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Micaela MayeroView profile

Researcher

• Formal Proofs
• Symbolic Computation
• Formalization of Mathematics
• +12 more

Micaela Mayero is a Researcher at the Laboratoire d'Informatique de Paris Nord (LIPN), affiliated with the Institut Galilée at Université Paris Nord. Her work focuses on formal proofs, numerical analysis, and verification using the Coq proof assistant. Education : PhD in Computer Science (2001) from Université Paris VI, Habilitation à Diriger des Recherches (2012). Her research spans formal verification , type theory , and numerical analysis , with a strong emphasis on mechanized proofs in mathematics and programming. Recent publications highlight applications in Lebesgue integration , polynomial approximations , and partial differential equations , reflecting her expertise in bridging theoretical mathematics with computational verification. Key trends across her articles include formalization of mathematical theorems in Coq, verification of numerical algorithms, and refinement of Petri net models. Collaborations with teams across France and Europe underscore her interdisciplinary approach.

Mridul AanjaneyaView profile

Assistant Professor

• Computational Physics
• Programming Languages
• Robotics
• +4 more

Mridul Aanjaneya is an Assistant Professor in Computer Science at Rutgers University, focusing on Computational Physics, Programming Languages, and Robotics. His research bridges numerical accuracy and algorithm design for mathematical libraries. PhD from Stanford University Postdoctoral studies at the University of Wisconsin-Madison His work addresses challenges in floating point arithmetic, emphasizing robustness in computational mathematics. Key contributions include polynomial approximations and convex hull methods for correctly rounded math libraries, improving performance in numerical computations. Recent publications highlight advancements in the RLIBM approach, numerical stability for linear programs, and hardware-aware math library design. These efforts span disciplines like Computer Science, Numerical Analysis, and Algorithm Development.

Eva DarulovaView profile

Associate Professor

• Programming Languages
• Software Verification
• Program Synthesis
• +3 more

Eva Darulova is an Associate Professor at the Department of Information Technology, Uppsala University, and Adjunct Faculty at Max Planck Institute for Software Systems (MPI-SWS). Previously held tenure-track faculty at MPI-SWS and completed her PhD at École Polytechnique Fédérale de Lausanne (EPFL) under Viktor Kuncak. Current research: Applications in numerical/embedded domains, program synthesis, software verification, approximate computing Key projects: Blossom (fuzzing), Lassie (HOL4 tactics), Pine (floating-point loops), LTLTalk (robot instruction), Incarnate (3D printing), Icing (compiler semantics), Flipper (NLP interface), FloVer (error checking), Daisy (accuracy analysis), Rosa (real compiler) Teaching: Program Design (2021/22), Program Synthesis (2021), Program Analysis (multiple years), Advanced Program Analysis (2019), Static Program Analysis (2017), Approximate Computing seminar (2016) Service: Artifact evaluation chair (ASPLOS'22), PC member (ECOOP'22, ESEC/FSE'22, EMSOFT'21, PLDI'21) Research Trends: Focus on numerical computing verification, approximate computing frameworks, and program synthesis techniques. Publications span TACAS, EMSOFT, ISSTA, POPL, CAV, FMCAD, TOPLAS, with emphasis on floating-point error analysis, mixed-precision optimization, and compiler verification. Scientific Recognition: Nominated for EAPLS best paper award (TACAS'21). Co-advisor for Anastasiia Izycheva and mentor for multiple MSc/BSc students including Mustafa Hafidi, Joachim Bard, Nikita Zyuzin, Youcef Merah, Safya Alzayat, Anastasiia Izycheva. Collaborations: Works with researchers at EPFL, MPI-SWS, University of Saarland, University of Washington, and Saarbrücken/Kaiserslautern institutions.

Raphaël MonatView profile

Researcher

• Static program analysis
• Abstract interpretation
• Formal methods
• +5 more

Raphaël Monat is a Research Scientist in the SyCoMoRES team at Inria Lille since September 2022, with an affiliation at the University of Lille. His work focuses on formal methods and static program analysis, with particular applications to computational law and realistic software systems. He is one of the core maintainers of the Mopsa static analysis framework and has developed tools for analyzing the French tax code. Monat holds a Ph.D. in computer science from Sorbonne Université, advised by Antoine Miné, where he worked on the static analysis of Python and Python+C programs within the ERC MOPSA project. Prior to his position at Inria, he completed research internships at MPI-SWS in Saarbrücken, University of Oxford, and École Normale Supérieure in Paris. His primary research interest lies in improving software quality through automated program verification, with a focus on making static analysis applicable to realistic systems. Monat has a particular interest in computational law, especially the implementation of tax and benefit systems. He is currently developing formal methods tools for Catala, a domain-specific language for legislative texts, in collaboration with Aymeric Fromherz. His work emphasizes the practical application of theoretical concepts in abstract interpretation to real-world problems. Monat's publication record demonstrates a consistent focus on advancing static analysis techniques, particularly for Python and C programs, and applying them to computational law. His recent work shows an increasing emphasis on compositional analysis, concolic execution for legal computations, and improving the maintainability of academic static analyzers. The integration of formal methods with legislative implementations represents a distinctive thread throughout his research career. SV-Comp 2025 Silver Medal in "Software Systems" category Amazon Research Award, Fall 2024 ETAPS 2024 best tool paper award SV-Comp 2024 Gold Medal SV-Comp 2023 Bronze Medal ECOOP 2023 Distinguished Artifact Reviewer Award SOAP 2020 Best Presentation Award Monat actively mentors students, currently supervising two PhD candidates (Milla Valnet and Pierre Goutagny) and having previously guided several Master's students. He has secured significant funding for his research, including an ANR Young Researcher Grant for the RAISIN project on Resource-Aware Conservative Static Analysis starting in Fall 2024, and the Inria-AEx AVoCAT project for Automated Verification of Catala programs. Monat is a core contributor to the Mopsa static analysis platform, which has achieved notable success in the Software Verification Competition (SV-Comp). He also co-developed Mlang, a modern compiler for the French tax code that is being transitioned to production use by the French tax administration. His work bridges theoretical formal methods with practical applications in public administration.

• Computational Fluid Dynamics
• Aerospace Engineering
• Quantum Computing Applications
• +7 more

Rene Steijl serves as a Senior Lecturer in Aerospace Engineering within the James Watt School of Engineering at the University of Glasgow, specializing in Autonomous Systems & Connectivity. His academic profile demonstrates a sustained research career focused on computational fluid dynamics with increasing emphasis on quantum computing applications in recent years. Dr. Steijl's research interests center on modeling and simulation of complex flows for aerospace applications, particularly focusing on multi-physics/multi-scale methods for challenging flow regimes including strong shock waves and expansions in supersonic and hypersonic flows. His work spans computational aerodynamics for high-speed transport aircraft, unmanned aerial vehicles, space launch systems, re-entry vehicles, and helicopters. Since approximately 2020, his research has significantly expanded into quantum computing applications for fluid dynamics, with numerous publications exploring quantum algorithms for computational fluid dynamics, quantum circuit implementations, and quantum simulation of fluid phenomena. This represents a strategic evolution of his expertise in advanced computational methods toward emerging quantum computing paradigms. Analysis of his recent publications reveals a clear trajectory from traditional computational fluid dynamics toward quantum computing applications, with nearly half of his 2023-2024 publications focusing on quantum algorithms and implementations. His work bridges fundamental fluid dynamics with cutting-edge computational approaches, maintaining relevance to practical aerospace applications while exploring next-generation computing technologies. The publications demonstrate consistent collaboration with colleagues at Glasgow and internationally, particularly with George Barakos and Craig White. Dr. Steijl maintains active research in both conventional computational fluid dynamics and emerging quantum computing applications, with recent work addressing challenges in shock wave interactions, gas kinetic modeling, rotorcraft aerodynamics, and quantum circuit optimization. His ORCID profile (0000-0002-1872-8687) documents over 100 publications spanning two decades of research contributions to aerospace engineering and computational science.

Hendrik RanochaView profile

Professor

• Numerical Analysis
• Partial Differential Equations
• Scientific Computing
• +6 more

Prof. Dr. Hendrik Ranocha is a Professor in Numerical Mathematics at Johannes Gutenberg University Mainz. His research focuses on numerical methods for partial and ordinary differential equations, emphasizing stability, structure-preserving techniques, and high-performance computing in Julia. University: Johannes Gutenberg University Mainz Research Interests: Numerical Analysis, Structure-Preserving Methods, Scientific Computing, and Uncertainty Quantification His work includes hyperbolic balance laws, Runge-Kutta methods, and applications in magnetohydrodynamics, compressible flows, and multi-physics problems. Recent publications address catastrophic floating point errors in biological models, stabilization of cut-cell meshes, and hyperbolic approximations for higher-order PDEs. His research integrates data-driven approaches and mimetic properties to ensure accurate discrete-level approximations. He contributes to open-source projects like Trixi.jl and SummationByPartsOperators.jl , supporting numerical methods and scientific computing.

Alexander IhlerView profile

Professor

• Artificial Intelligence
• Machine Learning
• Graphical Models
• +5 more

Alexander Ihler is a Professor in the Department of Information and Computer Science at the University of California, Irvine. His work bridges artificial intelligence and machine learning with a focus on statistical methods for data analysis and approximate inference in graphical models. Applications span sensor networks, computer vision, and computational biology. His research emphasizes graphical models for structuring probability distributions and enabling efficient reasoning. Key contributions include analyzing convergence in belief propagation, extending variational techniques to continuous systems, and developing bounds for marginal MAP and decision-making problems. He also explores adaptive inference algorithms for dynamic problems, such as tracking changes in sensor data or biological systems. Recent publications highlight advancements in AI for protein design and astrophysics UAV mobility modeling deep learning integration with classical algorithms dynamic mode decomposition for forecasting Scientific accolades include the NSF CAREER award and first-place UAI 2014 Approximate Inference Challenge in five categories. He has advised numerous PhD and MS students, with graduates in fields like robotics, computational biology, and statistical learning. His group collaborates with institutions like Microsoft Research, DARPA, and the NIH, focusing on scalable methods for real-world systems. Additional personal details include a PhD from MIT under Alan Willsky and a prior role as co-program chair of the 2016 Uncertainty in Artificial Intelligence (UAI) conference.

• Computer Security
• Operating Systems
• Hardware Security
• +3 more

C Giuffrida is an Associate Professor at the Faculty of Science, Vrije Universiteit Amsterdam, with affiliations to the Network Institute and the Systems and Network Security group. His research focuses on computer systems security, hardware vulnerabilities, and software reliability. Giuffrida holds a PhD in Computer Systems from Vrije Universiteit Amsterdam (2014). His academic contributions span multiple areas including transient execution attacks, fuzzing techniques, and hardware-software co-design for security. Research Interests: Hardware Security: Investigating vulnerabilities like Spectre, Rowhammer, and speculative execution risks. Software Security: Focusing on memory safety, compiler optimizations, and exploit mitigation strategies. Systems Research: Developing tools like BinRec for binary analysis and VPS for C++ vulnerability protection. His work has been recognized with awards such as the Distinguished Paper Award in 2021. Giuffrida supervises advanced courses in operating systems and hardware security, and has guided 16 PhD theses to completion.

Marc Marot-LassauzaieView profile

Researcher

• HPC Software Engineering
• Algorithm Efficiency
• Large-Scale Simulations
• +2 more

Marc Marot-Lassauzaie is a Research Associate at the Chair of Scientific Computing in Computer Science (SCCS), Technical University of Munich (TUM). He holds an M.Sc. in Computational Science and Engineering (2020) and a B.Sc. in Engineering Sciences (2018), both from TUM. His research focuses on HPC software engineering, algorithm efficiency optimization, and large-scale simulations. He currently contributes to the Exahype software project and explores mixed-precision algorithmics for high-order methods like the ADER-DG algorithm. His work emphasizes improving computational performance and numerical stability in scientific simulations. Teaching activities include leading the Master-Praktikum on Scientific Computing and High-Performance Computing in Winter 2021. He has presented research at conferences such as PASC24, focusing on the impact of numerical precision in high-order methods. His technical expertise includes software development for large-scale simulations and collaboration on exascale computing initiatives.

Jean-Baptiste JeanninView profile

Assistant Professor

• Verification of cyber-physical systems
• Logics and semantics of programming languages
• Programming with coinductive types
• +3 more

Jean-Baptiste Jeannin is an Assistant Professor in the Department of Aerospace Engineering at the University of Michigan — Ann Arbor, leading the MARVL group. He is also affiliated with the departments of Computer Science and Engineering and Robotics. His research focuses on formal verification of cyber-physical systems, logics and semantics of programming languages, and coinductive programming types. He holds a Ph.D. in Computer Science from Cornell University (2013) and a Diplôme d'Ingénieur from École polytechnique (2007), with prior roles at Samsung Research America and Carnegie Mellon University. His research interests include verification of aerospace applications, program logics, and formal methods applied to robotics and distributed systems. Notable work includes formally verified algorithms for collision avoidance, network consensus protocols, and numerical methods. He teaches courses such as AERO 552 (Aerospace Information Systems) and EECS 590 (Advanced Programming Languages). Jeannin's recent publications emphasize formal verification in hybrid systems, rounding error analysis, and hardware-software co-design. His work bridges theoretical foundations with practical applications in autonomous systems, cybersecurity, and computational physics. Collaborations include projects on certified approximations of mathematical functions and verified distributed system protocols.

Ulrich GrudeView profile

Professor

• Programming Languages
• Compiler Design
• Algorithms and Data Structures
• +3 more

Ulrich Grude is a Professor in the Department of Computer Science and Media at Beuth University of Applied Sciences Berlin. His teaching portfolio includes the following courses taught across multiple semesters (2012-2018): Programming 1 and 2 (Java, C, Ada, C++) Compiler Construction Algorithms and Data Structures Concepts of Programming Languages Programming with Erlang and VPL His research focuses on: Programming language semantics via buoy representation Compiler construction methodologies Algorithm visualization techniques Formal systems like Lambda Calculus and Petri Nets Computer science pedagogy innovations Low-level language implementation details He developed the buoy variable representation model for explaining language peculiarities in Java/C++, documented in technical papers, and created interactive educational tools including floating-point number explorers (GleitBitsFloatApplet) and Java formatting trainers (eTeachMePrintf). Professor Grude supervises Bachelor's and Master's theses in computer science. His course materials include comprehensive lecture notes, programming exercises, exams, and supplementary resources for over 10 distinct courses. He maintains extensive educational repositories covering language-specific implementations (GCC, LCC, Borland C++), floating-point arithmetic, and multilingual programming concepts.

Michael LamView profile

Associate Professor

• High-performance computing
• Scientific computing
• Systems tools
• +3 more

Michael Lam is an Associate Professor in the Department of Computer Science and Interim Assistant Dean of the College of Integrated Science & Engineering (CISE) at James Madison University (JMU). He holds a Ph.D. and M.S. in Computer Science from the University of Maryland (2014 and 2010, respectively), and a B.S. in Computer Science from JMU (2007). His research focuses on high-performance computing, scientific computing, and software engineering, with a particular emphasis on precision computing and algorithm optimization. Dr. Lam's work includes developing tools for mixed-precision analysis, such as HPC-MixPBench, and advancing techniques like ADAPT (Algorithmic Differentiation for Floating-Point Precision Tuning). His contributions span both computational theory and pedagogy, including innovative approaches to online systems education and curriculum design. He has held roles such as Post-doctoral Researcher at the University of Maryland and Graduate Research Intern at Lawrence Livermore National Lab. His research trends emphasize energy-efficient computing paradigms, automated precision tuning, and bridging the gap between theoretical algorithms and practical software implementation. While no awards are explicitly listed, his extensive publication record reflects sustained excellence in high-performance computing and educational innovation. As Interim Assistant Dean of CISE, Lam contributes to interdisciplinary initiatives and program development. His advising and grant activities are reflected in his scholarly work, though specific grant details are not provided here.

• High-performance computing
• Decimal arithmetic
• Parallel programming
• +4 more

Alvaro Vazquez Alvarez is an Assistant Professor at the Higher Technical School of Engineering, University of Santiago de Compostela. He holds a PhD in Computer Engineering (2009) from the same institution, focusing on High-performance decimal floating point units under the supervision of Dr. Elisardo Antelo Suárez. His research interests span high-performance computing, decimal arithmetic hardware design, parallel programming models, and LiDAR data processing. His work emphasizes FPGA implementation of arithmetic units, numerical algorithms optimization, and compiler/runtime systems for parallel environments. He leads the CograDe Research Group (Computer Graphics and Data Engineering), exploring advanced topics in parallel computing and data engineering applications. Key contributions include universal MPI binding frameworks for new languages and efficient LiDAR point cloud processing algorithms. Notable technical expertise includes: digit-by-digit algorithms, redundant number systems, CORDIC-based computations, and multi-operand decimal arithmetic implementations. His publications bridge theoretical numerical methods with practical hardware/software co-design solutions. Current research focuses on extending parallel programming interfaces, improving decimal arithmetic precision in modern architectures, and applying advanced geometric processing techniques to 3D data challenges.

• Numerical Analysis
• Scientific Computing
• Domain Decomposition Methods
• +4 more

Conor McCoid is a Research Fellow in Mathematics at McMaster University, specializing in numerical analysis, scientific computing, and computational geometry. His research focuses on domain decomposition methods, spectral collocation techniques, and algorithm development for computational problems such as phase field models and geometric intersections. He holds a PhD from the University of Geneva, where he worked on domain decomposition under Martin Gander, and a Master's from Simon Fraser University in spectral collocation methods with Manfred Trummer. His postdoctoral work at McMaster involves accelerating phase field models for fractures and optimizing Schwarz methods. McCoid's research interests include developing robust algorithms for geometric intersections (e.g., triangles and tetrahedra), improving spectral collocation methods for boundary value problems, and prototyping machine learning algorithms for music analysis (e.g., Mozart classification). His GitHub repository includes code for tetrahedral intersections, adaptive Schwarz methods, and music data processing using tools like music21 and scikit-learn. His publications explore numerical methods for multigrid cycles, nonlinear solvers, and geometric algorithms, often emphasizing robustness and efficiency in computational contexts. Collaborations include work with Blaise Bourdin (McMaster), Felix Kwok (Université Laval), and Martin Gander (Geneva).

Jean-Michel MullerView profile

Researcher

• Computer Arithmetic
• Floating-Point Arithmetic
• Algorithms
• +3 more

Jean-Michel Muller is a Research Director at CNRS, posted at Laboratoire LIP (common to CNRS, ENS Lyon, INRIA, and Université Claude Bernard Lyon 1). He is a member of the Pascaline team, a follow-up of the ARENAIRE and ARIC teams. He serves as Associate Editor-in-Chief of the IEEE Transactions on Emerging Topics in Computing . His research focuses on computer arithmetic, including floating-point arithmetic, algorithms, architectures, and elementary functions. He has authored influential books such as Handbook of Floating-Point Arithmetic and Elementary Functions: Algorithms and Implementation . Research Interests : Computer Arithmetic Floating-Point Arithmetic Standards (IEEE 754) Algorithm Design for Hardware/Software Systems Error Analysis and Correct Rounding Elementary Function Implementations Awards : Médaille d'Argent du CNRS (2013) "La Recherche" Award for Computer Science (2013, shared) IEEE Fellow (2017) Labs/Teams : Member of the Pascaline team at LIP, collaborating on advancing computer arithmetic and numerical algorithms.