جست‌وجوی استاد و پژوهشگر

Sandro Stuckiمشاهده پروفایل

مدرس

• Programming Languages
• Formal Methods
• Type Systems
• +۴ مورد دیگر

Sandro Stucki is a Lecturer in the Department of Computer Science and Engineering at Chalmers University of Technology. He has previously worked as an applied scientist at Amazon and as a postdoctoral researcher at Chalmers University of Technology and the University of Gothenburg. He completed his doctoral studies at the Programming Methods Laboratory (LAMP) at EPFL under the supervision of Professor Martin Odersky. His research focuses on programming languages, with particular interest in formal methods, type systems and theory, and the semantics and implementation of domain-specific languages. He applies formal methods and type theory to problems in privacy and security, investigates type safety of Scala and related type systems, and develops type soundness proofs using Agda. His work also includes designing domain-specific languages for modeling probabilistic and stochastic systems, especially biochemical systems. He has contributed to the Scala ecosystem by developing a GNU/Emacs mode for Kappa, a modeling language for systems biology. His recent publications demonstrate strong expertise across programming language theory, formal verification, privacy-preserving systems, and applications in systems biology. His work bridges theoretical foundations with practical applications, particularly in the Scala programming language ecosystem. Stucki is actively involved in academic service, having served on program committees for numerous conferences including ECAI, GPCE, NWPT, and as a steering committee member for the Scala Symposium series. He has also organized several academic events, including the Scala Symposium 2016. He currently teaches courses on Data Science and AI, Fundamentals of Program Development, and Neuro-symbolic AI at Chalmers/Gothenburg University, and has previously taught courses on Parallel Functional Programming, Principles of Concurrent Programming, and Types and Programming Languages.

• Formal Verification
• Parallel Programming
• Programming Languages
• +۳ مورد دیگر

Frédéric LOULERGUE is a Full Professor at the University of Orléans, affiliated with the LIFO (Laboratoire d'Informatique Fondamentale d'Orléans) laboratory and part of the LMV research team (Languages, Models, and Verification). His work focuses on formal methods for programming languages, parallel computing, and cybersecurity. He leads research in verified parallel frameworks, distributed systems reconfiguration, and TPM software stack validation. Contact him at frederic.loulergue@univ-orleans.fr or visit his homepage at https://www.univ-orleans.fr/lifo/Members/loulergu/ . Research interests include formal verification of parallel algorithms, deductive program analysis with tools like Frama-C and Coq, and the development of formally verified distributed systems. Recent work emphasizes combining deductive verification with shape analysis, runtime monitoring for security properties, and formalizing decentralized reconfiguration languages in Maude. His publications span topics from verified parallel functional programming (SyDPaCC framework) to cybersecurity in Trusted Platform Modules (TPM). He collaborates on frameworks like WhyBSML for scalable parallel computing and Concerto-D for distributed reconfiguration. No scientific awards are listed, but his contributions are highlighted through extensive peer-reviewed publications and conference activities. Labs/Teams: Member of LIFO's LMV team, contributing to the formal methods research group. Involved in cross-disciplinary projects on cloud computing security and high-performance parallel programming.

Christophe Dubachمشاهده پروفایل

دانشیار

• Data-Parallel Language Design
• Hardware Design Space Exploration
• Heterogeneous Computing
• +۴ مورد دیگر

Christophe Dubach is an Associate Professor at McGill University, jointly appointed in the Department of Electrical and Computer Engineering (ECE) and the School of Computer Science (CS). He holds a Canada CIFAR AI Chair at Mila. Previously, he was a Reader (Associate Professor) at the University of Edinburgh until 2019. His research focuses on data-parallel language design, high-level code generation for parallel hardware (GPUs/FPGAs), hardware design space exploration, and applying machine learning to these areas. He has advised numerous PhD and MSc students on topics like FPGA-based high-level synthesis, neural networks, and compiler optimization. Education: PhD in Informatics (2009, University of Edinburgh), MSc in Computer Science (2005, EPFL, Switzerland). Teaching includes courses on compiler design, computer organization, and high-level synthesis. His projects include Lift (a functional IR for GPU code generation) and High-Level Synthesis of Neural Networks on FPGAs. Collaborators include Michel Steuwer, Lu Li, and others in the fields of compilers, machine learning, and parallel computing.

Adam Chlipalaمشاهده پروفایل

استاد

• Programming Languages
• Formal Verification
• Compiler Verification
• +۲ مورد دیگر

Adam Chlipala is the Arthur J. Conner Professor of Computer Science at MIT, with appointments in the Computer Science and Artificial Intelligence Laboratory (CSAIL) and the Department of Electrical Engineering and Computer Science. His research integrates programming languages and formal methods to develop verified computer systems. Education: PhD in Computer Science, University of California, Berkeley MS in Computer Science, University of California, Berkeley BS in Computer Science, Carnegie Mellon University Research Focus: Dr. Chlipala's work spans: Verified compilers and programming tools Cryptographic protocol implementation Hardware description languages Proof automation in theorem provers High-performance parallel computing His current projects focus on rethinking abstractions for parallel systems with end-to-end formal verification. Teaching: He has created and teaches courses on verified software engineering (6.S057), programming fundamentals (6.1010), and formal reasoning about programs (6.5120). His educational materials include textbooks on Certified Programming with Dependent Types and Formal Reasoning About Programs. Awards and Honors: His research has been recognized with the German IT Security Award, Humies Gold Award for evolutionary computation, and multiple teaching prizes including the Jamieson Prize. He is an ACM Distinguished Member and NSF CAREER awardee. Research Teams: Dr. Chlipala leads the Programming Languages & Verification Group at MIT CSAIL. His startup Nectry develops no-code enterprise software based on the Ur/Web language and UPO proof system.

Jason Marsمشاهده پروفایل

استاد

• Computer Architecture
• Warehouse-Scale Computing
• Machine Learning Systems
• +۳ مورد دیگر

Jason Mars is a Professor at the University of Michigan's Department of Electrical Engineering and Computer Science, leading the Clarity-Lab. His research focuses on cross-layer architectures and runtimes for future computing systems, particularly in warehouse-scale computers. He has pioneered projects like Sirius (an open voice/vision personal assistant), Protean Code (dynamic code transformations), and Adrenaline (tail latency management). His work addresses challenges in energy efficiency, resource allocation, and performance optimization in large-scale systems. Key contributions include architectural designs for AI services, thermal management strategies, and compiler techniques for approximate computing. Mars collaborates with industry leaders like IBM and has advised numerous students who have published at top conferences such as ASPLOS and HPCA. Recent research spans AI ethics, scalable programming models (e.g., Data Spatial Programming), and edge-cloud collaboration frameworks like Neurosurgeon. He has delivered invited talks at Baidu, IBM Watson, and conferences worldwide, emphasizing the societal impact of computing systems.

Song Fuمشاهده پروفایل

استاد

• Artificial Intelligence
• Deep Learning
• Aerospace Engineering
• +۵ مورد دیگر

Song Fu is a Professor and Associate Chair for Research in the Department of Computer Science and Engineering at the University of North Texas. His research focuses on interdisciplinary applications of artificial intelligence in aerospace engineering, fault diagnosis systems, radiation belt dynamics, and autonomous vehicle technologies. He leads initiatives in predictive maintenance for mechanical systems, data privacy in vehicular edge computing, and cooperative perception frameworks for connected autonomous vehicles. Dr. Fu’s work bridges machine learning with domain-specific challenges, such as optimizing shield machine designs via deep reinforcement learning and developing fault diagnosis frameworks for aeroengines using transformer networks and large language models. His contributions extend to space physics, analyzing radiation belt electron dynamics and Martian magnetosphere interactions with solar wind. He has pioneered privacy-preserving techniques for vehicle-edge networks and contributed to additive manufacturing through generative modeling of microstructure simulations. Key research themes include: AI-driven predictive maintenance and system reliability Cooperative autonomous systems and edge computing Radiation belt modeling and space weather prediction Privacy-preserving machine learning architectures His publications emphasize real-world applications, such as improving aircraft brake monitoring, enhancing flash storage reliability in automotive systems, and optimizing laser-based additive manufacturing processes. Current projects explore multi-agent collaboration in fleet maintenance strategies and next-generation object detection frameworks for autonomous vehicles.

Prakash Ramananمشاهده پروفایل

استاد

• Algorithms
• Database Systems
• Distributed Computing
• +۳ مورد دیگر

Prakash Ramanan is a Professor at the School of Computing within the College of Engineering at Wichita State University. His research focuses on algorithms, distributed computing, database systems, and XML processing. He holds an office at 208 Jabara Hall on the university's main campus. His academic interests include parallel algorithms, MapReduce frameworks, computational complexity, and query processing in XML databases. His work spans theoretical algorithm analysis to practical implementations in distributed systems. Ramanan's publications emphasize algorithmic efficiency, lower bounds analysis, and optimization techniques. His recent work addresses MapReduce-based matrix multiplication and XPath query evaluation over XML streams. Earlier contributions include foundational studies on tree patterns, join operations, and parallel computing. No scientific awards or grants are explicitly listed in the provided information. He currently advises no listed students or supervisees. His research activities involve collaborations in distributed systems and database query optimization.

Nikos Ntarmosمشاهده پروفایل

مدرس ارشد

• Distributed Systems
• Big Data Management
• NoSQL Databases
• +۳ مورد دیگر

Nikos Ntarmos is a Senior Lecturer at the University of Glasgow's School of Computing Science, with affiliations to the IDEAS research lab. He holds a PhD in Computing Engineering & Informatics from the University of Patras and specializes in distributed systems and large-scale data management. Research focuses on: Distributed data storage and indexing for NoSQL systems Efficient query processing for graph databases Scalable algorithms for big data analytics Recommendation systems and collaborative filtering Publications demonstrate evolving interests from distributed hash tables to contemporary graph processing and recommendation systems. Recent work increasingly addresses practical applications in education technology alongside core database research.

Mark-Matthias Zymlaمشاهده پروفایل

پژوهشگر ارشد

• Computational Semantics
• Syntax/Semantics Interface
• Formal Pragmatics
• +۵ مورد دیگر

Mark-Matthias Zymla is a post-doctoral researcher at the University of Konstanz's Department of Linguistics specializing in computational linguistics. His work bridges theoretical linguistic frameworks with practical computational implementations, focusing on semantic representation and processing. His primary research interests include computational semantics, the syntax/semantics interface, formal and computational pragmatics, with particular expertise in tense and aspect systems, argumentation theory, discourse particles, and rhetorical questions. He develops computational tools like LiGER (Linguistic Graph Expansion and Rewriting) that integrate syntactic analyses from LFG and Universal Dependencies with semantic annotations using Glue Semantics. Zymla has taught numerous courses at the University of Konstanz including Computational Semantics, Lexical Functional Grammar, Large Language Models, and Grammar Development across multiple semesters since 2015. His research is supported by projects like CUEPAQ (Visual Analytics for Argument Quality), VALIDA, and tense/aspect research funded by the Nuance Foundation. He actively participates in the ParGram community, co-organizing meetings in Rochester (2023) and Groningen (2022), and co-founded the 'SuP' reading group on semantics and pragmatics at Konstanz. His publications demonstrate strong interdisciplinary work connecting computational linguistics with visual analytics, argumentation studies, and cross-linguistic semantic annotation.

Rajeev Baruaمشاهده پروفایل

استاد

• Computer Science
• Cybersecurity
• Parallel Computing
• +۵ مورد دیگر

Rajeev Barua is a Professor of Electrical and Computer Engineering at the University of Maryland, College Park, affiliated with the Institute for Systems Research (ISR) and the Computer Science Department. He holds a Ph.D. from MIT in Electrical Engineering & Computer Science (2000) and a B.Tech. from the Indian Institute of Technology Delhi (1992). His research focuses on compilers, cybersecurity, and parallel computing, with notable contributions to binary rewriting, automatic parallelization, and memory management for embedded systems. He has secured $6.95M in research grants and holds five patents. Barua founded SecondWrite Inc., commercializing cybersecurity software based on his research. He has received awards such as the NSF CAREER Award (2002) and the UMD George Corcoran Teaching Excellence Award (2003). Affiliations: Electrical & Computer Engineering Department, ISR, Computer Science Department Education: Ph.D., MIT (2000); S.M., MIT (1994); B.Tech., IIT Delhi (1992) Research Themes: Binary Rewriting, Cybersecurity, Parallel Programming, Embedded Systems, Compiler Optimization Key Projects: SecondWrite Inc., Raw Architecture chip development (IBM), ICE programming model for parallel computing Grants & Patents: $6.95M in grants, $2.4M in startup funding, five issued patents Teaching: Courses include ENEE 446 (Compilers), ENEE 350H (Embedded Systems), and advanced graduate topics. His work on binary rewriting addresses security and performance optimization, while his parallel computing research advances declarative programming models like ICE. Recent publications emphasize malware detection and efficient multiprogramming on multicore systems. Awards: NSF CAREER Award (2002) UMD George Corcoran Teaching Excellence Award (2003) UMD Jimmy Lin Innovation Award (2014) President of India Gold Medal (1992) Finalist, Inventor of the Year (2005) Labs & Teams: Leads research groups in compiler optimization and cybersecurity at UMD, collaborating with industry partners like IBM and Tilera. SecondWrite’s cybersecurity software has commercial applications in malware mitigation.

Elsa L Gunterمشاهده پروفایل

استاد پژوهشی

• Formal Methods
• Programming Languages
• Software Engineering
• +۷ مورد دیگر

Elsa L Gunter is a Research Professor and Senior Lecturer at the University of Illinois at Urbana-Champaign's Department of Computer Science. Her academic background includes a Ph.D. in Mathematics from the University of Wisconsin, Madison. She leads research in formal methods, programming languages, and human-computer systems. Research Interests: Her work spans formal verification, programming language semantics, automated theorem proving, and security. She develops tools for compiler optimization verification, human-automation system safety, and concurrent program analysis. Key projects include the VeriF-OPT framework for parallel program transformations and Tutela for human-computer system protection analysis. Publications Focus: Her recent research emphasizes compiler verification, concurrency models, and human-system interaction. Work includes symbolic analysis for CSP, dependently-typed session systems, and robustness verification for safety-critical interfaces. Awards: Most Influential 10 Year Paper award at Requirements Engineering (RE 2010) EASST Best Software Science Paper at ETAPS 2001 Best Paper award at Fourth International Conference on Requirements Engineering (2000) Funding and Labs: Secured NSF grants for projects on parallel program verification ($450K) and human task analysis ($500K). Leads the Formal Methods and Verification Lab, collaborating with NASA on NextGen aviation systems. Student Advising: Mentored 7 PhD graduates and 12+ Master's students. Current PhD candidates work on secure distributed programming (Dennis Griffith) and formal methods for concurrent systems (Liyi Li, Susannah Johnson).

• Deep Neural Networks
• Compiler Design
• Program Verification
• +۵ مورد دیگر

Thirimadura Charith Yasendra Mendis serves as an Assistant Professor at the University of Illinois Urbana-Champaign with dual appointments in the Siebel School of Computing and Data Science and the Department of Electrical and Computer Engineering. He maintains a strong affiliation with the Coordinated Science Lab, where he conducts interdisciplinary research bridging computer architecture, compilers, and artificial intelligence systems. His research program centers on deep neural networks, compiler design, and program verification, with significant contributions to soundness verification of DNN certifiers, domain-specific language development for neural network certification, and hardware-aware compiler optimizations. His work in parallel computing and graph neural networks specifically targets efficiency bottlenecks in AI infrastructure through novel vectorization and level parallelism techniques. Recent 2025 publications reveal a cohesive research trajectory focused on enhancing AI system reliability through formal methods and compiler innovation. Key themes include automated verification frameworks for tensor operations, declarative approaches to neural network certification, and GPU-optimized code generation for sparse attention mechanisms in transformer architectures—collectively advancing trustworthy AI deployment. Dr. Mendis has earned two prestigious national awards: DARPA Young Faculty Award (2024) NSF CAREER Award (2024) These competitive grants fund his research program investigating foundational aspects of AI safety and compiler technology, likely supporting graduate student mentorship in systems and programming languages research. Within the Coordinated Science Lab ecosystem, Mendis collaborates with cross-disciplinary teams on projects spanning hardware acceleration, programming language design, and neural network verification—leveraging this environment to drive innovation in computing systems reliability and performance.

Wei Yangمشاهده پروفایل

دانشیار

• Software Engineering
• Security
• AI on Edge Devices
• +۵ مورد دیگر

Wei Yang is an Associate Professor in the Department of Computer Science at the University of Texas at Dallas, working within the Erik Jonsson School of Engineering and Computer Science. He holds a regular faculty position with an office in ECSS 4.225 and is actively engaged in teaching, research, and mentoring graduate students. His academic journey spans multiple prestigious institutions, and he currently serves on editorial boards for major software engineering journals. Dr. Yang received his Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign in 2018, advised by Prof. Carl A. Gunter and Prof. Tao Xie. He earned his M.S. in Computer Science from North Carolina State University in 2013 under Prof. Tao Xie, and his B.E. in Software Engineering from Shanghai Jiao Tong University in 2011 under Prof. Jianjun Zhao. He was also a visiting researcher at the University of California, Berkeley, invited by Prof. Dawn Song. Dr. Yang's research spans multiple cutting-edge areas at the intersection of software engineering and security. His primary focus centers on software engineering for AI systems , particularly addressing challenges in deploying AI on edge devices like mobile phones, IoT devices, and autonomous vehicles. His pioneering work on efficiency robustness (initiated in 2019) explores how different inputs can trigger varying computational costs in neural networks, leading to novel attacks and defenses. He also develops infrastructure support for AI deployment , including compiler toolchains for dynamic-shaped neural networks and security analysis for IoT deployments. His research extends to mobile testing (since 2012), malware detection using expectation context analysis, and intelligent tools for software engineers and security researchers. Dr. Yang's publication record demonstrates a clear trajectory toward addressing critical challenges in AI security and efficiency. His recent work shows increasing focus on foundation models, large language models, and their security implications, while maintaining strong connections to practical software engineering challenges. The publications reveal a consistent pattern of high-impact research in top-tier venues across software engineering, AI, and security domains. NSF CAREER Award (2022) ACM SIGSOFT Distinguished Paper Award (2021) Amazon Research Award Dr. Yang actively mentors a large group of graduate and undergraduate students, with several PhD students currently working under his supervision. He serves as a faculty advisor for the ASTRO (AI Security and Trustworthiness Operations) team, which was selected as a red teaming participant in the Amazon Nova AI Challenge. His research is supported by significant grants, including the NSF CAREER award providing approximately $500,000 over five years. He emphasizes practical student development, helping them navigate the job market and transition from academic training to professional careers. Dr. Yang leads a vibrant research group focused on software engineering and security challenges in AI systems. His team, including the ASTRO group participating in the Amazon Nova AI Challenge, develops innovative techniques for testing, securing, and improving AI-based systems. The research environment fosters collaboration across multiple domains, with students working on projects ranging from mobile security to foundation model engineering.

Tobias Grosserمشاهده پروفایل

دانشیار

• Compilers
• Programming Languages
• Performance Programming
• +۷ 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 rethinking performance programming by bridging the gap between developers and compilers. He holds a PhD from École Normale Supérieure Paris and has held positions including Reader at the University of Edinburgh and Ambizione Fellow at ETH Zurich. His research interests span compilers, programming language design, static/dynamic analysis, and the integration of machine learning into compiler development. He emphasizes making compilation more modular, automatic, and trustworthy, with applications in quantum computing, climate science, and open-source hardware. Key projects include xDSL (a Python-native compiler framework), LoopOpt, and the Open Earth Compiler for climate simulations. Recent publications highlight advancements in multi-level intermediate representations (IR), formal verification in MLIR, and performance optimization for GPUs and FPGAs. His work often addresses barriers between programmers and compilers, aiming for intuitive collaboration between developers and automated systems. Tobias mentors a dynamic team of PhD students, postdocs, and researchers, including notable contributors like Siddharth Bhat, Arjun Pitchanathan, and Mathieu Fehr. His lab focuses on compiler toolchains for domain-specific hardware accelerators, quantum computing ecosystems, and verified compilation techniques.

Farhad Arbabمشاهده پروفایل

پژوهشگر

• Computer Science
• Cyber-Physical Systems
• Formal Methods
• +۴ مورد دیگر

Farhad Arbab is a researcher at the Centrum Wiskunde & Informatica (CWI) in Amsterdam, Netherlands, affiliated with the Computer Security department. His work focuses on formal methods for modeling and analyzing cyber-physical systems (CPS) and coordination models. Current affiliation: Researcher at CWI's Computer Security department Research areas: Cyber-Physical Systems, Formal Methods, Workflow Modeling, Constraint Automata Arbab's research develops component-based semantic models for CPS using constraint automata and the Reo coordination language. His framework enables: Algebraic composition of cyber-physical components Formal modeling of priority constraints in workflows Runtime composition with lazy expansion techniques Verification via Maude rewriting logic system Recent publications analyze: Parallel composition of constraint automata (2025) Concurrency in rule-based machines (2025) Runtime composition techniques (2023) Formal frameworks for distributed CPS (2022) Awards & Projects: FACS Best Paper Award (2015) Bronzen Achievement Award (2009) EU/NWO-funded initiatives: COMPAS (2008), WoMaLaPaDiA (2007), CREDO (2006)