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.
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.
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.
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.
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.
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.
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.
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.
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.
Sylvie Putot is a Professor of Computer Science at École Polytechnique, where she is affiliated with the LIX (Computer Science Laboratory) and a member of the Cosynus research team. Her office is located in the Alan Turing Building in Palaiseau. She is actively involved in research, teaching, and organizing the LIX Seminar series. PhD in Computer Science, École Polytechnique (specific year not provided) Previous affiliation: CEA LIST, where she contributed to the FLUCTUAT static analyzer Her research focuses on the formal verification of numerical programs, hybrid systems, and cyber-physical systems. She employs abstract interpretation, particularly using zonotopic domains, to analyze floating-point computations, rounding errors, and robustness. Her work bridges theoretical computer science with practical applications in safety-critical domains such as aerospace, robotics, and embedded systems. She is also extending these methods to ensure the safety and explainability of AI systems. The recent publications highlight a strong trend toward formal methods for AI safety, mobile robotics, and quantified reachability. Her work consistently involves collaboration with Eric Goubault and others, and spans from foundational static analysis to applied verification in complex systems. Keywords across publications include formal methods, verification, cyber-physical systems, abstract interpretation, and AI safety. Scientific Awards No specific awards mentioned in the provided text. She advises numerous PhD students on topics related to verification, robotics, and numerical analysis. Her research is funded by major projects such as SAIF (Safe AI through Formal methods), FARO (algorithmic foundations of robot swarms), and ANR projects like NusSCAP and COVERIF. She has led or participated in many national and international research initiatives. She is a key member of the Cosynus team at LIX, which focuses on the co-design of safe and intelligent systems. She organizes the monthly LIX Seminar, fostering academic exchange within the laboratory and the broader Institut Polytechnique de Paris community.
Nathan Wiebe is a Research Fellow in Quantum Information Science at the University of Toronto and Pacific Northwest National Laboratory, specializing in quantum algorithm development for simulating physical systems, machine learning, optimization, and quantum system characterization. His interdisciplinary work bridges theoretical computer science and quantum physics to advance computational methodologies. His academic credentials include: PhD in Physics from the University of Calgary MSc in Physics from Simon Fraser University BSc in Mathematical Physics from Simon Fraser University Dr. Wiebe's research focuses on designing quantum algorithms that enable efficient simulation of quantum dynamics, enhance machine learning models through quantum acceleration, and solve complex optimization problems. His theoretical contributions address fundamental challenges in quantum computational complexity and circuit design, with applications spanning physics, chemistry, and artificial intelligence. His publication history demonstrates a clear trajectory toward increasingly sophisticated quantum computational frameworks, particularly in quantum linear algebra (2019), foundational quantum machine learning (2017), and Hamiltonian simulation techniques (2012). These works collectively establish him as a key contributor to quantum algorithm theory with emphasis on practical implementability. His professional recognition includes: Yale Distinguished Lecture Award (2022) No information regarding student supervision or research funding sources was provided in the source material. He maintains affiliations with the Department of Computer Science at the University of Toronto and the Wellcome Centre for Human Neuroimaging, though specific research group structures or laboratory facilities were not detailed in the available documentation.
Trung Tuyen Truong is a Professor of Mathematics at the University of Oslo, Faculty of Mathematics and Natural Sciences, Department of Mathematics, since September 2023. Previously, he served as an Associate Professor at the same institution from September 2017 to September 2023. His academic journey includes a PhD from Indiana University (2006-2012) under Professor Eric Bedford, followed by postdoctoral positions at Syracuse University (2012-2014), Korea Institute for Advanced Study (2014-2015), and The University of Adelaide (2015-2017). His research spans several complex variables, dynamical systems, and related topics in algebraic geometry. He is particularly interested in applying dynamical systems theory and computational techniques to solve both theoretical and practical problems. His work includes developing optimization algorithms, exploring connections between Weil's Riemann hypothesis and dynamical systems, and investigating the Jacobian conjecture. Truong has made significant contributions to optimization methods, particularly developing Backtracking Gradient Descent and Backtracking New Q-Newton's method, which have applications in deep learning and solving complex mathematical problems. His research has been supported by various grants including a Seed grant from UiO Growth House for developing AI tools for mathematical education, membership in MSCA-Cofund-DP for ERC funding PhD positions, and as Principal Investigator for the Young Research Talents grant from the Research Council of Norway. Truong is actively involved in developing software tools to help students learn mathematics and optimization, as well as creating efficient computational tools. His NUV Optimization solvers have demonstrated superior performance compared to established tools like Matlab and IPOPT in certain optimization problems. He is collaborating with AI companies and medical doctors to use AI for diagnosing from lung data measurements. His publications demonstrate expertise across pure mathematics, computational methods, and interdisciplinary applications, with recent work focusing on dynamical systems approaches to number theory problems and practical optimization algorithms.
Dor Abrahamson is a Professor of Learning Sciences and Human Development in the Graduate School of Education at the University of California Berkeley. He directs the Embodied Design Research Laboratory (EDRL) and has established himself as a leading design-based researcher in the field of mathematics education. His work bridges cognitive science, socio-cultural theory, and embodiment paradigms to create innovative learning environments that transform how students understand mathematical concepts. Abrahamson's research focuses on the relationship between physical action and conceptual learning, with particular emphasis on how embodied interaction facilitates mathematical understanding. His work spans intensive quantities like ratio, likelihood, and slope, as well as early algebra concepts. He develops experimental technological materials and activities that have potential for scale-up, while simultaneously refining theoretical constructs in the learning sciences and developing frameworks for educational design. His current projects include developing embodied-interaction technological systems for the guided reinvention of mathematical concepts, with recent work involving computer-embedded animated avatars that incorporate naturalistic gesture in face-to-face mathematics tutoring. Analysis of Abrahamson's recent publications reveals a strong focus on multimodal learning approaches, with significant attention to how gesture, eye-tracking, and physical manipulation contribute to mathematical understanding. His work increasingly integrates learning analytics, complex systems theory, and inclusive design principles, particularly in developing educational technologies for diverse learners. The research demonstrates a consistent trajectory toward understanding the micro-dynamics of learning through embodied interaction while creating practical educational interventions. National Academy of Education/Spencer Postdoctoral Fellowship for Seeing Chance Co-recipient of NSF grants for developing naturalistically gesturing interactive pedagogical avatars Co-recipient of NSF grants for fostering children's productive dispositions toward failure when programming Best Submission—Dr. Arthur I. Karshmer Award for Assistive Technology Research Finalist, Best Paper, IJCCI 2021 Abrahamson has secured significant research funding including NSF grants for developing gesturing avatars and for understanding how children approach programming failures. His work emphasizes collaborative, interdisciplinary research that brings together experts from education, cognitive science, movement science, and computer science. He has mentored numerous doctoral students and collaborators who have gone on to contribute significantly to the field of educational design. The Embodied Design Research Laboratory operates as a hub for innovative research that combines theoretical rigor with practical educational applications. The Embodied Design Research Laboratory (EDRL) represents a multi-disciplinary approach to educational design, inspired by the belief that all students can deeply understand mathematics. EDRL projects involve creating mixed-media materials that are iteratively refined based on empirical studies of student multimodal behaviors including speech, gesture, and eye-gaze. The lab has produced numerous technological innovations including motion sensor applications, touch screen interfaces, and agent-based simulations that support embodied mathematical learning.
Dr. Lecturer Yeşim AKÜZÜM ÖZEN is a full-time faculty member at Kafkas University's Faculty of Arts and Sciences, Department of Mathematics. She holds a PhD in Mathematics (2019) and a Master's (2014) from Kafkas University, with a Bachelor's from Atatürk University (2009). Her research focuses on Algebra and Number Theory, particularly recurrence sequences (Fibonacci, Pell, Jacobsthal) in finite groups, matrix theory applications, and modulo m properties. PhD: Mathematics, Kafkas University (2014-2019) Master's: Mathematics, Kafkas University (2012-2014) Bachelor's: Mathematics, Kafkas University (2009-2012) and Atatürk University (2008-2009) Her work explores complex-type sequences, Hadamard matrices, and polyhedral group structures, with over 81 publications and collaborations with researchers like Ö. Deveci and A.G. Shannon. She supervises master's students in specialized courses like Matrix Theory and Special Integer Sequences . Recent articles analyze the Balancing-Pell sequence modulo m, Leonardo-Pell numbers, and Mersenne-Jacobsthal sequences, demonstrating her focus on hybrid number systems and group-theoretic applications. Her research has been cited 82 times, with an h-index of 4. Supervised theses: Arzu Genç (2025), Tan Barış Aydin (2025), Didem İnce Özüak (2024), Ayhan İncekara (2024) Co-authored projects with Ö. Deveci (2017-2025) and E. Karaduman (2015-2019)
Özgür ERDAĞ is a Lecturer in the Department of Mathematics at Kafkas University, Faculty of Arts and Sciences , Turkey. His academic career began as a Research Assistant in 2020, and he was promoted to Lecturer in 2023. PhD in Mathematics, Kafkas University, Institute of Science (2017) MSc in Mathematics, Kafkas University, Institute of Science (2013–2015) BSc in Mathematics, Atatürk University, Faculty of Science (2008–2012) His research focuses on Algebra and Number Theory , particularly exploring connections between complex-type sequences (e.g., Narayana, Padovan, Pell) and their matrix-based generalizations. He investigates periodicity, finite sums, and Binet formulas of these sequences modulo m. With over 52 publications and 62 citations, Özgür has collaborated extensively with mathematicians such as Ömer Deveci and Anthony G. Shannon . His work has been presented at international conferences like the International Hazar Scientific Research Conference and Bilsel International Congress .
