جستجوی استاد راهنمای دانشگاهی

Grégoire Montavon is a Professor at Charité – Universitätsmedizin Berlin and Research Group Lead at the Berlin Institute for the Foundations of Learning and Data (BIFOLD). His appointment commenced on April 1, 2025, as part of BIFOLD's institutional partnership with Charité. He holds a Master's in Communication Systems from École Polytechnique Fédérale de Lausanne (2009) and a Ph.D. in Machine Learning from Technische Universität Berlin (2013). Master's: Communication Systems, EPFL (2009) Ph.D.: Machine Learning, TU Berlin (2013) Montavon pioneers Explainable AI (XAI) for medical applications, developing methods like Layer-Wise Relevance Propagation (LRP) to verify deep learning models in diagnostics. His work bridges machine learning theory with clinical practice, focusing on model transparency for tumor classification, bias mitigation, and regression strategy analysis. Recent research emphasizes counterfactual explainers and spectral analysis for explanation quality assessment. His 2025 publications reveal a cohesive trajectory: advancing explainability frameworks for distance-based classifiers, diffusion models in oncology, and visual counterfactual systems. Key themes include robustness against dataset shifts, metadata integration for fairness, and formal desiderata for explainer design—consistently targeting real-world medical AI deployment. Award highlights: 2013 Dimitris N. Chorafas Award 2020 Pattern Recognition Best Paper Award 2022 Digital Signal Processing Best Paper Award 2025 XAI Conference Best Paper Award (for XpertAI) As BIFOLD Research Group Lead for Explainable Machine Learning in Medicine, Montavon directs projects funded through Charité-BIFOLD partnerships, including the agility project on deep generative model transparency. His team collaborates with Klaus-Robert Müller and others on NIH/DFG grants for AI-driven cancer treatment personalization and Clever-Hans strategy pruning. He leads the BIFOLD research unit Explainable Machine Learning in Medicine, focusing on clinical AI integration. Current initiatives include ICLR 2025 contributions on foundation model reliability and MedI diffusion frameworks for tumor classification bias reduction.

• Explainable AI
• Machine Learning
• Medical Diagnosis
• +۲ مورد دیگر

Dr. Krysia Broda is an Honorary Senior Lecturer in the Department of Computing at Imperial College London's Faculty of Engineering. She directs the HiPEDS CDT Programme and coordinates the PhD Teaching Scholarship Programme, actively supervising doctoral candidates. Her office is located at 180 Queen’s Gate, London SW7 2BZ, U.K., and she can be contacted via phone (+44 20 7594 8426) or email. Her research bridges logic programming , automated reasoning , and neural-symbolic integration , with applications in computational biology and multi-agent systems. Key focus areas include: Abductive/Inductive Logic Programming Answer Set Programming (ASP) for knowledge representation Probabilistic reasoning in biological networks Teleo-reactive agent policies Her publications emphasize logic-based methods in AI, spanning theoretical foundations and real-world applications like gene regulation analysis and legal case inference. Recent work shows a trend toward integrating probabilistic models with symbolic AI for complex system validation. She leads the Structured and Probabilistic Intelligent Knowledge Engineering (SPIKE) group and collaborates with the Machine Learning Group. Current projects involve distributed abductive reasoning and ASP-based theory refinement.

• Logic Programming
• Automated Reasoning
• Neural-Symbolic Integration
• +۳ مورد دیگر

Luxi Zhao is a Research Fellow at Technische Universität München's Embedded Systems and Internet of Things department, specializing in Time-Sensitive Networking (TSN) and real-time network calculus. Working under the supervision of Prof. Sebastian Steinhorst, Zhao contributes to projects like ReMiX and 6G-Life, focusing on security, performance analysis, and configuration optimization of deterministic networks. Research Focus: Worst-case latency analysis in TSN networks Runtime configuration and reconfiguration problems Hybrid scheduling of processing and communication Network calculus modeling for heterogeneous systems Security challenges in industrial IoT and autonomous systems Interoperability of IoT systems in Industry 4.0 Teaching Activities: Secure Autonomous Systems (2025) Software Architecture for Distributed Embedded Systems (2025) IoT Security (2025) IoT Remote Lab practical courses across multiple semesters Advanced Seminar series (2020-2025)

• Time-Sensitive Networking
• Network Calculus
• Real-Time Systems
• +۳ مورد دیگر

Prof. Dr. Martin E. Müller is a Professor in the Department of Computer Science at Bonn-Rhein-Sieg University of Applied Sciences, specializing in the mathematical and theoretical foundations of informatics. His research bridges abstract algebraic structures with practical computational applications, particularly in knowledge representation and reasoning systems. Dr. Müller's primary research interests include Algebraic Logic , Modal Logic , Relational Algebra , Universal Algebra , and Logic Knowledge Discovery (also known as explainable machine learning). His work demonstrates how theoretical mathematical frameworks can provide robust foundations for practical computational problems, particularly in the areas of rough set theory, formal concept analysis, and inductive logic programming. He approaches machine learning through logical structures, emphasizing transparency and explainability in AI systems. Analysis of his publication record shows a consistent scholarly trajectory from 1994 through 2023, with increasing emphasis on applying formal logical methods to contemporary machine learning challenges. His work spans theoretical foundations of computing, relational methods in program semantics, and practical applications in user modeling and knowledge discovery. Recent publications demonstrate growing interest in making machine learning more interpretable through logical frameworks. Among his professional recognitions is the Seahorse award from 1975 . Dr. Müller serves as a reviewer for numerous academic journals and conferences and is an active member of several professional associations in computer science and logic. Dr. Müller has led significant research projects including PARIA (2004-2008), which developed the PACME architecture for concurrent processes; Rela-X (2010-2019), which implemented libraries for efficient relation calculus with the R-Lang programming language; and the ongoing COMPARE project (2024-) focusing on pairwise multidimensional comparisons for survey analysis. His current work with the 'Sets, Structures, Semantics' project (2018-) aims to create a comprehensive resource on discrete mathematics and logics. His research group maintains strong international collaborations, particularly with researchers in the relational and algebraic methods community, including notable figures like Tony Hoare, Peter Höfner, Peter Jipsen, and Bernhard Möller. The Rela-X project established a productive student working group environment that produced both theoretical insights and practical software tools for relational calculus visualization.

• Algebraic Logic
• Modal Logic
• Relational Algebra
• +۷ مورد دیگر

Anirudha Majumdar is an Associate Professor in the Mechanical and Aerospace Engineering (MAE) department at Princeton University and Associated Faculty in Computer Science. He also holds a part-time visiting research scientist position at Google DeepMind in Princeton, focusing on robotics and AI integration. His research centers on enabling robots to generalize safely and reliably in human-centered environments, combining nonlinear control theory, optimization, motion planning, and machine learning. Key applications include autonomous UAV flight, robotic manipulation, and legged locomotion. He has published extensively on topics like uncertainty quantification, safe reinforcement learning, and formal guarantees for robotic systems. Educated with a Ph.D. in Electrical Engineering and Computer Science from MIT (2016) and a B.S.E. in Mechanical Engineering and Mathematics from the University of Pennsylvania (2011). Postdoctoral scholar at Stanford University's Autonomous Systems Lab (2016-2017). His work emphasizes algorithmic innovation and theoretical rigor. Recent publications highlight advancements in robust control, vision-language models for robotics, and frameworks for safe AI deployment. Scientific Awards Sloan Fellowship ONR Young Investigator Program (YIP) award NSF CAREER award Google Faculty Research Award (twice) Amazon Research Award (twice) He leads the Intelligent Robot Motion Lab and has received multiple accolades for teaching and research excellence at Princeton.

• Control
• Robotics
• Dynamical Systems
• +۷ مورد دیگر

Dr. Mike Stannett is a Senior Lecturer in the School of Computer Science at the University of Sheffield, where he serves as Sustainability Lead. His research bridges theoretical computer science and physics, focusing on the Logical Foundations of Relativity Theory , Unconventional Computing , and Isabelle/HOL verification of physical theorems. Key research areas: Relativity, Hypercomputation, Membrane Systems, General Topology, and Formal Verification Grants: EPSRC-funded Hypercomputation Research Network, Royal Society grant for relativity verification, RCUK project on Bitcoin risk modeling Professional memberships: London Mathematical Society, AISB, CiE, British Society for the Philosophy of Science His recent publications explore spacetime geometry, faster-than-light particles, and hypercomputation’s theoretical limits. Notable collaborations include work with Hungarian researchers (Andréka, Madarász, Németi, Székely) and the Foundations of Computation group.

• Unconventional Computing
• Logical Foundations of Physics
• Relativity Theory
• +۷ مورد دیگر

Martin Fabian is a Professor at the Department of Electrical Engineering , Chalmers University . As Head of the Automation research group , he leads research initiatives in automation and discrete event systems. His research focuses on Supervisory control theory Formal verification Cyber-physical systems Robotics Manufacturing automation Recent publications (2024) highlight advancements in timed automata synthesis, smart contract verification, and disturbance-handling controller design. Earlier works (2023-2022) explore compositional algorithms, automated driving safety, and vehicle routing optimization.

• Automation
• Discrete Event Systems
• Supervisory Control
• +۳ مورد دیگر

Dr Amin Karami is an Associate Professor in the School of Computer Science and Digital Technologies (CDT) at the University of East London, within the School of Architecture, Computing and Engineering. He serves as course leader for MSc Big Data Technologies and leads postgraduate programs, having secured £1.23 million in funding from the Office for Students to develop inclusive AI and Data Science courses for non-STEM and far-STEM graduates. His research spans Artificial Intelligence, Big Data Analytics, Blockchain, and Optimization, with focus on Industry 5.0 applications. Current work addresses federated learning heterogeneity, smart contract security, healthcare fraud detection, and ethical AI implementation. He develops cloud-based platforms for large-scale data processing and computational intelligence solutions for real-world industry challenges. Recent publications demonstrate strong trends in federated learning techniques, blockchain vulnerability mitigation, and big data applications across healthcare, finance, and social media. His work consistently bridges academic research with industry needs through partnerships with Multiverse and Cambridge Spark, emphasizing practical solutions for credit risk assessment, satellite telemetry, and personalized marketing. Scientific recognition includes: UEL Vice-Chancellor & President Impact & Innovation Award for Industry 4.0 readiness (2023) Fellow of the Higher Education Academy (FHEA) Dr Karami actively supervises UG/PG/PhD students while leading curriculum innovation through externally funded projects. His Chainlink Bootcamp initiative connects academia with industry practitioners, and he serves as external examiner and conference program chair. Significant grant achievements include developing diversity-focused STEM pathways that enhance graduate employability through industry-aligned training in AI and Data Science.

• Artificial Intelligence and Data Science
• Big Data Technologies
• Blockchain and Cryptocurrencies
• +۴ مورد دیگر

Munindar P. Singh is the SAS Institute Distinguished Professor of Computer Science at North Carolina State University . He serves as a core faculty member in the Science of Security Lablet and contributes to initiatives in responsible computing and ethical AI . His research spans artificial intelligence , software engineering , and computing ethics . Education: Ph.D. in Computer Sciences from University of Texas at Austin (1993) B.Tech. in Computer Science and Engineering from Indian Institute of Technology, Delhi (1986) Research Focus: Dr. Singh's work centers on trustworthy AI and sociotechnical systems , with key contributions in Multiagent systems and BDI architectures Defensive cyberdeception using hypergame theory Normative systems for blockchain applications Equitable transportation systems via AI Service-oriented computing and protocol engineering Scientific Recognition: Fellow of AAAI , IEEE , and ACM Recipient of NSF CAREER Award and multiple industry awards Editor-in-Chief of ACM Transactions on Internet Technology Grant Activities: Currently leading several major NSF-funded projects including: SCC: Serving Households in Food Insecurity ($2.018M) RI: Foundations of Ethics for Multiagent Systems ($500K) Science of Security Lablet ($3.65M)

• Artificial Intelligence
• Multiagent Systems
• Sociotechnical Systems
• +۵ مورد دیگر

Xavier Rival is a Senior Researcher at Inria, affiliated with multiple project teams including Antique , GraphDeco , Sierra , and Quantic . He focuses on data structures and algorithms within operating systems and embedded programs, with a strong emphasis on artificial intelligence and software engineering. Rival received the ERC Proof of Concept grant in 2018 for his work on formal verification tools. Current teams: GraphDeco (since 2016), Quantic (since 2021), Sierra (2016-2024) Key collaborations: European Research Council grants, Inria Startup Studio Research trends include neural network security, curiosity-driven machine learning, and AI safety verification. Notable projects involve SOFA open-source coordination and DeepTech initiatives at Inria Saclay. Scientific awards ERC Proof of Concept grant (2018)

• Computer Science
• Artificial Intelligence
• Software Engineering
• +۳ مورد دیگر

Parosh Abdulla is a Professor in the Division of Computer Systems within the Department of Information Technology at Uppsala University, Sweden. His research focuses on formal methods and program verification with emphasis on concurrent systems, memory models, and verification techniques. With an extensive publication record spanning over three decades, he has established himself as a leading researcher in his field. His primary research interests include formal verification, model checking, concurrent systems analysis, memory models (particularly TSO and PSO), string constraints, timed automata, parameterized verification, and program analysis. His work addresses fundamental challenges in verifying complex software systems, especially those involving concurrency and weak memory models. In recent years, he has expanded his research to include quantum systems verification and the application of machine learning techniques to formal methods. Analysis of his recent publications (2023-2025) reveals a continued focus on advancing verification techniques for concurrent systems, with growing interest in quantum computing verification and the integration of graph neural networks with traditional formal methods. His work demonstrates consistent innovation in reducing the state space explosion problem while maintaining verification completeness. Professor Abdulla has established significant collaborations with researchers including Mohamed Faouzi Atig, Bengt Jonsson, and others, resulting in numerous high-impact publications in top-tier venues. His research has practical implications for developing reliable concurrent software systems and advancing formal verification methodologies. While specific details about his advising activities and grants aren't explicitly mentioned in the available information, his extensive publication record suggests active supervision of graduate students and involvement in multiple research projects. His work likely contributes to formal methods research groups at Uppsala University, focusing on verification tools and theoretical foundations of program analysis.

• Formal Verification
• Model Checking
• Concurrent Systems
• +۶ مورد دیگر

Dr. Gaoyang Dai is an Assistant Professor at Uppsala University's Department of Information Technology, specializing in Computer Systems research. His work focuses on real-time systems, embedded computing, and scheduling algorithms for complex task models. Research interests include: Deterministic timing analysis in distributed systems Priority inversion handling in multicore environments Non-preemptive node scheduling for DAG tasks Cyber-physical system design paradigms Deep learning applications in wireless networks Recent publications demonstrate expertise in IEEE Transactions and DATE conference proceedings, with particular emphasis on sporadic DAG task scheduling and resource sharing protocols. Collaborative work includes development of the TIMES-Pro toolchain for CPS implementation.

• Real-Time Systems
• Embedded Systems
• Scheduling Algorithms
• +۵ مورد دیگر

Kristin Yvonne Rozier is the Dennis and Rebecca Muilenburg Associate Professor in the Department of Aerospace Engineering and Computer Science at Iowa State University, joining the faculty in Fall 2016 after 14 years as a NASA Research Scientist and a brief tenure at the University of Cincinnati. She is an NSF CAREER Award winner and recipient of the Women in Aerospace Initiative-Inspiration-Impact Award, with affiliations including Associate Fellow of AIAA and Senior Member of IEEE/SWE. Her educational background includes a PhD from Rice University and MS/BS degrees from the College of William and Mary. PhD, Rice University MS, College of William and Mary BS, College of William and Mary Rozier's research centers on formal methods for safety-critical systems, specializing in verification and validation techniques for aerospace applications. Her work spans design-time checking of system logic, runtime monitoring, and safety analysis for autonomous systems, with specific focus on Unmanned Aerial Systems (UAS), mission-time linear temporal logic (MLTL), and specification debugging. She develops theoretical frameworks for model checking, property-based design, and linear temporal logic satisfiability, addressing challenges in biomedical privacy, secure protocols, and automated control systems. Her recent publications (2023-2025) demonstrate a concentrated effort on advancing mission-time logic verification, neurosymbolic AI applications, and hardware-verified runtime monitors. Key themes include MLTL validation through regular expressions, multimodal verification for cyber-physical systems, and practical tool development like R2U2 and MoXI, all targeting real-world deployment in aerospace and autonomous systems where safety guarantees are non-negotiable. Major recognitions include: NSF CAREER Award for theoretical foundations of UAS in the National Airspace Women in Aerospace Initiative-Inspiration-Impact Award (2013) Distinguished AIAA Award Elevation to Associate Fellow of AIAA As Principal Investigator, she leads significant grants including an ARMD Seedling project for UAS safety management and NSF-funded initiatives like CCRI for open-source model checking and CPS for resource-aware runtime verification. Her NASA work involved critical contributions to NextGen Air Traffic Management and CubeSat health monitoring, with R2U2 deployed on CySat-I and sounding rockets. She actively shapes the field through the NASA Formal Methods Symposium Steering Committee (since 2008) and promotes ethical research dissemination through reproducibility principles. Rozier directs the R2U2 research group developing runtime verification tools for embedded aerospace systems, with current projects integrating formal methods into UAS traffic management and autonomous space systems. Her team collaborates with NASA centers on safety-critical applications, maintaining strong industry ties through AIAA and IEEE forums while mentoring next-generation researchers in formal methods.

• Formal Methods
• Verification and Validation
• Safety-Critical Systems
• +۵ مورد دیگر

Liqian Chen is a Professor of Computer Science at the National University of Defense Technology (NUDT), Changsha, China. His research focuses on program analysis, abstract interpretation, and automated program repair, with a strong emphasis on numerical stability and software verification. Recent publications address advanced topics in floating-point error detection, vulnerability analysis, and invariant generation for neural networks. Key contributions include tools like F-IKOS (static analyzer for Fortran programs), AutoRNP (floating-point error repair tool), and Software Apron (numerical abstract domain library). Scientific Awards: ACM SIGSOFT Distinguished Paper Award (ISSTA 2024) He serves on program committees for major conferences such as ASE 2025, SAS 2025, and SETTA 2025, and acts as Guest Editor for journals like Automated Software Engineering Journal and Journal of Systems Architecture .

• Program Analysis
• Abstract Interpretation
• Automated Program Repair
• +۶ مورد دیگر

Tong Chen is a Postdoc in the Machine Learning section at the Department of Computer Science, University of Copenhagen, focusing on theoretical and applied machine learning with applications in information retrieval, medical data analysis, remote sensing, sustainability, and biological data modeling. His research spans adversarial machine learning, neural network compression, and robustness verification using polynomial and semialgebraic optimization techniques. Chen develops mathematically rigorous frameworks to enhance deep learning model efficiency, security, and reliability while addressing real-world implementation challenges. Chen's publication trajectory reveals increasing emphasis on formal verification methods and compression-robustness trade-offs, with recent work integrating semialgebraic geometry for neural network certification. This demonstrates a clear trend toward foundational approaches that bridge theoretical optimization and practical AI safety requirements. As part of the Machine Learning section, Chen contributes to the SCIENCE AI Centre and TreeSense initiative, utilizing the department's dedicated compute cluster for resource-intensive remote sensing and deep learning experiments in global environmental monitoring.

• Machine Learning
• Adversarial Machine Learning
• Neural Network Compression
• +۳ مورد دیگر