Find Academic Supervisors

• Parallel Computing
• High Performance Computing
• Data-Intensive Computing
• +3 more

Geoffrey C. Fox serves as Professor and Director of the Digital Science Center at Indiana University Bloomington's Luddy School of Informatics, Computing, and Engineering, with over 1,200 publications spanning physics and computer science. His career encompasses foundational contributions to parallel computing methodology, algorithms, and data-intensive systems. Educational background includes a PhD in Theoretical Physics from Cambridge University. Fox's research focuses on synergies between high-performance computing and data analysis, pioneering decomposition principles for MIMD hypercubes and message-passing efficiency. He developed innovative systems like Twister (enhanced MapReduce for iterative computations) and SPIDAL (Scalable Parallel and Interoperable Data-intensive Application Library), bridging HPC with data-intensive applications through architectures comparable to Apache Spark and Flink. Major recognitions include: ACM-IEEE CS Ken Kennedy Award (2019) for foundational contributions to parallel computing methodology, algorithms, software, and data analysis interfaces with broad applications ACM Fellow (2011) for contributions to high-performance computing software applications and diversity outreach Fox demonstrates exceptional commitment to community engagement through teaching Java and parallel computing courses to Historically Black Colleges and Universities since 1997, creating a Computerworld-recognized 'Big Data Applications' MOOC, and leadership roles in Open Grid Forum and Java Grande Forum standards development. As principal investigator of the FutureGrid cyberinfrastructure testbed, he enabled novel scientific computing approaches while serving as General Chair for multiple conferences. He directs Indiana University's Digital Science Center and leads the SPIDAL project, which supports diverse data-intensive applications across high-performance computing platforms through interoperable library frameworks.

Michael L. ScottView profile

Professor

• Parallel Computing
• Operating Systems
• Programming Languages
• +7 more

Michael L. Scott is the Arthur Gould Yates Professor of Engineering in the Department of Computer Science at the University of Rochester's Hajim School of Engineering and Applied Sciences. He received his Ph.D. from the University of Wisconsin-Madison in 1985 and has been a faculty member at Rochester since 1985, serving as Department Chair multiple times (1996-99, 2007, 2017, 2020-2024). He is a Fellow of the ACM, IEEE, and AAAS, and recipient of numerous awards including the Edsger W. Dijkstra Prize in Distributed Computing. Dr. Scott's research focuses on parallel and distributed systems, with particular expertise in synchronization mechanisms, transactional memory, and persistent memory systems. His work spans theoretical foundations to practical implementations, with numerous influential publications and open-source systems like RSTM and Ralloc. His research has addressed critical challenges in concurrent programming, memory management, and system reliability. His publications show a consistent focus on improving the reliability and performance of concurrent systems, with recent work centered on persistent memory technologies. The trajectory of his research demonstrates a progression from fundamental synchronization algorithms to sophisticated systems addressing modern hardware challenges. His publications span top venues in systems, architecture, and programming languages. His scientific honors include: ACM Fellow (2006) IEEE Fellow (2010) AAAS Fellow Edsger W. Dijkstra Prize in Distributed Computing (2006) University of Rochester's Goergen Award for Teaching (2001) Hajim School Lifetime Achievement Award (2018) IEEE TCCA/HPCA Test of Time Award (2022) Dr. Scott has advised over 25 Ph.D. students who have gone on to successful careers in academia and industry at institutions including Lehigh University, Google, Intel, Facebook, and NVIDIA. His textbook 'Programming Language Pragmatics' is a standard reference in the field, now in its 5th edition. He also co-authored 'Shared-Memory Synchronization,' a comprehensive treatment of the field. He spent the 2014-2015 academic year as a Visiting Scientist at Google. His research group, the Rochester Concurrent Systems Group, has developed numerous influential systems including RSTM (a software transactional memory system), Ralloc (a persistent memory allocator), and Montage (a system for persistent data structures). His work often bridges theoretical correctness with practical performance considerations.

Hongyi WuView profile

Professor

• Security and Privacy
• Intelligent Computing Systems
• Communication Systems
• +7 more

Hongyi "Michael" Wu is the Department Head and Thomas R. Brown Leadership Chair in the Department of Electrical and Computer Engineering at the University of Arizona, part of the College of Engineering. He is a full-time Professor and a Fellow of IEEE, with a strong leadership presence in both academic administration and research. Education: PhD in Computer Science, State University of New York (SUNY) at Buffalo, 2002 MS in Electrical Engineering, State University of New York (SUNY) at Buffalo, 2000 BS in Scientific Instruments, Zhejiang University, China, 1996 His research is focused on security and privacy in intelligent computing and communication systems , with applications in cybersecurity, mobile and wireless networks, Internet of Things (IoT), and distributed computing. His work bridges theoretical foundations with real-world systems, emphasizing robustness and trust in next-generation networks. While specific article titles are not listed in the provided text, his publication record exceeds 160 technical papers in top-tier journals and conference proceedings, indicating sustained contributions in cybersecurity, mobile computing, and networked systems over two decades. His editorial roles in IEEE Transactions on Mobile Computing, IEEE Internet of Things Journal, and others reflect his influence in these domains. Scientific Awards and Honors: NSF CAREER Award (2004) UL Lafayette Distinguished Professor Award (2011) IEEE Percom Mark Weiser Best Paper Award (2018) IEEE Fellow Dr. Wu has served as Principal or Co-Principal Investigator on over 50 funded research projects totaling more than $23 million from NSF, NSA, DOD, DOE, NATO, state governments, and industry partners. He actively mentors graduate students, with recent advisees securing faculty positions at Old Dominion University, Chongqing University, and the University of Hawaii at Manoa. He currently has openings for postdoctoral researchers and research assistants in AI, security, and wireless networks. He has chaired major conferences including IEEE INFOCOM 2020 and WoWMoM 2021, and has served on the editorial boards of several leading journals such as IEEE Transactions on Computers, IEEE Transactions on Mobile Computing, IEEE Internet of Things Journal, and Elsevier Parallel Computing.

Ming YanView profile

Associate Professor

• Optimization Methods
• Sparse Recovery
• Inverse Problems
• +7 more

Ming Yan is an Associate Professor and Assistant Dean at the School of Data Science, The Chinese University of Hong Kong, Shenzhen (CUHK-Shenzhen), where he has been serving since 2022. Prior to this position, he was an Associate Professor at Michigan State University from 2021 to 2023 (on leave from 2022 to 2023) and an Assistant Professor from 2015 to 2021. His academic journey includes postdoctoral positions at UCLA and Rice University. His educational background includes: Ph.D. in Mathematics from University of California, Los Angeles (UCLA), 2012 M.S. in Mathematics from University of Science and Technology of China (USTC), 2008 B.S. in Mathematics from University of Science and Technology of China (USTC), 2005 Ming Yan's research focuses on optimization methods , particularly for sparse recovery and inverse problems . His work extends to federated and machine learning algorithms , parallel and distributed algorithms for large-scale data , and variational techniques in image processing . His approach often combines theoretical analysis with practical applications, developing algorithms that address computational challenges in modern data science. His recent publications demonstrate a strong focus on decentralized optimization , primal-dual algorithms , and sparse recovery techniques . These works span theoretical developments in convergence analysis and practical applications in machine learning and signal processing. His research has evolved from foundational work in image processing to more recent contributions in distributed learning and federated systems, reflecting the changing landscape of data science. Ming Yan is actively involved in mentoring students and has indicated that he is recruiting Ph.D. students, postdocs, and visiting students at CUHK-Shenzhen. His teaching portfolio includes courses in calculus, optimization, and computational methods across multiple institutions. His research is supported by various grants, though specific details are not provided in the available information. He has developed several algorithms and software implementations, including the PD3O framework for convex optimization algorithms.

Caleb StanfordView profile

Assistant Professor

• Programming Languages
• Formal Methods
• Distributed Systems
• +4 more

Caleb Stanford is an Assistant Professor in the Department of Computer Science at the University of California, Davis. He specializes in programming languages and formal methods with applications to distributed systems, data processing, and computer security. His research focuses on developing practical tools and theoretical foundations for secure and reliable systems. Research interests span programming languages theory, formal verification techniques for distributed systems, Rust ecosystem security, stream processing verification, and automata theory extensions. Specific projects include securing Rust crates through automated auditing tools, extending derivative-based abstractions for regular expressions, and developing verification frameworks for dataflow programming. His publications demonstrate consistent focus on formal methods applications, with recent emphasis on Rust program safety (2025), stream processing verification (2020-2024), and incremental graph algorithms (2023). Article keywords frequently include formal verification, programming languages, distributed systems, and automata theory. Dr. Stanford actively recruits students for research positions: Undergraduates should demonstrate excellence in relevant coursework Prospective PhD students must apply through UC Davis admissions Current openings focus on Rust security, formal methods, and distributed systems verification He leads the Guided Incremental Digraphs (GID) research project and collaborates with UCSD on Rust security initiatives. Community contributions include serving as Artifact Evaluation Co-Chair for POPL 2024 and committee member for PLDI and OOPSLA conferences.

Peter DienerView profile

Research Professor

• General Relativity
• Numerical Relativity
• Black Hole Physics
• +2 more

Peter Diener is a Research Professor at Louisiana State University's Department of Physics & Astronomy within the College of Science. His primary research focuses on computational astrophysics and numerical relativity, with specialized interests in general relativity, black hole physics, gravitational waves, and neutron star mergers. He contributes significantly to the development and application of the Einstein Toolkit, an open-source software framework for relativistic simulations. Diener's investigations center on modeling extreme astrophysical phenomena using advanced computational methods. His work on neutron star mergers examines ejecta dynamics, kilonovae emissions, and gravitational wave signatures. Through particle-based numerical approaches like SPHINCS_BSSN, he explores black hole dynamics, dynamical spacetimes, and relativistic hydrodynamics.

Dan HuView profile

Lecturer

• Biomechanical Engineering
• Vehicle Dynamics
• Estimation Algorithms
• +3 more

Dan Hu is a Lecturer in Mechanical Engineering at the CEES School of Engineering. He specializes in biomechanical modeling, vehicle dynamics, and estimation algorithms. His research focuses on human movement analysis, musculoskeletal systems, and advanced control systems for automotive applications. He is currently accepting PhD students in two projects: developing predictive modeling tools for personalized rehabilitation devices and creating experiment-free clinical diagnosis systems using machine learning. Dr. Hu holds a Doctorate in Biomechanics & Automotive Engineering from Jilin University (2014), where his thesis addressed musculoskeletal modeling of drivers' hands. He also earned an MSc in Automotive Engineering (2009), focusing on Kalman filter applications for vehicle state estimation. His research interests span biomechanical engineering, computational modeling of human motion, and vehicle state estimation using extended Kalman filters. Notable contributions include 3D whole-body walking models, metatarsophalangeal joint kinematics studies, and biomimetic prosthetics design. His work bridges biomechanics with automotive engineering, aiming to improve healthcare technologies and vehicle safety systems. Dr. Hu has peer-reviewed for journals like Scientific Reports , IEEE Transactions on Neural Systems , and Computer Methods in Biomechanics . His research collaborations span biomechanics, robotics, and automotive control systems. Current projects emphasize predictive modeling and clinical diagnosis innovation.

Wan FokkinkView profile

Professor

• Distributed Systems
• Formal Analysis of Protocols
• Concurrency Theory
• +3 more

Prof. Wan Fokkink is a Full Professor in Theoretical Computer Science at Vrije Universiteit Amsterdam (VU) and holds a part-time position as Professor of Model-Based System Engineering at Eindhoven University of Technology (TU/e). His research focuses on distributed systems, formal analysis of protocols, and supervisory control synthesis. He leads the Theoretical Computer Science group at VU and has authored three influential textbooks: Introduction to Process Algebra , Modelling Distributed Systems , and Distributed Algorithms: An Intuitive Approach . Education: MSc in Mathematics (University of Amsterdam, 1990) and PhD in Computer Science (University of Amsterdam, 1994). Postdoctoral work at Utrecht University and lectureship at Swansea University preceded his leadership roles at CWI (2001–2004) and VU (since 2004). Teaching includes courses on logic, concurrency, and distributed algorithms. He is editor of Logical Methods in Computer Science and co-founder of the Electronic Proceedings in Theoretical Computer Science . Active in professional organizations: co-founder of IFIP WG 1.8 (Concurrency Theory) and steering committee member of the CONCUR conference. Research interests span formal verification, concurrency theory, and practical applications of supervisory control in engineering systems (e.g., traffic management, ship locks). His work aligns with sustainable development goals through contributions to reliable system design and optimization. Recent articles explore model-based specification, distributed control under communication delays, and tools like Eclipse ESCET for supervisory control synthesis. Collaborations span international teams in Europe and beyond.

• Edge Computing
• Cloud Computing
• Artificial Intelligence
• +7 more

Hadi Tabatabaee is an Assistant Professor at the School of Computer Science, University College Dublin (UCD), leading the Sustainable Orchestration in Computing Continuum (SOC² Lab). His research focuses on sustainable orchestration of services across edge-cloud environments, emphasizing energy efficiency, carbon-aware systems, and AI-driven applications like large language models (LLMs). Key roles include Associate Editor for IEEE Access and Management Committee member of COST Action CA22151 (CYPHER). He holds a PhD in Computer Engineering from the University of Isfahan and has held academic positions at Maynooth University, Shahid Beheshti University, and Trinity College Dublin's CONNECT research program. Education: PhD (Computer Engineering, University of Isfahan), MSc (Computer Engineering), with a research visit at TU Delft (2010-2011). Certifications include Epigeum's Research Leadership and Research Integrity courses. Languages: Persian (fluent), Azerbaijani (spoken). Research Interests: Edge-cloud continuum, dynamic service placement, distributed AI workloads, LLM optimization, and sustainable resource management. Recent work includes zero-trust vehicular networks, parallel algorithms for recommender systems, and geospatial event processing. Awards: None explicitly listed, though his contributions include over 20 journal articles in IEEE/Elsevier/Springer venues. Professional Activities: IEEE Senior Member, TPC member for IEEE conferences, and reviewer for multiple journals. Teaching: Coordinates/teaches Cloud Computing, Computer Networks, and Principles of Computer Organization at UCD.

Maria Ribera SanchoView profile

Professor

• Software Engineering
• Conceptual Modeling
• Ontologies
• +4 more

Dr. Maria Ribera Sancho is a Professor at the Polytechnic University of Catalonia (BarcelonaTech), holding roles as Dean of the Faculty of Informatics of Barcelona (2004–2010), Vice-Dean (1998–2004), and currently Manager of the Education and Training Department at the Barcelona Supercomputing Center (BSC-CNS). She chairs the EQANIE Accreditation Committee and serves on the ACM-W Europe Executive Committee. Her research focuses on Software Engineering, Conceptual Modeling, Ontologies, Learning Analytics, and IoT applications. Notable contributions include work on automated design using conceptual models, model-driven software development, and semantic-based IoT infrastructure monitoring. She leads projects like LinDaFIX (social welfare data tools) and REMEDiAL (ontology-driven software automation). Key awards include the UPC Quality in Teaching Prize (2005, 2021), Jaume Vicens Vives Distinction (2005), Sapiens Award (2011), and Festibity Award (2011). She advises doctoral and master’s students on topics like IoT semantic monitoring and educational ontology development. Academic director of the inLab FIB Talent Program, she also directs the PRACE Advanced Training Center at BSC. Main projects include TINTIN (SQL integrity tool), e-Catalunya (collaborative government platform), and PILARES (learning analytics for secondary education). Her work bridges academia and industry through competitive projects with companies and institutions.

• Data Management
• Social Media Analysis
• User Behavior Analysis
• +4 more

Dr. Ying Zhou is a Lecturer in the School of Computer Science at The University of Sydney. She holds a BSc and MEng from Nanjing University (1997) and a PhD from the School of Computing at the National University of Singapore (2003). Her research focuses on human-centred data management, including large-scale data storage, user behavior analysis, and improving image query systems. Current projects include mining socially tagged images and accountability mechanisms for multitenant cloud platforms. Teaching responsibilities include courses on Advanced Data Models (COMP5338), e-Commerce Technology (COMP5347), and Cloud Computing (COMP5349). She collaborates with industry partners like Amazon and IBM, and advises four research students. She is a member of the Sydney Southeast Asia Centre. Her publication record spans 20 years, with recent work emphasizing machine learning applications, big data systems, and cloud security. Research trends include optimizing distributed computing frameworks (Hadoop/Spark/Flink), adversarial machine learning, and trustworthy database systems. Earlier work focused on social network analysis, web communities, and blogosphere dynamics. Current research projects address both technical challenges (e.g., efficient image search, cloud platform accountability) and applied solutions for leveraging social media data. Her lab activities involve interdisciplinary collaboration between computer science and information systems domains.

Ezer RasinView profile

Senior Lecturer

• Theoretical Phonology
• Computational Phonology
• Learnability
• +3 more

Ezer Rasin is a Senior Lecturer in the Department of Linguistics at Tel Aviv University (TAU), where he also leads the TAU Phonological Computation Lab. He holds a PhD in Linguistics from MIT (2018), an MA and BSc in Linguistics and Mathematics from TAU (both prior to his PhD). Before joining TAU, he was a postdoctoral researcher in Leipzig University's IGRA program. His research focuses on theoretical and computational phonology, learnability, and formal linguistics. Key areas include phonological opacity, optimality theory, and the interaction between phonology and morphosyntax. His work bridges empirical phonology with computational modeling, addressing questions about language acquisition and formal grammatical architecture. Recent publications examine challenges to size-based parallel analyses in Hebrew vowel deletion, opacity phenomena in Gua and Akan, and computational approaches to phonological learning. He has contributed to journals like Linguistic Inquiry and Journal of Language Modelling , and co-edited proceedings for NELS and other conferences. Rasin teaches courses on phonological opacity, computational phonology, and the phonology-morphology interface at TAU. His research also involves developing audio databases for endangered languages like Judeo-Baghdadi Arabic, reflecting his commitment to computational methods in linguistic documentation.

Seth GilbertView profile

Associate Professor

• Distributed Systems
• Algorithmic Techniques
• Blockchain Technology
• +3 more

Seth Gilbert is the Dean's Chair Associate Professor and Head of the Department of Computer Science at the National University of Singapore. His research focuses on designing robust, scalable algorithms for large-scale distributed systems, particularly in the domains of Byzantine agreement, dynamic networks, and contention resolution. He holds a PhD from MIT (advised by Nancy Lynch) and completed a postdoc at EPFL under Rachid Guerraoui. His work emphasizes three core techniques: accountability (identifying network failures), work-sharing (task distribution in asynchronous environments), and contention resolution (efficient resource allocation). Notable contributions include the DConstructor (PODC 2020) and Polygraph (ICDCS 2021) protocols for accountable Byzantine agreement. He has received multiple awards, including the Edsger W. Dijkstra Prize (2021) and Best Paper Awards at DISC, ICDCS, and IPDPS. Prof. Gilbert has served as program chair for DISC 2021, OPODIS 2019, and SPAA 2016, and currently chairs the DISC Steering Committee. His career includes industry experience at Microsoft (Visual Studio team) and early work on solar car engineering at Yale University.

Fredrik KjolstadView profile

Assistant Professor

• Compilers
• Programming Models
• Systems
• +5 more

Fredrik Kjolstad is an Assistant Professor of Computer Science at Stanford University. His research focuses on compilers, programming models, and systems for sparse computing, with an emphasis on separating algorithms from data representation. He leads efforts in developing compilers like TACO, Simit, and Legate Sparse to optimize sparse tensor algebra and distributed computations. Kjolstad's work spans compiler design, hardware-software co-design, and programming languages for high-performance computing. His research group aims to enable portable applications across diverse data representations and architectures. Notable projects include the TACO compiler for sparse tensor algebra, the Simit programming language for physical simulations, and the Copy-and-Patch compilation technique for fast runtime code generation. He has also contributed to distributed systems like Legate Sparse and hardware accelerators such as Onyx. Kjolstad has received prestigious awards including the NSF CAREER Award and the MIT EECS PhD Thesis Award. His publications cover topics like sparse tensor compilation, compiler optimization, and agile hardware design. He advises multiple PhD students and collaborates with researchers like Kunle Olukotun and Alex Aiken. Key areas of impact include efficient sparse data processing, compiler-driven hardware design, and scalable distributed computing frameworks. His work bridges theoretical compiler techniques with practical system implementations, aiming to simplify and accelerate complex computational tasks.

Atakan AralView profile

Academic

• Edge Computing
• Edge AI
• Internet of Things (IoT)
• +3 more

Atakan Aral is a Visiting Lecturer at the Department of Computing Science, Umeå University. His research focuses on Edge Computing, Edge AI, and the Internet of Things (IoT), with a particular emphasis on resource management and sustainable environmental monitoring. He is affiliated with the Autonomous Distributed Systems Lab and Green Distributed Computing Group, both part of the Wallenberg AI, Autonomous Systems and Software Program (WASP) initiative. His work spans theoretical frameworks and practical implementations in edge intelligence and distributed systems. Research Interests: Edge Computing architectures and workflows Neuromorphic and energy-efficient AI systems Federated learning and multi-cluster collaboration Sensor networks for environmental monitoring Optimization of resource allocation in distributed systems Key Publications Trends: Recent work emphasizes neuromorphic edge AI applications, hierarchical federated learning, and energy-efficient IoT deployments. His articles often intersect computing continuum concepts with real-world challenges like rural environmental monitoring and latency-critical systems. Scientific Awards: No awards explicitly listed in the provided text. Advising & Grants: No formal student advisees or grant details provided. However, he contributes to the De facto Center of Excellence in Autonomous Distributed Systems (2023–2029), indicating involvement in large-scale collaborative research. Labs & Teams: Member of the Autonomous Distributed Systems Lab (lead in distributed systems research) and Green Distributed Computing Group (focused on sustainability in computing).