جست‌وجوی استاد و پژوهشگر

René Thiemannمشاهده پروفایل

دانشیار

• Program Verification
• Interactive Theorem Proving
• Termination Analysis
• +۷ مورد دیگر

René Thiemann is an Associate Professor in the Department of Computer Science at the University of Innsbruck, Austria, where he is a key member of the Computational Logic Group. His work bridges theoretical computer science and practical formal verification, with a strong emphasis on automated reasoning and program correctness. Research Interests: Program Verification using interactive theorem proving (Isabelle/HOL) Termination and complexity analysis of programs Term rewriting systems and dependency pairs SAT/SMT solving and decision procedures Formalization of algebraic algorithms (LLL, Smith normal form, algebraic numbers) Development of the Certification Problem Format (CPF) and the CeTA tool His recent publications (2017–2025) reflect a consistent focus on formalizing advanced algorithms in Isabelle/HOL, especially those related to termination, complexity, and algebraic computation. These works are published in top venues like CPP, FSCD, LICS, and ITP, demonstrating rigorous, machine-checked proofs. His research often centers on verifying tools like AProVE and developing foundational libraries for number theory and rewriting. Scientific Projects: ARI : Automation of Rewriting Infrastructure (Task Leader, since 2022) Certifying Termination and Complexity Proofs : Project Leader (2014–2021) Constrained Rewriting and SMT : Task Leader (2012–2015) Improving Certifiers for Termination Proofs : Project Leader (2010–2014) Teaching Activities: Lecture and Proseminar: Program Verification (SS 2023–2025) Lecture: Constraint Solving (SS 2024–2025) Lecture and Proseminar: Functional Programming (WS 2021–2025) Lecture: Advanced Functional Programming (WS 2024/25) Lecture: Interactive Theorem Proving in Isabelle/HOL (SS 2022–2024) Lecture: Decision Procedures (SS 2021) He has supervised no listed students in the provided data but actively contributes to collaborative research. His email is rene.thiemann@uibk.ac.at.

• Numerical Analysis
• Applied Mathematics
• Sustainable Development
• +۳ مورد دیگر

Душан М. Милошевић је редовни професор на Катедри за Математику Електронског факултета у Нишу (Универзитет у Нишу) од 2016. године, са богатом академском каријером која почиње дипломом из Рачунарске технике 1988. и простиже врхунац докторатом из Математике 2005. на Природно-математичком факултету у Нишу. Као члан академске заједнице, доприноси настави и истраживању кроз 33 рада у часописима са Импакт фактором и више уџбеника. Његово образовање обухвата: Докторат из Математике, Природно-математички факултет Ниш (2005) Магистратуру из Математике, Електронски факултет Ниш (1992) Диплому из Рачунарске технике, Електронски факултет Ниш (1988) Истраживачки интереси професора Милошевића су фокусирани на Нумеричку анализу (посебно методе за локализацију нула полинома), те интердисциплинарне примене у области одрживог развоја, паметних градова и еколошког моделовања. Његов рад интегрише Фузи логику и вишекритеријумско доношење одлука за решавање проблема у процени обнове индустријских објеката, моделовању соларне рефлекције и анализи енергетских пројеката, што истиче његову способност превођења апстрактне математике у практична инжењерска решења. Анализа његових радова (2011–2020) открива еволуцију од чисте нумеричке анализе ка интердисциплинарним проблемима: рани радови (2011–2015) развијају методе за нумеричко решавање полиномских једначина, док каснији (2018–2020) примењују ове технике у контексту одрживог развоја, паметних градова и еколошког мониторинга. Овај прелаз од теоријских алгоритама ка применама у енергетици и урбанистици одражава савремене тенденције у математичким наукама. Тренутно учествује у једном националном и међународном истраживачком пројекту, иако детаљи нису наведени. Као аутор више уџбеника (укључујући "Нумеричко решавање нелинеарних једначина" и "Математика 4"), значајно доприноси образовном процесу на Електронском факултету.

• Software Reliability
• Theoretical Computer Science
• Formal Methods
• +۷ مورد دیگر

Prof. Javier Esparza is a full Professor in the Department of Computer Science at the Technical University of Munich (TUM), leading the Chair for Foundations of Software Reliability and Theoretical Computer Science since 2007. His academic career includes roles at the University of Stuttgart (2003–2007) and the University of Edinburgh (2001–2003). He holds a Ph.D. in Computer Science from the University of Zaragoza (1990) and completed his habilitation at the University of Hildesheim (1994). His research focuses on formal methods for software reliability, concurrency theory, program analysis, and model checking. Key interests include Petri nets, automata theory, probabilistic systems, and verification techniques for distributed systems. He has authored influential books such as Free Choice Petri Nets and Unfoldings: A Partial-Order Approach to Model Checking , and developed tools like Rabinizer and Peregrine. Prof. Esparza has received prestigious awards, including the ERC Advanced Grant (2018) and an honorary doctorate from Masaryk University (2009). His work bridges theoretical foundations and practical applications, contributing to advancements in verification algorithms and distributed computing.

• Algebra
• Number Theory
• Algebraic Geometry
• +۵ مورد دیگر

Prof. Dr. Marc Nieper-Wißkirchen is a University Professor of Algebra and Number Theory at the University of Augsburg's Institute of Mathematics, part of the Faculty of Mathematics, Natural Sciences and Technology. Since December 2008, he has held this W3 professorship, and since October 2021, he serves as Vice Dean of the Mathematisch-Naturwissenschaftlich-Technische Fakultät (MNTF). His academic career includes a Junior Professorship at Johannes Gutenberg University Mainz (2004–2008) and postdoctoral research at the University of Cambridge and the Max Planck Institute for Mathematics in Bonn. His research focuses on algebraic geometry, hyperkähler manifolds, Rozansky-Witten invariants, cohomology theories, and Hilbert schemes. Notable contributions include work on characteristic classes, periods for algebraic connections, and topological computations in differential equations. He has authored textbooks on algebra and contributed to advancing mathematical education through online resources. Publications highlight interdisciplinary connections between algebraic geometry and differential equations, with a focus on Stokes phenomena, moduli spaces, and Hodge structures. Despite no explicit awards listed, his prolific publication record and academic roles reflect significant scholarly impact. He advises students on thesis topics in algebra and number theory and maintains active involvement in teaching and institutional leadership.

Paul Balmerمشاهده پروفایل

استاد

• Tensor Triangular Geometry
• Algebraic Geometry
• Modular Representation Theory
• +۳ مورد دیگر

Paul Balmer is a Professor of Mathematics at the University of California, Los Angeles (UCLA), specializing in Algebra, Algebraic Geometry, and Representation Theory. He has held this position since 2008, following roles at ETH Zürich and other institutions. His research focuses on tensor triangular geometry, modular representation theory, homotopy theory, and Witt groups, with significant contributions to the field through over 70 publications. Education: Ph.D. in Mathematics from the University of Lausanne (1998), supervised by Manuel Ojanguren. Postdoctoral positions at the University of Western Ontario, Rutgers University, and the University of Münster. Research interests include tensor triangulated categories, algebraic K-theory, and geometric approaches to representation theory. He serves on the editorial board of the Annals of K-Theory and actively participates in conferences worldwide, including events in Ascona, Banff, Oberwolfach, and upcoming talks in Linz, Osaka, and Edinburgh. Teaching: Extensive experience in graduate and undergraduate courses at UCLA, including Algebra, Linear Algebra, Homological Algebra, and Complex Analysis. Current courses include Spring 2026 Graduate Algebra (Math 210C). Awards: Humboldt Research Award (2015-2016), Fellow of the American Mathematical Society (2012). Active in professional service, including organizing workshops and serving on academic committees.

• Arithmetic Groups
• Symmetric Spaces
• Lie Groups
• +۵ مورد دیگر

Lizhen Ji is a Professor in the Department of Mathematics at the University of Michigan. His research focuses on arithmetic groups, symmetric spaces, Lie groups, geometry, and topology. He has authored and edited numerous influential monographs and volumes, including works on arithmetic groups, compactifications of symmetric spaces, and geometric analysis. Ji has organized conferences and summer schools on topics such as cohomology, transformation groups, and moduli spaces. He serves as an editor for journals like the Asian Journal of Mathematics and Pure and Applied Mathematics Quarterly , and has contributed to popularizing mathematics through outreach activities. His work bridges pure mathematics, geometry, and topology, with a strong emphasis on international collaboration and academic leadership.

Yong Linمشاهده پروفایل

پژوهشگر

• Artificial Intelligence
• Machine Learning
• Automated Theorem Proving
• +۲ مورد دیگر

Yong Lin is a Postdoctoral Research Associate at the Princeton AI Lab, Princeton University. His work focuses on artificial intelligence, machine learning, and automated theorem proving, with a particular emphasis on improving the robustness, generalization, and alignment of large language models (LLMs). Research interests include Automated theorem proving for formal systems Reinforcement learning and reward modeling Out-of-distribution generalization in deep learning Efficient model merging and parameter control Recent publications highlight advancements in mathematical reasoning , LLM alignment , and robust AI systems , with a focus on addressing spurious correlations, invariant principles, and compositional reasoning challenges in multimodal and language models. Yong Lin collaborates with Chi Jin as part of the Princeton AI Lab, contributing to open-source frameworks like Goedel-prover while exploring theoretical foundations for practical AI applications.

Raimund Kirnerمشاهده پروفایل

استاد

• Parallel Computing
• Real-Time Systems
• Embedded Systems
• +۵ مورد دیگر

Dr. Raimund Kirner is a Reader in Cyberphysical Systems at the University of Hertfordshire, UK. His research focuses on embedded computing, parallel computing, and real-time systems, with notable contributions to worst-case execution time (WCET) analysis, compiler design for predictability, and cybersecurity. He leads several projects, including the EU-funded CRAFTERS initiative and the FWF-supported SECCO and FORTAS-rt projects. Kirner holds a PhD from TU Vienna (2003) and a Habilitation (2010). He has authored over 100 publications and holds two patents. His work bridges many-core and embedded systems, emphasizing reliability and predictability. Education: PhD in Computer Science, TU Vienna, 2003 Habilitation, TU Vienna, 2010 Research Interests: Kirner's research spans parallel computing architectures, WCET analysis for real-time systems, compiler optimizations for predictability, and cybersecurity in embedded systems. He explores machine learning applications for medical diagnostics (e.g., EEG-based Alzheimer detection) and quantum cryptography frameworks. His work on mixed-criticality systems aims to enhance reliability in automotive and aerospace domains. Grants and Projects: Principal Investigator (CRAFTERS): EU Artemis-JU project addressing multi-core predictability (2012–2015) Co-Investigator (ADVANCE): FP7 project optimizing parallel programs (2012–2015) FWF-funded projects: SECCO (coverage preservation), FORTAS-rt (WCET measurement), and CoSTA (compiler timing predictability) Awards: Two patents granted for innovations in cybersecurity and embedded systems Labs and Teams: Kirner contributes to the Centre for AI and Robotics Research and the Cybersecurity and Computing Systems Group at the University of Hertfordshire. His collaborations include projects with TU Vienna and industry partners like the Technology Strategy Board. Advising: He has supervised one doctoral student, as documented in his Scopus profile, and oversees interdisciplinary research teams in cyber-physical systems.

Richard Verasمشاهده پروفایل

استادیار

• High Performance Computing
• Parallel Algorithms
• Computational Linear Algebra
• +۴ مورد دیگر

Dr. Richard Veras is an Assistant Professor in the School of Computer Science at the University of Oklahoma . His research focuses on High Performance Computing (HPC), with emphasis on code synthesis, parallel algorithms, and optimizing computational workflows for modern hardware architectures. Education: Ph.D. and M.S. in Electrical and Computer Engineering from Carnegie Mellon University B.S. in Mathematics and Computer Science from The University of Texas at Austin Research Interests: High Performance Computing (HPC) Parallel algorithm design and implementation Computational linear algebra and signal processing Graph analytics and network modeling Compiler optimizations and automated code generation Performance portability across hardware architectures Professional Experience: Research Scientist at Louisiana State University Postdoctoral Researcher at Carnegie Mellon University Labs/Teams: Leads HPC research initiatives at OU, focusing on code synthesis tools and performance optimization frameworks.

Payman Kassaeiمشاهده پروفایل

استاد

• Arithmetic Geometry
• Modular Forms
• Galois Representations
• +۳ مورد دیگر

Professor Payman Kassaei is a leading academic in Number Theory at King's College London, Department of Mathematics. He holds the rank of Professor and specializes in Arithmetic Geometry, focusing on the interplay between modular forms, Galois representations, and Shimura varieties. His research emphasizes p-adic methods, including overconvergent modular forms and their role in the p-adic Langlands programme. Kassaei obtained his PhD from MIT in 1999 and has held positions at institutions like McGill University before joining King's in 2006. Education: PhD in Mathematics from MIT (1999) Affiliations: Member of the Number Theory group at King's College London His research explores congruences between modular forms modulo primes, leveraging cohomology studies over Shimura varieties. Key topics include Hilbert modular forms, canonical subgroups, and mod p dynamics of Hecke operators. Kassaei has contributed to major projects like the Langlands Programme and has held EPSRC grants. His work appears in prestigious journals such as Compositio Mathematica and Astérisque . Recent publications highlight advancements in Jacquet-Langlands relations, minimal weights of Hilbert modular forms, and p-adic dynamics. His 2023 paper on Jacquet-Langlands relations and Serre filtration exemplifies his innovative use of geometric methods. Kassaei has organized conferences and delivered invited lectures, including a 2024 inaugural lecture on 'Arithmetic and Geometry.' Grants include leadership in the EPSRC-funded 'Langlands reciprocity and the geometry of Shimura varieties.' Collaborations with scholars like Fred Diamond and Shu Sasaki underscore his international impact in arithmetic geometry.

• Algebra
• Computational Complexity
• Theoretical Computer Science
• +۳ مورد دیگر

Prof. Pawel M. Idziak is a distinguished academic at the Faculty of Mathematics and Computer Science, Jagiellonian University, where he holds a Professorship in the Department of Algorithmics. His primary research focuses on algebra, computational complexity, and theoretical computer science, with notable contributions to the study of algebraic structures, satisfiability problems, and modular circuits. Idziak has led and contributed to numerous grants, including managing the 'Numerical and Structural Invariants in Algebra, Logic and Constraint Satisfaction Problems' (2015–2022) and co-ordinating initiatives like 'Programmers of the Future 4.0'. His work bridges abstract algebra with computational challenges, addressing topics such as group theory, circuit satisfiability, and algorithmic complexity. His research interests span a broad spectrum of theoretical computer science and algebra, including the interplay between algebraic structures and computational problems. Idziak has advised over 20 PhD students, mentoring them in areas such as algorithm design, logic in computer science, and discrete mathematics. He has also contributed to foundational works like Generative Complexity in Algebra (2005) and co-authored influential papers in journals like SIAM Journal on Computing and Transactions of the American Mathematical Society . Idziak’s academic leadership is evident through his roles in institutional committees and his active participation in international conferences. His recent work (2024) on equation satisfiability in solvable groups highlights his ongoing contributions to advancing computational algebra and complexity theory.

• Psychophysiology
• Cognitive Neuroscience
• EEG Analysis
• +۳ مورد دیگر

Dr. Balazs Czigler is an Associate Professor in the Department of General Psychology and Methodology at the Institute of Psychology, Faculty of Humanities and Social Sciences, Károli Gáspár Reformed University. With a PhD in Behavioral Science from the Hungarian Academy of Sciences, Institute of Cognitive Neuroscience and Psychology, he specializes in psychophysiology and cognitive neuroscience research. Dr. Czigler earned his MA in Psychology from Eötvös Loránd University (ELTE) in 2000 and completed his medical degree from Semmelweis University in 2007. His doctoral dissertation, defended in 2010, focused on EEG changes in Alzheimer's disease, examining EEG spectrum, coherence, complexity, and event-related potentials. His primary research interests lie in psychophysiology and cognitive neuroscience, with a specific focus on quantitative EEG analysis, neural complexity, and event-related potentials in both healthy and pathological conditions. Dr. Czigler's work has particularly emphasized the application of EEG methodologies to understand cognitive changes in Alzheimer's disease, mental arithmetic processing, and the effects of substances like alcohol on cognitive functions. His methodological expertise spans spectral analysis, coherence measurement, and linear-nonlinear complexity features of EEG signals. Analysis of Dr. Czigler's publication record from 2006-2012 reveals a consistent research trajectory focused on quantitative EEG methodologies applied to cognitive neuroscience questions. His work demonstrates a progression from basic EEG analysis techniques to increasingly sophisticated applications examining neural complexity, synchronization patterns, and their relationship to cognitive performance in both healthy and pathological aging. A significant portion of his research addresses Alzheimer's disease, establishing him as a specialist in EEG biomarkers for early cognitive decline, while other studies explore how substances like alcohol affect cognitive processing as measured by EEG. Dr. Czigler is a member of the Hungarian Neuroscience Society and has contributed to the field through conference organization, notably participating in the organization of The 28th International Epilepsy Congress in Budapest in 2009. His collaborative research approach is evident through his numerous co-authorships with researchers including Molnár M., Gaál Zs. A., and Boha R., suggesting active participation in research networks focused on cognitive neuroscience and clinical applications of EEG. Fluent in English with conversational French skills, Dr. Czigler maintains an active research program that bridges psychology, neuroscience, and clinical applications. His work represents an important contribution to the understanding of brain-behavior relationships through electrophysiological methods, with particular relevance to cognitive aging and neurodegenerative conditions.

• Theoretical Computer Science
• Computational Complexity
• Randomness Extraction
• +۱۳ مورد دیگر

Eshan Chattopadhyay is a prominent researcher in theoretical computer science, focusing on computational complexity, pseudorandomness, and cryptography. His work centers on the explicit construction of randomness extractors, condensers, pseudorandom generators, and non-malleable codes, often improving entropy requirements and error bounds. He has made significant contributions to derandomization, space-bounded computation, and tamper-resilient cryptography. His research is published extensively in the Electronic Colloquium on Computational Complexity (ECCC), indicating deep engagement with foundational aspects of computer science. Research Interests: Eshan's research spans randomness extraction from weak sources, including sumset sources, polynomial sources, and adversarial models. He investigates pseudorandomness for branching programs, linear threshold functions, and Fourier-based constructions. His work in cryptography includes non-malleable codes, leakage resilience, and secret sharing under bounded collusion. He also contributes to combinatorics through extremal hypergraphs and designs, and to complexity theory via lower bounds and derandomization techniques. The recent articles (2021–2025) show a continued focus on improving extractor and condenser constructions under challenging models such as number-on-forehead protocols, online adversaries, and interleaved or adversarial sources. There is a strong trend toward handling sources with very low entropy, achieving near-optimal parameters, and extending results to two-sided and unbalanced settings in expander graphs. His collaborations with leading researchers like Xin Li, David Zuckerman, and Jesse Goodman reflect his central role in the community. Scientific Awards: No specific awards mentioned in the provided text. Advising and Grants: While no formal students or grants are listed, the volume and depth of publications suggest active mentorship and likely grant funding in theoretical computer science. His work often involves junior collaborators, indicating a role in guiding emerging researchers. Labs and Teams: No specific lab or team affiliations are mentioned in the scraped content. However, his frequent co-authorship with researchers from institutions like UT Austin, CMU, and others implies participation in collaborative research networks focused on complexity and cryptography.

• Formal analysis
• Software testing
• Programming languages
• +۶ مورد دیگر

Alastair Donaldson is a Professor and Director of Research in the Department of Computing at Imperial College London, where he has been a faculty member since 2011. He leads the FastPL research group (formerly Multicore Programming Group), focusing on formal analysis, software testing, and programming languages techniques for improving software reliability, with special emphasis on high-performance systems. Donaldson earned his BSc (First Class Honors) in Computing Science and Mathematics from the University of Glasgow in 2003, followed by a PhD in Computing Science from the same institution in 2007 under Alice Miller. His academic journey includes positions as an EPSRC Postdoctoral Research Fellow at Oxford, Visiting Researcher at Microsoft Research Redmond, and Research Engineer at Codeplay Software Ltd. His research spans automated reasoning, compiler verification, and GPU programming with significant contributions to software reliability. He pioneered metamorphic testing for graphics drivers through GraphicsFuzz (acquired by Google in 2018) and developed innovative compiler fuzzing techniques. His work addresses critical challenges in memory models, concurrency, and verification of complex systems. Recent publications show increasing focus on applying these techniques to modern challenges including AI-generated code and verification-aware programming languages like Dafny. Donaldson's scientific contributions have been recognized with the 2017 BCS Roger Needham Award, an EPSRC Early Career Fellowship, and multiple best paper awards including EuroSys 2024 (Best Paper), ICST 2024 (Best Industry Paper), and ISSTA 2023 (Distinguished Paper). His 2012 GPUVerify paper received the ACM SIGPLAN Most Influential OOPSLA Paper Award in 2022. As an advisor, Donaldson has mentored numerous PhD students and postdocs, many now in prominent academic and industry positions. His research is supported by Amazon Research Awards (2022-2023) for Dafny ecosystem testing and compiler validation. He previously served as Senior Software Engineer and Visiting Researcher at Google following the GraphicsFuzz acquisition. The FastPL group maintains strong industry connections with Google, Microsoft, and Amazon, ensuring practical relevance of their theoretical work. Donaldson currently serves as Editor-in-Chief of ACM TOPLAS (2025-present) and on program committees for major conferences including PLDI, ICSE, and ASPLOS.

• Industrial Analytics
• AI/Data Science for IoT-enabled Smart Systems
• Reliability Modeling and Optimization
• +۴ مورد دیگر

Akash Deep serves as an Assistant Professor in the Department of Industrial Engineering within the School of Industrial Engineering & Management at Oklahoma State University. His academic foundation includes a Ph.D. in Industrial Engineering from UW-Madison (2022), an M.S. in Statistics from the same institution (2020), and a B.Tech from IIT Roorkee, India (2017). Dr. Deep's research centers on industrial analytics , integrating statistical methodologies with industrial knowledge to model complex systems. Key focus areas include AI for IoT-enabled smart systems, reliability optimization of engineering systems, stochastic control processes, and domain-aware machine learning. His methodological expertise spans stochastic processes, Bayesian optimization, and reinforcement learning. His publication record demonstrates strong alignment with industrial IoT applications and system reliability, particularly through his 2021 IISE Transactions paper on degradation modeling with imperfect maintenance. This work exemplifies his approach of combining data-driven modeling with industrial process constraints. Dr. Deep actively mentors graduate students in industrial engineering and maintains professional engagement through GitHub repositories focused on machine learning implementation and algorithm development. His technical contributions include MATLAB-based deep learning tools and R packages for multivariate arithmetic reduction.