جست‌وجوی استاد و پژوهشگر

• Process Mining
• Anomaly Detection
• Blockchain
• +۳ مورد دیگر

Ko, Jonghyeon is a researcher affiliated with the Ulsan National Institute of Science and Technology (UNIST) , specifically the Department of Materials Science and Engineering within the College of Natural Science and Engineering. His work spans multiple disciplines including process mining, anomaly detection, blockchain technology, AI computing, and environmental engineering. His research interests include: Anomaly detection in business process event logs Blockchain-based systems for nuclear/radioactive waste management AI computing using neuromorphic devices Statistical leverage and information-theoretic approaches to process mining Optimization of autonomous vehicle safety systems Recent publications demonstrate expertise in developing formal languages for data quality simulation, probabilistic trace alignment methods, and practical tools for anomaly detection like AIR-BAGEL. While no explicit scientific awards are mentioned in the text, his work has been published in venues such as Information Systems , npj Unconventional Computing , and Expert Systems with Applications .

Rayna Dimitrovaمشاهده پروفایل

استادیار

• Verification
• Reactive Systems Synthesis
• Control and Robotics
• +۳ مورد دیگر

Rayna Dimitrova is a Tenure-track faculty member at the CISPA Helmholtz Center for Information Security in Saarbrücken, Germany. Previously, she held positions as Lecturer (Assistant Professor) at the University of Sheffield and University of Leicester, and postdoctoral roles at the University of Texas at Austin and the Max Planck Institute for Software Systems. She earned her PhD from Saarland University. Her research focuses on formal methods, including verification and synthesis of reactive systems, applications to control and robotics, quantitative analysis of probabilistic systems, and information-flow security. Key interests include strategic synthesis under partial observability, probabilistic uncertainty, and continuous dynamics, with applications to autonomous systems. She has served in numerous prestigious roles, including PC co-chair for VMCAI 2024, HYPER 2023, and SMT 2018. Her awards include the RS3 Best Paper Award (VMCAI 2012) and nominations for ETAPS (TACAS 2015) and EMSOFT (2014) Best Paper Awards. Her research group includes PhD students Rafael Dewes, Philippe Heim, and Saleh Soudijani. She has taught courses on reactive synthesis, program analysis, and decision procedures at multiple institutions, including the University of Sheffield and TU Kaiserslautern.

Yousra Aaferمشاهده پروفایل

استادیار

• Mobile and Smart Device Security
• System and Software Security
• Android Security
• +۴ مورد دیگر

Yousra Aafer is an Assistant Professor in the Department of Computer Science at the University of Waterloo. Her research focuses on mobile and smart device security, system security, and software security, particularly in the context of Android and cyber-physical systems. She holds a Ph.D. and M.Eng. from Syracuse University. Education: Ph.D., Syracuse University, United States (2016) M.Eng., Syracuse University, United States (2012) Her research interests include analyzing vulnerabilities in Android systems, binary analysis, IoT security, and fuzzing techniques. She explores methods to enhance security through formalized protocols, probabilistic protection recommendations, and leveraging large language models for vulnerability detection. Her work addresses critical areas such as cross-language buffer overflow detection, residual API audits in custom ROMs, and cyber-physical inconsistency in robotic vehicles. Her recent publications span topics like Android security, binary disassembly (e.g., D-ARM), and IoT security protocols (e.g., ProFactory). She has also contributed to frameworks like Poirot for probabilistic protection recommendations and StochFuzz for efficient binary fuzzing. While no awards are explicitly mentioned, her extensive publication record indicates active recognition in the security research community. She advises on multiple projects but no student names are listed here. Her work often involves collaboration on tools and frameworks for practical security applications.

• Stochastic Simulation
• Optimization
• Machine Learning
• +۵ مورد دیگر

Giulia Pedrielli is an Associate Professor at Arizona State University's School of Computing and Augmented Intelligence, with affiliations as a Senior Global Futures Scientist. She holds a Ph.D., M.Sc., and B.S. in Mechanical and Industrial Engineering from Politecnico di Milano, Italy. Her research focuses on stochastic simulation, optimization, and machine learning applications in biomanufacturing, digital twins, and cyber-physical systems. She has contributed to frameworks for pandemic modeling (PySIRTEM), RNA design, and high-dimensional optimization. Pedrielli has collaborated with institutions like NUS and UC Berkeley, and her work spans biomedical engineering, supply chain resilience, and security of critical infrastructure. Recent articles emphasize co-simulation tools, AI-driven logic translation, and falsification methods for CPS security. Her teaching includes advanced stochastic simulation courses and thesis supervision across multiple disciplines. Education: Ph.D. Mechanical Engineering, Politecnico di Milano, Italy M.Sc. Industrial Engineering, Politecnico di Milano, Italy B.S. Industrial Engineering, Politecnico di Milano, Italy Research interests integrate simulation-based optimization with machine learning, particularly in safety-critical systems like healthcare and smart infrastructure. She develops digital twin methodologies for real-time control under uncertainty, leveraging Gaussian processes and Bayesian approaches. Pedrielli's work bridges theoretical advancements (e.g., BASSO optimization algorithm) with practical applications in biomanufacturing and pandemic response.

• Software Engineering
• Model Checking
• Formal Verification
• +۴ مورد دیگر

Professor Tomoji Kishi is a distinguished faculty member at Waseda University's School of Creative Science and Engineering, where he has been serving since 2009. Previously, he held academic positions at Japan Advanced Institute of Science and Technology (2003-2009) following a 21-year career at NEC Corporation (1982-2003). He earned his Ph.D. in Information Science from Japan Advanced Institute of Science and Technology in 2002, building upon his earlier engineering graduate studies at Kyoto University. Professor Kishi's research focuses on software engineering, particularly in software product line development, model checking, formal verification, and aspect-oriented modeling. His work bridges theoretical formal methods with practical applications in embedded systems, automotive software, and IoT technologies. He has made significant contributions to scalability challenges in model checking for configurable systems and has pioneered approaches to variability management and approximate modeling techniques. His publication record demonstrates remarkable consistency and evolution, with 42 papers and 153 citations according to Scopus data (h-index: 7), spanning from foundational work in software architecture in the 1990s to cutting-edge research on AI-enhanced verification methods in 2025. His recent work shows increasing application of machine learning techniques to traditional formal methods problems, particularly in the context of highly configurable systems and IoT applications. ITS Standardization Activity Merit Prize (2022) from Society of Automotive Engineers of Japan IPSJ/ITSCJ Standardization Contribution Award (2017) IPSJ/ITSCJ Project Editor Award (2016 and 2013) Information Processing Society of Japan Society Activity Contribution Award (2010) IPA/SEC Journal Best Paper Award (2007) Information Processing Society of Japan Yamashita Memorial Research Award (1998) Professor Kishi has led multiple JSPS-funded research projects, including recent work on 'variability management methods prioritizing usability through variability mining' (2020-2023) and 'utility-first modeling method' (2017-2020). His industry collaborations, particularly with automotive systems developers, demonstrate the practical impact of his research. He maintains active membership in major professional societies including IEEE Computer Society, ACM, and the Information Processing Society of Japan.

Mateja Novakمشاهده پروفایل

استادیار

• Model Predictive Control
• Power Electronics
• Multilevel Converters
• +۶ مورد دیگر

Mateja Novak is an Assistant Professor at AAU Energy, Aalborg University, Denmark, within the Department of Applied Power Electronic Systems under the Faculty of Engineering and Science. Her research focuses on model predictive control, multilevel converters, machine learning, and reliability of power electronic systems, contributing to sustainable energy systems and renewable energy integration. She holds a Ph.D. from Aalborg University (2020) and an M.Sc. from Zagreb University (2014). Previously, she was a Postdoc at AAU Energy (2020-2023) and a visiting researcher at Kiel University (2018) and Danfoss (2023). Notable achievements include the EPE Outstanding Young EPE Member Award (2019) and 2nd place in the 2021 IEEE-IES Student and YP Competition. Her work spans projects like ALL2GaN (2023-2026) and AI-Power (2022-2027), addressing GaN IC solutions and AI-driven power electronics advancements. She is actively involved with IEEE societies including the Power Electronics Society and IEEE Women in Engineering. Her research outputs emphasize control strategies for power electronics, reliability analysis, and optimization techniques. Key areas of exploration include thermal stress balancing in converters, statistical model checking, and multiobjective control algorithms. Collaborations with industry partners like Danfoss and academic institutions like Kiel University underscore her interdisciplinary approach to advancing power electronics technology.

• Natural Language Processing
• Computational Linguistics
• Machine Learning
• +۴ مورد دیگر

Dr. Lea Frermann is an active researcher in computational linguistics and natural language processing, with a focus on media framing analysis, fairness in AI, and Bayesian modeling of semantic categories. Her work bridges machine learning with social science applications, particularly in political discourse and climate communication contexts. PhD in Bayesian models of category acquisition (University of Edinburgh, 2017) Key collaborations with University of Melbourne, ACL, EMNLP, and CoNLL communities Research emphasizes: Media framing analysis across cultures Explainable AI for bias detection Bayesian approaches to language understanding Word association modeling for value systems Computational methods for political discourse analysis Applications in mental health support forums Recent publications focus on large language models' limitations in hierarchical reasoning, multimodal categorization, and systematic fairness evaluation frameworks. Collaborations show strong connections with researchers like Timothy Baldwin, Trevor Cohn, and Omri Abend.

Mitra Nasriمشاهده پروفایل

استادیار

• Real-Time Systems
• Embedded Systems
• Scheduling Algorithms
• +۴ مورد دیگر

Mitra Nasri is an Assistant Professor at the Eindhoven University of Technology, affiliated with the College of Engineering's Department of Electrical Engineering. She contributes to the High Tech Systems Center and EAISI Foundational, focusing on interconnected resource-aware intelligent systems. Research Focus: Real-Time Systems, Scheduling Algorithms, Embedded Systems, Fault-Tolerant Computing, and Cyber-Physical Systems. Key Contributions: Development of scheduling frameworks for multi-rate task chains, response-time analysis techniques, and containerization strategies for real-time distributed applications. Her recent work includes advancements in weakly-hard timing constraints, parallel global scheduling, and cloud integration for embedded systems. She actively collaborates on projects like SAM-FMS and COMP4DRONES. Scientific Awards: Best Paper Award - RTAS 2022 Best Paper Award - RTNS 2016 Outstanding Paper Awards at RTAS 2017, 2022 and RTSS 2020 She teaches courses in Real-Time Systems, Operating Systems, and Automotive Software, and participates in organizing conferences like Embedded Systems Week and CompSys.

Ichiro Hasuoمشاهده پروفایل

استاد

• Formal Verification
• Mathematical Structures
• Cyber-Physical Systems
• +۳ مورد دیگر

Ichiro Hasuo is a Professor at the National Institute of Informatics (NII) in Tokyo, Japan, where he serves as Director of the Research Center for Mathematical Trust in Software and Systems. He holds a joint appointment at The Graduate University for Advanced Studies (SOKENDAI). Since 2016, he has been the Research Director of the JST ERATO Metamathematics for Systems Design Project, and founded Imiron Co., Ltd. in 2024. Education: PhD in Computer Science (cum laude) from Radboud University Nijmegen (2008) MSc in Mathematical and Computing Sciences from Tokyo Institute of Technology (2004) BSc in Mathematics from University of Tokyo (2002) His research focuses on foundational aspects of software science, particularly formal verification techniques using mathematical structures from category theory and coalgebra. He develops methods for ensuring reliability in cyber-physical systems and systems incorporating machine learning components. Current work emphasizes logical frameworks for autonomous vehicle safety and mathematical trust in complex systems. Hasuo's publications demonstrate consistent focus on theoretical foundations with practical applications. His recent work spans coalgebraic verification methods, temporal logic for hybrid systems, quantum programming semantics, and applications to autonomous driving systems. Key themes include compositional reasoning, probabilistic modeling, and the integration of discrete and continuous system verification. Awards and Honors: Best Paper Award at ICTAC 2024 Minister of Education, Culture, Sports, Science and Technology Commendation (2024) Distinguished Paper Award at CAV 2023 Outstanding Reviewer Award at EMSOFT 2022 Best Paper Award at ICECCS 2018 Best Paper Award at CONCUR 2014 Hiroshi Fujiwara Encouragement Prize (2012) PhD cum laude (2008) He leads multiple major research grants including: JST ASPIRE (2024-2029) for international collaboration on software trust JST START (2022-2025) for autonomous driving verification JST ERATO Metamathematics for Systems Design (2016-2025) Several JSPS KAKENHI grants As head of the MMM laboratory (Hasuo-Lab) at NII, he supervises PhD students and postdoctoral researchers in formal methods and mathematical systems design.

Sebastien Pillementمشاهده پروفایل

مدرس

• Computer Engineering
• Embedded Systems
• Computer Architecture
• +۳ مورد دیگر

Sébastien Pillement is a Lecturer at Polytech'Nantes, the College of Engineering of the University of Nantes, and a member of the ASIC research team within the IETR UMR 6164 laboratory. His career spans over 13 years at the IUT of Lannion (University of Rennes 1) before joining the University of Nantes in 2012. He holds a PhD in Computer Science from the University of Montpellier II (1998) and a Habilitation à Diriger des Recherches (HDR) from the University of Rennes 1 (2010). Current Research Focus: Dynamically Reconfigurable Architectures (DRA), Hardware Security, Fault-Tolerant Embedded Systems, NoC (Network-on-Chip), Design Methodologies. Teaching Areas: Digital Circuits, Computer Architecture, Operating Systems, Real-Time Systems, and Algorithms for Embedded Computing. His research explores flexible and efficient architectures for embedded systems, emphasizing real-time management, reliability, and security. Key article trends include RISC-V processors, neural network acceleration on MPSoC, formal verification of arithmetic circuits, and runtime FPGA scheduling. Collaborations span international institutions like the University of Toronto and CEA, as well as industrial partners such as STMicroelectronics and Thales. PhD Students : Current: Laureline Dubucq (Security & AI), Mustafa Ibrahim (Ultra-Low Power AI), Téo Biton (Runtime Anomaly Detection), Mohamed Amine Zhiri (Adaptive NoC), Juliette Pottier (RISC-V Protection). Graduates: Quentin Dariol (Neural Network Timing), Alexis Duhamel (FPGA Scheduling), Safouane Noubir (Security in Multi-Core Systems), Hai Dang Vu (Probabilistic Timing Analysis), David Pallier (Sensor Clock Synchronization). Projects : Current: ADAPTING (2023-2029), FITNESS (2023-2027), SEC-V (2022-2025), NOP (2021-2025). Past: SPARTE (2015-2018), ARDyT (2011-2015), FosFor (2008-2011).

Sally McCleanمشاهده پروفایل

استاد

• Process Mining
• Business Process Management
• Activity Recognition
• +۹ مورد دیگر

Professor Sally McClean is an academic at Ulster University's School of Computing , holding the title of Professor of Mathematics . Her research spans computational mathematics, machine learning, and process mining, with applications in healthcare, IoT, and smart cities. Focus areas include Process Mining , Activity Recognition , and Lead Toxicity Prediction Funded by organizations like The Royal Society and Invest Northern Ireland Her work contributes to the UN Sustainable Development Goals in health and technology domains. Recent publications analyze smart home IoT systems, emergency department logistics, and federated process mining privacy frameworks. Awarded Honorary award (2021) for heartbeat rhythm analysis Received TM Forum Catalyst Industry Contribution (2022)

Selman Hershfieldمشاهده پروفایل

استاد

• Condensed Matter Theory
• Transport Theory
• Many-Body Physics
• +۳ مورد دیگر

Selman Hershfield is a Professor in the Department of Physics at the University of Florida's College of Liberal Arts and Sciences. He specializes in condensed matter theory, particularly transport phenomena and many-body physics in nanostructures. His research combines theoretical rigor with practical applications to quantum systems and nanodevices. Hershfield investigates transport in quantum dots, magnetic multilayers, and tunneling through single atoms or defects. These systems exhibit novel many-body effects due to reduced dimensionality and can be driven far from equilibrium. His work connects fundamental theory with technological applications in nanoelectronics. His publications establish foundational frameworks for nonequilibrium quantum statistical mechanics and develop novel solutions for complex transport problems. Recent work extends to robust control of autonomous systems under uncertainty.

Eugene Starkمشاهده پروفایل

استاد

• Operating Systems
• Programming Language Semantics
• Concurrency Theory
• +۳ مورد دیگر

Eugene Stark is a Professor in the Department of Computer Science at Stony Brook University, affiliated with the College of Engineering and Applied Sciences. His work focuses on theoretical foundations and practical applications in systems and programming languages. Educational Background: He earned his Ph.D. in Computer Science from the Massachusetts Institute of Technology (MIT) in 1984. Research Interests: His primary areas include Operating Systems, Programming Language Semantics, Concurrency Theory, Distributed Algorithms Verification, and Functional Programming Languages. He explores formal methods to ensure correctness and efficiency in complex systems, leveraging category theory for rigorous semantic frameworks. His work often addresses challenges in indeterminate dataflow networks and probabilistic models. Publications Overview: Stark’s recent articles emphasize categorical structures (e.g., residuated transition systems, bicategories) and probabilistic I/O automata. They reflect a trajectory from foundational theory to tool development for concurrent system analysis. Academy of Teaching Scholars award Department Award for Undergraduate Teaching (2000) Advising & Grants: No formal advisees are listed in current records. His research projects include the Probabilistic I/O Automata framework, CARA Infusion Pump specifications, and SAMSON Network Memory Server initiatives. Grants and funding details are not explicitly mentioned. Labs/Teams: He is associated with the Laboratory for 體魯 Institute of (1988), though the lab name’s full English title remains unclear. Collaborations likely extend to interdisciplinary projects involving formal methods and systems engineering.

Ricardo Melo Czeksterمشاهده پروفایل

مدرس

• Cyber Threat Intelligence
• Cyber-Physical Systems
• Risk Assessment
• +۳ مورد دیگر

Ricardo Melo Czekster is a Lecturer in Software Engineering & Cybersecurity at Aston University's School of Computer Science and Digital Technologies. His research develops methods for securing critical infrastructure through formal modeling and threat intelligence. Education: PhD in Performance Evaluation, Pontifical Catholic University of Rio Grande do Sul (2010) MSc in Analytical and Environmental Chemistry, University of Benin (2009) BSc in Industrial Chemistry, Ambrose Alli University (2004) Research Focus: Czekster specializes in cybersecurity for cyber-physical systems, particularly smart grids and active buildings. His work develops STIX-based threat intelligence frameworks, attack path modeling with BDMPathfinder, and dynamic risk assessment methods. Recent projects address security in medical IoT and transportation systems. Teaching: Leads modules on Secure Programming, Security Governance, and Secure Software Delivery.

• Machine Learning
• Neural Networks
• Uncertainty Quantification
• +۴ مورد دیگر

Dr. Matias Valdenegro Toro is an Assistant Professor of Machine Learning at the University of Groningen within the Faculty of Science and Engineering and the Artificial Intelligence department of the Bernoulli Institute. He holds a PhD from Heriot-Watt University (2019) and a Master's in Autonomous Systems from Bonn-Rhein-Sieg University of Applied Sciences (2014). His research focuses on trustworthy machine learning models , particularly in uncertainty quantification , medical AI , and robotics , with applications in computer vision and explainable AI. He teaches courses like Introduction to Machine Learning and Deep Learning at the Bachelor and Master levels. His work emphasizes robustness in AI systems, including uncertainty estimation for medical applications, super-resolution techniques, and neuromorphic robotics. He has published widely on topics like Bayesian neural networks, prompt tuning, and sanity checks for explanations. Notable awards include Best Reviewer at ICML (2024) and Highlighted Reviewer at ICLR (2022). He collaborates with institutions like the German Research Center for Artificial Intelligence and actively contributes to open-source datasets (e.g., the Japanese Uncertain Scenes Dataset ). Key grants and activities include organizing the ENLIGHT BIP Course on Deep Learning for Forestry and teaching at the European Summer School on AI . His research also addresses regulatory challenges like the EU AI Act's implications for uncertainty quantification in general-purpose AI.