Find Academic Supervisors

• Storage Systems
• Persistent Memory
• Key-Value Stores
• +4 more

Oana Balmau is an Assistant Professor in the School of Computer Science at McGill University, where she leads the Data-Intensive Storage and Computer Systems Laboratory (DISCS Lab). She also holds a status-only appointment at the University of Toronto and serves as a working group chair for MLPerf Storage. Her research focuses on creating storage infrastructure that enables fast and energy-conscious insights from data, with particular emphasis on storage and persistent memory technologies for machine learning, data science, and edge computing workloads. Dr. Balmau's research interests span computer systems, with specific focus on: Design and implementation of efficient key-value stores Storage systems for machine learning workloads Edge computing infrastructure Persistent memory technologies Performance optimization of data-intensive systems Her recent work has led to significant contributions in storage benchmarking through the MLPerf Storage benchmark and in edge computing frameworks. The MLPerf Storage benchmark has become an industry standard for evaluating storage performance in machine learning environments, while her work on hierarchical edge computing addresses security and performance challenges in distributed edge environments. Her publications show consistent high-impact contributions to top systems venues, with recent work focusing on processing-in-memory virtualization, stream processing reconfiguration, and efficient data preprocessing pipelines. Dr. Balmau has received numerous awards for her research, including: SEC 2024 Best Paper Award for "Falcon: Live Reconfiguration for Stateful Stream Processing on the Edge" MLCommons Hero Award 2023 for leadership as MLPerf Storage working group chair ACM SIGOPS Dennis M. Ritchie Doctoral Dissertation Award 2021 Honorable Mention CORE John Makepeace Bennett Award 2021 for the best Computer Science doctoral dissertation in Australia and New Zealand USENIX ATC 2019 Best Paper Award for "SILK: Preventing Latency Spikes in Log-Structured Merge Key-Value Stores" As an educator, Dr. Balmau teaches courses on advanced computer systems, operating systems, and principles of computer systems design at McGill University. She has served on program committees for top systems conferences including SOSP, SIGMOD, FAST, and EuroSys, and has co-organized workshops on resource-efficient machine learning and edge computing. She leads the DISCS Lab, which focuses on two main research directions: Systems for ML (including the MLPerf Storage benchmark) and Edge computing (including frameworks for fast and secure edge computing in hierarchical edge environments).

Peter DindaView profile

Professor

• Parallel and Distributed Systems
• Virtualization
• Operating Systems
• +8 more

Peter Dinda is a Professor in the Department of Computer Science at Northwestern University , with a secondary appointment in the Department of Electrical and Computer Engineering . He has authored over 130 scientific papers, holds five patents, and is a Fellow of the IEEE . As the former head of the Computer Engineering and Systems division, he has contributed extensively to experimental computer systems. Education: B.S. in Electrical and Computer Engineering from the University of Wisconsin Ph.D. in Computer Science from Carnegie Mellon University Research Focus: Experimental computer systems, particularly parallel and distributed systems , virtualization , operating systems , and empathic systems that integrate user satisfaction with systems-level decision-making. His work also spans compiler design, memory management, and hardware-software co-design for performance optimization. Recent Trends: His publications emphasize virtualization efficiency, memory protection frameworks, parallel programming language design, and power management in heterogeneous computing environments. Key areas include exascale systems, IoT privacy, and physiological sensor-based user modeling. Scientific Awards: Fellow, IEEE Leadership: Served as Director of Graduate Studies and previously led the Computer Engineering and Systems division.

Phillip B. GibbonsView profile

Professor

• Parallel Computing
• Distributed Systems
• Databases
• +4 more

Phillip B. Gibbons is a Professor in both the Computer Science Department and Electrical & Computer Engineering Department at Carnegie Mellon University. He received his Ph.D. in Computer Science from the University of California at Berkeley in 1989 and has held research positions at AT&T Bell Laboratories, Lucent Bell Laboratories, and Intel Research Pittsburgh before joining CMU's faculty. His research spans parallel computing, distributed systems, databases, computer architecture, and machine learning. Gibbons' work bridges theory and systems, with publications in top-tier conferences including SOSP, OSDI, SIGMOD, VLDB, NeurIPS, and many others across computer science and engineering disciplines. His research has been supported by significant funding from NSF, Intel, and other organizations. Gibbons has made substantial contributions to streaming algorithms, parallel computing frameworks, distributed systems security, and large-scale machine learning systems. His work on data stream algorithms with Alon, Matias, and Szegedy has been particularly influential in the field. He has served in numerous leadership roles including Editor-in-Chief of ACM Transactions on Parallel Computing (2012-2018) and on the editorial boards of Journal of the ACM and IEEE Transactions on Cloud Computing. He has also been active on program committees for major conferences in systems, databases, and theory. IEEE Fellow (2014) - For contributions to parallel computing and databases ACM Fellow (2006) - For contributions to parallel computing, databases, and sensor networks Selected for Oral Presentation at NeurIPS '13 (only 20 selected out of 1420 submissions) Co-winner of the best paper award for NSDI '06 Gibbons has advised numerous students and mentored researchers who have gone on to make significant contributions in academia and industry. His research has been supported by major grants including the $15M Intel Science and Technology Center for Cloud Computing (2011-2015) where he served as Co-PI/Co-Director. He currently leads research projects on write-efficient algorithms, big learning systems, and visual cloud systems. His laboratory work focuses on bridging theoretical computer science with practical systems implementation, particularly in the areas of parallel and distributed computing. Current research directions include adapting algorithms for emerging memory technologies and optimizing machine learning systems for large-scale deployment.

Nils StieglitzView profile

Professor

• Strategic Management
• Organizational Behavior
• Strategic Decision-Making
• +3 more

Prof. Dr. Nils Stieglitz is a distinguished Professor of Strategy at Frankfurt School of Finance & Management since 2012 and serves as President & CEO since 2018. He leads the Management Department (2015–2018) and held the Executive Board role as Vice President Corporate Development (2017–2018). His research spans Strategic decision-making Organizational adaptation Human-agent interaction across dynamic environments. His scholarly work appears in top-tier journals like Strategic Management Journal , Management Science , and Organization Science , focusing on complex organizational trade-offs, incentive design, and scenario planning. He also contributes strategic insights to global media outlets such as Harvard Business Review and The Economist . As Senior Editor at the Journal of Organization Design and editorial board member of Organization Science , he shapes discourse on organizational theory. His teaching emphasizes strategy formulation and organizational design principles.

Trevor BrownView profile

Associate Professor

• Concurrent Data Structures
• Lock-Free Algorithms
• Transactional Memory
• +4 more

Trevor Brown is an Associate Professor in the Computer Science department at the University of Waterloo, affiliated with the Cheriton School of Computer Science. He leads the Multicore Lab and specializes in concurrent data structures, non-blocking algorithms, and memory management. His research bridges theory and systems, focusing on practical implementations of lock-free trees, transactional memory, and techniques for non-uniform memory architectures. Education includes a PhD in Computer Science from the University of Toronto and a B.Sc. in Computer Science and Mathematics from York University. Research interests center on concurrent systems, with recent work exploring hardware-accelerated indexing, memory reclamation techniques, and performance anomalies in microbenchmarks. His publications demonstrate consistent innovation in parallel computing, with articles frequently appearing at top conferences like PPoPP, SPAA, and DISC. Sustainable energy research includes optimizing hybrid power systems and battery storage solutions. Awards include multiple best paper/artifact recognitions at SPAA and PPoPP, teaching excellence honors, and nominations for the Governor General’s Gold Medal. Extensive advising includes 13+ graduate students and PDFs, with research grants exceeding $965K from NSERC, Huawei, and CFI. He directs the Multicore Lab, developing open-source tools like SetBench for rigorous performance benchmarking.

Yinzhi CaoView profile

Associate Professor

• Security and privacy of machine learning
• Web security and privacy
• Mobile security and privacy
• +1 more

Yinzhi Cao is an Associate Professor at the Johns Hopkins University Department of Computer Science . He serves as Technical Director of the Johns Hopkins Information Security Institute and is affiliated with the Data Science and Artificial Intelligence Institute and the Institute for Assured Autonomy . Cao joined JHU in 2018 from Lehigh University, where he was an Assistant Professor. Doctor of Philosophy (PhD) in Computer Science, Northwestern University (2014) Bachelor of Engineering (BE) in Electronic Engineering, Tsinghua University (2008) Research Interests focus on security and privacy of web, mobile, and machine learning systems . Key projects include Vulnerability Analysis of Web Applications and Security, Privacy, and Fairness Analysis of ML Systems . His work addresses prototype pollution in JavaScript, node.js vulnerabilities, browser fingerprinting, federated learning privacy, and automated exploit generation. Scientific Recognition includes the NSF CAREER Award (2021) DARPA Young Faculty Award (2022) & Director's Fellowship (2024) Amazon Research Awards (2022, 2017) IEEE Security & Privacy Test of Time Award (2025) Distinguished Paper Awards at IEEE S&P 2025, CCS 2023, USENIX Security 2022 Advising & Grants highlight mentorship of 20+ PhD and Master’s students across institutions. Major grants include $1.2M collaborative CICI TCR grant (2024-2026) with Dr. John Aucott $750K DARPA YFA grant (2022-2025) $500K NSF SaTC grant (2022-2025) NSF EAGER grant (2016-2017) Labs & Teams : Affiliated with Johns Hopkins Information Security Institute , Data Science AI Institute , and Institute for Assured Autonomy . Collaborates with institutions like Columbia, UC Santa Barbara, and SRI International. His group investigates real-world vulnerabilities in over 2,500 websites and NPM packages, uncovering 80+ zero-day issues.

Pavel PanchekhaView profile

Assistant Professor

• Programming Languages
• Program Synthesis
• Verification
• +4 more

Pavel Panchekha is an Assistant Professor in the School of Computing at the University of Utah, where he holds the Warnock Chair for Junior Faculty. His research spans programming languages, web browsers, and numerical analysis, with a focus on developing programming language techniques to address challenges across computer science. Dr. Panchekha received his educational training at prestigious institutions: PhD in Computer Science from the Paul G. Allen School for Computer Science and Engineering at the University of Washington, advised by Michael D. Ernst and Zachary Tatlock BS in Mathematics from MIT Panchekha's research program has two major thrusts. First, he works on web browser internals , with projects including fuzzing layout invalidation, multi-tenant garbage collection, and optimizing 2D graphics. He is also authoring a textbook on web browsers that informs much of this research. Second, he focuses on automatic numerical analysis , with projects such as automatic accuracy improvement, synthesis via term rewriting, scalable static accuracy analysis, and math library implementation. He leads the FPBench and Herbie projects, which are major deployments of his research. His scholarly output demonstrates consistent contributions across programming languages, verification, and numerical methods. Recent work shows a growing emphasis on bidirectional typing systems, layout invalidation in browsers, and robust floating-point error analysis. His publications reveal a trajectory from foundational work on floating-point accuracy (notably the Herbie tool that won a Distinguished Paper Award at PLDI 2015) toward more comprehensive systems for program synthesis, verification, and browser optimization. Panchekha has received significant recognition for his research contributions: NSF Fellowship ARCS Foundation Fellowship Adobe Research Fellowship Wissner-Slivka Foundation Fellowship 2015 PLDI Distinguished Paper Award for work on the Herbie numerical analysis and repair tool As an advisor, Panchekha mentors a substantial group of students across multiple levels. He currently advises six students: Marisa Kirisame (PhD), Bhargav Kulkarni (PhD), Yumeng He (PhD), Artem Yadrov (MS), Jesus Ponce (BS), and Jonas Regehr (BS). Previously, he has advised over twenty students including PhD candidates like Ian Briggs and numerous MS and BS students. His advising spans theoretical topics in programming languages and practical applications in web browsers and numerical computing. Panchekha leads research groups focused on programming languages applications to web browsers and numerical analysis. His work on the Herbie tool for floating-point accuracy improvement has become influential in the programming languages community, and his more recent work on browser internals is shaping how researchers understand and optimize modern web rendering engines. He is currently developing a textbook on web browsers that aims to synthesize knowledge about browser architecture and implementation.

• Virtualization
• Operating Systems
• Concurrency
• +4 more

Gaël Thomas is a Senior Researcher at Inria Saclay and a part-time Professor at École Polytechnique. He leads the Benagil team, a joint initiative between Inria and Telecom SudParis/IP Paris. His research focuses on virtualization, operating systems, concurrency, and runtime systems, with an emphasis on improving system performance and safety. He holds a PhD and Habilitation from Sorbonne Université and has extensive academic experience, including roles as a Professor at Telecom SudParis (2014–2023) and an Associate Professor at UPMC (2006–2014). Education: PhD in Computer Science, 2005, UPMC Sorbonne Université Habilitation à Diriger les Recherches (HDR), 2012, UPMC Sorbonne Université MSc in Computer Science, 2001, MIAIF Program Research Interests: Gaël’s work spans virtualization techniques, NUMA architecture optimization, garbage collection scalability, lock-free algorithms, and persistent memory systems. He has pioneered solutions like J-NVM for Java NVMM and NumaGiC for big data systems. Grants & Awards: Principal Investigator of DiVA (PEPR Cloud, 868k€) and Maplurinum (ANR PRC, 184k€) Contributions to VMKit and infra-web-cours development Labs/Teams: Benagil Team (Inria/Telecom SudParis) focuses on systems and runtime optimization.

John D. KubiatowiczView profile

Professor

• Computer Architecture
• Quantum Computing
• Distributed Systems
• +7 more

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

Yihan SunView profile

Assistant Professor

• Parallel Computing
• Algorithms
• Data Structures
• +12 more

Yihan Sun is an Assistant Professor at the University of California, Riverside (UCR) since January 2020. He earned his Ph.D. in Computer Science from Carnegie Mellon University (CMU) , advised by Guy Blelloch , and holds a Bachelor's degree in Computer Science from Tsinghua University . Research Interests: Yihan Sun focuses on the theory and practice of parallel computing , including Parallel algorithms and data structures Write-efficient algorithms for Non-Volatile Memory (NVM) Computational geometry (range trees, Delaunay triangulations) Graph algorithms (SSSP, SCC, cluster-based BFS) Concurrent and persistent data structures Multi-version concurrency control (MVCC) with garbage collection Applications in databases, transactional systems, and computational biology Recent Research Trends: His work on join-based parallel balanced trees has been foundational, supporting four balancing schemes (AVL, red-black, weight-balanced, treaps) and enabling efficient implementations in graph analytics, spatial queries, and dynamic programming. Recent publications focus on output-sensitive algorithms , scalable graph libraries (PASGAL) , and pedagogical approaches to teaching parallel algorithms. Teaching: He teaches CS260 (Parallel Algorithms) at UCR and has served as a guest lecturer for MIT 6.886 (Algorithm Engineering) and CMU 15-859 (Algorithms in the real world) . He also contributed to algorithm education through a tutorial at the ACM Symposium on Principles and Practice of Parallel Programming (PPoPP 2019) . Labs & Collaborations: Yihan is a core contributor to the PAM (Parallel Augmented Maps) library, which has been integrated into systems like Aspen (graph-streaming) and C-trees . He collaborates with teams at CMU-Parlay , PBBS , and Ligra , with his code available on Github for community feedback.

Nguyen KhanhView profile

Assistant Professor

• Programming Languages
• Compiler & Runtime Support
• Memory Management
• +5 more

Khanh Nguyen is an Assistant Professor in the Department of Computer Science & Engineering at Texas A&M University, part of the College of Engineering. His research focuses on improving scalability and efficiency in Big Data systems through compiler and runtime innovations, particularly in memory management and distributed computing. Education: Ph.D. in Computer Science, University of California, Los Angeles (2019) M.S. in Computer Science, University of California, Irvine (2015) B.S. in Computer Science, University of California, Irvine (2012) His research interests include programming languages, compiler design, memory management, and Big Data systems. He has developed techniques such as Gerenuk for thin computation over big data, Skyway for distributed heap connectivity, and Yak , a high-performance garbage collector. His work emphasizes resource efficiency and workload scalability, particularly for machine learning and data-parallel applications. Recent publications (2021–2024) highlight advancements in adaptive memory management for warehouse-scale computers, semantics-aware swapping in disaggregated systems, and query-driven distributed tracing. Awards: Google Ph.D. Fellowship (2017) Facebook Ph.D. Fellowship Finalist (2017) His research bridges compiler/runtime systems with large-scale data processing, addressing challenges in distributed systems and far-memory utilization. He collaborates with industry and academic partners to advance practical, scalable solutions for modern data-intensive workloads.

Alexandra FedorovaView profile

Professor

• Systems Software
• Memory and Storage Management
• Accelerator-Centric Computing
• +4 more

Dr. Alexandra Fedorova is a Professor in the Department of Electrical and Computer Engineering at the University of British Columbia (UBC), with an Associate Member role in the Computer Science department. She leads the Systopia systems research group, focusing on system software design, memory/storage management, and accelerator-centric computing. Her work emphasizes performance optimization, energy efficiency, and hardware-software co-design. She holds a PhD from Harvard University (2006), where she researched operating system scheduling under Margo Seltzer. Prior to UBC, she was an Associate Professor at Simon Fraser University (2006–2015). Fedorova is a recipient of the Alfred P. Sloan Research Fellowship and the Anita Borg Early Career Award. She consults for MongoDB's storage engine team and collaborates with industry on storage and performance challenges. Her research spans tools like Non-sequitur for program trace visualization, studies on storage-class memory (e.g., Optane), and frameworks for GPU acceleration. Recent efforts include Sunstone (spatial accelerator scheduling) and ExtMem (application-aware memory management). Her work bridges low-level systems with high-performance computing needs. Key contributions include optimizing NUMA systems, improving storage engine performance, and exploring processing-in-memory architectures. Fedorova’s projects often involve open-source collaboration, reflected in her GitHub repositories such as vividperf and perf-logging , which support performance analysis tools.

Kathleen StokesView profile

Assistant Professor

• Urban Governance
• Infrastructural Labour
• Sustainable Cities
• +3 more

Dr. Kathleen Stokes is an Assistant Professor in Urban Geography at Dublin City University's School of History and Geography. Her research focuses on urban governance, infrastructural labor, sustainable cities, urban livelihoods, waste management, and vacant space policies. She examines how state-society relations shape community waste labor in cities like Cape Town and South Africa, emphasizing the socio-economic dimensions of urban development. Her work bridges theoretical frameworks with empirical studies, addressing contemporary challenges such as infrastructural citizenship and urban resource reconfiguration. Her research interests also extend to the socio-economic impact of collaborative platforms and UK makerspaces, reflecting a broader engagement with innovation ecosystems. Despite no listed awards or grants, her publications highlight critical analyses of labor dynamics in waste sectors and urban policy evaluations, particularly in South African and Dublin contexts.

Matthew HertzView profile

Teaching Professor

• Computer science education
• runtime systems
• dynamic memory management

Matthew Hertz is a Teaching Professor in the Department of Computer Science and Engineering at the University at Buffalo's School of Engineering and Applied Sciences. His research focuses on computer science education, runtime systems, and dynamic memory management. Education: PhD, Computer Science, University of Massachusetts Amherst, 2006 MS, Computer Science, University of Massachusetts Amherst, 2001 BA, Computer Science, Carleton College, 1997 Research interests span computer science education and systems optimization. His educational research investigates learning factors in introductory programming courses, develops pedagogical tools like CloudCoder for programming exercises, and analyzes failure rates in CS1 courses. In systems research, he focuses on memory management innovations including garbage collection algorithms, adaptive resource allocation, and performance optimization in shared environments. Publications show dual focus: recent work emphasizes educational data analysis and programming pedagogy while earlier research concentrates on memory management efficiency and runtime systems. Trends include automated assessment tools and adaptive algorithms for resource-constrained environments. No scientific awards reported. No advising or grant information available. No labs or teams mentioned in available data.

Stefan MarrView profile

Researcher

• Concurrent Programming
• Language Implementation
• Virtual Machines
• +6 more

Stefan Marr is a faculty member at the University of Kent, United Kingdom, focusing on Concurrent Programming , Language Implementation , and Virtual Machines . His research spans projects like optimizing interpreters, improving concurrency models, and enhancing profiling tools for dynamic languages. Projects : Royal Society Industry Fellowship (FastStart), EPSRC-funded CaMELot, Oracle Labs-funded Automatic Superinstructions. Collaborations : With students and researchers across institutions, including Shopify, Vrije Universiteit Brussel, and Johannes Kepler University Linz. His recent work emphasizes profiling reliability for Java, interpreter performance , and tooling for complex concurrent systems . Projects like SOM (Simple Object Machine) and its variants (SOM++, TruffleSOM, PySOM) highlight his commitment to language implementation research and education. Scientific Awards include fellowships and grants from the Royal Society, EPSRC, and Oracle Labs. His contributions to conferences as program committee member , steering committee , and workshop organizer further underscore his academic leadership.