جست‌وجوی استاد و پژوهشگر

Sara Achourمشاهده پروفایل

استادیار

• Compilers
• Programming Languages
• Program Analysis
• +۵ مورد دیگر

Sara Achour is an Assistant Professor jointly appointed in both the Computer Science and Electrical Engineering Departments at Stanford University's School of Engineering. Her work bridges computer science and electrical engineering, focusing on enabling end-users to develop computations for emerging computing platforms with analog behaviors. Dr. Achour received her PhD in Computer Science from the Massachusetts Institute of Technology in 2021. Her academic journey led her to Stanford where she currently teaches courses including Introduction to Essential Software Systems and Tools (CS 104), Software Engineering (CS 295), and Software Techniques for Emerging Hardware Platforms (CS 349H/EE 349). Her research program centers on developing new programming languages, compilers, and runtime systems that address the challenges of emerging computing platforms. She specializes in creating tools that help developers harness the potential of analog and non-traditional hardware systems. Her work spans quantum computing, analog computing paradigms, hyperdimensional computing, and memory systems, with a particular emphasis on compiler techniques and hardware-aware optimization. Analysis of her recent publications reveals a strong focus on bridging the gap between software and emerging hardware platforms. Her work spans quantum computing (qubit/qutrit circuits), analog computing paradigms, hyperdimensional computing, and novel memory systems. A recurring theme is developing compiler techniques that optimize for specific hardware characteristics while maintaining programmer productivity. Dr. Achour actively mentors students across multiple levels of their academic careers. She serves as a Doctoral Dissertation Advisor, Co-Advisor, Reader, and Master's Program Advisor for numerous students working on cutting-edge research in compilers and emerging hardware. Her teaching portfolio includes both foundational courses like Introduction to Essential Software Systems and specialized advanced courses focused on emerging hardware platforms. She appears to be building a research group focused on programming languages and compilers for non-traditional computing architectures, with students working across quantum computing, analog systems, and memory technologies.

Pierre-Evariste Dagandمشاهده پروفایل

پژوهشگر

• Formal Verification
• Programming Languages
• Theorem Proving
• +۵ مورد دیگر

Pierre-Evariste Dagand is a CNRS Researcher at IRIF, Université de Paris, where he is a member of the Proofs, Programs, and Systems group. His research focuses on enhancing the safety of systems software through the application of domain-specific languages and formal verification techniques, particularly leveraging interactive theorem provers. His research interests span several areas of computer science, including: Formal Methods : Using rigorous mathematical techniques to verify software and hardware systems Programming Languages : Designing and implementing languages with a focus on safety and correctness Compilers : Developing verified compilers for domain-specific languages and synchronous languages Type Theory : Exploring dependent types and their applications in verification Operating Systems : Contributing to the design of systems for multicore architectures Cryptography : Creating high-throughput cryptographic implementations via formal methods Dagand's publications demonstrate a consistent focus on formal verification and theorem proving applied to practical systems. His work frequently involves the Coq proof assistant and addresses challenges in compilers, cryptography, and systems programming. Recent trends include the development of tools for secure cryptographic implementations (e.g., Usuba and Tornado) and formal models for intermittent computing. He has advised numerous students in internships, working on projects related to formal methods, compilers, and cryptography. These internships have contributed to various research outputs and tools. Dagand is part of the Proofs, Programs, and Systems group at IRIF, which focuses on foundational aspects of computer science and their application to software verification and language design.

David Greggمشاهده پروفایل

استاد

• Software performance optimization
• Embedded systems
• Accelerating deep neural networks on embedded systems
• +۱۲ مورد دیگر

David Gregg is a Professor in the Department of Computer Science at Trinity College Dublin's School of Computer Science and Statistics, where he serves as Global Director for Computer Science (since 2020) and previously as Head of the Discipline of Software and Systems (2018-2022). His research focuses on software performance optimization and embedded systems, with particular expertise in accelerating deep neural networks on resource-constrained platforms. He teaches Systems Programming (CS2014/5) and Concurrent Systems I (CS3014). Gregg's research spans multiple areas of computer systems including: Compiler optimization and program analysis Processor microarchitecture and parallelism (multi-core, vector, instruction-level) Computer arithmetic and domain-specific languages Low-energy embedded systems and FPGA implementations Deep neural network acceleration He has served on numerous program committees including PACT, PLDI, CC, and other major computer systems conferences, and has been on the Board of Distinguished Reviewers for ACM TACO multiple times. Professor Gregg has advised numerous PhD and MSc students, many of whom have gone on to prominent positions at companies like Intel-Movidius, Google, Amazon, and Synopsys. He leads the triNNity project which includes optimized libraries and compilers for implementing convolutional neural networks on CPUs.

Tobias Grosserمشاهده پروفایل

دانشیار

• Compilers
• Programming Language Design
• Performance Programming
• +۸ مورد دیگر

Tobias Grosser is an Associate Professor in the Department of Computer Science and Technology at the University of Cambridge. His research focuses on compiler technology, programming language design, and performance programming, with applications spanning hardware design, climate science, and quantum computing. He leads a research group developing innovative compiler frameworks and tools that bridge theoretical foundations with practical applications. Dr. Grosser completed his undergraduate studies in Computer Science at the University of Passau in Germany and pursued his PhD at École Normale Supérieure Paris as a Google PhD Fellow. Prior to joining Cambridge, he served as a Reader at the University of Edinburgh and held an Ambizione Fellowship at ETH Zurich. His research program centers on rethinking performance programming by re-connecting developers and compilers. He aims to make compilation more modular, predictable, automatic, and trustworthy while bringing open-source compiler innovation to increasingly diverse targets from GPUs to FPGAs and custom hardware. His work spans multiple domains including polyhedral compilation, constraint solving, quantum computing, and hardware design automation. Dr. Grosser is particularly interested in breaking down barriers between compilers and programmers by enabling their interaction through the programming language environment. His recent publications demonstrate a strong focus on compiler infrastructure development, particularly around the MLIR framework. He has pioneered work on Presburger arithmetic optimization with the FPL library, developed new intermediate representations for hardware description and quantum computing, and created tools for compiler education and prototyping like xDSL. His research shows a consistent theme of creating practical, high-performance compiler technologies that address real-world challenges across multiple domains. HiPEAC Technology Transfer Award 2021 for "Fast linear programming through transprecision computing on small and sparse data" OOPSLA 2021 Distinguished Paper Award for "FPL: Fast Presburger arithmetic through transprecision" Dr. Grosser actively mentors PhD students and postdoctoral researchers, currently supervising a team of over a dozen researchers working on various aspects of compiler technology. His group collaborates with industry partners including ARM and Xilinx, and maintains strong ties with the LLVM and MLIR open-source communities. He has secured funding for multiple research projects including work on verified compilation with Lean-MLIR, quantum compiler development, and hardware design automation. His research group operates at the intersection of multiple projects including Open-Source Electronic Design Automation, Seamless design of Smart Edge Processors, Lean-MLIR for verified compilation, FPL for fast Presburger arithmetic, and compilation frameworks for quantum computers. They maintain strong community engagement through regular Compiler Social events in Cambridge and active participation in LLVM developer meetings.

Aarti Guptaمشاهده پروفایل

استاد

• Formal verification
• Model checking
• Program analysis
• +۶ مورد دیگر

Aarti Gupta is a Professor in the Department of Computer Science at Princeton University, where she conducts research in formal verification, program analysis, and automated decision procedures. She has made significant contributions to the field of system analysis and verification, with her work being applied in industrial settings. Before joining Princeton, she led research in systems analysis and verification at NEC Labs America. Dr. Gupta received her PhD in Computer Science from Carnegie Mellon University. Her academic journey has positioned her as a leading figure in formal methods and verification techniques. Her research focuses on two main areas: theoretical foundations and practical applications. Foundations include formal methods, model checking, program analysis, automated synthesis, and SAT/SMT solvers. Applications span verification of software, hardware, networks, and distributed systems. Gupta's work bridges the gap between theoretical advances and real-world implementation, with particular emphasis on developing techniques that scale to handle complex industrial systems. Gupta's recent publications demonstrate a clear trajectory toward more modular, scalable, and practical verification techniques. She has increasingly focused on network verification, with several papers addressing the challenges of verifying distributed network control planes. Her work also shows growing interest in hardware verification, particularly for Systems-on-Chip (SoCs), and the intersection of formal methods with neural networks. A notable trend is the development of techniques that combine symbolic reasoning with learning approaches to improve verification scalability. Dr. Gupta has received numerous prestigious awards for her contributions: ACM Fellow (2017) - for contributions to system analysis and verification techniques and their transfer to industrial practice PLDI 2023 Distinguished Paper Award for "Synthesizing MILP Constraints for Efficient and Robust Optimization" IEEE ICNP 2022 Best Paper Award DATE 2021 Best Paper Award (Track D) ACM TODAES 2020 Best Paper Award IEEE Micro Top Pick 2018 Honorable Mention Dr. Gupta has advised numerous graduate students and postdoctoral researchers, including current PhD candidates Divya Raghunathan, Akash Gaonkar, Deyuan He, and Dexin Zhang. Among her former students are Timothy Alberdingk Thijm and Lauren Pick. She has also mentored postdoctoral fellow Grigory Fedyukovich and visiting student Yueling Zhang. Her research has been supported by significant grants that have enabled her to lead projects such as SyLVer (Synthesis, Learning, and Verification) and network verification initiatives in collaboration with researchers like Dave Walker and Ryan Beckett. Dr. Gupta leads the SyLVer research group at Princeton, which focuses on developing techniques that improve the scalability of algorithmic verification by combining deductive learning with learning on data and examples. Her group collaborates extensively on network verification projects, particularly through the Minesweeper initiative with Dave Walker and Ryan Beckett, and on Instruction Level Abstraction (ILA) for System-on-Chip verification in collaboration with Sharad Malik's group.

Luc Marangetمشاهده پروفایل

پژوهشگر

• Weak Memory Models
• Concurrency
• Formal Verification
• +۵ مورد دیگر

Luc Maranget is a Researcher at Inria Paris working within the Cambium project. His primary research focuses on weak memory models, concurrency, formal verification, and computer architecture. He has made significant contributions to understanding memory models in modern architectures including ARM, x86, and RISC-V. Maranget's research interests center around the formal modeling of memory consistency in modern processor architectures. He has developed theoretical frameworks for understanding relaxed memory models across multiple architectures and has created practical tools like the diy testing suite for validating memory models. His work bridges theoretical computer science with practical hardware implementation concerns, particularly in the domain of concurrent programming. His publication record shows a consistent focus on memory models and concurrency since 2016, with increasing emphasis on formal verification of hardware specifications. His research spans both theoretical aspects (operational semantics for memory models) and practical applications (testing suites, compiler implementations). Maranget teaches courses on programming shared memory multicore machines at MPRI (Master Parisien de Recherche en Informatique) and has previously taught at École polytechnique. He is also the author of H E V E A, a fast LaTeX to HTML translator, demonstrating his interest in programming language implementation. As the main developer of the diy testing suite, Maranget has created an important tool for the memory models research community. His work provides practical validation methods for theoretical models of processor behavior, making significant contributions to the verification of concurrent systems.

Grigore Roşuمشاهده پروفایل

استاد

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

Grigore Roşu is a Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign's Grainger College of Engineering. He leads the Formal Systems Laboratory, focusing on foundational research in programming languages and formal methods. His work bridges theoretical computer science with practical verification tools for real-world systems. Roşu's research spans programming language semantics, formal verification, and language design, with particular emphasis on matching logic and the K framework. His approach enables language-agnostic verification techniques applicable across diverse programming languages and systems, from high-level web technologies like JavaScript to low-level architectures like x86-64. His publications demonstrate a consistent focus on creating executable formal semantics that can be directly used for verification and analysis. Analysis of his recent publications reveals a clear progression toward unifying verification frameworks that work across language boundaries. His work has increasingly focused on blockchain systems (Ethereum, general blockchain finality) while maintaining strong foundations in formal semantics for traditional programming languages. The recurring theme across his research is creating practical verification tools grounded in rigorous mathematical foundations. Throughout his career, Roşu has been actively involved in the programming languages research community, serving on program committees for major conferences including PLDI, POPL, SPLASH, and ICFP. His contributions span both theoretical foundations and practical implementations of verification tools. His laboratory work connects formal methods with real-world applications, particularly in the domains of smart contract verification and processor architecture semantics. The Formal Systems Laboratory under his direction has produced significant tools and frameworks that have influenced both academic research and industrial practice in program verification.

• Computer Architecture
• Memory Systems
• Hardware/OS Interaction
• +۷ مورد دیگر

Vasileios Karakostas is an Assistant Professor at the Department of Informatics and Telecommunications, National and Kapodistrian University of Athens. He is a member of the Computer Architecture Lab and focuses on computer architecture, memory systems, and resource management. Previously, he was a postdoctoral researcher at the National Technical University of Athens' Computing Systems Lab. He holds a PhD in Computer Architecture from Universitat Politècnica de Catalunya and Barcelona Supercomputing Center. Education: Ph.D. in Computer Architecture, Universitat Politècnica de Catalunya (2016) M.Sc. in Computer Architecture, Networks, and Systems, UPC (2012) B.Eng. in Electrical and Computer Engineering, NTUA (2009) Research Interests: Memory systems (virtual memory, NVM) Hardware/OS interaction Resource management in data centers Parallel systems and serverless computing RISC-V architectures and cloud infrastructure Projects: Active in Horizon Europe projects Vitamin-V, Neuropuls, and REBECCA. Previously contributed to DAPHNE and ACTiCLOUD (EU H2020). Awards: 2024: Distinguished Artifact Award (ASPLOS) 2011: Best Paper Award (ICPE) Selected for IEEE Micro's Top Picks (2015, 2016) Teaching: Courses include Logic Design, Parallel Systems, and Large-scale Computing Systems at undergraduate and graduate levels. Labs/Teams: Leads research in the Computer Architecture Lab, collaborating on resilient architectures and cloud computing innovations.

• Computer Architecture
• Reconfigurable Computing
• Application Acceleration
• +۵ مورد دیگر

Professor Dionisios Pnevmatikatos holds the position of Professor at the School of Electrical and Computer Engineering, National Technical University of Athens (NTUA), where he leads research in Computer Architecture and Reconfigurable Computing. He previously served as a Professor at the Technical University of Crete (TUC) from 2000 to 2019, directing the Microprocessor and Hardware Laboratory (MHL) and chairing the department. His academic journey includes a B.Sc. from the University of Crete (1989), M.Sc. and Ph.D. from the University of Wisconsin-Madison (1991 and 1995). Research Interests: Focuses on Computer Architecture, Reconfigurable Computing, Application Acceleration, Custom Architectures, and Hardware Acceleration of Bioinformatics Algorithms. His work spans FPGA-based systems, parallel computing, and energy-efficient designs. Key Projects: Coordinator of FASTER (EU FP7), Principal Investigator in DeSyRe, AXIOM, dRedBox, and EDRAH2020 projects. Active in EU initiatives like H2020 OPTIMA and Vitamin-V for RISC-V ecosystems. Leadership roles in conferences include SAMOS 2018 and FPL 2011 program chairs. Teaching: Courses include Computer Architecture, Digital Systems Design, and Parallel Processing Systems at NTUA. Former roles include teaching at University of Crete and TUC. Labs: Affiliated with Computing Systems Laboratory (CSLab) at NTUA and FORTH-ICS since 1997. Involved in prototyping manycore architectures and network processors.

Kontogiannis Spyridonمشاهده پروفایل

دانشیار

• Algorithmic Game Theory
• Direct Response Algorithms and Approximation Algorithms
• Foundations of Parallel and Distributed Computing Systems
• +۱ مورد دیگر

Kontogiannis Spyridon serves as an Associate Professor in the Division of Applications and Foundations of Computer Science at the University of Patras, where he contributes to both teaching and research activities within the Department of Computer Engineering and Informatics. His research expertise spans theoretical computer science with particular emphasis on: Algorithmic Game Theory - analyzing strategic interactions through computational models Direct Response Algorithms and Approximation Algorithms - developing efficient computational methods Foundations of Parallel and Distributed Computing Systems - researching architectures for multi-processor and networked environments Computational Complexity - investigating the inherent difficulty of computational problems As part of the Division of Applications and Foundations of Computer Science, his work contributes to examining fundamental mathematical properties of computing systems, exploring computational boundaries, and investigating algorithm design principles across various computational models. His research aligns with the division's specialized laboratories including the Combinatorial Algorithms Laboratory and Distributed Systems and Telematics Laboratory. Professional activities include participation in academic conferences such as DOCSS and ICALP mentioned in connection with the division's research portfolio. His work supports the department's mission of bridging theoretical computer science with practical applications in evolving technological domains.

• Operating Systems
• Parallel Computing
• Scheduling in Parallel Systems
• +۳ مورد دیگر

Ioannis Venetis is an Assistant Professor at the University of Piraeus, School of Information and Communication Technologies, Department of Informatics, specializing in Operating Systems and Parallel Computing. He earned his PhD from the Department of Computer Engineering and Informatics at the University of Patras. His research spans programming models for parallel systems, scheduling optimization, and applications in computational neuroscience and seismology. Research Interests Operating Systems Parallel Computing Scheduling Algorithms High-Performance Computing Computational Neuroscience Seismology Projects Participation in European and national research programs Development of Gisola (GPU-accelerated seismic inversion tool) His teaching portfolio includes courses like Operating Systems, Parallel Processing, and Symbolic Programming. Articles highlight expertise in GPU acceleration, tridiagonal solvers, sensor networks, and many-core architectures. Notable contributions include work on Chimera states in neuronal dynamics and hierarchical workload scheduling frameworks.

Qirun Zhangمشاهده پروفایل

دانشیار

• Program Analysis
• Programming Languages
• Software Engineering
• +۴ 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. His research focuses on program analysis, compiler optimization, and formal language theory, with numerous publications in top-tier programming language and software engineering conferences including PLDI, POPL, OOPSLA, and FSE. He teaches courses on compilers, program analysis, and software testing. Dr. Zhang's research interests center on improving software reliability and security through advanced program analysis techniques. He approaches problems from perspectives including computational complexity, analytic combinatorics, graph theory, and formal languages. His work often bridges theoretical foundations with practical applications in compiler design and program verification. His recent publications show a strong focus on context-free language reachability, Dyck-language based analyses, and SMT solving techniques. His research demonstrates consistent innovation in making program analysis more precise while maintaining scalability, with applications ranging from debug information validation to software debloating and type inference. PLDI Distinguished Paper Award (2020) SIGSOFT Distinguished Paper Award (2023) OOPSLA Distinguished Artifact Award (2022) Dr. Zhang actively mentors PhD and MS students, with current advisees including Camille Bossut and Benjamin Mikek. His service to the academic community includes Artifact Evaluation Co-Chair roles for PLDI 2025 and 2026, and program committee membership for numerous top conferences including PLDI, POPL, and OOPSLA. He leads research projects including SLOT, Context-Free Language Reachability with Transitive Redundancy Elimination, and Debug Information Validation.

Syed Asad Alamمشاهده پروفایل

پژوهشگر ارشد

• Machine Learning
• Embedded Systems
• Quantization
• +۵ مورد دیگر

Syed Asad Alam is a Post-Doctoral Research Fellow at the School of Computer Science and Statistics, Trinity College Dublin, The University of Dublin, Ireland, specializing in optimization and efficient implementation of digital systems for signal processing and communication algorithms. Education: Bachelors in Computer and Information Systems Engineering, N.E.D. University of Engineering and Technology, Karachi, Pakistan M.Sc. in Electrical Engineering (System-on-Chip Design), Linköping University, Sweden PhD in Electrical Engineering (Computer Engineering/Electronics Systems), Linköping University, Sweden His research integrates Machine Learning with Embedded Systems through novel approaches to Quantization and Logarithmic Number Systems , targeting efficient Neural Networks implementation on FPGAs and ASICs . This work builds on extensive industry experience with multimedia processors, communication devices, and SOC bus design, including specialized projects on filters, wireless systems, frequency multipliers, and DSP processors. His 2020 LCTES publication on non-base-2 logarithmic systems exemplifies his focus on numerical representation efficiency for resource-constrained embedded applications, reflecting broader trends in hardware-aware machine learning optimization. No scientific awards or grant information was documented in the source material. He has collaborated with multiple industry teams on FPGA-based design and Integrated Circuit Development across startup environments, though specific advising roles or student supervision were not indicated.

Adam Chlipalaمشاهده پروفایل

استاد

• Programming Languages
• Formal Methods
• Computer Systems
• +۵ مورد دیگر

Adam Chlipala is a Professor at the Massachusetts Institute of Technology working at the intersection of programming languages, formal methods, and computer systems. His research focuses on building practical verified systems with end-to-end machine-checked proofs, particularly using the Coq proof assistant. His educational background includes a Computer Science undergraduate degree from Carnegie Mellon University (2003) and a PhD in Computer Science from the University of California, Berkeley (2007). Following a postdoctoral position at Harvard University through 2011, he joined MIT as faculty. Chlipala's research spans multiple domains with strong emphasis on dependent types , verified compilation , and hardware-software co-verification . His work consistently bridges theoretical foundations with practical implementation, as evidenced by his development of the Ur/Web programming language and his focus on creating clean-slate hardware-software stacks with formal guarantees. Key research thrusts include cryptographic constant-time verification, side-channel security, and verified tensor compilation. His recent publications (2020-2025) reveal a clear trajectory toward increasingly complex verified systems, with growing emphasis on hardware-software integration, cryptographic implementations, and performance-critical applications. The work consistently leverages Coq for machine-checked proofs while addressing real-world constraints like timing channels and hardware interfaces. Chlipala is the author of the influential textbook Certified Programming with Dependent Types , which serves as a primary educational resource for Coq at numerous institutions worldwide. His professional activities include significant service to the PL community through program committees for major conferences including PLDI, POPL, ICFP, and CPP. He leads research initiatives connecting hardware and software verification, most notably through the DeepSpec project which aims to build fully verified computing stacks. His current work focuses on practical applications of dependent types for business applications through Ur/Web and verified cryptographic implementations.

Stefano Markidisمشاهده پروفایل

استاد

• High-Performance Computing
• Heterogeneous Systems
• Quantum Computing
• +۳ مورد دیگر

Stefano Markidis is a Professor of Computer Science specializing in high-performance computing systems at KTH Royal Institute of Technology in Sweden. He works in the Division of Computational Science and Technology, focusing on supercomputers, quantum computers, and computational methods for scientific simulations. His research spans multiple domains including plasma physics, computational fluid dynamics, and quantum computing. Markidis holds an MS degree from Politecnico di Torino and a PhD in Nuclear Engineering from the University of Illinois at Urbana-Champaign. Prior to joining KTH, he was a graduate research assistant at Los Alamos National Laboratory and Lawrence Berkeley National Laboratory, followed by a postdoc at KU Leuven. His academic journey reflects a strong foundation in both engineering and computational science. His primary research interests include High-Performance Computing , Heterogeneous Systems , and Quantum Computing . Markidis develops computational methods for plasma physics, particle-in-cell simulations, and fluid dynamics. His work bridges theoretical physics and practical computing, with applications in space physics, fusion energy, and materials science. He is particularly known for contributions to parallel computing, GPU acceleration, and the development of scalable simulation frameworks like Neko for computational fluid dynamics. His research increasingly integrates machine learning techniques with traditional numerical methods. Analysis of Markidis' recent publications reveals a strong focus on quantum-classical hybrid computing, advanced particle-in-cell methods, and high-fidelity computational fluid dynamics. His work demonstrates expertise in programming models for heterogeneous architectures including GPUs and quantum processors, with growing emphasis on AI-enhanced scientific computing. R&D100 award (2005) for the CartaBlanca project R&D100 award (2017) for the SHIELDS project Markidis teaches multiple courses at KTH including Applied GPU Programming, Quantum Computing for Computer Scientists, and High-performance Computing for Computational Scientists. He has supervised numerous degree projects across various specializations in computer science and electrical engineering. His research has been supported by various grants related to high-performance computing and quantum technologies, with applications spanning from space physics to medical treatments. Markidis leads research in computational science with a focus on developing frameworks like Neko for extreme-scale computational fluid dynamics. His team works on integrating traditional HPC methods with emerging technologies including quantum computing and AI, contributing to advancements in scientific simulation across multiple disciplines.