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نمایش ۱۶-۳۰ از ۷۰ استاد راهنما

اطلاعات پروفایل‌ها از صفحات عمومی دانشگاه‌ها گرفته شده و ممکن است به‌روز نباشد.

Philipp Slusallekمشاهده پروفایل

استاد

Max Planck Institute for Software SystemsGermany

Philipp Slusallek is a Professor for Computer Graphics at Saarland University since 1999 and Scientific Director at the German Research Center for Artificial Intelligence (DFKI), leading the "Agents and Simulated Reality" research area since 2008. He has held leadership roles such as Director for Research at the Intel Visual Computing Institute (2009-2017) and Dean of the Faculty of Computer Science and Mathematics (2002-2004). His affiliations include being a Principal Investigator in the German Excellence Cluster "Multimodal Computing and Interaction" (2007-2019) and a Visiting Professor at NVIDIA Research (2007-2008). PhD in Computer Science from Erlangen University Diploma in Physics from Frankfurt and Tübingen University His research spans computer graphics, artificial intelligence, and high-performance computing, with contributions to real-time ray tracing, digital reality, motion synthesis, and domain-specific languages (DSLs). Recent work includes visual programming tools for image processing (2022), DSL frameworks for high-performance libraries (2018), and compiler optimizations for vectorized code (2018). Key themes include simulation technologies, programming models, and computational sciences. Scientific honors include Fellow of Eurographics, Associate Editor of Computer Graphics Forum , membership in acatech (German National Academy of Science and Engineering), and advisory roles in AI policy. He has led research groups at institutions like Stanford University (Visiting Assistant Professor, 1998-1999) and the University of Tübingen (Researcher, 1990-1992).

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Laure Gonnordمشاهده پروفایل

استاد

Grenoble INPFrance

Laure Gonnord is a Full Professor in Computer Science at Grenoble INP , affiliated with the Esisar Engineer School in Valence, France, since September 2021. She is a member of the CTSYS research team at the LCIS laboratory and an external member of the CASH team at the University of Lyon / CNRS / LIP / Inria. Her research focuses on compilation , static analysis , and applications to safety , security in high-performance and embedded systems . Fields of Interest : Compiler Design Static Analysis for Safety & Security Abstract Interpretation Embedded Systems High-Performance Programming Hardware Security Engineering Research Trends (from recent publications): Her work explores modular verification through monadic abstract interpreters, complexity bounds in term rewriting , and educational tools for theorem proving . Notable contributions include compiler hardening schemes for hardware security and memory layout optimizations for algebraic data types. Academic Leadership : Scientific Director of the Summer School EJCP (École Jeune Compilation et Programmation) Board Member of the French national research group GDR GPL Teaching Responsibilities at Grenoble INP include courses in architecture , compilation , programming languages , algorithms , and databases . She has also taught at University of Lyon, ENS Lyon, Polytech'Lille, and INSA.

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Gordon Kindlmannمشاهده پروفایل

دانشیار

University of ChicagoUnited States

Gordon Kindlmann is an Associate Professor of Computer Science at the University of Chicago, affiliated with the Systems Group research community. His work bridges computational imaging science and visualization theory, focusing on biomedical applications and machine learning integration. He leads projects in diffusion MRI analysis, surgical planning tools like SlicerDMRI, and theoretical advancements in visualization design. Research Interests: Biomedical Image Analysis Scientific Visualization Theory High Performance Computing Medical Imaging Algorithms Machine Learning Applications Recent Articles Trends: His work emphasizes cardiovascular modeling (e.g., aortic dissection prediction) and visualization validation techniques. Recent collaborations include optimizing visualization tools for scalability and accuracy in threaded data exploration. Awards: None explicitly listed in provided text. Grants/Advising: Involved in CDAC Discovery Grants (2019) and actively supports student research, though specific advisees are not listed here. His lab develops open-source tools like Diderot for tensor field visualization. Labs & Teams: Member of the Systems Group, a collaborative environment advancing systems research, programming languages, and software engineering.

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Nina Herrmannمشاهده پروفایل

پژوهشگر

University of MünsterGermany

Dr. Nina Herrmann is a researcher at the Chair of Machine Learning and Data Engineering (School of Business and Economics, University of Münster). Her work focuses on parallel programming, domain-specific languages (DSL), and algorithmic skeletons for heterogeneous computing environments. Education: Bachelor of Information Systems (WWU Münster, 2017) Master of Science in Information Systems (WWU Münster, 2019) Semester abroad at University of Trento (Italy, 2018–2019) Research Interests revolve around optimizing high-performance computing through algorithmic skeletons, DSL design, and machine learning applications in resource-constrained systems. She has supervised multiple theses on topics including EdgeML methods, memory-efficient ML models, and parallel programming frameworks. Project Contributions include the ongoing Musket (Muenster Skeleton Tool) for DSL-based parallel code generation, and the completed Muesli (Muenster Skeleton Library), which simplified parallel application development using algorithmic skeletons. Teaching Activities span courses like Computer Science I/II , Parallel Programming , and specialized seminars on topics including graph machine learning and process mining. Contact: nina.herrmann@uni-muenster.de

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Fritz Hengleinمشاهده پروفایل

استاد

IMDEA Software InstituteSpain

Fritz Henglein is a Professor at the Department of Computer Science, University of Copenhagen (DIKU), with affiliations also at Deon Digital. His career spans over three decades, with notable roles including Head of the Algorithms and Programming Languages (TOPPS) research group and Director of the HIPERFIT research center. Research interests include Programming languages and type systems Functional programming and DSLs High-performance computing Contracts and distributed ledger technology Algorithmic logic and formal semantics His work often bridges theoretical rigor with practical applications, particularly in financial information technology and compiler design. Collaborations include leadership in conferences like POPL, ICFP, and PEPM, with a focus on mentoring through academic events and community-building initiatives.

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Louis-Noël Pouchetمشاهده پروفایل

دانشیار

Colorado State UniversityUnited States

Louis-Noël Pouchet is an Associate Professor in the Department of Computer Science at Colorado State University, with a joint appointment in the Electrical and Computer Engineering department. He leads research in high-performance computing, focusing on polyhedral compilation, performance portability, and hardware-software co-design. His research interests include polyhedral compilation, iterative and adaptive compilation, machine learning for compilers, performance-oriented domain-specific languages, energy-aware program optimization, and high-level synthesis. He develops compiler technologies to optimize and parallelize code for heterogeneous platforms, with applications in scientific computing and embedded systems. The 15 most recent publications highlight a strong focus on compiler optimization for high-performance systems, particularly using the polyhedral model. Key themes include data locality, parallelization, vectorization, memory access optimization, and performance modeling. His work spans both theoretical advances in program transformation and practical implementations in tools like PoCC and PolyOpt. Member, Center for Domain-Specific Computing (NSF) Member, DSL Technology for Exascale Computing (DoE) Lead, Polyhedral Compilation Research (NSF and Intel ISRA) Former member, Platform-Aware Compilation Environment (DARPA) He teaches courses on polyhedral compilation and has developed widely used software tools such as PoCC, PolyBench/C, and PolyOpt/C. His research is supported by major funding agencies including NSF, DoE, and Intel.

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Matthew J. Parkinsonمشاهده پروفایل

پژوهشگر

Max Planck Institute for Software SystemsGermany

Matthew J. Parkinson is a Principal Researcher at Microsoft Azure Research in Cambridge, UK. His work focuses on memory and concurrency safety , with significant contributions to formal verification , systems programming , and language design . He actively participates in academic communities as a session chair and committee member for conferences like OOPSLA , PLDI , and ISMM . Research Trends : His recent publications (2023-2025) emphasize dynamic region ownership , wait-free reference counting , and allocator optimization for concurrent systems. These works span systems programming , formal verification , and domain-specific language tools , reflecting his interdisciplinary approach. Conference Contributions : Parkinson has served as session chair for tracks including MPLR Session 2 (2023) and PLDI: Memory Models & Program Logics (2023). He has also contributed to program committees for POPL , IWACO , and The Future of Weak Memory (2024).

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Jacques Caretteمشاهده پروفایل

دانشیار

McMaster UniversityCanada

Jacques Carette is an Associate Professor at McMaster University , affiliated with the Department of Computing and Software . His research bridges formal and applied domains, including Mechanized Mathematics , Correct-by-Construction Software , and Game Design , with a focus on User Experience and Visual Scaling in games. He actively explores Reversible Computing , Quantum Programming , and DSLs for software generation. Research Interests: Mechanized Mathematics : Formalizing mathematical theories for software correctness. Game Design : Visual scale impact on user experience and cognitive components in gaming. Reversible & Quantum Computing : Reversible program transformations and quantum algorithm design. Software Engineering : Metaprogramming, code generation, and productivity analysis. Trends in Articles : His work spans Quantum Computing , Game Design , Formal Methods , and Software Sustainability . Recent publications emphasize DSLs , reversible models , and emotional computational models in games. Contact Information: Email: c@rtte@mcmaster.ca

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Clément Pit-Claudelمشاهده پروفایل

استادیار

Swiss Federal Institute of Technology in LausanneSwitzerland

Clément Pit-Claudel is a Tenure-Track Assistant Professor at École Polytechnique Fédérale de Lausanne (EPFL), leading the SYSTEMF Laboratory in the School of Computer and Communication Sciences. His work bridges programming languages, formal verification, and systems engineering to build high-assurance software and hardware components. PhD in Computer Science from MIT (2016), thesis on proof-producing compilers MSc in Computer Science from MIT BSc in Computer Science from École Polytechnique (France) Research Interests focus on correct-by-construction program synthesis , domain-specific compilers , and formal verification . He develops tools like Alectryon for interactive Coq proof visualization and Rupicola for verified compiler construction. His work spans hardware description languages (e.g., Kôika ), software verification (e.g., Fiat ), and regex engine formalization (e.g., Elk and Warblre ). Recent publications include foundational work on JavaScript regex mechanisms (ICFP 2024), relational compilation (PLDI 2022), and hardware simulation (ASPLOS 2021). His lab SYSTEMF emphasizes full assurance without compromise through machine-checked proofs and hardware-software co-design. Awards include the MIT Frederick C. Hennie III Teaching Award (2016) and MIT William A. Martin Thesis Award (2016). He has served as program committee member for conferences like PLDI, POPL, and Coq Workshop. Teaching includes Software Construction (400+ students at EPFL) and Interactive Theorem Proving graduate course. He has advised doctoral students in formal methods and compiler design.

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Damien Andersonمشاهده پروفایل

استاد آموزشی

University of StrathclydeUnited Kingdom

Dr. Damien Anderson is a Teaching Professor in the Computer and Information Sciences Department at the University of Strathclyde. He serves as Programme Leader for the MSc in Software Development and has been affiliated with the university since 2011, completing both his undergraduate (BSc in Software Engineering, 2015) and doctoral studies (PhD in General Video Game Playing, 2020). His research focuses on AI applications in video games, gamification, and ensemble decision systems. He actively supervises PhD students in areas like General Video Game Playing and AI in Interactive Education. Anderson’s work emphasizes leveraging game mechanics to enhance education through gamification, aiming to make learning more engaging and resilient. He has contributed to projects such as AI in Early Learning and AI on the Assembly Line , funded internally at the university. His research has been published in venues like GECCO and IEEE Congress on Evolutionary Computation, with topics ranging from evolutionary algorithms to deceptive game strategies. He received the Robertson Medal (2016) and serves on peer-review panels for conferences like IEEE Conference on Games. His activities include education outreach programs like the Accelerate Schools Computer Science Challenge.

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Christopher Brownمشاهده پروفایل

مدرس

University of St AndrewsUnited Kingdom

Christopher Brown is a Lecturer in the School of Computer Science at the University of St Andrews, where he conducts research in programming languages, refactoring, parallel programming, and energy-efficient computing. He is a member of the Programming Languages research group and leads the EPSRC-funded Energise project, focused on energy-aware refactorings for embedded systems. Previously, he led the EU Horizon 2020 TeamPlay project, addressing time, energy, and security in parallel software. Research Interests: Programming Languages: semantics, compilers, static analysis Refactoring: sequential to parallel transformation, correctness verification Parallel and Concurrent Programming: tools, skeletons, GPUs, multi-core systems Energy Optimization: compiler and refactoring techniques for low-energy software Verification: dependent types, formal soundness, non-functional properties The recent publications reflect a strong focus on dependently-typed parallel languages, algorithmic skeletons, and specification-guided refactoring, with applications in energy efficiency and safety. His work bridges theoretical foundations with practical tool development, including open-source systems like Skel and ParaFormance. Scientific Awards: No scientific awards explicitly mentioned in the provided text. Advising and Grants: Supervision: Accepting postgraduate research students; no named students listed. Grants: Principal Investigator on two major projects: Energise (EPSRC): Refactorings and skeletons for energy-aware embedded systems (2021–2024) TeamPlay (EU Horizon 2020): Time, energy, and security analysis for heterogeneous platforms (2018–2021) Labs and Teams: Member of the Programming Languages group at the University of St Andrews. Actively involved in developing open-source tools such as Skel (Erlang parallel library) and ParaFormance (C/C++ refactoring suite), and contributes to collaborative research in parallelism and verification.

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Shizhi Tangمشاهده پروفایل

پژوهشگر

Max Planck Institute for Software SystemsGermany

Shizhi Tang is a researcher affiliated with Tsinghua University in China. His work focuses on domain-specific languages (DSLs) for high-performance computing, particularly in tensor programming optimization and numerical methods for partial differential equations. He has contributed to top-tier conferences including SPLASH, CGO, PLDI, and PPoPP. Research Highlights: Developed Mat2Stencil , a modular DSL for efficient PDE solvers on structured grids. Created IntelliGen , a tensor program auto-tuning framework with memory optimization. Conference Involvement: 2023 SPLASH (OOPSLA track) 2025 CGO (Main conference track) 2022 PLDI and PPoPP (Main conference track)

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Elizabeth Polgreenمشاهده پروفایل

استادیار

University of EdinburghUnited Kingdom

Elizabeth Polgreen is a Lecturer (~Assistant Professor) in the School of Informatics at the University of Edinburgh, where she conducts research in programming languages and formal methods, particularly program synthesis and verification. She is affiliated with the Laboratory for Foundations of Computer Science and contributes to the Programming Languages and Software Engineering research area. Her research focuses on formal program synthesis, leveraging techniques such as symbolic reasoning, genetic algorithms, and large language models to improve the scalability and automation of software verification. She explores applications in code modernization, tensor program lifting, and explainable AI. Her work bridges programming languages, AI, and formal methods to build trustworthy systems. Her recent publications (2020–2025) span top venues including PLDI, CGO, AAAI, CAV, OOPSLA, and NeurIPS, showing a strong trend in neuro-symbolic synthesis, grammar inference, and verification of AI and low-resource code. She has secured competitive funding, including a Royal Academy of Engineering fellowship and an Amazon Research Award. Scientific awards include: Royal Academy of Engineering Research Fellowship Amazon Research Award Best Paper Award at GPCE Distinguished Paper Award from CGO She advises multiple PhD students, including Yixuan Li, Alexander Brauckmann, and José Wesley de Souza Magalhães, often in collaboration with Professor Mike O'Boyle. Her research is supported by grants from the Royal Academy of Engineering and Amazon. She has delivered keynotes, tutorials, and a TEDx talk, contributing actively to outreach and education in formal methods. She leads a research group within the Laboratory for Foundations of Computer Science, focusing on trustworthy systems through synthesis and verification. Her team includes PhD students and collaborators working on cutting-edge topics in program analysis, AI for code, and compiler design.

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Jonathan Protzenkoمشاهده پروفایل

پژوهشگر

InriaFrance

Jonathan Protzenko is a Principal Researcher at Microsoft Azure Research , focusing on advancing the theory and practice of software verification through formal methods and type systems. His work bridges critical security gaps in modern programming languages, with verified code integrated into major software like BoringSSL, Linux kernel, Python, and Firefox. PhD in Computer Science from INRIA Paris (2014) Formerly at Microsoft Research (9 years) and INRIA's Gallium team (2010–2014) His research spans verified cryptography (EverCrypt, HACL*), Rust verification (Aeneas, Eurydice), and computational law (Catala). Recent projects include: Rust verification (2022–present): Aeneas and Eurydice toolchains for bidirectional Rust-C compilation Verified protocol stacks (2019–present): Noise*, MLS*, and Signal* implementations Formal computational law (2020–2022): Catala for formalizing legal texts Open-source impact (2009–present): Maintaining Thunderbird add-ons His 15 most recent publications (2025–2021) highlight trends in Rust verification , secure messaging protocols , and formal methods for legal frameworks . Scientific accolades include the Internet Defense Prize and SIGPLAN Research Highlight . Notable advisees include Théophile Wallez and Denis Merigoux , who won the Gilles Kahn PhD Award . His work involves collaborations with startups like Cryspen and formal verification toolchains like KreMLin .

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Nada Aminمشاهده پروفایل

استادیار

InriaFrance

Nada Amin is an Assistant Professor of Computer Science at Harvard University's John A. Paulson School of Engineering and Applied Sciences (SEAS). She previously served as a University Lecturer in Programming Languages at the University of Cambridge and was a key contributor to the Scala programming language at EPFL during her PhD. Her research at the Metareflection Lab focuses on neurosymbolic systems that integrate programming languages (PL) and artificial intelligence (AI) to enable correct-by-construction software in domains like program synthesis and precision medicine. Her work emphasizes three pillars: Safer systems via formal verification and type theory; Faster development through generative programming and multi-stage interpreters; Easier access by bridging neural (learnable) and symbolic (interpretable) representations. She has contributed to the POPL, ICFP, PLDI, and OOPSLA conferences, with recent work on Dafny proof assistants and Persimmon's polymorphism techniques. Scientific awards include: Fellow of Jesus College (Cambridge, 2017-2019); Michigan Cambridge Research Initiative Grant (2018); Teaching Assistant Team Award (EPFL, 2015); 6.170 Letter of Commendation (MIT, 2005); ArsDigita Prize Finalist (1999). She has advised committees at ICFP, PLDI, POPL, and SPLASH , and taught courses like Neurosymbolic Programming (2025) and Advanced Semantics of Programming Languages (2022). Her lab explores language design through projects like Persimmon , LURK , and DafnyBench .

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