جستجوی استاد راهنمای دانشگاهی

• Computer Science
• Cryptography
• Number Theory
• +۲ مورد دیگر

Nirvana Coppola is a researcher affiliated with Vrije Universiteit Amsterdam, specializing in formal methods, cryptography, and computational mathematics. She presented her work on formalizing class group computations and integral points on Mordell elliptic curves at the CPP 2023 workshop co-located with POPL 2023 in Boston, Massachusetts. Her research focuses on applying programming language techniques to analyze theoretical properties of deep learning and advancing computational approaches in number theory. The work on Mordell elliptic curves highlights her contributions to formal verification and cryptographic algorithms. While no specific awards, students, or grants are listed in the provided materials, her involvement in POPL 2023 underscores active participation in academic research communities. Further details about her institutional roles or collaborative projects remain unspecified in the given text.

Osman Hasanمشاهده پروفایل

استاد

• Formal Methods
• Theorem Proving
• Hardware Verification
• +۷ مورد دیگر

Professor Dr. Osman Hasan is a faculty member at the School of Electrical Engineering & Computer Science (SEECS) , National University of Sciences & Technology (NUST) , Islamabad, where he currently serves as Pro-Rector (Academics). His research primarily lies in formal methods, hardware verification, reliability analysis, and embedded systems, with applications in smart grids, robotics, and cybersecurity. His research interests include: Formal Methods and Theorem Proving Hardware and Software Verification Reliability and Safety Analysis of Critical Systems Smart Grids and Power Systems Approximate Computing and Energy-Efficient Design Robotics and Biomedical Systems His recent publications show a strong trend toward formal verification of hardware and cyber-physical systems, integration of machine learning with formal methods, and applications in power systems and robotics. He frequently employs higher-order logic theorem proving (e.g., HOL, ACL2) and model checking to ensure correctness and reliability. He has been actively involved in numerous international conferences such as FMCAD, FSEN, CICM, and ICTAC, serving on program committees and organizing workshops. His leadership as Pro-Rector highlights his commitment to enhancing academic quality and research excellence at NUST. He has supervised numerous students who have co-authored papers with him, indicating an active research group. His work bridges theoretical formal methods with practical engineering applications, particularly in safety-critical domains.

Uwe Waldmannمشاهده پروفایل

پژوهشگر

• Computer Science
• Artificial Intelligence
• Formal Logic
• +۳ مورد دیگر

Dr. Uwe Waldmann is a researcher at the Max Planck Institute for Informatics in Saarbrücken, Germany, affiliated with the Automation of Logic research group. He leads research initiatives in Combinations of Deductive Systems and First-Order Model Checking , contributing to formal logic and automated reasoning. Contact: uwe@mpi-inf.mpg.de . Research areas: Automated reasoning, model checking, deductive systems As an academic researcher, Waldmann's work focuses on theoretical foundations of logic in computer science. His contributions span formal methods, theorem proving, and computational complexity. He also serves as Ombudsperson for the institute, ensuring ethical standards in research practices.

Omar Rivasplataمشاهده پروفایل

مدرس ارشد

• Machine Learning
• Deep Learning
• Reinforcement Learning
• +۶ مورد دیگر

Omar Rivasplata is a Senior Lecturer in Machine Learning and Robotics, specializing in theoretical and applied machine learning. He is affiliated with the MCAIF: Centre for AI Fundamentals, focusing on advancing foundational aspects of artificial intelligence. His research spans reinforcement learning, Bayesian analysis, neural networks, and generalization bounds. Key research interests include the theoretical underpinnings of deep learning architectures, optimization strategies for large-scale models, and probabilistic methods in machine learning. Notable contributions include work on gradient clipping for wide/deep networks and semi-pessimistic reinforcement learning frameworks. Leading the MCAIF project (2021–2026), a multidisciplinary initiative exploring core AI principles. Active collaborations with institutions worldwide, emphasizing open-access publications (e.g., Transactions on Machine Learning Research). His articles explore topics like PAC-Bayesian theory, convergence of diffusion models, and meta-analyses of Bayesian methods. He advises a diverse cohort of postgraduate researchers in AI fundamentals.

• Large Language Models
• Formal Language
• Mathematical Formalization
• +۳ مورد دیگر

Lan Zhang holds a Doctor of Philosophy in the Department of Computer Science. His research focuses on formalization of mathematical statements using large language models (LLMs), syntactic analysis, and information retrieval systems. He has contributed to constructing mathematical libraries through consistent autoformalization techniques and explored epistemic ensemble methods for formal mathematical reasoning. His work bridges AI with symbolic mathematics, emphasizing automated theorem proving and latent space modeling. Research interests include large language models, formal verification, and mathematical logic. His recent studies address challenges in de-noising formal languages and evaluating unsupervised text representations using synthetic datasets. Zhang's publications span topics from latent variable analysis to ensemble methods in mathematical reasoning systems. His research outputs demonstrate expertise in applying LLMs to formalize complex mathematical definitions and derive multi-operational mathematical processes in latent spaces. While no awards or grants are explicitly mentioned, his active contributions to mathematical formalization and VAE-based text representation indicate significant academic engagement.

Sean Holdenمشاهده پروفایل

استاد

• Automated Theorem Proving
• Machine Learning
• Artificial Intelligence
• +۵ مورد دیگر

Sean Holden is a Professor in the Department of Computer Science and Technology at the University of Cambridge, affiliated with The Computer Laboratory. He holds positions at both the University and Trinity College. His research focuses on automated theorem proving, machine learning, and their intersections with formal methods and AI. He leads the development of the Connect++ theorem prover, which won the Best Newcomer award at CASC 2024. His work spans areas including connection calculus, graph neural networks, and reinforcement learning in games like Mahjong. Holden's academic contributions include over 50 publications in top venues such as IJCNN, ICPRAM, and Journal of Automated Reasoning. Notable works include foundational research on Bayesian methods in theorem proving and machine learning applications in bioinformatics and medical imaging. He serves as an Associate Editor for IEEE Transactions on Artificial Intelligence since 2023. His research group explores topics like protein graph embeddings, medical AI (e.g., breast cancer classification via MUGI-MRI), and the integration of machine learning with automated reasoning systems. Collaborations span institutions including Trinity College and international partners in computational biology and AI.

Ronald Garciaمشاهده پروفایل

دانشیار

• Programming Languages
• Gradual Typing
• Type Systems
• +۶ مورد دیگر

Ronald Garcia is an Associate Professor in the Department of Computer Science at the University of British Columbia, affiliated with the Faculty of Science and the Software Practices Lab. His research focuses on programming language theory, emphasizing gradual typing, type systems, and end-user programming. He teaches courses such as Programming Language Principles (CPSC 509) and Compiler Construction (CPSC 411). Education details are not explicitly stated in the provided texts, but his extensive academic contributions span over two decades. Research interests include formal semantics of programming languages, typestate systems, dependent types, and improving software practices for end-users. He has advised numerous Ph.D. and Master's students, contributing to over 30 academic publications. Recent work explores hybrid programming environments for end-users, runtime type slicing for debugging, and foundational frameworks for gradual dependent types. Garcia actively participates in academic conferences as a PC member and keynote speaker, notably in PLDI, POPL, and ICFP. His lab and collaborations focus on advancing theoretical foundations while addressing practical challenges in software development and education.

• Formal Methods
• Model-Driven Engineering
• SAT/QBF Solving
• +۴ مورد دیگر

Martina Seidl is a researcher and principle investigator at TU Wien's Institut für Softwaretechnik und Interaktive Systeme (E188). Her main affiliation is within the Faculty of Informatics. She leads the FAME Project (Formalizing and Managing Evolution in Model-Driven Engineering), focusing on advancing formal methods in software engineering. Her research interests include formal verification techniques, SAT/QBF solving algorithms, model-driven engineering, and automated reasoning. She has contributed to advancements in quantified Boolean formula (QBF) solving, parallel computing methodologies for logical problems, and formal methods in software model analysis. Her work spans theoretical computer science and practical applications in automated theorem proving and model checking. Notable contributions include expansion-based QBF solving approaches, parallel solving frameworks, and feature-based classifications of formal verification techniques. Seidl co-organized the QBF Gallery initiative, which curates benchmark suites for quantified Boolean formula competitions. She has also published extensively on clause redundancy optimization, blocked clause analysis, and the integration of formal methods in educational contexts like UML@Classroom.

Jeremy Avigadمشاهده پروفایل

استاد

• Formal Methods
• Automated Reasoning
• Mathematical Logic
• +۷ مورد دیگر

Jeremy Avigad is a Professor in the Department of Philosophy and the Department of Mathematical Sciences at Carnegie Mellon University, where he serves as Director of the Hoskinson Center for Formal Mathematics and holds a Dean's Chair in Logic and Philosophy of Mathematics. His primary research interests include Formal Methods and AI for mathematics, mathematical logic, and the history and philosophy of mathematics. His work bridges deep theoretical inquiry with practical applications, particularly in formal verification and automated reasoning. His recent publications reflect a strong focus on the formalization of mathematics, automated reasoning, and the integration of AI techniques into theorem proving. Key themes include the development and application of the Lean theorem prover, premise selection, proof optimization, and the formal verification of computational claims, especially in the context of blockchain technology. CADE-25 Skolem Award Avigad advises PhD students, such as Chase Norman, and is involved in significant research grants and collaborations, including work with StarkWare. He is an active organizer of major academic events like Big Proof and the Formalization of Mathematics workshops. He leads the Hoskinson Center for Formal Mathematics, a research hub dedicated to advancing the field of formal mathematics.

Armando Solar-Lezamaمشاهده پروفایل

استاد

• Program Synthesis
• Neurosymbolic Programming
• Machine Learning
• +۵ مورد دیگر

Armando Solar-Lezama is a Professor at the MIT Schwarzman College of Computing , Associate Director and COO of MIT CSAIL, and leads the Computer-Aided Programming Group . He earned his BS in Computer Science and Mathematics from Texas A&M University and PhD (2008) from UC Berkeley under Rastislav Bodik. His research focuses on program synthesis at the intersection of Programming Systems and Artificial Intelligence, with recent work on neurosymbolic programming. Education: BS (Texas A&M), PhD (UC Berkeley) Labs: CSAIL, Center for Deployable Machine Learning His research explores automated reasoning and learning to reduce programming effort, including the development of the Sketch programming language. Current neurosymbolic work combines deep learning with logical reasoning for applications in multi-agent systems, RNA splicing, and interpretable policy generation. Recent projects include VLMaterial for procedural asset generation and CRUXEval for code evaluation benchmarks. Scientific awards include: Robin Milner Young Researcher Award (2024) Best Paper (PLDI 2005, PPoPP 2016) Outstanding Paper (EMNLP 2023) He advises graduate students through MIT's EECS PhD program and has developed courses like 6.820 Foundations of Program Analysis and Program Synthesis . His work impacts software synthesis automation, programming language design, and deployable machine learning.

• Modal Logic
• Coalgebra
• Automated Reasoning
• +۳ مورد دیگر

Prof Dirk Pattinson is a Professor in the School of Computing at Australian National University (ANU). His research focuses on modal logic, coalgebraic systems, automated reasoning, and formal methods. He holds a PhD in Computer Science and has supervised numerous research students. Research interests include coalgebraic logic, non-classical modal logics, automated theorem proving, and applications in computational social choice. His work bridges theoretical foundations with practical tools like the COOL reasoner for modal fixpoint logics. Notable contributions span over 70 peer-reviewed publications since 2008, with recent work on non-iterative modal resolution calculi (2024), Hennessy-Milner properties via topological methods (2022), and formal verification of voting systems (2021). His research often integrates algebraic, categorical, and coalgebraic perspectives.

Alex Potaninمشاهده پروفایل

دانشیار

• Programming Languages Design
• Software Security
• Type Systems
• +۵ مورد دیگر

Roles & Affiliations: Associate Professor at the Australian National University (ANU), School of Computing. Previously held positions at Victoria University of Wellington (2006–2013), Carnegie Mellon University (visiting scholar), and Kyoto University (sabbatical). Served as editor for multiple journals and conference proceedings, including OOPSLA and APSEC. Education: PhD in Programming Languages (Victoria University of Wellington, 2006) BSc(Hons) in Mathematics (Victoria University of Wellington) Research Interests: Focuses on foundational aspects of programming languages, including ownership, immutability, and capabilities. Designs secure languages like Wyvern (collaboration with CMU) and contributes to Rust's lifetime parameters. Explores module systems, effects, and verified software. Recent work addresses reproducibility debt in scientific software and quantum computing models. Article Trends: Recent publications emphasize reproducibility challenges, reactive system verification, and deductive program synthesis. Highlights include systematic reviews on reproducibility debt and formal methods for secure reactive systems. Awards & Recognition: ECOOP 2024 Distinguished Artifact Award FORTE 2022 Best Paper Award ESEC/FSE 2007 Distinguished Paper Award Advising & Grants: Supervises 15+ current students across PhD, Masters, and Honours. Co-leads projects on secure module systems and verified traits. Collaborates with industry (e.g., Alibaba's CUE language) and academia (CMU, KIT). Labs & Teams: Active in the Wyvern project, secure language design initiatives, and international collaborations on formal verification and programming language semantics.

Paul Krogmeierمشاهده پروفایل

پژوهشگر ارشد

• Programming Languages
• Logic
• Program Synthesis
• +۷ مورد دیگر

Paul Krogmeier is a postdoctoral fellow at Harvard University's School of Engineering and Applied Sciences (SEAS), advised by Nada Amin and Walter Fontana. His research bridges programming languages, logic, and program synthesis with applications in interpretable machine learning and computational universality. Starting Fall 2026, he will join the University of Colorado Boulder as an assistant professor in computer science. His educational background includes a PhD from the University of Illinois under Madhusudan Parthasarathy. Dr. Krogmeier investigates computational universality under errors, formal expressivity of neural networks, and symbolic language emergence. His work connects evolutionary processes with learning mechanisms and rigorously characterizes creativity through computational models. Ongoing projects focus on DSL synthesis for few-shot learning and developing frameworks where symbolic language emerges from computational processes. His publication record shows consistent contributions to programming languages theory (OOPSLA, POPL) with increasing integration of machine learning techniques. Key themes include program synthesis, logic-based learning, and formal analysis of neural architectures, demonstrating interdisciplinary rigor across theoretical computer science and AI. His scientific achievements include: Distinguished Paper Award at OOPSLA 2023 Distinguished Paper Award at POPL 2022 While currently not advising students as a postdoc, he will establish an advising program at CU Boulder. His research is supported by institutional resources at Harvard SEAS with potential future funding from NSF and computing industry partnerships. At Harvard, he collaborates within Nada Amin and Walter Fontana's research group, which explores foundational programming language concepts applied to machine learning systems and biological computation models.

• Formal Methods
• Programming Languages
• Program Verification
• +۳ مورد دیگر

Adithya Murali is an Assistant Professor at the University of Wisconsin-Madison's Department of Computer Science. His research focuses on Formal Methods and Programming Languages , specifically democratizing software verification through data-driven logic learning and neuro-symbolic approaches. He holds a Ph.D. from the University of Illinois at Urbana-Champaign (UIUC), advised by P. Madhusudan Parthasarathy. Education: Ph.D. in Computer Science (UIUC, 2024), B.Tech. from BITS-Pilani (2017) Roles: Subreviewer for PLDI, CONCUR, ICALP, and LICS; Teaching Assistant for courses in Logic, Compilers, and Trustworthy AI His research interests center on reducing the cognitive burden of software verification, enabling non-experts to verify code through innovative techniques like logic learning from data. Cross-disciplinary work integrates machine learning with symbolic reasoning, exemplified in projects like the CLEVR VDP Dataset and GQA VDP Dataset for visual discrimination puzzles. Recent publications span formal verification frameworks (e.g., FO-Complete Heap Logics), neuro-symbolic systems, and automated reasoning. His work has received the ACM Europe Best Paper Award (OOPSLA 2023) . Awards: Ray Ozzie Fellowship (2018), Gold Medal (BITS-Pilani 2017), INSPIRE Scholarship (2012-2016) Grants: UIUC Travel Grants, SIGPLAN Funding He advises students across UIUC and UW-Madison, focusing on program synthesis, verification, and AI integration. Collaborations include the Formal Methods Seminar at UIUC and volunteer roles at major conferences.

Clare Dixonمشاهده پروفایل

استاد

• Verification
• Automated Reasoning
• Temporal Logic
• +۵ مورد دیگر

Clare Dixon is a Professor of Computer Science in the Department of Computer Science at the University of Manchester, where she leads the Autonomy and Verification research group. Previously, she held a Professorship at the University of Liverpool (2001–2020) and was a Senior Research Fellow at Manchester Metropolitan University (1995–2000). She also holds an honorary professorship at the University of Liverpool (2020–2023). Her research focuses on verification for robotics and autonomous systems, formal methods, temporal and modal logics, and theorem proving. Key applications include ensuring systems meet safety and reliability requirements through tools like model checkers and theorem provers. She collaborates with robotics engineers to apply formal verification techniques in dynamic environments and swarm systems. Research Interests: Verification of autonomous systems, formal specification, temporal logics, automated reasoning, and reliable robotics. Her work aligns with UN Sustainable Development Goals, particularly in advancing reliable technology and improving quality of life. Awards: Best Paper Award (2018) Invitation to Sister Conference Best Paper Track (2017) Springer Best Paper Award (2014) Projects: She is a Co-Investigator in major initiatives such as the Centre for Robotics and Artificial Intelligence , FAIR-SPACE Hub , and S4: Science of Sensor System Software , focusing on autonomous systems, space robotics, and secure sensor networks. Labs/Teams: Leads the Autonomy and Verification group at Manchester and contributes to interdisciplinary teams in robotics, formal methods, and AI ethics.