جست‌وجوی استاد و پژوهشگر

Michael D. Bondمشاهده پروفایل

استاد

• Programming Languages
• Systems
• Security
• +۴ مورد دیگر

Michael D. Bond is a Professor in the Department of Computer Science and Engineering at Ohio State University, where he leads the Programming Languages and Software Systems (PLaSS) Research Group. His work focuses on designing program analyses and software and hardware systems that enhance computing reliability, scalability, and security. As an active member of the programming languages and systems research community, he serves in leadership roles including General Chair for PLDI 2027 and ISMM 2024. Professor Bond's research interests span programming languages, systems, and security with a particular focus on information flow control, concurrency, memory management, and Rust programming language systems. His group has made significant contributions to data race detection, predictive analysis, information flow control in Rust, and memory-disaggregated systems. Recent work includes Carapace (static-dynamic information flow control in Rust), Cocoon (static information flow control in Rust), and IsoPredict (predictive analysis for weakly isolated data applications). His research group has secured substantial funding, including multiple NSF grants such as SaTC-2348754 (2024-2027) on information flow control in Rust, CyberCorps-2336531 (2024-2029), and CSR-2106117 (2021-2025). Professor Bond has advised numerous PhD and MS students, many of whom have gone on to prestigious positions at Google, Amazon, Huawei, and academic institutions. Scientific Awards: Outstanding Teaching Award, Department of Computer Science and Engineering, Ohio State University (2018) Lumley Research Award, College of Engineering, Ohio State University (2016) OOPSLA 2015 Distinguished Paper and Artifact Awards NSF CAREER Award ACM SIGPLAN Outstanding Doctoral Dissertation Award Professor Bond actively contributes to the research community through service as program committee member for top conferences including PLDI, ASPLOS, and OOPSLA. He is currently the General Chair for PLDI 2027 and served as General Chair for ISMM 2024. His group's open-source implementations accompany many publications, demonstrating commitment to reproducibility and practical impact.

• Artificial Intelligence
• Cybersecurity
• Security of AI
• +۴ مورد دیگر

Isabel Cecília Correia da Silva Praça Gomes Pereira is a Coordinator Professor at the School of Engineering of the Polytechnic Institute of Porto (ISEP), where she serves as Director of the Master on Informatics Engineering and Advisor of ISEP Presidency for R&D. She is also a Senior Researcher at GECAD (Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development), a research unit ranked as Excellent by the Portuguese Science & Technology Foundation. Dr. Praça holds a PhD in Electrical and Computer Engineering - Industrial Informatics from the University of Trás-os-Montes e Alto Douro, and completed her post-doctoral studies in Artificial Intelligence - Multi-Agent Systems with support from the Portuguese National Science Foundation (SFRH/BPD/30111/2006). Her academic journey includes progressive appointments from Assistant to Adjunct Professor and finally to Coordinator Professor at ISEP. Her research focuses on Artificial Intelligence applied to cybersecurity and security of AI , with significant contributions to applying AI in various domains including cybersecurity (projects like AIDA, SAFE, VESTA), industry (SeCoIIA, Cyberfactory), and energy systems (SPET, MAS-Society). She leads a research team at GECAD working on these topics and has strong connections with over 100 companies and technology transfer centers across more than 30 countries. Analysis of her recent publications reveals a strong trend toward AI security, with particular emphasis on adversarial attacks against AI systems, secure implementation of AI in critical domains, and privacy-preserving AI techniques. Her work spans both theoretical foundations and practical applications across multiple sectors including energy, healthcare, and network security, demonstrating her ability to bridge academic research with real-world applications. Dr. Praça has been recognized as an expert by several prestigious organizations including the European Union Agency for Cybersecurity (ENISA), where she contributes to working groups on Security of AI and the European Cybersecurity Skills Framework. She also serves as an expert for NATO's Defence Innovation Accelerator for the North Atlantic (DIANA) and represents ISEP in the European Cybersecurity Organization (ECSO). As an educator, she teaches Machine Learning and Multi-agent Systems in the MSc in AI program, and Security of Communications and Infrastructures in the Cybersecurity branch of the MSc in Informatics. She currently supervises 4 PhD students in AI security and privacy and has guided 45 Master's theses, demonstrating her commitment to developing the next generation of researchers and practitioners in her field. Her research is supported by numerous international and national grants, including multiple Horizon Europe and Horizon 2020 projects where she serves as Principal Investigator or Co-PI. She has participated in over 35 R&D projects totaling significant funding, with a strong track record of translating research into practical applications through industry partnerships.

Calvin Linمشاهده پروفایل

استاد

• Computer Architecture
• Parallel Computing
• Programming Languages and Compilers
• +۲ مورد دیگر

Calvin Lin serves as a University Distinguished Teaching Professor at the University of Texas at Austin, concurrently directing the Turing Scholars Honors Program and coaching UT's Men's Ultimate Frisbee team. His career uniquely integrates cutting-edge research in computer systems with transformative educational initiatives. His academic foundation includes: BS from Princeton University PhD from the University of Washington Lin's research spans computer architecture, parallel computing, programming languages, compilers, security, and privacy, with recent emphasis on computer science education. His work consistently addresses performance optimization challenges across hardware-software boundaries, particularly in memory systems and processor design, while pioneering efforts to modernize K-12 CS curricula through NSF funding. Publication trends reveal enduring focus on system-level efficiency: early breakthroughs in machine learning-based branch prediction (2001) evolved into contemporary innovations in data prefetching and GPU synchronization. His articles demonstrate methodological progression from theoretical compiler techniques to practical security-aware architectures, maintaining relevance across 20 years of computing evolution. Recognition for his dual excellence includes: William David Blunk Memorial Professorship (2019) President’s Associates Teaching Excellence Award (2015) Senior Provost Teaching Fellow (2015) UT Academy of Distinguished Teachers (2013) Jean Holloway Award for Excellence in Teaching (2013) Best Paper Award, Eurographics Symposium on Parallel Graphics and Visualization (2012) University of Texas Regents’ Outstanding Teaching Award (2011) Best Paper Award, International Symposium on Code Generation and Optimization (2011) Best Paper Award, ACM SIGPLAN Conference on Programming Language Design and Implementation (2007) IEEE Micro Top Picks (2006) HPCA Test of Time Award (2019) As Turing Scholars Director, Lin mentors elite undergraduate researchers while leading an NSF grant to redesign high school CS education. His advising extends beyond traditional graduate supervision into curriculum development and competitive programming initiatives, fostering talent pipelines from secondary education through doctoral research. Beyond formal academic roles, Lin cultivates community engagement through Ultimate Frisbee coaching and maintains active research collaborations. His current work focuses on security-enhanced architectures and compiler-driven performance optimization, positioning his team at the intersection of hardware innovation and educational outreach.

• Timber Engineering
• Wood Mechanics
• Structural Engineering
• +۵ مورد دیگر

Min Hu is a Lecturer in the Department of Building Technology at Linnaeus University's Faculty of Technology. With a PhD in Building Technology completed in 2018, Dr. Hu has established a research program focused on improving machine strength grading of structural timber through advanced analysis of fiber orientation and local bending stiffness related to knots in sawn timber. Dr. Hu's research interests center on timber engineering, wood mechanics, and structural analysis, with particular expertise in non-destructive testing methods including laser scanning and X-ray computed tomography. Their work bridges computational modeling with practical applications in wood construction, developing sophisticated models to predict mechanical properties of timber based on internal structure. The publication record demonstrates a consistent focus on understanding fiber orientation in Norway spruce and other timber species, with recent work expanding into Cross-Laminated Timber (CLT) applications. The research shows progression from fundamental studies of wood microstructure to practical engineering applications for improving timber grading and construction systems. Dr. Hu actively supervises bachelor's and master's degree projects while teaching courses in Structural Mechanics, Construction Technology, and the Finite Element Method. Their pedagogical approach emphasizes creating an encouraging environment that conveys the joy of learning and inspires critical thinking. Department of Building Technology Faculty of Technology Linnaeus University Dr. Hu leads research within multiple collaborative frameworks including the Linnaeus University Centre for Competitive Timber Structures (Lnuc TiSt) and participates in several ongoing research projects focused on sustainable wood construction, detection of internal timber properties, and improving CLT-based building systems through engineering design.

• Abstract Interpretation
• Programming Language Semantics
• Formal Methods
• +۱۲ مورد دیگر

Giorgio Levi is a Full Professor in the Department of Computer Science at the University of Pisa, Italy. He works on theoretical aspects of programming languages and formal methods. Research includes interpreter design and abstract interpretation Teaches courses on Abstract Interpretation in Declarative Languages Contributed to projects like FIRB - Model B - Pisa Research Interests : His work focuses on abstract interpretation, programming language semantics, and code analysis. He has developed interpreters for functional languages, denotational semantics, operational semantics, and type inference systems. Teaching : He teaches a course on abstract interpretation in declarative languages, with materials including denotational interpreters, operational semantics, and run-time structures. Projects : Involved in the FIRB - Model B - Pisa research initiative, which relates to interpreter refinements and semantic evaluation functions.

Tolga Ayavمشاهده پروفایل

استاد

• Formal Methods for Software Testing
• Hardware Component and On-Chip System Tests
• Real-Time and Fault-Tolerant Embedded Systems
• +۵ مورد دیگر

Professor Tolga Ayav is affiliated with the Department of Computer Engineering at Izmir Institute of Technology , where he has served as faculty since 2006. He received his BSc in Electrical and Electronics Engineering (1995) from Dokuz Eylül University, MSc in Computer Engineering (1999) from Izmir Institute of Technology, and PhD in Computer Engineering (2004) from Ege University. He was a researcher at INRIA Rhone-Alpes in 2005. Education BSc: Electrical and Electronics Engineering, Dokuz Eylül University (1995) MSc: Computer Engineering, Izmir Institute of Technology (1999) PhD: Computer Engineering, Ege University (2004) Research Interests include formal methods for software testing, hardware component and on-chip system tests, real-time and fault-tolerant embedded systems, blockchain applications, and machine learning. His work focuses on: Formal verification techniques Real-time system optimization Hardware-software co-design Blockchain-based security solutions Mathematical modeling in testing Recent Publications span topics like: Deep learning for livestock monitoring Blockchain in IoT security Fourier expansion in fault analysis Optimized data replication strategies Finite state machine testing Scientific Awards include: Best Paper Award at IEEE CBDCom 2016 for cloud data replication research Teaching includes courses such as: CENG 312: Computer Networks CENG 523: Advanced Topics in Real-Time Systems CENG 215: Circuits and Electronics Projects he has led include: TÜBİTAK TEYDEB: Spectrally Efficient Small Cell Base Station Design BAP: Dedicated Server for Physical Network Applications Lab Leadership : He leads the DCS Research Group at Izmir Institute of Technology, focusing on distributed computing and real-time systems.

Rui Abreuمشاهده پروفایل

استاد

• Software Quality
• Software Testing
• Debugging
• +۵ مورد دیگر

Rui Abreu is a Full Professor at the Faculty of Engineering of the University of Porto, Portugal. His academic career spans multiple institutions including IST, ULisbon where he served as an Associate Professor, and the University of Porto where he previously held the position of Assistant Professor. He has also been a Visiting Researcher at Google NYC (2019-2020) and a member of the Model-Based Reasoning group at PARC's System and Sciences Laboratory. Dr. Abreu earned his Ph.D. in Computer Science - Software Engineering from the Delft University of Technology, The Netherlands, and his M.Sc. in Computer and Systems Engineering from the University of Minho, Portugal. His research revolves around software quality with emphasis on automating testing and debugging phases of the software development life-cycle as well as self-adaptation. With extensive expertise in both static and dynamic analysis algorithms, his work spans traditional software engineering, quantum software testing, and security-focused development practices. His recent publications demonstrate a strong focus on applying AI techniques to software engineering problems, quantum software testing, and security vulnerability detection. The trend shows increasing integration of large language models in debugging tools and expanding research into quantum software verification. Dr. Abreu has received significant recognition for his work, including 6 Best Paper Awards and a Distinguished Paper Award at ESEC/FSE 2019. His research has attracted considerable attention in the software engineering community. As an entrepreneur, he co-founded DashDash in January 2017, a platform that allows users to create web applications using only spreadsheet skills. The company secured $9M in Series A funding in May 2018. His industry experience complements his academic work, particularly in the areas of practical software tools and applications. Dr. Abreu has been actively involved in numerous conferences, serving as General Chair for ICST 2020, Program Committee member for multiple tracks across various conferences, and as Steering Committee member for ICST. His leadership in the software engineering community is evident through his extensive service and contributions to the field.

• Cyber-Physical Systems
• Interval Analysis
• Robotics
• +۵ مورد دیگر

Alexandre Chapoutot is a Lecturer and researcher at ENSTA Paris within the Computer Science and Systems Engineering Unit (U2IS). He leads the Semantics of Hybrid Systems (SSH) research group and serves as Head of the 2nd year specialization of the engineering cycle in Computer Science. His academic work bridges theoretical computer science with practical engineering applications, focusing on the rigorous analysis and verification of complex systems. Chapoutot's research interests span the verification of cyber-physical systems, with a strong emphasis on interval analysis and mobile robotics. He has made significant contributions to the static analysis of programs through abstract interpretation, particularly for improving the accuracy of floating-point arithmetic calculations. His work bridges theoretical computer science with practical engineering applications, developing methods that ensure the reliability and safety of autonomous systems. The research has direct applications in robotics, control systems, and safety-critical embedded software where numerical precision and system correctness are paramount. His recent publications reveal a strong trend toward the integration of formal methods with practical robotics applications. The work spans theoretical foundations in hybrid systems verification, practical implementations for robotics navigation and control, and innovative approaches to combining different verification techniques. A significant portion of his recent work focuses on Signal Temporal Logic applications, trajectory optimization, and the development of verified algorithms for autonomous systems. The publications demonstrate a consistent progression from theoretical foundations to practical implementations with real-world robotics applications. Chapoutot actively supervises multiple PhD students working on cutting-edge research topics at the intersection of formal methods and robotics. His research group collaborates extensively with other institutions and researchers, as evidenced by the numerous co-authored publications across various domains. He contributes to the development of the DynIbex software library, which provides validated solutions for ordinary differential equations using Runge-Kutta methods. His laboratory work centers around the Semantics of Hybrid Systems (SSH) group, which develops theoretical frameworks and practical tools for the analysis and verification of cyber-physical systems. The group's research spans formal methods, numerical analysis, and robotics, with a strong emphasis on developing mathematically rigorous approaches that can be applied to real-world engineering problems.

Maciej Norasمشاهده پروفایل

دانشیار

• Electrical Engineering
• Electrostatics
• Power Engineering
• +۴ مورد دیگر

Maciej Noras is an Associate Professor in the Engineering Technology and Construction Management department at the University of North Carolina at Charlotte . His academic background includes a Ph.D. in Engineering Science from Southern Illinois University and an M.S. in Electrical Engineering from UNC Charlotte and Wroclaw University of Technology. Ph.D. in Engineering Science, Southern Illinois University, Carbondale, IL (2000) M.S. in Electrical Engineering, University of North Carolina at Charlotte and Wroclaw University of Technology, Poland (1994) Noras's research focuses on electrostatics , power engineering , and sensor design , with applications in semiconductor manufacturing , actuator technology , and non-contact charge measurement . He has explored Electrostatic voltmeter design for semiconductor wafers Ionizer monitoring in ESD-sensitive environments Charge feedback systems for piezoelectric actuators Surface potential measurement techniques His recent publications highlight advancements in electrostatic sensor technology , actuator control systems , and charge accumulation analysis across semiconductors and data storage devices. Key trends include Development of high-resolution electrostatic voltmeters Optimization of ionizer systems in manufacturing Integration of piezo-driven sensors for precision applications Charge feedback amplifiers for actuator control No scientific awards or honors are mentioned in the provided data. He has submitted multiple research grants , including DOE Grant (2010-2012) for a Net-Zero Energy Building Operator Training Program as Co-PI Unfunded UNC Charlotte Faculty Grant (2008) on Doherty Amplifier feasibility Pending Army Research Laboratories proposal (2008) on geophysical anomaly detection Multiple declined NSF grants (2009-2010) for power engineering and pharmaceutical blend research

Xueyuan Michael Han-Vanbastelaerمشاهده پروفایل

استادیار

• Systems Security
• Privacy
• Data Provenance
• +۲ مورد دیگر

Xueyuan Michael Han-Vanbastelaer is an Assistant Professor in the Department of Computer Science at Wake Forest University , focusing on systems-level security and privacy research. His work integrates data provenance, machine learning, and distributed systems to develop robust security mechanisms. Ph.D. in Computer Science (2022) from Harvard University Master of Science in Computer Science (2022) from Harvard University B.Sc. in Computer Science (2015) from University of California, Los Angeles His research interests span systems security , privacy , data provenance , and graph analysis , where he develops operating system infrastructures, language-level frameworks, and algorithms to enhance system transparency and detect sophisticated attacks. His publications reveal a trend of advancing provenance-based security solutions, including intrusion detection systems like KAIROS and Unicorn, eBPF security frameworks like SafeBPF, and data deletion techniques like Splice. He has contributed to 15 major publications since 2016, with recent work (2024–2025) focusing on hardware-assisted kernel security and real-time provenance auditing. His teaching includes courses like CSC 111: Introduction to Computer Science and CSC 250: Computer Systems I , covering programming fundamentals, system architecture, and memory management.

• Online Learning Theory
• Machine Learning Theory
• Reinforcement Learning
• +۳ مورد دیگر

Andrew Jacobsen is a Postdoctoral Research Fellow affiliated with the Polytechnic University of Milan and the University of Milan, working under the supervision of Nicolò Cesa-Bianchi. His research focuses on theoretical aspects of machine learning, particularly online learning and adaptive algorithms for dynamic environments. His educational background includes: PhD in Computing Science (2024) from the University of Alberta, supervised by Ashok Cutkosky and Martha White. MSc and BSc in Computing Science from the University of Alberta, supervised by Martha White and Adam White. Music program at Grant Macewan University, majoring in composition and minoring in music technology. Andrew's primary research interest lies in online learning theory, with a focus on developing algorithms that require no hyperparameter tuning and can adapt to time-varying objectives. He is also broadly interested in machine learning theory, reinforcement learning, and bandits. His work bridges theoretical guarantees with practical algorithmic design, creating robust systems for non-stationary environments through parameter-free optimization and regret minimization frameworks. His publication trends reveal a strong emphasis on dynamic regret analysis, equivalence proofs between static and dynamic regret, and hyperparameter-free methods across online optimization. Recent work extends into reinforcement learning applications and continual learning systems, consistently targeting theoretical foundations with practical implementations. Scientific recognition includes: Outstanding PhD Thesis Award for "Adapting to Non-stationarity in Online Learning" (2024). Andrew has no documented student advising or grant leadership in the provided materials. He maintains active research collaborations at his institutions while pursuing interdisciplinary interests in music composition and physical activities like skateboarding and grappling.

• Granular Materials
• Social Network Modeling
• Morphodynamics of Pebbles
• +۳ مورد دیگر

János Török is an Associate Professor at the Department of Theoretical Physics, Budapest University of Technology and Economics, affiliated with the Morphodynamics group. His research spans granular materials, social network modeling, and morphodynamics of pebbles. Granular materials: Quasi-static shearing, shear band formation, particle shape effects, hopper flow Social science: Conflicts on Wikipedia, consensus modeling, social network dynamics Morphodynamics: Collective abrasion, fragmentation of pebbles His recent publications focus on computational modeling of granular physics and social dynamics, with applications in machine learning for malaria detection. Articles highlight interdisciplinary approaches to phase transitions, network analysis, and material deformation. Shear zones in granular materials Deep learning for social network parameters Malaria detection software He is involved in open-source projects (WWM, Mozi) and teaches courses in mathematical methods, mechanics, and scientific programming.

Kathleen Fisherمشاهده پروفایل

استاد مدعو

• Programming Languages
• Domain-Specific Languages (DSL)
• Program Synthesis
• +۴ مورد دیگر

Kathleen Fisher is an Adjunct Professor in the Computer Science Department at Tufts University and currently serves as the Director of the Information Innovation Office at DARPA. She previously held roles as Professor and Department Chair at Tufts (2016-2021), Program Manager at DARPA, and Principal Member of Technical Staff at AT&T Labs Research. Her academic journey began with a PhD in Computer Science from Stanford University. Kathleen’s research focuses on advancing programming languages through domain-specific languages (DSLs), program synthesis, and formal methods. Her work addresses challenges in ad hoc data management, secure systems, and integrating machine learning with programming language design. Notable projects include the Hancock and PADS systems for data processing, Forest for filestore management, and verified parser generators. She has received prestigious accolades, including ACM Fellow, Hertz Foundation Fellow, and SIGPLAN Distinguished Service Award. Her service includes leadership roles in ACM SIGPLAN, CRA-W, and as General Chair for ICFP 2015. Kathleen has advised PhD student Matt Ahrens and led impactful DARPA programs like HACMS and PPAML. As co-founder of the Programming Language Mentoring Workshop (PLMW), she actively contributes to diversity initiatives in computer science. Her research group, TuPL, explores DSLs, program synthesis, and language-based security, maintaining projects such as Autobahn and PADS.

Coen De Rooverمشاهده پروفایل

استاد

• program analysis
• program transformation
• software engineering
• +۸ مورد دیگر

Coen De Roover is a Professor at the Software Languages Lab (SOFT) of Vrije Universiteit Brussel (VUB) since October 2015, where he leads the Code Analysis and ManiPulation (CAMP) subgroup. His research focuses on program analysis design and its applications to software quality, including soft verification of contracts, incremental abstract interpretation, vulnerability detection in infrastructure code, and mining change patterns in commits. Key Research Areas: static analysis, dynamic analysis, mining software repositories, software engineering tools, security, and empirical studies. Conference Roles: Organizing Committee Chair for ECOOP Academy (2026), Steering Committee Member for GPCE, Program Committee Member for ICFP, SANER, and VMCAI, and Session Chair for multiple research tracks. Notable Contributions: Publications on WebAssembly analysis, Ansible security, concolic testing, and abstract interpretation frameworks. He also serves as Programme Director for the Bachelor in Computer Science at VUB since 2019-2020.

Tomoki Nakamaruمشاهده پروفایل

استادیار

• Domain-Specific Languages
• Mining Software Repositories
• Notebook Programming
• +۵ مورد دیگر

Tomoki Nakamaru is an Assistant Professor at the Department of Multidisciplinary Systems Science, Graduate School of Arts and Sciences, The University of Tokyo. He holds a Ph.D. in Information Science and Engineering from The University of Tokyo, advised by Prof. Shigeru Chiba. Education: PhD (2021), The University of Tokyo M.Sc. (2018), The University of Tokyo B.Eng. (2016), The University of Tokyo His research focuses on Domain-Specific Languages , Mining Software Repositories , and Notebook Programming . He develops tools like Multiverse Notebook and Silverchain to enhance code exploration and API usability. He has published extensively at top venues like OOPSLA and PLDI , covering topics from Fluent Interface Generation to Runtime Monitoring . His 15 most recent works span 2017–2025. Scientific Awards: Yamashita Memorial Research Award (2023) University of Tokyo Business Reform Special Award (2021) JSST Student Encouragement Awards (2019–2020) Distinguished Artifact Award at OOPSLA (2019) Dean's Award, UTokyo (2018) Nakamaru serves on program and artifact committees for conferences like ECOOP , OOPSLA , and ‹Programming› . He teaches courses including Practical Programming and Introduction to Information Systems .