جستجوی استاد راهنمای دانشگاهی

• Program Specification and Verification
• Concurrent Separation Logics
• Symbolic Analysis Tools
• +۳ مورد دیگر

Philippa Gardner is a Professor in the Department of Computing at Imperial College London, holding a UK Research and Innovation Established Career Fellowship (2018–2023). She leads the Research Institute on Verified Trustworthy Software Systems (VeTSS, 2017–2023) and is renowned for her work on program specification and verification, particularly in concurrent separation logics and the development of the Gillian platform for symbolic analysis of real-world languages like C and JavaScript. PhD from the University of Edinburgh (1992), supervised by Professor Gordon Plotkin FRS. EPSRC Advanced Fellowship at Cambridge (1998), followed by a lectureship at Imperial (2001) and promotion to Professor (2009). Her research bridges program verification, symbolic execution, and bi-abduction, focusing on robust mechanised language specifications. She has pioneered library specifications for JavaScript and WebAssembly, emphasizing environment robustness and scalable compositional reasoning. The Gillian platform unifies classical symbolic execution, separation-logic verification, and bi-abduction-based testing. Philippa has received prestigious awards, including the Royal Academy of Engineering Fellowship (2020), President and Rector’s Award for Teaching (2013), and Microsoft Research — Royal Academy of Engineering Senior Fellowship (2005–2009). She has secured significant grants from Amazon, Facebook, EPSRC, and GCHQ for projects like Gillian and JSVerify. She actively supervises PhD students and teaches the Scalable Software Verification course. As General Chair of POPL ’24 and organizer of the Isaac Newton Institute’s Verified Software program (2022), she shapes academic discourse. Her editorial roles and conference committee memberships further underscore her influence in programming languages and formal methods.

Alastair F. Donaldsonمشاهده پروفایل

استاد

• Programming Languages
• Compilers
• Verification
• +۵ مورد دیگر

Alastair F. Donaldson is a Professor in the Department of Computing at Imperial College London, where he leads the Multicore Programming Group. His academic career spans positions as a Visiting Researcher at Microsoft Research Redmond, a Postdoctoral Research Fellow at the University of Oxford, a Research Engineer at Codeplay Software Ltd., and PhD studies at the University of Glasgow. Notably, he served as Director of GraphicsFuzz, an Imperial College spinout acquired by Google in 2018, demonstrating significant industry impact from his academic research. Donaldson's research focuses on Programming Languages, Compilers, Verification, Testing, and Multicore Programming, with particular expertise in randomized testing methodologies for compilers and program analyzers. His work bridges theoretical computer science with practical software engineering applications, developing innovative techniques like metamorphic testing for graphics shader compilers and systematic approaches for testing memory persistency models. His research output shows consistent progression toward increasingly sophisticated testing frameworks for verification-aware languages and GPU programming interfaces. Analysis of his 15 most recent publications reveals a strong trend toward applying fuzzing and randomized testing techniques to increasingly complex systems, including zero-knowledge proof circuits, large language models for code generation, and WebGPU APIs. His work consistently addresses the challenge of verifying correctness in systems where traditional testing approaches fall short, with growing emphasis on industrial deployment of compiler testing techniques. Throughout his career, Donaldson has maintained exceptional service to the academic community, serving on program committees and as chair for numerous prestigious conferences including SPLASH, PLDI, POPL, ECOOP, and ISSTA. His leadership roles have included Program Chair for ECOOP and General Chair for PLDI, reflecting his standing within the programming languages research community. As an advisor and mentor, Donaldson has contributed to the PLMW (Programming Languages Mentoring Workshop) with talks on 'The Lean Researcher' and 'Hacks to Compensate for Lack of Novelty in Programming Languages Research,' demonstrating his commitment to nurturing the next generation of researchers. His work on integrating compiler fuzzing into continuous integration pipelines represents significant practical impact on software development practices.

Harry Xuمشاهده پروفایل

استاد

• Programming Languages
• Compilers
• Computer Systems
• +۳ مورد دیگر

Harry Xu is a Professor in the Computer Science Department at the University of California, Los Angeles (UCLA), with a research focus on computer systems spanning programming languages, compilers, runtime/operating systems, distributed systems, and computer architecture. Prior to UCLA, he was an Associate Professor at UC Irvine (UCI). His current work centers on infrastructures for future cloud computing (user-defined clouds) and scalable, cost-effective AI/ML systems. Education : Ph.D. in Computer Science and Engineering from Ohio State University (2011) His research has pioneered techniques for combating software bloat, optimizing data analytics through programming language innovations, and developing systems like Yak GC, VQPy, and Niijima. He co-founded BreezeML, a startup for GenAI risk management, and has held visiting roles at Microsoft Research and IBM Watson Research Center. Recent publications include work on resource-disaggregated datacenters, cloud computing fault tolerance, and ML system scalability, presented at top venues like SOSP, OSDI, and NSDI. He has advised numerous Ph.D. and M.S. students, many of whom now hold academic or industry roles. Awards : 2018 Dahl-Nygaard Junior Prize, ACM Distinguished Scientist Harry actively contributes to academic service as a committee member and co-chair at conferences like PLDI, SPLASH, and VMCAI. He maintains the VQPy project and other open-source systems.

Graham Huttonمشاهده پروفایل

استاد

• Mathematics of program construction
• Functional programming
• Programming language semantics
• +۶ مورد دیگر

Graham Hutton is a Professor of Computer Science at the University of Nottingham , where he leads the Functional Programming Lab and serves as Director of the Midlands Graduate School . He co-founded the Quotient Haskell project and maintains key roles in Journal of Functional Programming editorial work and Haskell Foundation governance. Co-leader, Functional Programming Lab (2008–date) Director, Midlands Graduate School (2023–date) ACM Distinguished Scientist Principal investigator for £912k EPSRC project (2024–2027) His research focuses on mathematical approaches to program construction , particularly through functional languages like Haskell and Agda . He develops techniques for compiler correctness , type system design , and program optimization , with recent work on quotient polymorphism and denotational cost models . Key article themes include: Compiler derivation from formal semantics Effect handling in functional languages Graph-based code generation over traditional tree structures Quotient type systems with SMT solver integration Concurrency semantics using choice trees Operational improvement with parametric polymorphism Scientific awards include: ACM Distinguished Scientist (2013) Best Paper & Best Student Paper (2018) EPSRC funding for compiler correctness projects He advises current PhD students in compiler calculation and memory safety , while maintaining extensive educational contributions through his widely-used textbook Programming in Haskell and open course materials.

Thibaut Balabonskiمشاهده پروفایل

استادیار

• Programming Languages
• Operational Semantics
• Type Systems
• +۶ مورد دیگر

Thibaut Balabonski is an Assistant Professor of computer science at Paris-Saclay University, affiliated with the Formal Methods Lab. His research focuses on programming languages, operational semantics, type systems, lambda calculus, and concurrency, with notable work on formal verification of mobile robot swarms and functional programming optimizations. Published key works on strong call-by-need calculus (LMCS, 2023), weak optimality in lambda calculus (ICFP'13), and separation logic for permission-based languages (TOPLAS, 2016). Co-authored educational books for high school and preparatory classes, including Digital and Computer Science series. Teaching includes object-oriented programming , compilation , and lambda calculus courses at L2, M1, and M2 levels. Labs & Teams : Formal Methods Lab (LMF), CNRS, with collaborations in Coq formalization (Pactole framework) and concurrency theory.

• Metaphysics of Mind
• Physicalism
• Ontology
• +۴ مورد دیگر

Gregor M. Hörzer is a Research Fellow at the Institute of Cognitive Science, University of Osnabrück, Germany, where he also serves as Coordinator of Bachelors' and Masters' Cognitive Science programs and Dean of Studies. Previously, he was Scientific Coordinator for the DFG-funded research training group "Situated Cognition" (2021-2023) and held a substitute professorship (2020) at the same institute. His dual PhD background bridges computational neuroscience and analytic philosophy. Education: PhD in Philosophy (Cognitive Science), University of Osnabrück (2018, summa cum laude) PhD in Computer Science, Graz University of Technology (2011) Master's in Philosophy, University of Graz (2014, with distinction) Master's in Telematics (Information and Computer Engineering), Graz University of Technology (2008, with distinction) Bachelor's in Telematics, Graz University of Technology (2006) Hörzer's research is grounded in analytic philosophy, with Metaphysics of Mind and Physicalism as core focuses. He critically examines the mind-body problem and phenomenal consciousness, arguing against eliminative materialism's ability to account for subjective experience. In philosophy of science, he investigates metaphysics of mechanisms within new mechanist explanation frameworks, particularly regarding biologically inspired learning processes. His early computational neuroscience work informs current interdisciplinary approaches to consciousness and cognition. His publications demonstrate a consistent trajectory in philosophy of mind, highlighted by the 2015 article analyzing Sandro Nannini's cognitive naturalism. The work challenges analogies between relativity physics and consciousness theory, emphasizing the irreducibility of phenomenal feel—a theme expanded in his 2020 book "Understanding Physicalism". Scientific Awards: Wolfgang Stegmüller Award (2022) from the German Society for Analytic Philosophy (GAP) for "Understanding Physicalism" Advising and Grants: While no individual PhD students are listed, Hörzer oversees academic guidance as Dean of Studies for Cognitive Science programs. His grant leadership includes coordinating the DFG-funded "Situated Cognition" research training group (2021-2023), facilitating doctoral training across Osnabrück and Bochum universities. This role involved structuring interdisciplinary research projects and supervision frameworks. Labs and Teams: Hörzer collaborates within the Institute of Cognitive Science's interdisciplinary ecosystem and is an associated member of Osnabrück's Institute for Philosophy. His past neurophysiological work involved cross-institutional teams, including the Max Planck Institute for Biological Cybernetics in Tübingen analyzing macaque monkey working memory data. Current team involvement centers on the "Situated Cognition" research group and Cognitive Science program coordination.

Felix Blankenburgمشاهده پروفایل

استاد

• Cognitive Neuroscience
• Neuroimaging
• Somatosensory Processing
• +۵ مورد دیگر

Felix Blankenburg is Professor and Chair of the Neurocomputation and Neuroimaging Unit at the Department of Education and Psychology, Freie Universität Berlin. His research investigates neural mechanisms of perception, cognition, and action using multimodal neuroimaging (fMRI, EEG) and neurostimulation (TMS, tDCS), with emphasis on somatosensory processing, sensory attenuation, and perceptual decision-making within virtual reality environments. His primary research focuses on cognitive neuroscience and neuroimaging, exploring how the brain processes tactile information, integrates multisensory cues, and generates conscious perception. Key areas include Bayesian models of sensory prediction, neural correlates of working memory for tactile stimuli, cross-modal influences (e.g., language on touch), and computational mechanisms of perceptual decisions. His work bridges experimental neuroscience with theoretical frameworks like active inference. Recent publications (2023-2025) reveal strong trends in multimodal integration methodologies, predictive coding applications in somatosensation, and clinical translation through neurostimulation. He pioneers concurrent TMS-fMRI and virtual reality paradigms to probe causal brain mechanisms, with growing emphasis on therapeutic applications for mental health as evidenced by the PREACT research unit. Professor Blankenburg supervises PhD students including: Gianluigi Giannini Guido Caccialupi Jona Förster He serves as principal investigator for the DFG-funded Research Unit 5187 PREACT, developing neuroimaging biomarkers to predict psychotherapy response in non-respondent patients through fMRI-based signatures. The Neurocomputation and Neuroimaging Unit, under his leadership, integrates computational modeling with empirical neuroscience. The team comprises postdocs, PhD students, and research staff collaborating on projects spanning tactile perception, decision neuroscience, and clinical applications of neuroimaging.

Frank Kammerمشاهده پروفایل

پژوهشگر

• Computer Science
• Algorithms and Data Structures
• Graph Theory
• +۴ مورد دیگر

Frank Kammer is a researcher at the Technical University of Central Hesse , actively engaged in teaching and research projects. He co-leads the RegioTainment platform with Biebertal municipality and contributes to the SEA Project , an open C++ library for space-efficient algorithms. Teaching : Database Systems (CS1020), Software Engineering (CS1023), Efficient Algorithms (CS2353), Artificial Intelligence (CS2364/WK1613), etc. Research : Focuses on space-efficient graph algorithms, temporal graph exploration, and algorithm engineering. His work addresses computational challenges in huge datasets and memory-constrained devices , with applications in planar graph encoding, vertex separators, and student feedback systems. Advising : Supervised theses on topics including data-driven analytics platforms, low-code integration, cloud orchestration, and AI-optimized marketing campaigns. Collaboration : Works with students like Max Stephan, Timon Pellekoorne, and Johannes Meintrup on algorithm implementation and educational tools. Projects like RegioTainment emphasize digital community networking to preserve rural culture while embracing modernity.

Nikolaus Weiskopfمشاهده پروفایل

استاد

• Magnetic Resonance Imaging
• Neuroimaging
• Brain Microstructure
• +۵ مورد دیگر

Professor Nikolaus Weiskopf serves as Managing Director of the Department of Neurophysics at the Max Planck Institute for Human Cognitive and Brain Sciences (MPI-CBS) in Leipzig, Germany. With over 230 journal articles and numerous conference presentations, he is a leading authority in quantitative MRI techniques for studying brain microstructure in vivo. Dr. Weiskopf specializes in developing advanced MRI methods that provide non-invasive histological information about brain tissue. His research focuses on: Quantitative multi-parameter mapping (MPM) for in vivo histology High-resolution imaging of cortical layers and columns Spinal cord microstructure imaging Applications of ultra-high field (7T) MRI Neurodegenerative disease biomarkers His recent publications reveal a strong emphasis on pushing MRI technology to achieve microscopic-level insights about brain organization. His work bridges basic neuroscience with clinical applications, particularly in spinal cord injury and neurodegenerative conditions like Alzheimer's disease. Through correlative MRI-histology studies, his team establishes the biological basis of MRI signals, transforming quantitative MRI into a powerful tool for in vivo histology. Dr. Weiskopf leads the ERC-funded hMRI project focused on non-invasive in vivo histology of the brain and contributes significantly to the NISCI clinical trial investigating Nogo-A antibodies for spinal cord injury recovery. His laboratory develops and shares important neuroimaging tools including the hMRI toolbox for quantitative MRI analysis, which has been widely adopted by the research community.

Stefan Lüdtkeمشاهده پروفایل

استادیار

• Neuro-symbolic machine learning
• Machine learning for heterogeneous data
• Machine learning for marine ecology
• +۵ مورد دیگر

Stefan Lüdtke is an Assistant Professor (Juniorprofessor) for Marine Data Science at the University of Rostock (since July 2023) and concurrently serves as a junior research group leader at ScaDS.AI Leipzig . Previously, he was a postdoctoral researcher at the Institute for Enterprise Systems, University of Mannheim (2021-2023) and completed his PhD at the University of Rostock (2016-2021). Education & Career Timeline: 2023 – present: Juniorprofessor (Assistant Professor) for Marine Data Science, University of Rostock 2023 – present: Junior research group leader, ScaDS.AI Leipzig 2021 – 2023: Postdoc, Institute for Enterprise Systems, University of Mannheim 2016 – 2021: PhD studies, University of Rostock Research Interests: Dr. Lüdtke’s research integrates neuro-symbolic machine learning with practical applications spanning heterogeneous tabular data , marine ecology , and underwater technology . His work bridges symbolic reasoning and modern gradient-based learning to tackle complex real-world problems such as hyperspectral imaging for environmental monitoring, knowledge-graph completion, and robust human-activity recognition. Key focus areas include: Design of memory-augmented decision-tree ensembles and gradient-based tree learning. Data-centric evaluation and quality assessment of machine-learning models on tabular and sensor data. Domain adaptation and self-training techniques for activity recognition in changing environments. Application of AI to marine robotics and glacier-dynamics mapping. Publication Trends: Across 40+ peer-reviewed works (2017-2025), Lüdtke demonstrates a steady shift from foundational probabilistic-filtering and lifted-inference methods toward cutting-edge neural-symbolic hybrids and tabular-data-centric learning. Recent high-impact venues show contributions in machine-learning theory , computer vision , and environmental informatics , with a notable uptick in interdisciplinary projects combining AI and marine science. Scientific Awards & Honors: No specific awards are listed in the provided material. Advising & Funding: No named students or explicit grant details are disclosed in the text. Laboratories & Teams: He leads the Marine Data Science junior research group at the University of Rostock and the ScaDS.AI Leipzig junior research group, fostering cross-institutional collaboration in AI and data science.

Andreas C. Schneiderمشاهده پروفایل

پژوهشگر

• Physics
• Neural Networks
• Information Theory
• +۳ مورد دیگر

Andreas C. Schneider is a researcher at the Max Planck Institute for Dynamics and Self-Organization in Göttingen, Germany. His work focuses on understanding the fundamental principles of intelligence through the lens of physics, neural networks, and information theory. Key research questions include how intelligence emerges from simple components, the interplay between memory and computation in neural systems, and the role of locality constraints in shaping neural structures. His research combines information-theoretic measures to analyze the development of structure and information flow in neural networks, aiming to uncover biases and improve the safety of AI applications. Recent work introduces locally learning neurons via information-theoretic rules. Deep Learning for Computer Vision (Winter 2022/23) Machine Learning (Summer 2022) Physics of Complex Systems (Winter 2019/20) Physics preparatory course for life sciences (Fall 2019) Scientific computing (Summer 2019) His recent publications emphasize interpretable neural learning, partial information decomposition, and complexity metrics. Despite no explicit awards listed, his work targets critical challenges in AI safety and neural computation.

Abdullah Makkehمشاهده پروفایل

پژوهشگر

• Information Theory
• Computational Neuroscience
• Artificial Neural Networks
• +۴ مورد دیگر

Dr. Abdullah Makkeh is a Senior Scientist at the University of Göttingen's Department of Data-driven Analysis of Biological Networks, headed by Michael Wibral, and a Guest Scientist at the Max Planck Institute for Dynamics and Self-Organization in Göttingen under Viola Priesemann's Complex Systems Theory group. Previously, he served as a Postdoc at the University of Tartu in both Theoretical Computer Science (Dirk Oliver Theis) and Computational Neuroscience (Raul Vicente) groups. Education: PhD in Informatics (2018, University of Tartu, Supervisor: Dirk Oliver Theis) MSc in Mathematics (2013, Lebanese University, Supervisor: Bassam Mourad) BSc in Mathematics (2011, Lebanese University) His research focuses on extending information theory to study computation in intelligent systems like the brain and artificial neural networks (ANNs). He has developed interpretable information-theoretic learning rules for ANNs (Makkeh et al., 2025) and analyzed reinforcement learning agents to reveal emergent computation mechanisms (Engel et al., 2022; Ehrlich et al., 2023). Current work applies these methods to enhance large language model (LLM) interpretability. His publications span information-theoretic frameworks, predictive coding, and neural oscillation analysis, with a 2024 Royal Netherlands Academy of Arts and Sciences (KNAW) recognition. He co-teaches courses in Bayesian Inference, Information Theory, and Discrete Mathematics, and organizes annual workshops on information theory in computational neuroscience. Scientific Awards: Royal Netherlands Academy of Arts and Sciences (KNAW) (2024) Dr. Makkeh contributes to open-source research tools via GitHub and collaborates with interdisciplinary teams across neuroscience, computer science, and mathematics. His work bridges theoretical foundations with applied machine learning through rigorous mathematical frameworks.

• Formal Verification
• Hardware Design
• Quantum Computing
• +۵ مورد دیگر

Professor Rolf Drechsler is affiliated with the Department of Mathematics and Computer Science at the University of Bremen, where he maintains an active research profile in formal verification, hardware design, and quantum computing. His office is located in the Multi-purpose high-rise building (MZH) 4330, and he can be reached at drechsler@uni-bremen.de or drechsler@informatik.uni-bremen.de. Dr. Drechsler's research focuses on formal verification techniques, particularly polynomial formal verification methods, binary decision diagrams (BDDs), in-memory computing architectures, and quantum circuit verification. His work bridges theoretical computer science with practical hardware implementation challenges. Notably, he has recently explored the integration of large language models (LLMs) with hardware verification and design automation, representing an emerging interdisciplinary research direction. An analysis of his 2024-2025 publications reveals a strong emphasis on verification methodologies for emerging computing paradigms. His research spans quantum computing verification (qSAT, quantum circuit debugging), in-memory computing (MAGIC-based architectures, memristive crossbars), and traditional hardware verification enhanced by AI techniques. The publications show a pattern of addressing verification challenges in novel computing architectures while maintaining theoretical rigor in formal methods. Professor Drechsler has made significant contributions to Binary Decision Diagram optimization, formal verification of arithmetic circuits, and hardware security. His work on polynomial formal verification represents a distinctive research thread that has evolved over recent years, addressing verification challenges for sequential circuits, approximate adders, and multi-valued logic circuits.

Matthias Müllerمشاهده پروفایل

پژوهشگر

• Quantum Physics
• Quantum Computing
• Quantum Optimal Control
• +۳ مورد دیگر

Matthias Müller is a researcher at the Research Center Jülich GmbH , specifically affiliated with the Peter Grünberg Institute in the Quantum Control (PGI-8) department. His work focuses on quantum optimal control, spintronics, and quantum computing technologies, with a particular emphasis on diamond-based quantum systems and Rydberg atom applications.

Gudrun Schwarzerمشاهده پروفایل

استاد

• Developmental Psychology
• Cognitive Neuroscience
• Emotional Development
• +۵ مورد دیگر

Gudrun Schwarzer is a Professor of Developmental Psychology at the Department of Developmental Psychology, Faculty 06 Psychology and Sports Science, Justus-Liebig-University Giessen, Germany. With a habilitation in Psychology (University of Tübingen, 1999) and a professorship since 2003, her research spans infant cognition, emotional development, language acquisition, and the interplay between motor skills and cognitive abilities. She leads a research team investigating how sensory-motor experiences shape predictive processing and social-emotional development in children. Education: Diploma in Psychology (University of Marburg, 1988), Dr. phil. nat. (University of Frankfurt/Main, 1991) Her research focuses on developmental changes in emotional perception, mental rotation, reinforcement learning, and the impact of music training on executive functions. She examines cross-domain interactions between motor, cognitive, and emotional systems, particularly in infants and preschoolers. Recent publications highlight her work on multimodal emotion processing , perceptual narrowing in speech and face recognition, and the effects of locomotion training on cognitive development. Her studies often employ longitudinal designs and integrate behavioral, neuroimaging, and computational approaches. Scientific Awards: Heinz-Heckhausen-Award (DGPs, 1991), DFG scholarship (1997-1998), Volkswagen Foundation funding (1988-1991) She has led an Independent Research Group at the Max Planck Society and maintains ongoing collaborations in developmental neuroscience. Her lab at Giessen University investigates sensorimotor integration, early language-emotion interactions, and interventions for developmental disorders.