Find Academic Supervisors

• Computer Architecture
• Memory Hierarchies
• Cache Coherence Protocols
• +5 more

Ricardo Fernández Pascual is an Associate Professor at the Computer Engineering Department (DITEC) of the Universidad de Murcia , Spain. He teaches introductory and advanced computer architecture courses like ' Estructura y Tecnología de Computadores ' and ' Organización y Arquitectura de Computadores '. His academic work focuses on computer architecture , particularly in memory hierarchies for chip multiprocessors cache coherence protocols fault tolerance energy-efficient design His PhD thesis (2009) at the Universidad de Murcia, titled ' Fault-tolerant Cache Coherence Protocols for CMPs ', was supervised by José Manuel García Carrasco and Manuel Eugenio Acacio Sánchez. He has also collaborated with institutions like Intel Barcelona Research Center and FORTH. Recent research trends in his publications include optimizing coherence directories for manycore scalability hybrid photonic-electronic interconnects fault-tolerant mechanisms in CMP architectures transactional memory enhancements dynamic resource management private-shared cache organization He has advised PhD student Antonio García Guirado, who completed his thesis on energy-efficient cache-coherent multi-cores in 2013. His work has been published in leading journals like IEEE Transactions on Parallel and Distributed Systems, Journal of Parallel Distributed Computing, and conferences including ICS, HPCA, and SBAC-PAD.

Cosmin Eugen OanceaView profile

Associate Professor

• Programming Language Design and Implementation
• Compiler Optimization
• Automatic Parallelization
• +7 more

Cosmin Eugen Oancea serves as an Associate Professor in the Programming Languages and Theory of Computation section at the Department of Computer Science (DIKU), University of Copenhagen, Faculty of Science. His academic profile shows active engagement in research, teaching, and collaboration within the computer science community. Education: PhD in Computer Science from Western University (completed November 20, 2005) Dissertation title: "Parametric Polymorphism for Software Component Architectures and Optimizations" Oancea's research spans multiple domains within computer systems, with primary focus on static, dynamic and hybrid techniques for automatic parallelization (optimizing compilers), programming language design and implementation, memory management, and computer algebra. His recent work demonstrates strong connections between functional programming, array languages, and high-performance computing, with applications in financial modeling, automatic differentiation, and time series analysis. He has made significant contributions to the Futhark programming language ecosystem, focusing on compiler optimizations and performance tuning for GPU architectures. His publication record shows consistent output in top-tier venues including SC (International Conference for High Performance Computing), IFL (Symposium on Implementation and Application of Functional Languages), and ARRAY (Workshop on Libraries, Languages and Compilers for Array Programming), with a notable concentration of publications from 2021-2023. His research demonstrates interdisciplinary connections between computer science theory and practical applications in finance, environmental data analysis, and machine learning infrastructure. Oancea maintains active international collaborations across multiple countries, as indicated by the network visualization of his research partnerships. His work bridges theoretical computer science with practical implementations, particularly in the domain of array programming and compiler optimization for parallel architectures.

Akshitha SriramanView profile

Assistant Professor

• Computer Architecture
• Software Systems
• Sustainable Computing
• +4 more

Akshitha Sriraman is an Assistant Professor at Carnegie Mellon University with joint appointments in Electrical and Computer Engineering and Computer Science. Her research bridges computer architecture and software systems to enable sustainable, equitable, and efficient hyperscale web infrastructures. She holds a PhD in Computer Science from the University of Michigan (2021) and an M.S. in Embedded Systems from the University of Pennsylvania (2015). Research Focus: Dr. Sriraman redesigns computing stacks to achieve high-performance web systems while addressing critical ethical implications like sustainability (e.g., reducing hardware carbon footprint) and equity (e.g., eliminating demographic bias in web scheduling). Her solutions span from analytical models to production deployments at hyperscale, influencing industry hardware designs including Intel's CPUs and Infrastructure Processing Units. Publications: Her 15 most recent publications demonstrate consistent focus on optimizing data center systems through architectural innovations. Key themes include carbon-efficient cloud servers, hardware-accelerated metadata management, and profile-guided microarchitectural optimizations. Work appears in premier venues like ISCA, MICRO, ASPLOS, and IEEE Micro, with two papers receiving IEEE Micro Top Picks distinctions. Awards & Honors: Google ML and Systems Junior Faculty Award (2025) NSF CAREER Award (2023) Intel Rising Star Award (2023) George Tallman Ladd Research Award (2024) IEEE Micro Top Picks (2020, 2024) ACM SIGARCH/TCCA Outstanding Dissertation Award HM (2022) Meta Systems Research Award Facebook Fellowship Students & Funding: Advises 10+ PhD/Master's students working on sustainable systems, equitable computing, and hyperscale architectures. Student achievements include NSF Graduate Fellowships and K&L Gates Presidential Fellowships. Research supported by NSF, Google, Meta, Intel, VMWare, and AWS grants. Lab & Service: Leads the Ethical Computer Systems Lab focusing on hardware/software co-design for societal impact. Founded workshops like HotEthics and initiated DEI activities recognized by CMU's Service Award. Regularly serves on program committees for OSDI, ISCA, and ASPLOS.

Xiaoxuan YangView profile

Assistant Professor

• Processing-in-Memory Systems
• Neuromorphic Computing
• Hardware Accelerators
• +3 more

Xiaoxuan Yang is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Virginia . Her research focuses on processing-in-memory-based system design , biologically plausible systems , and hardware accelerators for emerging applications. She has held postdoctoral positions at Stanford University's Robust Systems Group and served as a research scientist at the University of Virginia. Ph.D. : Electrical and Computer Engineering, Duke University M.S. : Electrical Engineering, University of California, Los Angeles B.S. : Electrical Engineering, Tsinghua University Her research integrates neuromorphic computing , LLM acceleration , and hardware-software co-design , with specific interests in ReRAM crossbars , photonic neural networks , and memristor synapses . Current projects address stochastic noise resilience , quantization optimization , and energy-efficient AI . The 15 most recent publications explore PIM architectures (38%), neuromorphic systems (30%), ML hardware (25%), and optical computing (7%). Key trends include large language model acceleration , hardware robustness , and emerging memory technologies . Third Place ACM Student Research Competition (ICCAD) Best Research Award ACM SIGDA Ph.D. Forum (DAC) Best Paper Award GLSVLSI 2025 Rising Star in EECS NSF iREDEFINE Fellow Machine Learning and Systems Rising Star Rising Scholars Postdoc Fellow

Gail RattingerView profile

Professor

• Health Outcomes
• Neurodegenerative Diseases
• Pharmacoepidemiology
• +3 more

Gail Rattinger is a Professor in the Department of Pharmaceutical Sciences at the School of Pharmacy and Pharmaceutical Sciences, Binghamton University. She previously held roles as Associate Dean for Academic Affairs and Assessment, and Vice Dean at the same institution. Her career includes tenure as Director of the Pharmaceutical Research Computing Center at the University of Maryland School of Pharmacy, and as Associate Professor at Fairleigh Dickinson University. She holds a BA in Chemistry from Occidental College (1979), a PhD in Physical Inorganic Chemistry from the University of Illinois (1984), a PharmD from the University of California, San Francisco (2007), and completed a postdoctoral fellowship in pharmacoeconomics at the University of Maryland (2007–2009). She is licensed as a pharmacist in New Jersey and Maryland. Dr. Rattinger’s research focuses on unmet healthcare needs of geriatric and vulnerable populations, particularly dementia care and its socioeconomic impacts. Her work includes studies on dementia progression using the Cache County Study cohort, examining risk factors like neurotrophin genetics, sleep disturbances, and modifiable lifestyle factors. She also investigates HIV testing access in underserved communities and opioid use disorder interventions. Her research has been funded by NIH (K12 grants) and state agencies. Her academic leadership emphasizes curricular innovation in healthcare education, advocating for interprofessional teamwork and evidence-based practice. She collaborates on projects addressing medication safety in long-term care, fall risk assessment, and integrating social determinants of health into clinical training. Her recent work explores disparities in PrEP availability and college student substance misuse trends. Dr. Rattinger’s contributions span academic administration, clinical research, and public health policy. She serves as a key figure in advancing geriatric pharmacotherapy research and training the next generation of healthcare professionals through structured learning initiatives and assessment frameworks.

Yannis KotidisView profile

Professor

• Data Management
• Sensor Networks
• Complex Event Processing
• +4 more

Yannis Kotidis is a Professor at the Department of Informatics of the Athens University of Economics and Business (AUEB). He holds a Diploma in Electrical Engineering and Computer Science from the National Technical University of Athens (1995), and an M.Sc. (1997) and Ph.D. (2000) in Computer Science from the University of Maryland. Prior to joining AUEB, he worked as a Senior Technical Specialist at AT&T Labs-Research (1995–2006). Research Interests: Data Management, Sensor Networks, Complex Event Processing, Blockchain Technology, Time Series Analysis, and Distributed Systems. Projects: Led initiatives like DBSENSE (sensor network data management), RECOST (real-time stream processing), and INFORE (cross-platform analytics). Collaborated on Smart-Views (blockchain-based OLAP systems) and EasyFlinkCEP (streaming analytics). Recent publications focus on HPC resource allocation (RATS), time-series compression (Chimp/Sim-Piece), and decentralized data management using blockchain. His work emphasizes scalable solutions for big data challenges in distributed and sensor environments. Collaborations include teams at Siemens and Statoil, and academic partnerships addressing semantic data integration. He contributes to open-source tools like FlinkCEP, enhancing accessibility of big data analytics.

Yueqi ChenView profile

Assistant Professor

• System and Software Security
• Quantum Control Systems
• Machine Learning for Systems
• +5 more

Yueqi Chen is an Assistant Professor in the Department of Computer Science at the University of Colorado Boulder, part of the College of Engineering and Applied Science. His research focuses on system and software security, with specialties in exploit analysis/synthesis, quantum control systems, and ML-driven system management. He holds a Ph.D. from Penn State University (2022) and completed a visiting scholar stint at Northwestern University (2022). Recipient of the IBM PhD Fellowship (2020-2022) and multiple DEFCON CTF accolades, his work has been recognized at top venues like USENIX Security and IEEE S&P. Research emphasizes 'weird machine' formalization, kernel vulnerability mitigation (e.g., SeaK allocator), and cross-disciplinary projects blending quantum computing with traditional systems. Advises the Abstract Team, actively seeking students with expertise in cybersecurity, system programming, or quantum computing. Leads the CU Cyber Club and teaches courses on operating systems and modern cybersecurity defense mechanisms. Key publications address kernel exploit reliability, vulnerability assessment tools (GREBE/KLAUS), and memory protection mechanisms (PET/HotBPF). His work has been commercialized through initiatives like DARPA AIxCC projects. Recognized for industry-academic impact, with multiple patents pending and open-source contributions including SLAKE and TGRop frameworks.

• Internet of Things (IoT)
• Context-aware computing
• Edge and cloud computing
• +7 more

Prem Prakash Jayaraman is a Professor in the Department of Computer Science and Software Engineering within the Faculty of Science, Engineering and Technology at Swinburne University of Technology. His research focuses on the intersection of Internet of Things, context-aware computing, and distributed systems, with particular emphasis on practical applications in smart cities, digital manufacturing, and healthcare. Dr. Jayaraman's research interests span a wide range of topics within the IoT ecosystem, including context-aware computing, edge and cloud computing integration, digital twins for industrial applications, sensor network optimization, and performance evaluation of IoT middleware platforms. His work often addresses the challenges of real-world deployment, focusing on practical solutions for context management, data quality assurance, and resource optimization in distributed environments. He has made significant contributions to frameworks for context query generation, adaptive context monitoring, and caching strategies that enhance the efficiency of IoT applications. His recent publications demonstrate a strong trend toward applied research with practical implementations in smart city infrastructure, digital manufacturing, and healthcare applications. His work often combines theoretical frameworks with real-world validation, particularly evident in his studies on 5G-enabled smart cities, AI-driven roadside infrastructure monitoring, and digital twin implementations for manufacturing quality control. The research spans multiple sub-disciplines including context-aware systems, performance evaluation methodologies, and resource optimization techniques for distributed IoT environments. Dr. Jayaraman maintains an extensive collaborative network with researchers across Australia and internationally, particularly with Dimitrios Georgakopoulos, Rajiv Ranjan, Arkady Zaslavsky, and Abdur Forkan. His work appears in top-tier journals and conferences including IEEE Transactions on Industrial Informatics, Future Generation Computer Systems, IEEE Internet of Things Journal, and major conferences like MDM, HICSS, and IEEE Cloud.

Muhammad Ali GulzarView profile

Assistant Professor

• Software Engineering
• Automated Debugging
• Testing
• +6 more

Muhammad Ali Gulzar is an Assistant Professor in the Department of Computer Science at Virginia Tech and an Amazon Scholar at Amazon Web Services. He holds a Ph.D. (2020) and B.S. (2014) in Computer Science from UCLA and Lahore University of Management Sciences, respectively. His research focuses on improving developer productivity through automated debugging and testing for emerging software domains, including data-intensive systems, machine learning applications, and computational notebooks. Education Ph.D., Computer Science, University of California, Los Angeles (2020) B.S., Computer Science, Lahore University of Management Sciences, Pakistan (2014) Research Interests Dr. Gulzar redesigns software productivity tools for emerging applications across three areas: (1) automated tracking-code localization in web applications, (2) re-engineering testing/debugging for data-intensive systems (e.g., Apache Spark), and (3) advancing testing/debugging in federated learning. His work includes projects like TraceFL for federated learning interpretability, NoT.JS for JavaScript tracking blocking, and MeanCache for LLM caching. Recent Contributions Recent papers address accessibility issues in ad-driven web pages (ICSE 2025), interpretability in federated learning (ICSE 2025), and notebook executability (MSR 2025). His team received the ACM CCS 2024 Distinguished Artifact Award for tracking JavaScript function analysis and a 2024-25 Amazon-VT Award for semantic caching research. Grants & Projects CCI-funded project on web ad transparency/accessibility engineering Amazon Scholar collaboration on productivity tools Student Contributions Recent advisees include Abdul Haddi Amjad (ad accessibility), Waris Gill (Federated Learning), and Tien Nguyen (notebook analysis). Students have contributed to industry-relevant tools like GitMeter for evidence-based hiring and DeSQL for SQL debugging.

Yu LiuView profile

Professor

• Sustainable Computing
• Energy-Efficient Software
• Programming Languages
• +3 more

Yu Liu is a Professor at the School of Computing, Binghamton University, where he has been a faculty member since 2008. His research focuses on sustainable computing, energy efficiency, and security in emerging software systems, with significant contributions to programming languages, robotics, and software-hardware interfaces. Education: MSE, Johns Hopkins University PhD, Johns Hopkins University Professor Liu's research centers on sustainable computing and energy-aware software systems. He investigates programming language designs for energy prediction, develops reliable robotics software through managed languages, and creates secure software-hardware interfaces. His work bridges theoretical foundations with practical applications in data centers, UAV systems, and multi-threaded environments, emphasizing verifiable sustainability metrics and energy-efficient resource management. His publication trends reveal a consistent focus on sustainable computing evolution, with recent work advancing verifiable sustainability frameworks in data centers (2023-2024), energy accounting for multi-threaded applications (2020), and secure cache management techniques (2022). The research spans programming language theory, robotics safety, and hardware security, demonstrating interdisciplinary integration across computer science subfields. Scientific Awards: NSF CAREER Award Google Faculty Research Award Fulbright Scholarship Professor Liu has secured significant research funding through his NSF CAREER Award and Google Faculty Research Award, supporting innovations in sustainable computing infrastructure. His Fulbright Scholarship enabled international collaboration in Slovenia, advancing global research in energy-efficient software systems. He regularly teaches core courses including Programming Languages (CS471/571) and Sustainable Computing (CS680G), mentoring the next generation of computer scientists. While specific lab affiliations aren't detailed in the source text, his research on UAV software (Jcopter) and cache security (Composable cachelets) suggests active involvement in robotics and systems security research groups at Binghamton University.

Minlan YuView profile

Professor

• Computer Science
• Networking
• Distributed Systems
• +9 more

Minlan Yu is the Gordon McKay Professor of Computer Science at Harvard University's John A. Paulson School of Engineering and Applied Sciences (SEAS). She leads the Harvard Theory and Systems group and co-leads the Harvard Power and AI initiative. Her research focuses on data networking, distributed systems, and software-defined networking, with recent emphasis on sustainable computing and AI-driven network management. She is also Assistant Director of the SRC/DARPA JUMP 2.0 ACE Center for Evolvable Computing. Key research interests include network optimization, edge AI serving, large-scale resource allocation, and fault tolerance in distributed systems. Recent work highlights include innovations in energy-efficient data centers, homomorphic encryption acceleration, and real-time network telemetry using FPGA coprocessors. Teaching responsibilities include advanced courses on networking (CS 145/243) and systems programming. She actively advises PhD students and postdocs across systems and networking domains. Her lab's work has led to impactful contributions in both academia and industry, with a focus on bridging theory and practical system implementations. Current research initiatives include the Harvard Power and AI initiative exploring energy-efficient AI workflows and the development of evolvable computing infrastructure through the ACE Center.

Yiming ZengView profile

Assistant Professor

• Quantum Networking
• Quantum Computing
• Edge Computing
• +3 more

Yiming Zeng is an Assistant Professor at the School of Computing, Binghamton University. He holds a PhD from Stony Brook University and a BS from Shanghai Jiao Tong University. His research focuses on quantum networking, quantum computing, edge computing, distributed systems, differential privacy, and decentralized optimization. He has been recognized with awards including the 2023 Sigcomm Workshop Travel Grant and the 2017 Stony Brook Academic Excellence Fellowship. Education: PhD in Electrical and Computer Engineering, Stony Brook University (2024 expected) BS in Engineering, Shanghai Jiao Tong University Research Interests: Quantum Networking: designing protocols for entanglement routing and management in quantum internets Edge Computing: optimizing edge caching, data offloading, and privacy-preserving techniques in 5G networks Decentralized Optimization: applying game theory to blockchain systems and distributed resource allocation Differential Privacy: developing methods for federated learning and data marketplaces Teaching Experience: Teaching Assistant for courses including Digital Logic Design, Electronics Laboratory, and Introduction to Electrical and Computer Engineering Labs/Teams: Active in quantum internet research and edge computing systems development, though specific lab affiliations are not explicitly stated.

Josep TorrellasView profile

Professor

• Computer Architecture
• Parallel Processing
• Hardware Reliability
• +1 more

Josep Torrellas is the Saburo Muroga Professor of Computer Science at the University of Illinois at Urbana-Champaign. He directs the SRC/DARPA JUMP 2.0 ACE Center for Evolvable Computing and leads research in computer architecture, with focus on parallel processing, energy efficiency, and hardware reliability. Dr. Torrellas has received major awards including the IEEE Harry H. Goode Memorial Award, UIUC Campus Mentoring Award, and IEEE Technical Achievement Award. He is a Fellow of IEEE, ACM, and AAAS. His recent publications demonstrate strong focus on cloud security, serverless computing, and AI acceleration. Research consistently addresses hardware/software co-design for emerging computing paradigms. He has graduated 48 PhD students, many now faculty at top institutions. Dr. Torrellas teaches courses in parallel computer architecture and advises students in the ACE Center for Evolvable Computing.

Fan LaiView profile

Assistant Professor

• Machine Learning Systems
• Cloud Computing
• Networked Systems
• +3 more

Dr. Fan Lai is an Assistant Professor at the Department of Computer Science, University of Illinois Urbana-Champaign (UIUC) , where he leads research at the intersection of Machine Learning, Systems, and Computer Networking . His work focuses on building practical systems for efficient GenAI applications across cloud and planetary scales, with real-world adoption by companies like Meta, LinkedIn, and Cisco . Education : Ph.D. in Computer Science from the University of Michigan (2023) Previous Roles : Systems Research@Google, Meta AI Research areas include: Cloud ML Systems (LLMs, Image/VideoGen, DLRMs) Collaborative Learning (Federated Learning, Edge-Cloud ML, Hyperscale Training) ML for Systems (Resource Management, Network Optimization) Recent publications span top conferences like SOSP'25, MICRO'25, MLSys'25, SIGMOD'25 , covering topics from LLM serving to distributed training compression . Awards include ACM SIGOPS Honorable Mention , David J. Kuck Dissertation Prize , and ML and Systems Rising Stars . Teaching includes CS 498: Machine Learning Systems and CS 598: Systems for GenAI . Actively mentoring PhD and MS students in ML systems research, with prior mentees transitioning to institutions like Princeton and UW .

• Distributed Machine Learning
• Cyber Security
• Edge Computing
• +2 more

Zhengxin Yu is a Lecturer at Lancaster University , affiliated with the School of Computing and Communications . She obtained her Ph.D. in Computer Science from the University of Exeter (UK) in 2021. Current Position: Lecturer in Security and Protection Science Department: Computing and Communications Research Groups: Security Lancaster (Distributed Systems, Secure Machine Learning and Intelligence, Systems Security) Her research focuses on Distributed Machine Learning , Cyber Security , and Edge Computing . Key contributions include advancing federated learning frameworks, robust caching mechanisms in multi-edge systems, and privacy-preserving decentralized learning architectures. Recent publications (2022–2025) highlight her work in federated learning, edge computing, reinforcement learning, and adversarial defense. Trends include optimizing resource allocation in blockchain-enabled systems, modeling mobile edge computing performance, and enhancing resilience in distributed environments. PhD Students: Marta Adamska, Daria Smirnova, Yushi Wang Labs/Teams: Security Lancaster, with subgroups in Distributed Systems and Systems Security