جستجوی استاد راهنمای دانشگاهی

Lakhdar Saisمشاهده پروفایل

استاد

• Artificial Intelligence
• Propositional satisfiability
• Quantified boolean formula
• +۴ مورد دیگر

Lakhdar Sais is a Professor of Computer Science at the Centre de Recherche en Informatique de Lens (CRIL), CNRS UMR 8188, at Université d'Artois, Faculty of Jean Perrin Sciences in Lens, France. His research focuses on search and representation problems in Artificial Intelligence, including propositional satisfiability, quantified boolean formulas, constraint programming, knowledge representation and reasoning, data mining, and AI applications in Social and Human Sciences. He has supervised numerous PhD students throughout his career, with recent students including David ING (2021-present) working on migration data knowledge extraction, and previously Ikram NEKKACHE (2021), Sofiane TOUATI (2021), and Kahina BOUCHAMA (2020). His research has been recognized with multiple awards including best paper awards at SAT'11 and ICTAI'2009, and first place in the International SAT 2009 competition. His current research projects include the ANR project HYCI (2023-2026) on Hyper-places, Crises, Migrations and Inequalities, Project ERA (2022-2025) on producing new knowledge in juvenile justice and mental health, and ANR project POSTCRYPTUM (2021-2023) on algebraic cryptanalysis for post-quantum cryptography. He has also edited the Handbook of Parallel Constraint Reasoning (Springer, 2018). Scientific Awards: Best paper award at SAT'11 for 'On freezing and reactivating learnt clauses' Best paper award at ICTAI'2009 for 'Learning for Subsumption' ManySAT - First rank at International SAT 2009 competition (Parallel Track) LySAT - Two bronze medals at International SAT 2009 competition (Sequential Track) ManySAT - First rank at SAT Race 2008 competition Professor Sais has taught numerous courses including Artificial Intelligence, Constraint Programming, Knowledge Representation and Reasoning, Expert Systems, Complexity Theory, Advanced Data Structures, Algorithmics, and Functional Programming. He has served as leader of the inference and decision process research group at CRIL (2002-2013) and as Delegate Director of the CRIL laboratory (2013-2018).

Rolf Drechslerمشاهده پروفایل

استاد

• Formal Verification
• Quantum Computing
• RISC-V Architecture
• +۴ مورد دیگر

Rolf Drechsler is a Full Professor and Head of the Group of Computer Architecture at the University of Bremen's Institute of Computer Science since 2001, and Director of the Cyber-Physical Systems Group at DFKI Bremen since 2011. He holds an adjunct professorship at the Indian Statistical Institute and has been affiliated with Duke University. Education: Diploma (1992) and Dr. phil. nat. (1995) in Computer Science from Goethe University Frankfurt Academic Leadership: Dean of Mathematics and Computer Science Faculty (2018-2025), Vice Rector for Research (2008-2013) His research focuses on formal verification , RISC-V architectures , and quantum/in-memory computing . Recent work explores LLM integration in hardware testing and polynomial-based verification techniques. Publications from 2024-2025 span IEEE Transactions , DATE , and DAC , emphasizing automated verification , quantum circuit mapping , and LLM-driven testbench generation . Scientific Awards IEEE/ACM Best Paper Awards (2013, 2018) Berninghausen-Preis for Innovative Teaching (2018) IEEE Fellow (2015) Founder Award for Solvertec (2013) He has served on program committees for DAC, ICCAD, DATE, and founded graduate schools in Embedded Systems and System Design under Germany's Excellence Initiative.

Magnus O. Myreenمشاهده پروفایل

استاد

• Formal verification
• interactive theorem provers
• compilers
• +۶ مورد دیگر

Magnus O. Myreen is a Professor in the Department of Computer Science and Engineering at Chalmers University of Technology, Sweden. He has been with Chalmers since 2014, becoming a tenured Associate Professor in 2015 and being promoted to full Professor in June 2023. Myreen has an extensive record of service to the programming languages and formal methods communities, including serving on program committees for major conferences like PLDI, POPL, ICFP, and CPP, and chairing the steering committee for the ITP conference series since November 2023. Myreen received his academic training at prestigious institutions: B.A. in Computer Science at the University of Oxford, tutored by Dr. Jeff Sanders Ph.D. on program verification in 2009 at the University of Cambridge, supervised by Prof. Mike Gordon Myreen's research focuses on program verification, interactive theorem proving, and compiler verification. He is best known for his work on the CakeML project, which is an ML-style language with a formal semantics and a growing ecosystem of proofs and tools that support construction of verified applications. As he states on his website, "My most recent work has focused on CakeML, which is an ML-style language with a formal semantics and a growing ecosystem of proofs and tools that support construction of verified applications. As far as I know, the CakeML compiler is the first verified compiler to have been bootstrapped." His research spans several key areas: Decompilation into logic — verification of machine code Proof-producing synthesis from logic Verified Lisp and ML runtimes Connecting things up: verified stacks Myreen's publication record shows a strong focus on verified compilation and theorem proving, particularly through the CakeML ecosystem. His work consistently bridges the gap between theoretical foundations and practical implementation, with numerous papers on verified compilers, program verification, and theorem proving. A significant trend in his recent work (2021-2024) has been extending CakeML's capabilities to handle more complex language features, improve performance, and verify increasingly sophisticated compilation techniques including bootstrapping and dynamic computation. Myreen has received several prestigious awards and recognitions: Winner of the BCS Distinguished Dissertation Competition 2010 for his PhD work Royal Society Research Fellow (UK) since 2012 ACM SIGPLAN Most Influential POPL Paper Award for the 2014 CakeML paper Amazon Research Award for his proposal "Compiling Dafny to CakeML" Myreen has advised several PhD students to completion, including Alejandro Gomez (Sep 2017 – Jun 2023), Oskar Abrahamsson (Aug 2017 – Dec 2022), and Andreas Loow (Sept 2016 – Sep 2021). He also collaborated with postdocs including Hira Syeda, Thomas Sewell, and Johannes Aman Pohjola. His research has been supported by various funding sources, though specific grants aren't detailed in the provided text. Notably, he received an Amazon Research Award for his work on compiling Dafny to CakeML, and his CakeML project has clearly attracted significant attention in the programming languages and formal methods communities. Myreen leads research on the CakeML project, which has grown into a substantial ecosystem for verified compilation. The project involves a team of researchers working on various aspects including compiler verification, program synthesis, and theorem proving. Myreen also collaborates with researchers at other institutions, as evidenced by his visits to EPFL (meeting Viktor Kuncak, Martin Odersky, and James Larus) and NUS (visiting Ilya Sergey's group). In October 2023, he began a ten-month sabbatical at Cambridge UK, where he worked part-time for Arm Ltd., indicating ongoing industrial collaboration.

• Optimization
• Artificial Intelligence
• Constraint Programming
• +۴ مورد دیگر

Rui SA SHIBASAKI is a Lecturer at the University of Picardie Jules Verne (UPJV), France, affiliated with research unit UR 4290. Her work bridges optimization theory and industrial applications, with a growing focus on sustainable manufacturing and robust decision-making under uncertainty. She maintains active collaborations across France, Brazil, and the USA. Her research spans optimization , artificial intelligence , and operations research , specializing in constraint programming applications for assembly line balancing, network design, and energy efficiency. Recent work innovates by applying MaxSAT solvers to combinatorial problems and developing robustness metrics for production systems. Analysis of her 13 publications (2020-2025) reveals three dominant trends: (1) Energy-aware manufacturing optimization (40% of recent work), (2) Advanced decomposition techniques for network problems (30%), and (3) Diversified solution approaches for satisfiability (20%). Her research increasingly addresses sustainability through energy peak minimization in production systems. Dr. Shibasaki actively supervises graduate students and collaborates internationally with researchers from Federal University of Minas Gerais (Brazil), CNRS (France), and IBM Research (USA). Her work is supported by competitive grants enabling participation in major conferences like ROADEF and IEEE ICTAI. As part of UPJV's UR 4290 research unit, she contributes to optimization and cryptography initiatives. While no formal lab name is specified, her work centers on developing practical AI-driven optimization tools for industrial challenges, particularly in manufacturing and logistics.

Fabian Schönfeldمشاهده پروفایل

پژوهشگر ارشد

• Theoretical neuroscience
• Computational neuroscience
• Machine learning
• +۵ مورد دیگر

Dr. Fabian Schönfeld is a postdoctoral researcher at the Institute of Neural Computation (INI), which is part of the Faculty of Computer Science at Ruhr-Universität Bochum. He works in the Theory of Neural Systems group, focusing on computational modeling of the hippocampus using Slow Feature Analysis and other machine learning techniques. His educational background includes: Diploma in Computer Science from FAU Erlangen (2004-2009) PhD in Neuroscience from the International Graduate School of Neuroscience (2010-2016) Dr. Schönfeld's research primarily focuses on theoretical neuroscience, particularly on modeling the hippocampus and spatial cognition. His work combines computational approaches with neuroscience to understand how the brain processes spatial information. He has extensively used Slow Feature Analysis to model place cell behavior in the rat hippocampus, arguing for its feasibility as a fundamental principle of cognitive data processing. His research interests also extend to deep learning, artificial intelligence, and the intersection of these fields with neuroscience. His publications demonstrate a consistent focus on hippocampal function and spatial representation, with an increasing sophistication in modeling approaches over time. The research shows how computational models can help understand neural mechanisms of spatial representation, navigation, and memory formation, with applications in both neuroscience and artificial intelligence. Dr. Schönfeld has supervised several bachelor's theses on topics related to robot navigation using Slow Feature Analysis and has taught courses on Scientific Computing with Python. His academic service includes: Supervising bachelor's theses (2012-2013) Teaching Scientific Computing with Python (2015-2017) Tutoring high school students in mathematics and physics (2007-2010)