جست‌وجوی استاد و پژوهشگر

• Modelling and Requirements Engineering
• Systems Architecture
• Process and Project Management
• +۵ مورد دیگر

Ricardo J. Machado is a Full Professor of Information Systems Engineering at the University of Minho, where he founded this disciplinary area. He currently serves as President of the CCG/ZGDV Institute and has held strategic roles including Vice-Rector of UMinho. He is a member of the Board of GraphicsVision.AI, the leading academic network in Europe in computer graphics and vision. DEng in Electrical and Computer Engineering (U.Porto) MSc and PhD in Computer Science and Engineering Dr. Habil in Information Systems Engineering (UMinho) Certified Software Product Manager (ISPMA) Cybersecurity and Crisis Management programme from Portuguese National Defence Institute Professor Machado's research focuses on modeling and requirements engineering, systems architecture, process and project management, information semantics, ontologies, and cognitive computing. He has developed several methods and tools including the 4SRS method and the shobi-PN meta-model for requirements analysis, software architecture, ontology computation, and project portfolio management. His work bridges theoretical frameworks with practical industrial applications across various sectors. His recent publications demonstrate a strong trend toward digital transformation across multiple domains including smart cities, healthcare systems, industry 4.0, and higher education. The research shows increasing integration of semantic interoperability, logical architecture design, and data management frameworks. His work consistently addresses the challenges of aligning business processes with technical implementations while focusing on practical applications in real-world settings. IEEE MGA Achievement Award APLOG Excellence Award TAA Textbook Award nomination by Springer Professor Machado has supervised nearly one hundred PhD and MSc students, many of whom now hold leadership roles in academia and industry. He has led over 50 R&I projects funded by FCT, ANI, IAPMEI, and the European Union, partnering with institutions including Carnegie Mellon University, MIT, Fraunhofer, and Bosch. His project portfolio spans requirements engineering, systems architecture, and digital transformation initiatives across multiple sectors including healthcare, manufacturing, and smart cities. He founded the SEMAG research group at ALGORITMI and the EPMQ department on Software Engineering and Intelligent Data at CCG/ZGDV. As Director of the ALGORITMI Research Centre, he coordinated UMinho's participation in strategic initiatives such as CEDT, CMU | Portugal, EIT Digital, EUHubs4Data, and Gaia-X Portugal. He also co-founded DTx and ProChild CoLabs, and TICE.pt and CSCP Clusters.

• Embedded Systems
• Hardware/Software Co-design
• Energy-efficient AI Accelerators
• +۷ مورد دیگر

Oliver Bringmann is a full Professor and head of the Chair of Embedded Systems at the University of Tübingen, Germany, and a member of the board of directors at the FZI Research Center for Information Technology. His research integrates embedded-system design, energy-efficient AI accelerators, dependable automotive perception, and medical AI for capsule endoscopy. Education & Career Ph.D. in Computer Science, University of Tübingen, 2001 Diploma in Computer Science, University of Karlsruhe (KIT) Head, Chair of Embedded Systems, University of Tübingen (since 2012) Deputy spokesperson & spokesperson, Dept. of Computer Science, University of Tübingen (2014-2022) Board of Directors, FZI Research Center for Information Technology Research Interests Bringmann’s group pioneers hardware/software co-design for ultra-low-power Edge-AI , developing RISC-V based accelerators, compiler-aware neural-architecture search, and real-time perception systems for autonomous driving and medical devices. Key topics include: Energy-efficient AI architectures (“Edge AI”) and custom accelerator generation Robust collective perception under adverse weather (LiDAR, camera, V2X fusion) Timing/power-predictable embedded software and system-on-chip design automation Hardware-assisted security and safety for automotive & IoT systems AI-driven capsule endoscopy localization and anomaly detection Recent Publication Trends His 2024-2025 articles reveal a strong shift toward robust multimodal perception for automated driving (snow, fog, collective LiDAR fusion) and Edge-AI medical devices (capsule endoscopy with multi-task CNNs). Core contributions span dataset generation (SCOPE, SnowyLane), safety metrics (LSM), and fast performance modeling for DNN accelerators. Professional Service & Projects Executive/Steering Committees: IEEE/ACM DATE, CODES+ISSS, CASES, ITSS conferences EU CATRENE EDA roadmap chapter lead (Embedded Software & ESL-to-RTL) Principal investigator in Scale4Edge, OCEAN12, enerDAG and other national projects on energy-efficient sensorics and secure energy trading. His group maintains extensive collaborations with automotive and semiconductor industry, focusing on dependable, energy-aware embedded intelligence.

• Software Engineering
• Formal Methods
• Software Architectures
• +۷ مورد دیگر

Patrizia Scandurra is an Associate Professor at the Department of Management, Information and Production Engineering, University of Bergamo, Italy. Her academic appointment in Computer Science (01/B1) spans from 2022 to 2033. She previously held roles as a researcher at the University of Bergamo (2009-2017) and postdoctoral fellow at the University of Milan (2006-2008). She earned her PhD in Computer Science (2006) and Bachelor's degree (2002) from the University of Catania. Research Focus : Software architectures and formal methods for modeling, validation, and verification of software-intensive systems. Specializations : Runtime analysis of self-adaptive, autonomous, and uncertain systems including IoT-Edge-Cloud applications, embedded systems, and system-on-chip. Collaborations : STMicroelectronics, Atego, Bialetti, and ENEA. Conference Involvement : Program/organizing committees for ICSE, ASE, ISSRE, ICSA, ECSA, SEAMS@ICSE, ABZ, SA-TTA@SAC, FAACS@ECSA. Research Projects : Model-driven development for robotics, adaptive architectures for pervasive systems, big data in smart cities, and digital twins for medical systems. Notable Contributions : Development of the ASMETA formal method community tools and frameworks for rigorous system design. She has published over 100 peer-reviewed works in international journals and conferences.

Frank Piessensمشاهده پروفایل

استاد

• Software Security
• System Security
• Formal Methods
• +۲ مورد دیگر

Frank Piessens is a Professor in the Department of Computer Science at Katholieke Universiteit Leuven (Belgium). His work bridges software security, systems security, formal methods, and programming languages. He actively investigates information flow security, with a dual focus on attack techniques and defense mechanisms. Current role: Professor at KU Leuven (Belgium) Research areas: Software Security, System Security, Formal Verification, Programming Languages Research Interests: His defense work includes formal verification for C-like languages, memory safety hardening, micro-architectural side-channel mitigation, and embedded security architectures. On the attack side, he studies transient execution attacks, memory safety exploits, and controlled channel attacks. Recent Article Trends: His publications span secure compilation techniques (2025), control-flow linearization (2024), functional reactive programming (2021-2014), formal verification (2020-2016), and hardware/software co-design security (2022). Keywords cluster around Computer Science , Security , and Programming Languages .

Ruzica Piskacمشاهده پروفایل

استاد

• Software Verification
• Security and Applied Cryptography
• Automated Reasoning
• +۴ مورد دیگر

Ruzica Piskac is a Professor of Computer Science at Yale University, where she leads the Rigorous Software Engineering (ROSE) group. She has made significant contributions to the fields of software verification, security, automated reasoning, and code synthesis, focusing on improving software reliability and trustworthiness through formal techniques. Dr. Piskac received her PhD from the Swiss Federal Institute of Technology (EPFL) in 2011, where her dissertation won the Patrick Denantes Prize. Prior to joining Yale, she led an independent research group at the Max Planck Institute for Software Systems in Germany (2012-2013). Her research spans several key areas: symbolic execution for Haskell (G2), privacy-preserving formal methods (PPFM), functional reactive synthesis, verification of configuration files, and analysis of software updates. Her work consistently bridges theoretical formal methods with practical applications in real-world systems. Dr. Piskac's recent publications demonstrate a strong trend toward applying formal verification techniques to emerging challenges including large language models, quantum computing security, legal accountability of automated systems, and cyber-physical systems. Her research increasingly intersects with AI, cryptography, and legal domains while maintaining strong foundations in formal methods. Her scientific achievements have been recognized with numerous prestigious awards: Multiple Amazon Research Awards Yale University's Ackerman Award for Teaching and Mentoring Facebook Communications and Networking Award Microsoft Research Award for the Software Engineering Innovation Foundation (SEIF) Patrick Denantes Prize for her PhD dissertation Dr. Piskac has graduated five PhD students, four of whom have gone on to become assistant professors of computer science. She has served as Program Chair of the 37th International Conference on Computer Aided Verification and is on the Steering Committee of the Formal Methods in Computer-Aided Design conference. She leads the Rigorous Software Engineering (ROSE) group at Yale, which focuses on several key projects including: Symbolic Execution Engine for Haskell (G2) Privacy Preserving Formal Methods (PPFM) Functional Reactive Synthesis Verifications for Configuration Files Analysis of Software Updates and Configuration Files

• Mechanical Engineering
• Artificial Intelligence
• Tribology
• +۳ مورد دیگر

Hassan Shirvani is a Professor of Engineering Design and Simulation at the School of Engineering and the Built Environment, Anglia Ruskin University. He serves as Director of the Engineering Analysis Simulation and Tribology (EAST) Research Group, focusing on industry collaborations to solve engineering challenges. PhD in Mechanical Engineering, University of Bath MSc in Mechanical Engineering, University of Birmingham Member, Institute of Mechanical Engineers (IMechE) His research spans mechanical engineering, artificial intelligence, and biomedical applications, including: Thermal system optimization Machine learning in clinical decision-making Composite metal foil manufacturing Virtual reality medical training systems Flow dynamics in heat exchangers and nozzles AI-assisted diagnostics Hybrid manufacturing processes Hassan's publications reflect expertise in computational modeling, multi-physics simulations, and industrial applications. Notable areas include deep learning for suicide prediction, thermodynamic analysis of sustainable energy systems, and tribology in mechanical components.

• Wireless Networks
• Health Informatics
• Sensor Networks
• +۳ مورد دیگر

Ibrahim Orhan serves as a Lecturer at KTH Royal Institute of Technology within the Division of Health Informatics and Logistics. His primary responsibilities include teaching core courses such as Communication Networks (HE1033), Mobile Communications and Wireless Networks (HI1035), and Routing in IP Networks (HI2002), while also supervising first-cycle degree projects in Computer and Electrical Engineering. His research centers on wireless sensor networks with specialized applications in healthcare and education. Key focus areas include time synchronization protocols for multi-sensor systems, performance monitoring in contention-based networks, and data fusion techniques for health monitoring applications like fall detection. Recent work demonstrates a strategic expansion into educational technology through serious games for engineering education. Analysis of his 15 most recent publications (2006-2023) reveals an evolving research trajectory: initial work concentrated on fundamental wireless network performance (2008-2011), shifting toward healthcare applications (2012-2016), and culminating in educational technology innovations (2023). Persistent themes include Bluetooth synchronization, mobile sensor integration, and quality-of-service management in resource-constrained environments. Dr. Orhan actively supervises undergraduate degree projects in computer and electrical engineering, though specific student names aren't publicly listed. His research has been supported through KTH institutional channels, with publications spanning IEEE conferences, specialized journals like the International Journal of Serious Games, and collaborative projects focused on ambient assisted living solutions.

• Algorithm development
• Data analytics
• Time-series data analysis
• +۶ مورد دیگر

Emran Ali is a Graduate Researcher (Ph.D. candidate) and Part-Time Lecturer at Deakin University's School of Information Technology within the Faculty of Science, Engineering and Built Environment. He holds concurrent faculty appointments at Hajee Mohammad Danesh Science & Technology University (HSTU) in Bangladesh where he teaches computer science courses while on study leave. His academic journey includes a Master of Science (Research) in Information Technology from Deakin University (2022) and a Bachelor of Science in Computer Science and Engineering from HSTU. Doctor of Philosophy (Ph.D.) in Information Technology, Deakin University (2023–present) Doctor of Philosophy (Ph.D.) in Machine Learning, Coventry University (Cotutelle program, 2023–present) Master of Science (Research) in Information Technology, Deakin University (2020–2022) Bachelor of Science in Computer Science and Engineering, HSTU Bangladesh (2007–2012) Ali's research focuses on algorithm development and applied machine learning in health informatics, specializing in biosignal processing for neurological and sleep disorder detection. His work integrates time-series data analysis with explainable AI techniques to develop clinical decision support systems. Current projects include ML/DL modeling of sleep-stage transitions in aging populations and causal relationship analysis in sleep disorders using EEG data. Analysis of his 10 recent publications reveals strong concentration in biomedical ML applications (60%), particularly EEG-based neurological disorder detection and mental health diagnostics. Secondary focus areas include environmental monitoring systems (20%) and foundational computer science (20%). His work consistently employs ensemble methods and feature optimization techniques across diverse datasets, with increasing emphasis on real-world clinical applicability in recent publications. Deakin University Post-graduate Research Scholarship (DUPRS) through Cotutelle program with Coventry University National Fellowship from Bangladesh Ministry of Science and Technology (2020) Best Presentation Award at Deakin School of IT Conference (2021) AWS AI/ML Scholarships (2023, 2024) Next Generation Tech Booster Scholarship (2024) Ali provides research supervision at HSTU while serving as a Graduate Research Teaching Fellow at Deakin University for Machine Learning and Data Analytics units. His industry collaborations include projects with Monash University, Alfred Health, and AETMOS Australia focused on health informatics applications. Current funding includes AWS-sponsored nanodegrees and Deakin University research scholarships supporting his sleep disorder research. His technical work integrates cloud-based AI/ML platforms (AWS, Azure) with biosignal processing pipelines, utilizing collaborations across Australian healthcare institutions to validate clinical applications. Recent projects emphasize explainability in deep learning models for medical diagnostics, particularly in resource-constrained environments relevant to Bangladesh healthcare contexts.

• Software Security
• Software Engineering
• Cybersecurity
• +۳ مورد دیگر

Wei Huo is a researcher at the Institute of Information Engineering, Chinese Academy of Sciences, specializing in software security and engineering. His work focuses on vulnerability detection, program analysis, and cybersecurity across diverse systems including cloud infrastructure, firmware, and web applications. His research interests encompass Software Security , Program Analysis , Binary Analysis , and AI-driven security techniques . He develops practical tools for identifying vulnerabilities in Kubernetes ecosystems, baseband firmware, and binary code, with emphasis on real-world applicability in industrial contexts. Analysis of his publications (2019-2025) reveals a consistent focus on empirical security studies and tool development. Key trends include cloud-native security (Kubernetes), firmware analysis (printers/baseband), and AI-enhanced binary similarity detection, demonstrating progression from component-level to system-level security challenges. While no specific awards are documented in the source material, his contributions to top venues like ASE, ICSE, and ISSTA reflect significant impact in software engineering research. His work bridges academic rigor with practitioner-oriented solutions, particularly in vulnerability management and automated testing. Wei Huo actively contributes to the security research community through publications addressing critical infrastructure vulnerabilities, though details about student supervision or grant funding are not available in the provided text.

Chung Hwan Kimمشاهده پروفایل

استادیار

• Computer Systems Security
• Cyber-Physical Security
• Software Security and Reliability
• +۳ مورد دیگر

Chung Hwan Kim serves as an Assistant Professor in the Department of Computer Science at the University of Texas at Dallas, where he directs the Software & Systems Security Laboratory (S³ Lab). His research focuses on critical security challenges in cyber-physical systems, embedded devices, and cloud infrastructure, with recognition including the NSF CAREER Award and UT Dallas New Faculty Research Symposium Grant. His expertise spans Computer Systems Security , Cyber-Physical Security , and Software Security and Reliability , emphasizing practical solutions for robotic vehicles, autonomous systems, and trusted execution environments. Current projects address signal injection attacks, resilience testing, and confidential computing through innovative fuzzing frameworks and hardware-assisted protections. Recent publications (2020-2026) reveal three dominant research thrusts: (1) Security for autonomous/robotic systems ( DriveFuzz , IMUFUZZER ), (2) Trusted execution in constrained environments ( Vessels , GEVisor ), and (3) Automated vulnerability discovery ( HFL , TZ-DATASHIELD ), consistently appearing in top venues like IEEE S&P and USENIX Security. Key honors include: NSF CAREER Award (premier early-career recognition) UT Dallas New Faculty Research Symposium Grant Top 10 finalist for CSAW Best Applied Research Paper Award (2018) As principal investigator of the S³ Lab, Kim mentors graduate researchers and secures competitive funding for projects spanning robotic vehicle security, embedded systems hardening, and confidential computing. His teaching portfolio includes Operating Systems, Information Security, and specialized courses on CPS/IoT security. The S³ Lab develops deployable security tools like TZ-DATASHIELD for embedded data protection and IMUFUZZER for resilience testing of aerial vehicles, collaborating with industry partners to translate research into real-world solutions for autonomous systems and critical infrastructure.

Zhenyu Chenمشاهده پروفایل

استاد

• AI for Testing
• Testing for AI
• Software Testing
• +۳ مورد دیگر

Zhenyu Chen is a Full Professor and Director of the iSE Laboratory at Nanjing University, specializing in AI-driven software testing methodologies. His research bridges artificial intelligence and software engineering with dual focus areas: leveraging AI to enhance testing processes ( AI for Testing ) and validating AI/ML systems ( Testing for AI ). His research interests center on deep learning framework testing , crowdsourced testing optimization , and Large Language Model applications in verification . Recent work demonstrates innovative approaches to metamorphic testing of neural networks, LLM-based test report analysis, and security hardening of code models against backdoors. Key contributions include the development of mooctest.com and frameworks like DevMuT for mutation testing of deep learning APIs. His publication trajectory reveals evolving focus from crowdsourced testing (2018-2020) to deep learning system validation (2021-2023) and current emphasis on LLM-powered testing solutions. Major venues include ASE, ICSE, and ISSTA where he serves regularly on program committees.

• Software Engineering
• Model Checking
• System Modeling
• +۵ مورد دیگر

Seung Yeob Shin serves as a Research Scientist at the University of Luxembourg's Interdisciplinary Centre for Security, Reliability and Trust (SnT), where he contributes to the Software Verification and Validation (V&V) Lab under Prof. Lionel Briand. His active participation in major software engineering conferences includes serving on ASE 2025's Research Papers Program Committee and authoring multiple journal-first publications presented at premier venues like ICSE and ESEC/FSE. Shin earned his PhD in 2016 from the Laboratory for Advanced Software Engineering Research (LASER) at the University of Massachusetts Amherst's College of Information and Computer Sciences. His research expertise centers on applying formal methods to complex software systems, with particular emphasis on model-based verification techniques. His current research program bridges theoretical modeling and practical system validation across critical domains. Key focus areas include developing simulation frameworks for software-defined networks, creating model-checking approaches for cyber-physical control loops, and establishing probabilistic methods for real-time system verification. This work consistently targets reliability challenges in safety-critical infrastructure through rigorous engineering methodologies. Recent publications demonstrate a cohesive trajectory in applying formal verification to emerging system paradigms. The 2024 journal-first papers reveal increasing sophistication in handling non-deterministic behaviors across networking, embedded systems, and requirements engineering domains, with notable emphasis on failure induction and probabilistic safety guarantees. No scientific awards were documented in the available materials. While conference contributions indicate significant scholarly engagement, no information regarding student advising or research grants appears in the provided documentation. As a core member of the V&V Lab at SnT, Shin contributes to Luxembourg's national cybersecurity research infrastructure. The lab specializes in developing mathematical frameworks for system validation, with current projects addressing verification challenges in autonomous systems, critical infrastructure, and secure communications protocols through model-driven approaches.

Hui Hanمشاهده پروفایل

مدرس ارشد

• Edge AI
• TinyML
• Sustainable Supply Chain Management
• +۷ مورد دیگر

Dr. Hui Han is a Senior Lecturer at Luleå University of Technology (LTU) in Sweden, working within the Machine Learning Group with a Wallenberg AI, Autonomous Systems and Software Program (WASP) professorship. She is affiliated with the Department of Computer Science, Electrical and Space Engineering, specifically within the Embedded Intelligent Systems LAB. Her office is located in Luleå at room A3424. Dr. Han earned her Ph.D. in Industrial Engineering from the Technical University of Madrid (UPM), Spain. Following her doctoral studies, she completed postdoctoral research at the Data Science department of Fraunhofer IESE in Germany and later served as a postdoctoral fellow in the Department of Computer Science at the Norwegian University of Science and Technology (NTNU), Norway. Dr. Han's research focuses on data science with specific expertise in Edge AI and TinyML (Tiny Machine Learning). She leads research efforts in developing sustainable modeling for Sustainable Supply Chain Management, particularly in reverse logistics and circular economy. Her work also encompasses social commerce and Industry 4.0 applications. As the first author, she has published 17 papers including 10 journal papers, 4 conference papers, and 3 magazine papers, accumulating over 1,000 citations according to Google Scholar. Her publications appear in high-impact journals such as Technological Forecasting & Social Change (IF 13.3), Expert Systems with Applications (IF 8.5), and Education and Information Technologies (IF 5.5). Dr. Han's teaching portfolio includes Management Information Systems, Project in Industrial AI, and TinyML. She has created an introductory TinyML course available on her YouTube channel. Her research spans multiple interdisciplinary areas including Industry 4.0, IoT, Cloud Computing, Big Data Analytics, Cyber-Physical Systems, Multicriteria decision support systems (particularly Fuzzy TOPSIS), Complex Relationships Analysis (PLS-SEM), Data Science, TinyML, Edge AI, Random Forest techniques, Information Systems, Social commerce, E-commerce, Sustainable Supply Chain Management, Reverse logistics, Closed-loop Supply Chains, Circle Economy, and Healthcare applications. Her Embedded ML for Health Project demonstrates practical applications of TinyML in healthcare, using Arduino Nano 33 BLE Sense and OV7675 Camera for melanoma detection through image processing and machine learning algorithms. This project showcases her ability to apply theoretical research to real-world healthcare challenges.

Priyank Kalgaonkarمشاهده پروفایل

استادیار

• Autonomous Embedded Systems
• Artificial Intelligence
• Real-time Decision Systems
• +۳ مورد دیگر

Priyank Kalgaonkar serves as an Assistant Professor in the Department of Electrical and Computer Engineering at Lafayette College, joining the faculty in 2024. He teaches ECE 414: Embedded Systems and Laboratory, emphasizing hands-on integration of hardware and software components through practical projects that prepare students for real-world engineering challenges. His research focuses on developing autonomous embedded systems and artificial intelligence applications capable of independent operation, real-time decision-making, and environmental adaptation to enhance efficiency, safety, and quality of life. Dr. Kalgaonkar's primary research areas center on autonomous embedded systems and AI, with specific expertise in real-time decision systems and adaptive technologies. His work targets revolutionary applications in robotics, automotive systems, and defense sectors by enabling complex task execution with minimal human intervention. He is driven by the challenge of pushing technological boundaries to create societal impact through innovative engineering solutions that address critical operational demands in dynamic environments. No current information is available regarding scientific awards, grant funding, or student advising activities. As a new faculty member, Dr. Kalgaonkar is establishing his research trajectory while maintaining an active teaching role. His laboratory affiliations and collaborative research teams have not been publicly documented in available sources, though his focus on embedded systems suggests potential connections to automotive and defense-related engineering initiatives.

• Cyber-Physical Systems
• Mobile Computing
• Internet of Things
• +۲ مورد دیگر

Pius Lee is a Lecturer of Computer Science and Coordinator of the BSc (Computer Science) Cyber-Physical Systems Track at Singapore Management University. His teaching focuses on computing fundamentals, IoT applications, and mobile computing systems. Lee develops curriculum for cyber-physical systems integration across IoT, embedded systems, and pervasive computing domains. His pedagogical interests include technology-enhanced learning methods for computing education. As programme coordinator, he oversees the academic progression and specialisation pathways for cyber-physical systems students.