جستجوی استاد راهنمای دانشگاهی

Matthias Tichyمشاهده پروفایل

استاد

• Domain Specific Languages
• Model-Driven Software Engineering
• Self-Adaptive Systems
• +۴ مورد دیگر

Prof. Dr. Matthias Tichy is a Full Professor and head of the Institute of Software Engineering and Programming Languages at Ulm University, Germany, since 2015. His research focuses on domain-specific languages (DSLs), model-driven engineering (MDE), self-adaptive software , and cyber-physical systems , with an emphasis on safety-critical applications and graph transformation formalisms. He employs empirical research methods to evaluate technical contributions and human factors in software engineering. University: Ulm University Role: Professor & Institute Head Research Interests span domain-specific languages for mechatronic systems, collaborative modeling , performance prediction in model transformations, and software evolution in industrial contexts. His work often bridges graph transformations and safety assurance for self-adaptive systems. Recent Publications highlight trends in model versioning (e.g., operation-based caching), DSL design (e.g., flowR for R code analysis), and automotive software testing (e.g., clustering test case specifications). He frequently collaborates with international institutions on topics like cyber-physical systems and IoT resilience . Key Collaborations include projects with Chalmers University, University of Gothenburg, and industrial partners like dSPACE GmbH. His grants and industry partnerships focus on automotive software , robotics , and self-healing systems .

• Automated Reasoning
• Theorem Proving
• Artificial Intelligence
• +۳ مورد دیگر

Stephan Schulz is a Professor at the Baden-Wuerttemberg Cooperative State University Stuttgart (DHBW Stuttgart) in the Faculty of Engineering, where he serves as the Program Director for Computer Science. His office is located in room B 3.14 at Lerchenstraße 1, 70174 Stuttgart, Germany. Professor Schulz is a leading researcher in automated reasoning and theorem proving, with extensive experience teaching computer science courses including Formal Languages and Automata, Logic and Foundations of Computer Science, Compiler Construction, and Algorithms. Professor Schulz's primary research interest lies in automated reasoning, specifically developing efficient algorithms and intelligent search control for automatic theorem proving. His long-term goal is integrating high-performance inference mechanisms with machine learning techniques to create robust reasoning systems across diverse domains. He is the principal developer of the E Theorem Prover, a high-performance system for full first-order logic with equality that has performed exceptionally well in international competitions like CASC. His recent publications demonstrate a clear trend toward extending theorem proving capabilities to higher-order logic while maintaining performance. Schulz has made significant contributions to practical aspects of automated reasoning, including watchlist implementations, contradiction detection in large theories, and the integration of machine learning techniques to improve search heuristics in theorem provers. Professor Schulz has received multiple prizes for his work on the E Theorem Prover, though specific award names are not detailed in the available information. His contributions to the field have been recognized through leadership roles in major conferences. Professor Schulz actively mentors students through project work (Studienarbeiten) at DHBW Stuttgart and has taught numerous courses throughout his career at institutions including the University of Miami, Mona Institute of Applied Sciences, Universität Hildesheim, and INRIA/MPI. While specific grant information isn't provided, his sustained development of the E Theorem Prover suggests ongoing research support. Professor Schulz is significantly involved with several workshop and conference series including the International Workshop on the Implementation of Logics (IWIL), Practical Aspects of Automated Reasoning (PAAR), and Artificial Intelligence and Theorem Proving (AITP). His current roles include PC co-chair for the 15th IWIL (2024) and 9th AITP (2024), and PC member for multiple other conferences including the 25th LPAR (2024) and 12th IJCAR (2024).

• Immunology
• Nutritional Immunology
• Agricultural Immunology
• +۵ مورد دیگر

Willem van Eden is a Full Professor in Immunology and Head of the Division of Immunology within the Department of Veterinary Medicine at Utrecht University, Netherlands. His academic journey began with an MD from the University of Groningen (1978) followed by a PhD from Leiden University (1983) under Professor J.J. van Rood. After completing postdoctoral training at the Weizmann Institute of Science in Israel (1983-1985), he joined Utrecht University where he has served as Associate Professor since 1985 and Full Professor since 1994. Dr. van Eden's research spans multiple immunological domains with particular emphasis on agricultural immunology - a field he helped pioneer through organizing the 2016 Leiden University Lorentz Center workshop 'Innate Immunity of Crops, Livestock and Fish: The Dawn of Agricultural Immunology.' His work bridges veterinary medicine, nutrition, and immunology, focusing on reducing antibiotic use in agriculture through enhanced innate immunity in plants and animals. His research encompasses dendritic cell biology, mucosal immunity, probiotic applications, and the role of heat shock proteins in autoimmune regulation. His publication record demonstrates consistent focus on immune mechanisms applicable to agricultural settings, with recent work examining probiotic interactions with intestinal epithelium, stress-immunity connections, and novel approaches to stimulate immune functions in both livestock and poultry. These studies collectively aim to establish drug-independent food safety systems by strengthening innate immune activity in agricultural production. Dr. van Eden serves as Specialty Chief Editor for Nutritional Immunology at Frontiers in Immunology and as Review Editor for Antigen Presenting Cell Biology. He co-edited the significant Research Topic 'New Horizons in Food Science via Agricultural Immunity' which compiled 11 articles exploring innovative approaches to reduce antibiotics and pesticides in agriculture through enhanced immune function. His professional activities include extensive editorial contributions, with 231 publications and 65 editorial contributions documented. He maintains active research collaborations internationally, particularly with Japanese institutions through the Core-to-Core Program of the Japan Society for the Promotion of Science focused on establishing an international agricultural immunology research core.

Paolo G. Giarrussoمشاهده پروفایل

پژوهشگر

• Programming Language Theory
• Domain-Specific Languages
• Distributed Systems
• +۵ مورد دیگر

Paolo G. Giarrusso is a researcher at the Institute for Programming Languages and Software Engineering within the Faculty of Informatics at the University of Tübingen . Previously, he was a Ph.D. student at the University of Marburg , where he defended his thesis Optimizing and Incrementalizing Collection Queries by AST Transformation in January 2018.

Robert Clarisóمشاهده پروفایل

مدرس

• Formal Methods
• Software Engineering
• Model-Driven Engineering
• +۳ مورد دیگر

Robert Clarisó is a Lecturer at Universitat Oberta de Catalunya (UOC) in the IT, Multimedia and Telecommunication Department. He has a strong background in Computer Science with a BSc (2000) and PhD (2005) from UPC-Barcelona Tech. His academic career includes positions as a part-time associate professor at UPC-Barcelona Tech and at Universitat Autònoma de Barcelona. Dr. Clarisó's educational background includes: BSc in Computer Science from UPC-Barcelona Tech (2000), graduated as the top of his class with a Special mention in the Spanish National University Degree Awards PhD in Computer Science from UPC-Barcelona Tech (2005), thesis titled "Abstract Interpretation Techniques for the Verification of Timed Systems" under Dr. Jordi Cortadella Fortuny His research primarily focuses on Formal Methods and Software Engineering, with recent expansion into AI-related topics. Dr. Clarisó has made significant contributions to Model-Driven Engineering, particularly in the area of Object Constraint Language (OCL). His recent work has shifted toward examining bias and fairness in Large Language Models and exploring the application of Generative AI in software engineering education, reflecting a strategic pivot to address contemporary challenges in AI ethics and deployment. Dr. Clarisó has held several leadership positions including Coordinator of the Research Group on Software Engineering at UOC (2008-2015), Academic Director of the Official Master in Computing Engineering (2011-2019), and Academic Director of the Bachelor's Degree in Techniques for Software Development (2020-2023). He currently leads the university's working group on "AI in education" and is a principal investigator of the SOM Research Lab. His notable recognition includes a Special mention in the Spanish National University Degree Awards. Dr. Clarisó has served on program committees for major conferences including MODELS and ASE across multiple years, demonstrating his standing in the academic community. As an educator, Dr. Clarisó has taught courses on automata theory, compilers, algorithms and data structures, graph theory, and software development. He has coordinated undergraduate theses and led postgraduate programs, showing his commitment to academic mentorship and program development. Dr. Clarisó is actively involved in research teams, currently leading the SOM Research Lab which focuses on software engineering research. His recent work has centered on the intersection of formal methods, model-driven engineering, and AI technologies, particularly examining ethical implications and educational applications.

Yu Fengمشاهده پروفایل

دانشیار

• Programming Languages
• Program Synthesis
• Program Analysis
• +۳ مورد دیگر

Yu Feng is an Associate Professor in the Computer Science Department at the University of California, Santa Barbara. He received his Ph.D. in Computer Science from the University of Texas at Austin in 2018 and his MS in Computer Science from Beihang University in 2008. His research focuses on programming languages, program analysis and verification, program synthesis, and security. Specifically, he aims to build automated tools that guarantee the desired behavior of systems in a sound, precise, and scalable way. He is particularly interested in applying program synthesis and analysis to tackle security and performance problems in mobile devices and blockchain applications. Professor Feng's recent publications demonstrate a strong focus on program synthesis for security applications, particularly in blockchain and zero-knowledge proofs. His work spans multiple top-tier conferences including PLDI, POPL, ASE, and CCS, showing consistent contributions to both programming languages and security communities. His research often combines logical reasoning with machine learning techniques to develop innovative solutions for complex problems in program analysis and synthesis. Ethereum Academic Award (2025, 2024, 2023) ACM SIGPLAN Distinguished Paper Award, PLDI 2022 ACM SIGCHI Best Paper Award, CHI 2021 Google Faculty Research Award (Security), 2021 ACM SIGSOFT Distinguished Paper Award, ASE 2020 DARPA HARDEN grant (2022-2025) NSF-SaTC grant (2019-2022) ACM SIGPLAN Distinguished Paper Award, PLDI 2018 Professor Feng actively mentors students in his research group, currently advising several PhD students including Yanju Chen, Junrui Liu, Hongbo Wen, and Hanzhi Liu. He has served on program committees for numerous top conferences including CCS, PLDI, OOPSLA, ICSE, ASPLOS, and POPL. His research is supported by prestigious grants including DARPA HARDEN and NSF-SaTC. His laboratory focuses on developing cutting-edge program synthesis and analysis frameworks that combine logical reasoning with machine learning. The team works on applications in blockchain security, zero-knowledge proofs, and secure protocol implementation.

Gary Tanمشاهده پروفایل

استاد

• computer security
• formal methods
• programming languages
• +۳ مورد دیگر

Gang (Gary) Tan is a Professor in the Computer Science and Engineering Department at Pennsylvania State University, co-directing the Institute for Networking and Security Research (INSR). His research bridges computer security, formal methods, and programming languages to develop practical solutions for software vulnerabilities and AI fairness. Education: B.E. in Computer Science from Tsinghua University Ph.D. in Computer Science from Princeton University Dr. Tan specializes in applying compiler techniques and formal verification to security challenges, with seminal work on cache side-channel attacks and fairness in machine learning. His Security of Software (SOS) Group develops frameworks that integrate theoretical guarantees into real-world systems, emphasizing measurable security outcomes and ethical AI. Recent projects focus on quantifying bias in neural networks and mitigating speculative execution vulnerabilities. Analysis of his 2021-2025 publications reveals a strategic pivot toward AI security, where he pioneers methods for fairness testing (e.g., information-theoretic debugging) and repair (e.g., NeuFair). Concurrently, his security work evolves from foundational side-channel research (SpecSafe, 2021) toward hardware-software co-design solutions, demonstrating consistent innovation across theoretical and applied domains. Scientific Awards: James F. Will Career Development Professorship NSF CAREER Award Google Research Award (two instances) Distinguished Reviewer Award at 2018 IEEE Symposium on Security and Privacy Outstanding Research Award at Penn State Ruth and Joel Spira Excellence in Teaching Award Best Paper Award at PLDI 2024 Dr. Tan leads the SOS Group with funding from NSF (including CAREER), DARPA (ISAT study group membership), and industry partners like Google. His grants support interdisciplinary projects spanning secure compilation, fairness engineering, and hardware security, while his teaching excellence award reflects commitment to pedagogy in core systems courses. He co-directs Penn State's Institute for Networking and Security Research (INSR), fostering collaboration between systems, security, and AI researchers. The SOS Group maintains active partnerships with industry security teams and contributes to open-source tools for vulnerability detection, with recent work expanding into fairness certification for machine learning pipelines.

Đorđe Žikelićمشاهده پروفایل

استادیار

• Formal Methods
• Program Analysis and Verification
• Trustworthy AI
• +۵ مورد دیگر

Đorđe Žikelić is an Assistant Professor of Computer Science at the School of Computing and Information Systems at Singapore Management University (SMU) in Singapore. He completed his PhD in 2023 at the Institute of Science and Technology Austria (ISTA) under Krishnendu Chatterjee and Petr Novotný, receiving both Outstanding PhD Thesis and Outstanding Scientific Achievement awards. Prior to his doctorate, he earned bachelor's and master's degrees in mathematics from the University of Cambridge. His educational background includes: PhD in Computer Science, Institute of Science and Technology Austria (ISTA), 2023 Bachelor's and Master's in Mathematics, University of Cambridge Dr. Žikelić's research focuses on advancing formal methods to ensure software and AI systems are correct, safe, and trustworthy. His work bridges theoretical aspects of formal reasoning about probabilistic systems with practical automated verification methods. His primary research interests span three interconnected areas: Program Analysis and Verification: He develops techniques for analyzing probabilistic programs, numerical programs, and efficient quantifier elimination methods, addressing fundamental challenges in verifying complex software systems. Trustworthy AI and Safe Autonomy: He creates formal verification frameworks for learning-enabled control systems and neural networks, ensuring AI operates safely in uncertain environments through methods like runtime monitoring and certificate repair. Probabilistic System Verification: He explores broader applications including bidding games on graphs and blockchain protocol analysis, extending formal methods to novel domains beyond traditional finite-state verification. His publication trajectory shows a consistent progression from theoretical foundations to practical applications, with recent work increasingly focused on integrating formal verification with machine learning. His 2024-2025 publications demonstrate growing expertise in verifying learning-based systems and developing practical tools like PolyQEnt for quantified entailment solving. His scientific achievements have been recognized with: Outstanding PhD Thesis Award at ISTA Outstanding Scientific Achievement Award at ISTA Distinguished Paper Award at FM 2024 Dr. Žikelić serves on program committees for major conferences including TACAS, PLDI, AAAI, and CAV. He actively mentors through the Programming Languages Mentoring Workshop (PLMW) at PLDI 2025. His research group at SMU focuses on developing novel algorithms for verifying correctness of programs and AI systems, with current projects spanning formal methods, artificial intelligence, and programming languages.