with the Theoretical Astrophysics Center at UC Berkeley. PhD in Astronomy, University of Texas at Austin (2018) Bachelor in Physics, Peking University (2013) Professor Lu specializes in extreme astrophysical events that serve as natural laboratories for studying physics under conditions of high energy density, strong gravity, and intense magnetic fields. His work integrates multiple physical domains including plasma physics, relativistic hydrodynamics, radiative transfer, and stellar dynamics. He maintains active collaborations with researchers worldwide and encourages student involvement in his projects. Analysis of his recent publications reveals a strong focus on tidal disruption events and fast radio bursts, with increasing emphasis on multi-messenger approaches and theoretical modeling of observational data from facilities like JWST, Chandra, and radio telescopes. His work demonstrates consistent theoretical innovation in explaining complex transient phenomena. Burke Fellow at Caltech (2018-2021) Lyman Spitzer Fellow at Princeton University (2021-2022) Professor Lu actively mentors students and postdocs, with many projects originating from discussions with junior researchers. He teaches courses in Radiation and Stars at UC Berkeley. His research is supported by multiple grants that enable computational modeling and observational collaborations across various wavelengths. His theoretical work often involves complex numerical simulations of astrophysical phenomena, particularly focusing on the hydrodynamic evolution of stellar debris in tidal disruption events and plasma processes in fast radio burst emission mechanisms.

Barzan Mozafariمشاهده پروفایل

دانشیار

• Database Systems
• Data Management
• Machine Learning
• +۳ مورد دیگر

Barzan Mozafari is an Associate Professor of Computer Science and Engineering at the University of Michigan, Ann Arbor, and leads a research group focused on scalable database systems and approximate computing. He holds a PhD from UCLA (2011) and was a Postdoctoral Associate at MIT. His research emphasizes data-intensive systems, combining statistical models, optimization, and machine learning to enhance database performance and predictability. Notable projects include BlinkDB (approximate query processing), DBSeer (database diagnosis), and VerdictDB (platform-independent AQP). He co-founded Keebo, advancing data learning technologies, and contributed to SnappyData (acquired by TIBCO). His work spans technical innovations like CATS/VATS scheduling algorithms (adopted in MySQL/MariaDB) and grants such as ConFlux (NSF-funded supercomputing-big data integration) and a smart black-box for autonomous vehicles. Awards include the NSF CAREER Award and Best Paper recognitions at SIGMOD and EuroSys. Teaching includes EECS 484/584 (Database Management Systems) and advanced topics courses. He advises students like Yongjoo Park (SIGMOD Dissertation Runner-Up), Jiamin Huang, and Boyu Tian.