Compiler Optimization Professors and Supervisors in Austria

Keshav Pingaliمشاهده پروفایل

استاد

Graz University of TechnologyAustria

Keshav Pingali is currently the W. A. "Tex" Moncrief Chair of Computing and a Professor in the Department of Computer Science at The University of Texas at Austin. He also serves as Director of the Center for Grid and Distributed Computing at the Oden Institute for Computational Engineering and Sciences. Previously, he held positions as India Chair of Computer Science at Cornell University (2003-2006) and Professor in both the Department of Computer Science and the Department of Electrical and Computer Engineering there (1999-2006). Education: Sc.D. (1986) and S.M./E.E. (1983) from MIT B.Tech. (1978) from Indian Institute of Technology, Kanpur Research Interests: Keshav works in programming languages, compilers, and runtime systems for program optimization and parallelization, with a focus on multicore processors and irregular applications from domains like social networks, data mining, and graphics. Scientific Awards: ACM SIGPLAN Programming Languages Achievement Award (2024) ACM/IEEE CS Ken Kennedy Award (2023) IEEE Charles Babbage Award (2023) Foreign Member, Academia Europaea (2020) IIT Kanpur Distinguished Alumnus Award (2013) Fellowships: ACM (2012), AAAS (2010), IEEE (2010) NSF Presidential Young Investigator Award (1989-1994) IBM Faculty Development Award (1986-1987) President's Gold Medal and Lalit Narain Das Memorial Gold Medal at IIT Kanpur (1978) Leadership & Service: He has served on steering committees of major conferences (PLDI, PPoPP), chaired editorial boards (ACM Transactions on Programming Languages and Systems), and contributed to NSF advisory committees. His research group maintains a homepage at http://iss.ices.utexas.edu/ .

پروفایل رسمی دانشگاه

Michael Szvetitsمشاهده پروفایل

مدرس

University of Applied Sciences Wiener NeustadtAustria

Dr. Michael Szvetits is a Lecturer and Researcher at the Institute of Computer Sciences at the University of Applied Sciences Wiener Neustadt. He holds a PhD in Computer Science from the University of Vienna (2019), an MSc in Computer Science (2012), and a BSc in Information Technology (2010), all from the University of Applied Sciences Wiener Neustadt. His research focuses on software architecture, domain-specific languages, model-driven software development, functional programming, logic programming, and compiler construction. He has contributed to projects such as 'Care about Care' (C^C), which aims to enhance home care through ICT solutions, and 'CARU cares,' which integrates emergency call systems with care documentation tools. His publications span topics like runtime event analysis, model-driven engineering, and software architecture decisions. He collaborates on initiatives funded by the FFG and Active Assisted Living Programme. Dr. Szvetits is actively involved in research projects targeting healthcare technology, software systems optimization, and innovation in assistive living solutions.

پروفایل رسمی دانشگاه

Friedrich Slivovskyمشاهده پروفایل

پژوهشگر

Vienna University of TechnologyAustria

Friedrich Slivovsky is a researcher at the Institute of Logic and Computation within the Faculty of Informatics at Technische Universität Wien (Vienna University of Technology). His work focuses on theoretical and practical aspects of computational logic, with particular expertise in Quantified Boolean Formulas (QBFs), Propositional Model Counting (#SAT), and Knowledge Compilation. His research interests span the theoretical foundations and practical applications of computational logic. Slivovsky investigates the complexity of logical reasoning problems, develops efficient algorithms for solving them, and creates practical tools that implement these theoretical advances. His work bridges the gap between theoretical computer science and practical applications in areas like hardware verification, artificial intelligence, and electronic design automation. Analysis of his publication trends reveals a consistent focus on QBF solving techniques, with increasing emphasis on circuit minimization, proof complexity, and practical solver engineering. His recent work (2023-2024) shows a strong focus on circuit minimization techniques, combining QBF and SAT approaches to solve complex optimization problems in hardware design. Earlier work (2019-2021) emphasized dependency schemes, certification methods, and theoretical foundations of QBF solving. Slivovsky leads several significant software projects that have become important tools in the computational logic community: Qute : A dependency learning QBF solver with GitHub repository showing active development (latest commit December 2024) Unique : A preprocessor for (D)QBF that computes unique Skolem and Herbrand functions Pedant : A certifying DQBF solver These projects demonstrate his commitment to translating theoretical advances into practical tools that benefit the broader research community.

پروفایل رسمی دانشگاه

Alexey Ignatievمشاهده پروفایل

دانشیار

Vienna University of TechnologyAustria

Alexey Ignatiev is an Associate Professor in the Optimisation research group at Monash University's Faculty of Information Technology. Previously, he was a postdoctoral researcher and researcher at the University of Lisbon's Faculty of Sciences, focusing on SAT/SMT-based decision procedures. He holds a Ph.D. from the Matrosov Institute for System Dynamics and Control Theory (Russian Academy of Sciences), where his thesis explored parallel CDCL-BDD integration. His research emphasizes formal methods in AI, including explainable AI (XAI), SAT-based reasoning, and optimization for applications like software upgradability, model-based diagnosis, and fault localization. His work spans over 100 publications, with notable contributions to MaxSAT solving (RC2 solver), neuro-symbolic frameworks (NEUSIS), and rigorous explanations for machine learning models. He has collaborated extensively with institutions like the University of Lisbon and Monash University, contributing to advancements in formal verification and interpretable machine learning.

پروفایل رسمی دانشگاه

Thomas Eiterمشاهده پروفایل

استاد

Vienna University of TechnologyAustria

Thomas Eiter is a Professor at TU Wien's Institute of Logic and Computation. His research focuses on declarative programming paradigms, knowledge representation, and artificial intelligence. He leads projects in neurosymbolic systems, answer set programming (ASP), and stream reasoning, with applications in visual question answering, scheduling optimization, and semantic scene generation. Eiter has contributed to foundational work in ASP semantics, computational complexity, and hybrid reasoning frameworks. His work bridges logical formalisms with practical AI challenges, emphasizing explainability and scalability. Projects like ALASPO and neurosymbolic integration showcase his focus on advancing both theoretical and applied aspects of AI. Projects: HumanE AI Network, WASP, REWERSE Research Themes: Neurosymbolic AI, Answer Set Programming, Stream Reasoning Notable achievements include pioneering work on semiring-based reasoning frameworks and developing efficient ASP solvers like Alpha. His contributions span over 471 publications, emphasizing interdisciplinary applications in computer vision, robotics, and automated planning.

پروفایل رسمی دانشگاه

Sebastian Schrittwieserمشاهده پروفایل

پژوهشگر

University of ViennaAustria

Sebastian Schrittwieser is a Researcher in the Research Group Security and Privacy, part of the Faculty of Computer Science. His work focuses on cybersecurity, code obfuscation, malware analysis, and machine learning applications in security. He leads and contributes to projects like INODES (Cyber Defense Strategies) and EMRESS (Resilience Evaluation Models). His research bridges theoretical foundations and practical applications, addressing challenges in software protection and threat detection. Key research interests include: Code Obfuscation Techniques and Resistance Adversarial Machine Learning and Risk Assessment Malware Analysis and Program Simulation User Behavior in Cybersecurity Contexts Recent publications emphasize empirical studies on IT/OT infrastructure security, graph neural network vulnerabilities, and quantum-inspired machine learning. He actively collaborates with institutions like SBA Research and presents at international conferences. Grants include Research Funding for projects on optimal cyber defense strategies (INODES) and software resilience evaluation (EMRESS). His work aligns with interdisciplinary efforts in security engineering and privacy-preserving technologies.

پروفایل رسمی دانشگاه