جست‌وجوی استاد و پژوهشگر

• Compilers
• Parallel Computing
• Automatic Differentiation
• +۳ مورد دیگر

Billy Moses is an Assistant Professor in the Department of Computer Science at the University of Illinois Urbana-Champaign (UIUC), with affiliate roles in Electrical and Computer Engineering (courtesy) and the Coordinated Science Library. He holds a PhD and dual S.B. degrees in Electrical Engineering and Computer Science from MIT (2023, 2017), as well as an S.B. in Physics from MIT (2017). His research focuses on compilers, parallel computing, and compiler-driven optimization techniques for high-performance systems. Moses has pioneered work on the Tapir framework for fork-join parallelism, the MLIR compiler infrastructure, and Enzyme for automatic differentiation. Moses' research spans compiler design, GPU acceleration, and AI-driven compiler optimization. Notable contributions include the Polygeist compiler for C-to-MLIR transformation, the Autophase reinforcement learning system for HLS phase ordering, and the Enzyme framework for GPU kernel differentiation. His work emphasizes practical compiler solutions for parallelism, performance portability, and end-to-end code generation in domains like deep learning and scientific computing. Awards: 2024 SIGHPC Doctoral Dissertation Award Courses Taught: CS 598 APE (Advanced Performance Engineering) His recent projects include compiler-based approaches to GPU-to-CPU transpilation, performance portability in heterogeneous systems, and AI-driven compiler decision-making. Moses collaborates with industry and academic partners on advancing compiler technologies for exascale computing and machine learning acceleration.

• Compilers
• Program Verification
• Formal Methods
• +۶ مورد دیگر

Nuno P. Lopes is an Associate Professor at Instituto Superior Técnico (University of Lisbon) and affiliated with INESC-ID . His research focuses on compiler optimization, program verification, formal methods, and hardware-software co-design for AI workloads. He advises students at both PhD and MSc levels in areas such as compilers, machine learning frameworks, and blockchain security. Education: PhD in Computer Science from Instituto Superior Técnico (2014), Master's from the same institution, and Bachelor's in Computer Science. Research Interests: Compilers, static analysis, LLVM optimization validation, undefined behavior exploitation, tensor contraction processors, and secure smart contract verification. He leads projects like Alive2 for LLVM optimization validation and Torchy , a tracing JIT compiler for PyTorch. Awards: Distinguished Paper at PLDI 2015, Best Paper at SPIN 2013. His student Juneyoung Lee won the EuroLLVM'19 SRC award and best PhD thesis in Seoul National University's Engineering College. Grants: Supported by Google, Matter Labs, Oracle, PRACE, and Woven by Toyota. His lab focuses on sustainable AI computing and compiler-driven optimizations. Current Projects: Alive2, TCP (Tensor Contraction Processor), and FuriosaAI collaborations. Advises students on topics like blockchain mining efficiency, Rust-C++ translation, and secure smart contract execution.

• Programming Languages
• Static Analysis
• Software Engineering
• +۷ مورد دیگر

Qirun Zhang is the Catherine M. and James E. Allchin Early Career Associate Professor in the School of Computer Science at Georgia Institute of Technology. He earned his Ph.D. in Computer Science and Engineering from The Chinese University of Hong Kong (2013) and bachelor's degree in Computer Science from Zhejiang University (2009). Zhang teaches graduate courses in compilers, software analysis, and program analysis, including CS 6340 Software Analysis and Test (Fall 2024) and CS 4240 Compilers and Interpreters (Spring 2025). His research focuses on improving software reliability and security through program analysis and compiler optimization techniques. Key research areas include computational complexity, analytic combinatorics, graph theory, and formal languages. Current projects include SLOT (SMT-LLVM Optimizing Translation) , Mutual Refinements of Context-Free Language Reachability , and Context-Free Language Reachability with Transitive Redundancy Elimination , among others. Zhang's research has produced award-winning work including the SIGSOFT Distinguished Paper Award (FSE 2023) and PLDI Distinguished Paper Award (2020). He has advised multiple students including Ph.D. graduates Shuo Ding (2024) and Yuanbo Li (now at Facebook), with Benjamin Mikek and Camille Bossut currently pursuing their Ph.D.s. As an academic leader, Zhang has served as: Artifacts Chair: PLDI'26, PLDI'25 Program Committee: PLDI'26, POPL'26, SAS'25, ASPLOS'25, SAS'24, FSE'24 External Reviewer: PLDI'19, PLDI'18 His group maintains active GitHub repositories for tools like Perses (syntax-guided program reduction) and Skeletal Program Enumeration (compiler testing framework). Zhang also contributes to educational resources by maintaining course materials on static analysis, symbolic execution, and webassembly analysis.

David Zwickerمشاهده پروفایل

استادیار

• Biological Physics
• Soft Matter Physics
• Phase Separation
• +۵ مورد دیگر

David Zwicker serves as a Max Planck Research Group Leader at the Max Planck Institute for Dynamics and Self-Organization in Göttingen, Germany, where he heads the Theory of Biological Fluids group. His research investigates how soft and fluid-like biological materials achieve precise spatial and temporal organization through physical principles. Dr. Zwicker's work centers on biomolecular condensates, pattern formation, and smart materials design. His group employs statistical physics, dynamical systems theory, and fluid dynamics to model biological processes including phase separation in cells, active matter systems, and nasal airflow mechanics. Current projects explore how phase transitions might enable cellular information processing and how chemical activity drives droplet self-propulsion. Recent publications (2024-2025) reveal strong focus on phase separation dynamics in biological contexts, with emerging themes in active condensates, multicomponent mixtures, and computational modeling tools. His group develops open-source software like flory for phase diagram analysis and py-pde for partial differential equations. Scientific recognition includes: ERC Starting Grant for biomolecular condensates research Dr. Zwicker leads an active research team currently comprising postdoctoral researchers Guido Kusters and Filipe Thewes (joined October 2024), with ongoing recruitment for ERC-funded projects. His group maintains strong computational focus while collaborating across physics and biology disciplines. The Theory of Biological Fluids group operates within the Max Planck Institute's Department of Fluid Dynamics, utilizing advanced modeling to bridge theoretical physics and cellular biology.

• Programming Languages
• Formal Methods
• Software Engineering
• +۴ مورد دیگر

Elsa Gunter is a Research Professor and Senior Lecturer at the Siebel School of Computing and Data Science, part of the Grainger College of Engineering at the University of Illinois Urbana-Champaign. She holds a Ph.D. in Mathematics from the University of Wisconsin (1987). Her research focuses on programming languages, formal methods, software engineering, and interdisciplinary computing education. Key research areas include the semantics of programming languages, compiler optimization, formal verification frameworks, and integrating computing into interdisciplinary education. She teaches courses like CS 421 (Programming Languages & Compilers), CS 431 (Embedded Systems), and CS 477 (Formal Software Development Methods). Her work emphasizes practical applications of formal methods in software development and pedagogical innovations in computing education. Recent projects explore challenges in interdisciplinary computing curricula and the formal semantics of programming languages. Elsa’s academic contributions span over four decades, with notable work on theorem proving tools (e.g., HOL90), compiler correctness frameworks, and educational technologies for programming courses.

Abid M. Malikمشاهده پروفایل

دانشیار

• High-performance computing
• Parallel programming models
• Compiler optimizations
• +۴ مورد دیگر

Abid M. Malik is an Associate Professor in the Department of Computer Science at Stony Brook University, joining in September 2023. His research focuses on high-performance computing, parallel programming models, and compiler optimizations targeting LLVM and MLIR frameworks. Prior roles include positions at Brookhaven National Laboratory, the University of Houston, Rice University, INRIA (France), and IBM's Center for Advanced Studies in Toronto. Education: PhD from the University of Waterloo, Canada. Research interests span compiler technology, parallel programming languages, and constraint programming. He has over 15 years of academic and industry experience, combining theoretical and applied work in computing systems. Teaching responsibilities include courses such as CSE 220, CSE 307, CSE/ISE 337, CSE 416, and CSE 304. No specific scientific awards or grants are listed in the provided text.

• Computer Architecture
• Embedded Systems
• RISC-V
• +۵ مورد دیگر

Prof. Daniel Müller-Gritschneder is a Full Professor of Computer Architecture at TU Wien's Institute of Computer Engineering (Faculty of Informatics) since 2024. Previously, he held roles as research group leader at TU Munich's Chair of Electronic Design Automation and acting professor for Real-Time Systems at TU Munich. He earned his Dipl.-Ing. (2003), Dr.-Ing. (2009), and Habilitation (2019) from TU Munich. His research focuses on Electronic System Level Design, RISC-V domain-specific architectures, embedded ML compiler toolchains, functional safety, and hardware security. He collaborates extensively with industry partners like Infineon, Bosch, BMW, and Mercedes. Active in the RISC-V community, he co-initiated the RISC-V Summit Europe and serves on steering committees for conferences such as DAC, ICCAD, and DATE. Key Research Themes: RISC-V ISA extensions and toolchain development Edge AI/ML on embedded systems Secure and safety-critical processor design High-level synthesis and performance simulation Awards: Best Student Paper Award (2024), IEEE Winter Conference on Computer Vision Habilitationspreis (2019), TU Munich Alumni Association Senior IEEE Member (2019) Teaching includes courses like 'High-Level Synthesis', 'Computer Organization', and 'Embedded Systems Design'. Current projects involve lightweight software for IoT devices and pre-silicon validation of safety-critical RISC-V systems. His work bridges academic research with industrial application through collaborative projects and open-source tool development.

Daniel Lohmannمشاهده پروفایل

استاد

• Operating system construction
• Embedded systems
• System software for dependable real-time systems
• +۶ مورد دیگر

Daniel Lohmann is a Full Professor and Fachgebietsleiter (Head of Department) at the Department of Operating Systems and Middleware within the College of Engineering and Computer Science at Leibniz Universität Hannover . His research focuses on operating system construction , embedded systems , and dependable real-time systems , with a strong emphasis on generative approaches , software product lines , and hardware-RTOS co-design .

Stephane Vialleمشاهده پروفایل

پژوهشگر

• High-Performance Computing
• GPU Computing
• Quantum Computing
• +۳ مورد دیگر

Stephane Vialle is a researcher at CentraleSupélec, leading the Interdisciplinary Laboratory of Digital Sciences. His research focuses on High-Performance Computing (HPC), GPU Computing, Quantum Computing, and Quantum Machine Learning. He has extensive experience in developing scalable fine-grained computing environments and optimizing parallel algorithms for distributed systems. His work spans financial engineering, energy management, and railway infrastructure through digital twin technology. Recent contributions include advancements in GPU cluster energy efficiency, stochastic control algorithms, and hybrid classical-quantum architectures for data clustering. Research interests emphasize optimizing parallel computing frameworks for diverse applications, including financial modeling, material science simulations, and transportation systems. His publications demonstrate expertise in distributed computing, fault-tolerant architectures, and algorithmic innovation across multiple computational paradigms. Current projects explore quantum computing integration with classical systems, GPU-based large-scale data processing, and real-world applications of parallel simulation techniques. Notable contributions include the parXXL development environment for coarse-grained platforms and the MINERVE digital twin for railway infrastructure management. His work bridges theoretical computing advancements with practical implementations in engineering and finance domains.

Alastair Reidمشاهده پروفایل

پژوهشگر

• formal verification
• security
• functional languages
• +۱ مورد دیگر

Alastair Reid is a researcher at Intel (2021–present) focusing on formal instruction set architecture (ISA) specifications and model checking processor pipelines. His career spans roles at Google Research (2019–2021) developing Rust verification tools, Arm Ltd (2004–2019) working on formal ISA specifications and vectorizing compilers, University of Utah (1998–2004) on component-based operating systems, Yale University (1994–1998) contributing to Haskell functional reactive programming, and University of Glasgow (1988–1994) in formal specification and verification. Ph.D. in Defining interfaces between hardware and software: Quality and performance , Glasgow University M.Sc. in A precise semantics for Ultraloose Specifications , Glasgow University B.Sc., University of Strathclyde His research interests include formal verification, security analysis, functional programming languages (particularly Haskell and Rust), and computer architecture. He has contributed to the development of machine-readable ISA specifications for ARM, RISC-V, and Intel architectures, and worked on symbolic execution tools like KLEE for Rust verification. Key publications include work on RISC-V specification improvements (2024), formal methods for persistent programming (2023), PLARCH workshop contributions on ISA specification (2023), and foundational papers on ARM processor verification with ISA-Formal (2016). His technical blog posts cover topics in symbolic execution, SMT solvers, and practical verification challenges. Contributions to conferences include committee roles in PLDI (2024), PriSC (2024), and workshop organization in formal methods and programming language design.

Brian Railingمشاهده پروفایل

استاد آموزشی

• Computer Science Education
• Computer Architecture
• Systems Programming
• +۳ مورد دیگر

Brian Railing is an Associate Teaching Professor in the Computer Science Department at Carnegie Mellon University, part of the School of Computer Science. He specializes in educational technologies and systems programming, with a focus on improving teaching methods and student engagement in computer science courses. His recent courses include 15213, 15346, and 15513 across multiple semesters. His research spans electronic examination analysis, computer architecture education tools like CADSS, and optimizing parallel computing systems through Contech and LLVM instrumentation. His publications highlight advancements in student-centered learning, such as enhancing office hours and analyzing exam-taking behaviors. He also contributes to compiler optimization and memory systems evaluation. Despite extensive teaching and research, no awards or grants are explicitly mentioned. Brian’s work emphasizes bridging theory and practice in systems education, with ongoing efforts to develop scalable tools for parallel program analysis and student-driven learning environments.

Simone Campanoniمشاهده پروفایل

دانشیار

• Compilers
• Computer Architecture
• Runtime Systems
• +۵ مورد دیگر

Simone Campanoni is an Associate Professor in the Department of Computer Science at Northwestern University, with a courtesy appointment in Electrical and Computer Engineering. His research focuses on compilers, computer architecture, runtime systems, and parallel computing. He leads the research lab, emphasizing co-design approaches to address hardware and application trends. Education: Ph.D., M.S., and B.S. in Information Technologies and Computer Engineering from Politecnico di Milano. Research Interests: Compilers, memory-centric optimizations, parallelization of sequential code, hardware-software co-design, and resilient architectures. His work includes the NOELLE compiler framework, LLVM extensions, and energy-efficient parallelization techniques. Recent Work: Contributions include energy-efficient bitwidth speculation, GPU scheduling (GhOST), and compiler-based memory protection (CAMP). His projects often involve collaborations with industry (e.g., Google) and academic partners. Awards: Best Teacher Award (2018), HiPEAC awards (2014, 2012), and multiple top-tier conference recognitions. His research has been funded by NSF, DOE, and industry grants totaling over $10 million. Grants & Labs: Leads NSF-funded projects on parallelism, compiler-architecture co-design, and neural network training. His lab develops tools like NOELLE and GINO, open-sourced to advance compiler research. Team & Students: Advises PhD and Master’s students in parallel computing, memory systems, and compiler design. Notable advisees include Riley Boksenbaum, Tommy McMichen, and Enrico Deiana.

• Electronic Design Automation (EDA)
• RISC-V Architecture
• TinyML (Tiny Machine Learning)
• +۳ مورد دیگر

Philipp van Kempen is a researcher at the Chair of Design Automation (Prof. Schlichtmann) at the Technical University of Munich. His work focuses on electronic design automation, specifically targeting RISC-V architecture, TinyML optimization, and LLVM compiler infrastructure for custom hardware accelerators. Key Research Areas: RISC-V ISA extensions, autovectorization, neural network accelerators, and compiler toolchain automation. Collaborations: Works with Daniel Mueller-Gritschneder, Ulf Schlichtmann, and Jefferson Parker Jones. His recent publications highlight contributions to benchmarking TinyML CNN kernels on RISC-V vector hardware, developing semi-automated LLVM support for ISA extensions, and optimizing TinyML inference through frameworks like MLonMCU and muRISCV-NN.

• High-Performance Computing
• Parallel Programming
• Task Parallelism
• +۶ مورد دیگر

Stephen L. Olivier is a prominent researcher in high-performance computing at Sandia National Laboratories, with a distinguished publication record spanning nearly two decades. His work focuses on parallel programming models, performance optimization, and energy-efficient computing across diverse architectures including CPUs, GPUs, and FPGAs. Olivier has made significant contributions to OpenMP standards and Kokkos programming model development, collaborating extensively with Department of Energy national laboratories and international research teams. Olivier's research interests center on task parallelism, memory management in distributed systems, and performance portability across heterogeneous architectures. His work addresses critical challenges in exascale computing, including efficient task scheduling for unbalanced workloads, power management in large-scale systems, and optimization of communication patterns. More recently, he has expanded his research into medical imaging applications, applying high-performance computing techniques to tuberculosis detection in rural healthcare settings. Analysis of Olivier's recent publications (2021-2024) reveals a strong focus on practical performance engineering for next-generation computing platforms. His work spans traditional HPC domains while increasingly incorporating data science applications and medical imaging analysis. The research demonstrates consistent innovation in parallel programming models, particularly around OpenMP tasking and Kokkos abstractions, with growing emphasis on energy efficiency and hardware-specific optimizations for emerging architectures. Olivier has maintained a prolific research output with numerous publications in top-tier conferences including SC, IPDPS, and IWOMP. His collaborative work extends across multiple Department of Energy laboratories and international institutions, reflecting the interdisciplinary nature of modern high-performance computing research. While specific grant information isn't detailed in the publication record, his work on DOE systems suggests significant involvement in national supercomputing initiatives.

Björn Fiedlerمشاهده پروفایل

مدرس

• Real-Time Systems
• Operating System Engineering
• Static Analysis
• +۴ مورد دیگر

Björn Fiedler is a Lecturer at Leibniz Universität Hannover's Department of Computer Science, specializing in Real-Time Systems and Operating System Engineering. He leads the SRA Group and focuses on static analysis, compiler optimization, and embedded systems specialization. His work emphasizes improving non-functional properties of system software through automated hardware abstraction and compiler-driven specialization. Education: PhD in Computer Science (Leibniz Universität Hannover, 2023). Research Interests: Real-Time Operating Systems (RTOS), multi-core specialization, system call optimization, static analysis, and compiler frameworks like ARA. His projects include AHA (Automated Hardware Abstraction) and MultiSSE, targeting performance and predictability in embedded and real-time systems. Publications: Focus on static analysis techniques, RTOS optimization, and compiler-driven system specialization. Notable work includes ARA's whole-system compiler framework and the MultiSSE syscall elision method. Awards: Best Paper Award at OSPERT 2018 Advising: Supervised 8+ theses on topics like static system object instantiation, sparse data structures, and FreeRTOS kernel specialization. Active in grant projects funded by DFG (e.g., AHA: LO 1719/4-1). Labs/Teams: Core member of the SRA Group, collaborating on projects involving LLVM-based compilation, embedded RTOS development, and real-time system analysis.