جست‌وجوی استاد و پژوهشگر

Lindsey Kuperمشاهده پروفایل

استادیار

• programming languages
• distributed computing
• concurrency
• +۵ مورد دیگر

Lindsey Kuper is an Assistant Professor in the Computer Science and Engineering Department at the Baskin School of Engineering, University of California, Santa Cruz. Her research bridges programming languages, distributed systems, and software verification with a focus on developing programming-language-based approaches to building concurrent and distributed software systems that are elegant, correct, and efficient. Her research interests center around programming languages, distributed computing, concurrency, parallelism, and software verification. Recent projects include library-level choreographic programming (in Haskell, Rust, and TypeScript), dependently-typed diagrams for inductive reasoning about concurrent executions, and expressing and verifying causal message delivery with refinement types. Her group's research has been supported by an NSF CAREER Award, a Stellar Development Foundation Academic Research Grant, a Google Faculty Research Award, and a gift from Amazon Web Services. She leads the CASL (Concurrency and Safety Lab) group, part of the larger Languages, Systems, and Data (LSD) Lab at UC Santa Cruz. NSF CAREER Award Stellar Development Foundation Academic Research Grant Google Faculty Research Award ICFP 2023 Distinguished Paper Award Professor Kuper advises multiple PhD students including Jonathan Castello, Tim Goodwin, Nathan Liittschwager, Patrick Redmond, Gan Shen, and Yan Tong. She previously supervised MS student Shun Kashiwa (who completed his thesis on ChoRus in 2024) and BS student Ali Ali (who graduated in 2024). She teaches courses including Foundations of Programming Languages, Distributed Systems, and Programming Abstractions at UC Santa Cruz. The CASL research group (which stands for Concurrency and Safety Lab, Creative, Ambitious Systems and Languages, or other creative expansions) focuses on building reliable distributed systems through programming language techniques. The group's work emphasizes safety, correctness, and efficiency in concurrent and distributed programming contexts.

David Monniauxمشاهده پروفایل

پژوهشگر

• program verification
• abstract interpretation
• static analysis
• +۸ مورد دیگر

David Monniaux is a senior researcher (directeur de recherche) at CNRS and an adjunct professor at École polytechnique. He works at VERIMAG, a computer science laboratory jointly operated by CNRS and the University of Grenoble. Dr. Monniaux obtained his PhD in 2001 from Université Paris Dauphine under Professor Patrick Cousot, with a dissertation on the static analysis of probabilistic programs by abstract interpretation. He later earned his habilitation in computer science in 2009 from Université Joseph Fourier, Grenoble, and also holds an agrégation in mathematics. Monniaux's research focuses on program verification, with particular emphasis on proving software correctness. His work spans theoretical foundations in computability theory and practical applications in safety-critical systems. He has made significant contributions to abstract interpretation, static analysis, and the verification of numerical properties in programs. His research bridges computer science theory with practical engineering challenges, particularly in the context of critical embedded systems where software failures can have severe consequences. His work connects to diverse fields including game theory, algebra, and convex optimization. His recent publications demonstrate a strong focus on improving the precision and efficiency of static analysis techniques. Key themes include polyhedral approximation, program analysis with local policy iteration, abstraction of arrays and maps, synthesis of ranking functions, and computing worst-case execution times. These works collectively advance the field of program verification by addressing challenges in handling nonlinear constraints, branching, and complex data structures while maintaining computational feasibility. Monniaux has supervised several students including Julien Henry, Alexis Fouilhé, George (Egor) Karpenkov, and Alexandre Maréchal (now at LIP6), with current students Hang Yu and Valentin Touzeau. He has led significant research projects including VERASCO (2012-2015), which aimed at integrating a static analyzer into the CompCert certified compiler, and STATOR (2012-2017), an ERC starting investigator grant exploring advanced techniques for automatic inference of program invariants. At VERIMAG, Monniaux is part of a vibrant research community focused on critical systems. His work connects with broader efforts in formal methods, with applications in aviation, automotive systems, and other safety-critical domains where software reliability is paramount.

Francesco Ranzatoمشاهده پروفایل

استاد

• Abstract Interpretation
• Static Analysis
• Program Verification
• +۳ مورد دیگر

Francesco Ranzato is a Professor at the University of Padova , affiliated with the Department of Mathematics. His research spans abstract interpretation, static analysis, program verification, and formal methods in machine learning and concurrency theory. University: University of Padova Department: Department of Mathematics Research Interests: Abstract Interpretation, Static Analysis, Program Verification, Machine Learning Robustness Research Trends Francesco's recent work bridges abstract interpretation with machine learning verification, focusing on robustness certification for classifiers like decision trees, k-NN, and SVMs. He also advances algorithms for simulation and bisimulation in concurrent systems. Scientific Awards Distinguished POPL Paper Award (2019) Distinguished LICS Paper Award (2021) Facebook/WhatsApp Privacy-Aware Program Analysis Award (2022) Amazon Research Award (2023) Francesco has mentored research groups through open-source projects like SAVer , Meta Silvae , and ERC , and contributed to ACM, IEEE, and Springer publications.

Annie Liuمشاهده پروفایل

استاد

• Programming Languages
• Algorithms
• Systematic Design and Optimization
• +۶ مورد دیگر

Annie Liu serves as Professor of Computer Science at Stony Brook University, holding a PhD from Cornell University alongside prior degrees from Peking and Tsinghua Universities. Her research centers on systematic methods for language and algorithm design, with emphasis on optimization frameworks applicable across critical computing domains. Her educational credentials include: BS in Computer Science from Peking University MEng in Computer Science from Tsinghua University PhD in Computer Science from Cornell University Liu’s research program investigates systematic analysis and transformation techniques for design optimization, particularly through incrementalization—the discrete counterpart of calculus differentiation. Her work spans optimizing compilers , interactive environments , database systems , distributed computing , and security , with projects addressing modeling, verification, code generation, and testing for real-time systems, semantic Web, and big data applications. The Design and Analysis Research Laboratory she directs bridges theoretical foundations with practical implementations. Analysis of her 2012-2025 publications reveals an evolution from foundational distributed algorithm design toward unifying frameworks for solver semantics and assurance. Key trends include increasing integration of incremental computation principles across compiler optimizations, database queries, and distributed system reliability. Her scientific recognition includes: State University of New York Chancellor’s Award for Excellence in Scholarship and Creative Activities Professor Liu has delivered over 100 international talks and maintains active conference leadership, serving on program committees for POPL, PEPM, and SPLASH through 2026. While specific grant details aren’t provided, her sustained involvement in high-impact venues like PADL and LPOP demonstrates significant research influence. She directs the Design and Analysis Research Laboratory where interdisciplinary teams advance optimization methodologies for emerging computational challenges.

• Programming Languages
• Functional Programming
• Type Systems
• +۴ مورد دیگر

Jeremy Yallop is a researcher at the University of Cambridge specializing in programming languages, type systems, and compiler design. He actively contributes to academic conferences such as PLDI, POPL, ICFP, and GPCE, focusing on staged computation, generic programming, and language design. Key Research Areas: Functional Programming, Generative Programming, Type Systems, Partial Evaluation Conference Roles: PC Co-Chair (PEPM 2017), Steering Committee Chair (PEPM 2019), Session Chair (OCaml 2023, PEPM 2022), Tutorial Presenter Notable Contributions: Verified Scheme compiler via CakeML, Macros in OCaml (MacoCaml), Dependent Types in Defunctionalization, Algebraic Simplification (Frex) Recent Trends: 2026 work on TEAL (Verified Assembly Language), 2025 developments in Dependently Typed Algebraic Simplification Awards: No explicit honors mentioned in provided data

Erika Ábrahámمشاهده پروفایل

استاد

• Computer Science
• Satisfiability Checking
• SMT Solving
• +۷ مورد دیگر

Erika Ábrahám is a Full Professor at RWTH Aachen University , Germany, leading the Theory of Hybrid Systems research group. Her academic journey includes positions as a Junior Professor (2008-2013) and postdoctoral researcher at institutions like Jülich Research Centre and Albert-Ludwigs-University Freiburg. Her research interests span formal methods, SMT solving, hybrid systems verification, and probabilistic systems. She has contributed to symbolic computation, railway timetables, and probabilistic hyperproperties, with recent work focusing on cylindrical algebraic decomposition, conflict-driven search algorithms, and stochastic modeling. Scientific contributions include 15+ articles (2021-2025) on topics like probabilistic hyperproperties , hybrid automata , and symbolic arithmetic , often published in LNCS, Springer, and Elsevier venues. She has collaborated on tools like HyPro and SMT-RAT, and edited proceedings for conferences including NASA Formal Methods and QEST.

Manu Sridharanمشاهده پروفایل

استاد

• Programming Languages
• Software Engineering
• Program Analysis
• +۴ مورد دیگر

Manu Sridharan is a Professor of Computer Science and Engineering at the University of California, Riverside, where he leads research in programming languages and software engineering. He is a member of the RIPLE research group and actively recruits PhD students for cutting-edge research in software reliability, security, and performance. Prior to his academic career, Sridharan worked at prominent industry research labs including IBM Research, Samsung Research America, and Uber, where he developed practical tools like NullAway to eliminate Java null pointer errors. His research focuses on developing tools and techniques to make large-scale software more reliable, performant, secure, and maintainable. Sridharan's work spans static analysis, program verification, type systems, memory safety, and resource management. He has made significant contributions to JavaScript analysis, call graph construction, nullability checking, and resource leak detection. His research bridges theoretical foundations with practical applications, resulting in tools that have been deployed in industry settings. Sridharan's publication record shows a consistent trajectory of high-impact research in top venues like PLDI, OOPSLA, ECOOP, and FSE. His recent work (2021-2025) demonstrates continued innovation in static analysis techniques, particularly in nullability inference, taint analysis, resource management, and call graph construction. His research shows a clear progression from foundational program analysis techniques to increasingly practical and deployable tools that address real-world software engineering challenges. Outstanding Artifact Award for Effective Race Detection for Event-Driven Programs (OOPSLA 2013) Best Paper Award for Predicting your own effort (AAMAS 2012) Best Paper Award for Debugging overconstrained declarative models using unsatisfiable cores (ASE 2003) Sridharan has mentored numerous students through their academic journeys, including current PhD candidates and Master's students working on cutting-edge research problems. His former PhD student Narges Shadab graduated in 2023. He has served in leadership roles for major conferences, including as Program Committee Chair for ECOOP 2021 and Area Chair for ECOOP 2022. His research has been supported by various grants that enable his team to pursue ambitious projects in program analysis and software engineering. As a member of the RIPLE research group at UC Riverside, Sridharan collaborates with other faculty and students on interdisciplinary projects that span programming languages, systems, and security. His team's work often involves building practical tools that can be deployed in real-world settings, as evidenced by his industry experience and tools like NullAway that were developed during his time at Uber.

Clark Barrettمشاهده پروفایل

استاد

• Formal Methods
• Automated Reasoning
• Verification
• +۷ مورد دیگر

Clark Barrett is a Professor in the Department of Computer Science at Stanford University, where he conducts research in formal methods, automated reasoning, and verification. He is affiliated with several research centers including the Stanford Center for Automated Reasoning, Stanford Center for AI Safety, Stanford Agile Hardware Project, and Stanford Center for Blockchain Research. His research focuses on developing formal methods and tools for verifying complex systems, with particular emphasis on satisfiability modulo theories (SMT), verification of neural networks, hardware design verification, and security. His work bridges theoretical foundations with practical applications across multiple domains. Over the past decade, Barrett's research has evolved from foundational work in SMT solving to increasingly diverse applications including neural network verification, hardware verification, and AI safety. His recent publications demonstrate a strong focus on practical verification techniques for real-world systems, particularly in the areas of hardware design, neural networks, and programming languages. The trend shows an expansion from core verification techniques to broader applications in AI safety and secure systems design. 2021 CAV (Computer Aided Verification) Award Barrett leads several major research initiatives and collaborates extensively with industry partners. His work on the Marabou neural network verification framework, SMT-LIB standard, and Symbolic QED verification methodology have had significant impact in both academic and industrial settings. He has supervised numerous PhD students and postdoctoral researchers who have gone on to successful careers in academia and industry. He is a key member of the Stanford Center for Automated Reasoning, which develops foundational technologies for automated reasoning, and the Stanford Center for AI Safety, where he focuses on formal methods for ensuring the safety and reliability of AI systems. His work on the Stanford Agile Hardware Project aims to revolutionize hardware design through formal methods and verification techniques.

Rupak Majumdarمشاهده پروفایل

پژوهشگر

• Formal Verification
• Computer-Aided Verification
• Reactive Systems
• +۱۰ مورد دیگر

Rupak Majumdar is a Scientific Director at the Max Planck Institute for Software Systems (MPI-SWS), with a distinguished career in formal verification, control systems, and programming languages. His research spans reactive, real-time, hybrid, and probabilistic systems, focusing on verification and synthesis problems in distributed and concurrent environments. Majumdar earned his B.Tech. in Computer Science from IIT Kanpur (where he received the President’s Gold Medal) and his Ph.D. from UC Berkeley (awarded the Leon O. Chua Award). His work has been recognized through prestigious honors including an NSF CAREER Award, Sloan Fellowship, ERC Synergy Grant, and Most Influential Paper Awards from PLDI and POPL. Research Areas: Formal Verification, Hybrid Systems, Stochastic Systems, Programming Languages, Automata Theory Leadership: Scientific Director, MPI-SWS; ISEC 2026 PC Chair His publications address critical problems in software verification, with recent work focusing on probabilistic systems, distributed protocol testing, and reinforcement learning for formal methods. Awards and grants reflect his impact on computer science and software engineering. Current students include Mahmoud Salamati, Ashwani Anand, V.R. Sathiyanarayana, and Mohammad Khoshechin, while graduated advisees hold positions at institutions like Amazon, Google, and academic research centers. Scientific Awards: President’s Gold Medal (IIT Kanpur) Leon O. Chua Award (UC Berkeley) NSF CAREER Award Sloan Foundation Fellowship ERC Synergy Award Distinguished Alumnus Award (IIT Kanpur) Most Influential Paper Awards (PLDI, POPL) Best Paper Awards (SIGBED, EAPLS, SIGDA)

Mohsen Lesaniمشاهده پروفایل

دانشیار

• Reliability and Security of Software Systems
• Concurrent and Distributed Systems
• Secure Replicated Systems
• +۴ مورد دیگر

Mohsen Lesani is an Associate Professor at the Computer Science and Engineering Department of University of California, Santa Cruz . He obtained his PhD from UCLA , MS in Artificial Intelligence from Sharif University of Technology , and BS in Software Engineering from University of Tehran . His research focuses on reliability and security of software systems , particularly concurrent and distributed systems , with recent work on secure replicated systems and distributed machine learning . NSF CAREER Award (2020) DARPA Young Faculty Award (2022) SIGPLAN Research Highlight (2019) Distinguished Paper Award at OOPSLA 2018 Best Paper Award at ISSRE 2015 His recent publications tackle challenges in automated synthesis of distributed protocols , heterogeneous replication , secure blockchain transactions , and verified RDMA-based data types . He advises PhD students Xiao Li , Eric Chan , Javad Saber-Latibari , and Tejas Mane in the Safe and Secure Software (S3) lab . He has taught courses on Distributed Systems , Parallel Programming , and Compiler Design .

Loris D'Antoniمشاهده پروفایل

دانشیار

• Programming languages
• Verification
• Synthesis
• +۱ مورد دیگر

Loris D'Antoni is an Associate Professor in the Department of Computer Science and Engineering at the University of California at San Diego (UCSD). He also holds a visiting academic position at AWS. His research focuses on helping people write trustworthy software through advances in programming languages, program verification, and synthesis techniques. Education : Bachelor's and Master's degrees in Computer Science from the University of Torino (2008-2010); PhD in Computer Science from the University of Pennsylvania (2015). His research interests span programming languages, formal verification, program synthesis, automata theory, and trustworthy machine learning systems. Recent work includes developing frameworks for semantics-guided synthesis (SemGuS), formal verification of fairness in machine learning, and methods for constraining large language models of code. Recent publications address topics like access control policy analysis, grammar-constrained decoding for language models, and automated specification synthesis. These works intersect computer science, formal methods, and machine learning. Awarded multiple prestigious accolades including the NSF CAREER Award, Microsoft Research Faculty Fellowship, and Google Faculty Award, he has also received the Morris and Dorothy Rubinoff Dissertation Award and was a Phillip R. Certain-Gary D. Sandefur Distinguished Faculty Award recipient. D'Antoni advises PhD students including Keith Johnson, Shaan Nagy, Jinwoo Kim, and Kanghee Park. Former advisees like Yuhao Zhang, Qinheping Hu, and Kausik Subramanian have moved on to prominent roles at companies such as Amazon, Google, and Facebook. He leads the Programming Systems Group at UCSD and collaborates with AWS on specification-aligned LLMs. His work also involves tools like AutomataTutor for education and projects at the intersection of program synthesis and machine learning robustness.

Amir Kalevمشاهده پروفایل

استاد مدعو

• Quantum Computing
• Quantum Information
• Quantum Simulations
• +۹ مورد دیگر

Amir Kalev is a Lead Quantum Scientist at the Information Sciences Institute (ISI) of the University of Southern California (USC) and an Adjunct Research Professor in the Department of Physics and Astronomy at USC. He holds a Ph.D. in Physics from Technion - Israel Institute of Technology and a B.S. in Physics from Tel-Aviv University. His research explores multidisciplinary topics in quantum information science, including: Quantum simulations and optimization Quantum machine learning algorithms Quantum system characterization and verification Quantum signal processing and compressed sensing Foundations of quantum measurement theory His recent publications (15 most recent) demonstrate strong focus on quantum algorithms for practical applications, including Hamiltonian simulation, quantum machine learning, error mitigation, and quantum information processing. Theoretical and experimental works frequently intersect with statistical mechanics and computational physics. Awards & Fellowships: Hartree Postdoctoral Fellowship (University of Maryland) Research Leadership: Leads quantum research team at ISI-USC with 2 research staff and 4+ graduate students Current advisees: Devika Mehra, Naman Jain; Alumni: Arnav Sharma, Chitra Vadlamani, Charu Jain, Aakash Ravindra Active Grants: NSF PHY QIS: Solving Optimization Problems on NISQ Computers ($300K, 2020-2023) NSF CCF FET: Quantum information leakage detection ($200K, 2021-2023) NSF-BSF: Fast Quantum Optimal Control ($400K, 2022-2025)

Martina Maggioمشاهده پروفایل

استاد

• Control Theory
• Cyber-Physical Systems
• Real-Time Systems
• +۴ مورد دیگر

Martina Maggio is a Professor at the Department of Computer Science , Saarland University (since March 2020) and holds a 20% position at the Department of Automatic Control , Lund University (since 2023). Her research bridges control theory with software engineering to address predictability in computing systems. PhD : Politecnico di Milano (completed with work on control-theoretical tools for computing systems) Visiting Graduate Student : MIT CSAIL (collaborated on Self-Aware Computing project) Her research focuses on: Control theory for resource allocation in cloud infrastructures and embedded systems Formal verification of event-triggered systems and deadline misses Robustness of cyber-physical systems under computational faults Automated methodologies for runtime adaptation in software Timing requirements in big data applications Security vulnerabilities in control systems Key article trends include: control-theoretic approaches to computing systems, stochastic modeling of deadline misses, formal verification of timing constraints, and security-aware control algorithms . Scientific recognition: Co-authored paper selected as Most Influential Paper at SEAMS 2025 Best Paper Award at ECRTS 2021 ACM SIGSOFT Distinguished Paper Award (FSE 2020) Outstanding Reviewer (ICPE 2020) Mentorship includes supervising PhD candidates (e.g., Clara Rubeck, Robert Pietsch) and serving as an academic advisor for industry collaborations (e.g., with Bosch Corporate Research). She also contributes to open-source tool development (WeaklyHard.jl) and has held leadership roles like Program Co-Chair for ICCPS 2020.

• Cryptography
• Computer Security
• Electronic Voting Systems
• +۲ مورد دیگر

Daniel Rausch is a Researcher at the University of Stuttgart , affiliated with the Institute of Information Security . His work focuses on cryptographic protocols, particularly in electronic voting systems, universal composability, and accountable security frameworks. Key research areas include Cryptography , Computer Security , and Formal Verification . Recent publications explore password-hardened encryption, zero-knowledge proofs, and tally-hiding mechanisms for e-voting. Scientific contributions span: Provably secure password-hardened encryption with round-optimal schemes Accountable bulletin boards with UC security guarantees Zero-knowledge proofs for ballot validity in ElGamal-encrypted elections Publicly tally-hiding protocols preserving vote privacy UC-IITM model unification for composability frameworks Collaborations include researchers like Ralf Küsters , Nicolas Huber , and Marvin Bechtold . Notable implementations include Fabric*BB for distributed ledger applications and Kryvos for verifiable elections.

Sven-Hendrik Voßمشاهده پروفایل

استاد

• Digital Signal Processing
• High-Speed Communication Systems
• FPGA Technology
• +۳ مورد دیگر

Prof. Dr. Sven-Hendrik Voß is a Professor at the Berlin University of Applied Sciences (BHT Berlin) since 2011. His academic role includes mentoring first-year students, representing practical phase programs, and leading the Digital Laboratory (Department VI). He has extensive experience in high-speed hardware architectures, FPGA design, and optical communication systems. Doctorate in Electrical Engineering (Microelectronics) from TU Berlin Diploma in Electrical Engineering (Communications) from TU Berlin Research Interests span digital signal/image processing, FPGA-accelerated data processing, high-speed communication systems, embedded vision systems, and methods for image synthesis. He has contributed to light field imaging, real-time processing, and optical interconnects for maskless lithography. Publications focus on FPGA-based solutions for high-speed data processing, optical communication systems, and hardware implementations in fields like 3D media and industrial applications. His work often combines digital circuit design with optical technologies. Teaching includes digital systems design, computer architecture, machine-oriented programming, and image processing. He offers thesis topics involving FPGA development for audio/video applications and communication systems. Professional Background includes leadership roles at Fraunhofer HHI, where he headed the High-Speed Hardware Architectures department (2010-2014) and led hardware groups in earlier roles. He has industry experience in VHDL implementation and PCB design.