جست‌وجوی استاد و پژوهشگر

Cristiano Politowskiمشاهده پروفایل

استادیار

• Software Engineering
• Video Game Development
• Artificial Intelligence for Software Engineering (AI4SE)
• +۳ مورد دیگر

Cristiano Politowski is an Assistant Professor in the Department of Computer Science at Ontario Tech University's Faculty of Science. His research focuses on software engineering for video games, automated testing, AI4SE, and empirical studies. He holds a Ph.D. in Software Engineering from Concordia University (2022), an M.Sc. in Computer Science from Universidade Federal de Santa Maria (2017), and a B.Sc. in Computer Science from Universidade Regional do Estado do Rio Grande do Sul (2014). Prof. Politowski has extensive postdoctoral experience at Université de Montréal (GEODES group) and École de Technologie Supérieure (ÉTS), focusing on game engine architecture, requirements specification, and certifiable RTOS systems. He has organized workshops like ASE4Games 2021/2022 and FaSE4Games 2024, emphasizing game software engineering. His research interests span video game development challenges, software testing methodologies, and leveraging AI for software engineering. Notable projects include analyzing game engine subsystem coupling, assessing video game balance with autonomous agents, and curating datasets like PlayMyData. Prof. Politowski has secured $20K from MITACS for a 5G infrastructure project during the pandemic and received the Concordia Accelerator Award (2021). His work has been published in venues like MSR, ICSE, and IEEE Transactions.

Yves Le Traonمشاهده پروفایل

استاد

• Machine learning systems
• Software testing
• Software security
• +۸ مورد دیگر

Dr. Yves Le Traon is a Full Professor of Computer Science at the University of Luxembourg, where he serves as Vice-Director of the Interdisciplinary Centre for Security, Reliability and Trust (SnT). He leads the 25-member SerVal research group (SEcurity, Reasoning and VALidation), focusing on software testing, security, and data-intensive systems. Previously, he chaired the CSC Research Unit (2013-2016) and pioneered model-driven engineering at INRIA. PhD and engineering degree in Computer Science from Institut National Polytechnique, Grenoble (1997) Former Associate Professor at University of Rennes (1998-2004) His research spans three main areas: innovative software testing and repair , Android security through static analysis and machine learning , and robust machine learning system design . Collaborations include industry leaders like PayPal, CREOS, and Cebi in fintech, smartgrid, and industry 4.0 domains. Awarded IEEE Fellow (2022) and Facebook Testing & Verification Research Award (2019) , he chairs editorial boards for STVR, SoSym, and IEEE Transactions on Reliability. His team has produced 20+ PhD graduates including Li Li (Monash University), Donia El Kateb (European Investment Bank), and Alexandre Bartel (SnT Research Associate). Commercial impact includes co-founding Datathings for runtime AI decision systems.

Heyuan Shiمشاهده پروفایل

دانشیار

• Software Quality Assurance
• Kernel Fuzz Testing
• Operating System Security
• +۳ مورد دیگر

Heyuan Shi is an Associate Professor at the School of Electronic Information, Central South University since 2023. He earned his B.S. (2015) and Ph.D. (2020) from Central South University and Tsinghua University respectively. His research focuses on software quality assurance with emphasis on kernel fuzz testing , open source software security , and AI application security . Presided over 10+ projects including NSFC General Program (No.62472448) and National Key R&D Sub-Project Published 30+ CCF-A/B papers across software security, machine learning, and quantum testing Supervised 15+ graduate students in software quality assurance areas His recent 2024-2025 publications demonstrate expertise in: LLM-enhanced patch classification Quantum neural network verification Hypergraph adversarial attacks RTOS fuzzing techniques Scientific recognition includes: 2024 Beijing Science & Technology Progress Award (First Prize) Hunan Province Xiaohe Sci-Tech Talent (2023) China Association for Science & Technology Young Talent (2025-2027) Active in academic service as PC member for FM2024 and reviewer for IEEE Transactions journals. Leads industry collaborations with Alibaba and Beijing Institute of Aerospace Metrology.

Nashid Shahriarمشاهده پروفایل

استادیار

• Computer Networks
• 5G Mobile Networks
• Resource Allocation
• +۵ مورد دیگر

Nashid Shahriar is an Assistant Professor in the Department of Computer Science at the University of Regina, Faculty of Science. His research addresses resource allocation challenges in next-generation networks including 5G, elastic optical networks, cloud infrastructures, and IoT systems. He holds a Ph.D. in Computer Science from the University of Waterloo, an M.Sc. from Bangladesh University of Engineering and Technology (BUET), and a B.Sc. from BUET. His work leverages optimization, machine learning, and AI for network management. Recent publications focus on 5G network slicing, intrusion detection, and NFV security. Research emphasizes practical AI-driven solutions for telecommunications and cloud systems.

Liang Dongمشاهده پروفایل

دانشیار

• Signal Processing
• Wireless Communications
• Cyber-Physical Systems
• +۱۲ مورد دیگر

Dr. Liang (Leon) Dong is an Associate Professor in the Department of Electrical and Computer Engineering at Baylor University, where he conducts research and teaches in the areas of signal processing, wireless communications, and artificial intelligence. He leads the Laboratory of Signal Processing, Communications, and Artificial Intelligence, fostering innovation in next-generation communication systems, IoT, and AI-driven applications. PhD, Electrical & Computer Engineering, The University of Texas at Austin (2002) MS, Electrical & Computer Engineering, The University of Texas at Austin (1998) BS, Applied Physics with Minor in Computer Engineering, Shanghai Jiao Tong University (1996) Dr. Dong's research focuses on advancing digital signal processing and wireless communications, with strong emphasis on artificial intelligence applications. His work spans NextG wireless systems , IoT and smart cities , cyber-physical system security , and AI in healthcare and industrial automation . He applies deep learning to domains such as autonomous driving and drug discovery, and investigates energy-efficient, secure, and reliable communication protocols. The recent publications highlight a strong trend toward integrating AI into traditional signal processing and communications. Topics include mRNA vaccine stability prediction , smart city infrastructures , secure cyber-physical systems , and deep learning for biomedical and industrial applications . His work bridges theoretical innovation with real-world impact in defense, transportation, and public health. Dr. Dong has earned recognition as a Senior Member of IEEE and a Member of the American Physical Society. He has also served as Faculty Advisor for Baylor University's InterVarsity chapter. Senior Member, Institute of Electrical and Electronics Engineers (IEEE) Member, American Physical Society (APS) He has successfully advised numerous graduate and undergraduate students, many of whom now hold academic and industry positions at institutions like Stanford, Intel, NASA, L3Harris, and Cummins. His research is generously supported by Baylor's VP for Research, the National Science Foundation, NASA, the Department of Defense (TARDEC), the Michigan Department of Transportation, and industry leaders including Intel, L3Harris, ExxonMobil, and Denso. He actively mentors students through research assistantships and senior design projects. Dr. Dong leads the Laboratory of Signal Processing, Communications, and Artificial Intelligence, which provides a collaborative environment for advancing research in signal processing, communications, and AI. The lab supports graduate and post-doctoral researchers and offers opportunities for undergraduate involvement in AI programming, circuit design, and embedded systems.

Xuan Zhangمشاهده پروفایل

دانشیار

• Artificial Intelligence Hardware
• Machine Vision Sensors
• Electronic Design Automation
• +۳ مورد دیگر

Xuan Zhang serves as Associate Professor in Electrical and Computer Engineering at Northeastern University, leading the Sensory AI Lab since joining in January 2024. Her research bridges computer architecture, integrated circuits, and artificial intelligence to develop miniaturized AI systems for autonomous physical platforms. She earned her PhD in Electrical and Computer Engineering from Cornell University in 2012. Her educational background forms the foundation for her interdisciplinary work spanning hardware and software co-design. Dr. Zhang's research focuses on artificial intelligence hardware, machine vision sensors, and security for autonomous systems. She pioneers techniques for efficient in-sensor computing, analog circuit optimization via machine learning, and hardware-level privacy preservation. Her work addresses critical challenges in energy efficiency, robustness, and security for edge AI deployment, particularly in resource-constrained environments like medical devices and autonomous vehicles. Analysis of her 2023-2025 publications reveals three dominant trends: (1) hardware-accelerated privacy mechanisms for sensors, (2) machine learning-driven analog circuit design automation, and (3) energy-efficient architectures for neural network inference. These works consistently target real-world applications in healthcare, autonomous systems, and semiconductor design. Her accolades include the prestigious NSF CAREER Award (2020) and leadership in a $10 million federal semiconductor initiative. She contributes to national efforts in AI-powered chip design through the National Center for the Advancement of Semiconductor Technology. Dr. Zhang advises graduate researchers in the Sensory AI Lab, securing significant funding for projects spanning hardware security, in-sensor computing, and autonomous system assurance. Her lab collaborates with federal agencies and industry partners on cutting-edge semiconductor research. The Sensory AI Lab operates at the hardware-software interface, developing novel architectures for intelligent edge devices. Current projects include optical privacy preservation, robust analog design tools, and energy modeling frameworks for in-sensor visual computing systems.

• Computer Graphics
• Virtual Reality
• Augmented Reality
• +۶ مورد دیگر

Francesco Strada is a Fixed-term Assistant Professor at the Department of Control and Computer Engineering (DAUIN) at Politecnico di Torino. He serves as a Course Lecturer for Virtual Reality and Technical Art for Cinema and Video Games, and as a Course Collaborator for multiple courses across Computer Engineering, Film and Media Engineering, and Architecture programs. He is an active member of the College of Computer, Film and Mechatronics Engineering and the College of Architecture and Design teaching committees. Dr. Strada's research focuses on Computer Graphics, Virtual Reality, Augmented Reality, and Human-Computer Interaction with particular emphasis on Embodied Conversational Agents, emotion recognition, and serious games applications. His work bridges technical computer science with psychological and human factors considerations to create more believable and effective virtual experiences. His research spans applications in education, healthcare, automotive interfaces, cultural heritage, and emergency response training. His recent publications demonstrate a strong trend toward emotionally intelligent virtual agents, VR/AR applications in specialized domains, and technical innovations in latency management and digital human representation. His work often combines psychological principles with technical implementations to enhance user experience and system effectiveness. Dr. Strada actively supervises PhD students including Alessandro Emmanuel Pecora, Stefano Calzolari, and Leonardo Vezzani, whose research focuses on Emotionally Aware Embodied Conversational Agents (E2CA) and Car AR-HUD design. He leads significant research projects including Holo-BLSD (2024-2025), a Mixed Reality tool for first aid emergency response training, and '50 shadows of AI' (2025), focusing on personalized education in corporate settings. He is a member of the CGVG - Computer Graphics and Vision Group at DAUIN, where his team develops cutting-edge applications in AR/VR, Human-Computer Interaction, User Experience, Computer Vision, Machine Learning, and Artificial Intelligence. His research has practical applications in education, training, healthcare, automotive interfaces, and cultural heritage preservation.

Yingyan Zengمشاهده پروفایل

استادیار

• Data Quality
• Data Valuation
• Data Sharing
• +۴ مورد دیگر

Dr. Yingyan Zeng is an Assistant Professor at the Department of Mechanical and Materials Engineering, University of Cincinnati, leading the Data-Centric Intelligence Lab. Her research focuses on creating data quality assurance paradigms to enhance AI modeling performance in Manufacturing Industrial Internet and Healthcare Systems. Ph.D. in Industrial and Systems Engineering, Virginia Tech (2024) M.S. in Computer Engineering, Virginia Tech (2024) B.Eng. in Industrial and Systems Engineering, Shanghai Jiao Tong University (2019) Her work spans data generation, acquisition, valuation, and sharing, with applications in additive manufacturing, bio-manufacturing, semiconducting, and IoT infrastructure design. She employs statistical and machine learning models, optimization methods, and interpretable AI techniques. Key trends in her publications include data-centric AI, privacy-preserving frameworks, synthetic data generation, and reinforcement learning in manufacturing. Her research often integrates domain knowledge and addresses challenges in industrial cyber-physical systems. 2022 Finalist, QSR Best Paper Competition (INFORMS Annual Meeting) She has secured industry grants, such as the MME Industry 4.0/5.0 Institute project on AI-driven reinforcement learning for factory operations. Her service roles include VP Marketing and Finance at the INFORMS VT Student Chapter and membership in IEEE Technical Committees.

• Software Engineering
• Software Testing
• Program Analysis
• +۴ مورد دیگر

Alessandro (Alex) Orso is the Dean and Professor at the University of Georgia College of Engineering , starting in 2025. Previously, he spent 25 years at Georgia Institute of Technology , where he served as a Professor in the College of Computing , Director of the Scientific Software Engineering Center , and Associate Dean for Off-Campus and Special Initiatives . Education: Ph.D. in Computer Science – Politecnico di Milano, Italy M.S. in Electrical Engineering – Politecnico di Milano, Italy B.S. in Electrical Engineering – Politecnico di Milano, Italy Research Focus: Alex Orso is a leading expert in software engineering , with a focus on software testing , program analysis , and debugging . His work aims to improve software reliability, security, and trustworthiness , and has been validated on real-world systems. He has led initiatives to integrate AI and large language models into testing workflows, particularly for REST APIs and mobile applications . Scientific Recognition: Distinguished Member of the Association for Computing Machinery (ACM) Fellow of the Institute of Electrical and Electronics Engineers (IEEE) ISSTA Impact Paper Award (2021) Research Funding & Leadership: Orso has secured major funding from Google, IBM, Microsoft , and government agencies. In 2021, he led the acquisition of $11 million from Schmidt Futures to establish the Scientific Software Engineering Center at Georgia Tech, where he served as inaugural director. Teaching & Innovation: He was an early advocate of Georgia Tech’s Online Master of Science in Computer Science (OMSCS) and has taught a virtual software engineering course since its launch in 2014.

Ondrej Vaculinمشاهده پروفایل

استاد

• Passive Vehicle Safety
• Automated Driving
• Vehicle Dynamics
• +۳ مورد دیگر

Prof. Ondrej Vaculin, Ph.D., is a Professor at Technische Hochschule Ingolstadt (THI), specializing in passive vehicle safety, mechatronic systems, and automated driving. He joined THI in 2018 and previously held roles at TÜV SÜD Prague (2008–2018) as Vice President for Safety and Security in Automotive, and as a researcher at Czech Technical University (2005–2008) and DLR Oberpfaffenhofen (2000–2005). His research focuses on enhancing vehicle safety through advanced simulation, sensor integration, and machine learning applications. Education: Ph.D. in Mechanics of Solids, Deformable Bodies and Continua from Czech Technical University in Prague (2001), and a degree in Technical Cybernetics from the Faculty of Electrical Engineering at the same university (1991). His work bridges theoretical research and practical implementation, addressing challenges in automated driving systems, crash simulation, and human body diversity in safety design. Research interests include passive safety systems, autonomous vehicle dynamics, and infrastructure integration for safety enhancements. Recent publications emphasize collaborative smart infrastructure, sensor fusion for free space detection, and machine learning in crash detection. He actively contributes to FISITA, serving as a Council Delegate and member of technical committees. Awards/memberships: Active in FISITA, holding leadership roles in technical committees. No explicit awards listed, though his contributions to automotive safety standards are notable. Grants and advising: While no specific grants or students are detailed, his extensive industry and academic collaborations (e.g., IN2Lab testing field projects) highlight his role in applied research and development.

• Software Engineering
• Artificial Intelligence
• Cybersecurity
• +۵ مورد دیگر

Xiaofei Xie is an Assistant Professor at the School of Computing and Information Systems (SCIS), Singapore Management University (SMU). He received his PhD from Tianjin University in 2018 and was a postdoctoral researcher at Nanyang Technological University (2018-2021) before joining SMU in 2022. His research focuses on software engineering, AI systems, and cybersecurity. Dr. Xie's primary research areas include program analysis, software testing, vulnerability detection, and quality assurance of AI systems. His work spans: Testing methodologies for autonomous systems and games AI security including backdoor detection and model robustness Automated program repair and code generation Formal methods and semantic code analysis His recent publications demonstrate strong emphasis on AI/ML system testing, cybersecurity applications, and program analysis techniques. Research trends show increasing focus on LLM-based program repair, autonomous system validation, and federated learning security. Major Awards: ACM SIGSOFT Distinguished Paper Awards (ASE'23, ISSTA'22, ASE'19, FSE'16) CCF Outstanding Doctoral Dissertation Award (2019) 3rd place in AI Singapore's Trusted Media Challenge (2022) Wallenberg-NTU Presidential Postdoctoral Fellowship (2019) APSEC Best Paper Award (2020) He currently advises 7 PhD/Master's students including CHENG Mingfei, KONG Jiaolong, and YU Jiongchi. Dr. Xie leads research in software reliability and AI security at SMU's SCIS.

• Power and Energy Systems
• Condition Monitoring
• Fault Diagnosis and Prognosis
• +۵ مورد دیگر

Dr Xiandong Ma is a Reader in Power and Energy Systems at Lancaster University's School of Engineering, where he has been a faculty member since December 2008. His research focuses on intelligent condition monitoring and fault diagnosis of power systems, with particular expertise in wind energy systems and smart grid technologies. His educational background includes: BEng in Electrical Engineering from Jiangsu University (1986) MSc in Power Systems and Automation from Nanjing Automation Research Institute (1989) PhD in Partial Discharge based High-voltage Plant Condition Monitoring from Glasgow Caledonian University (2002) Dr Ma's research spans intelligent condition monitoring and fault diagnosis/prognosis of wind power systems and electrical assets, condition-based operations and maintenance of power and energy systems, modeling, optimization, and control of smart/micro grids with renewable energy resources, power conversion and renewable energy integration, and associated machine learning and AI technologies and digital twin solutions. His work bridges theoretical advances with practical engineering applications in the renewable energy sector. His recent publications demonstrate a strong focus on quantum machine learning applications for wind turbine monitoring, electric vehicle-grid integration challenges, wave energy conversion systems, and nuclear fuel inspection technologies. The research shows a clear trajectory toward more sophisticated AI-driven solutions for energy systems, with increasing emphasis on multi-physics modeling and cross-domain applications. Dr Ma has received several prestigious recognitions: Chartered Engineer Fellow of the Institution of Engineering and Technology (FIET) Fellow of the Higher Education Academy (FHEA) Member of EPSRC Peer Review College KTP Fellowship awarded by University of Technology Sydney (2018) Ranked in the world's top 2% scientists by Stanford University He actively supervises numerous PhD students and postdoctoral researchers, with current projects including the Leverhulme Trust-funded "Self-Aware Power Networks: Autonomous Operation at Scale" and several EPSRC-funded initiatives. Dr Ma has secured significant research funding and collaborates extensively with industry partners to translate research into practical applications. Dr Ma leads research within Lancaster's Energy research group, focusing on the integration of advanced sensing, AI, and control techniques for next-generation power and energy systems. His team works closely with industrial partners including ALSTOM Power and various renewable energy companies to develop innovative solutions for real-world energy challenges.

Davide Di Ruscioمشاهده پروفایل

استاد

• Model Driven Engineering
• Model evolution
• Open Source Software
• +۷ مورد دیگر

Davide Di Ruscio is a Full Professor at the Department of Information Engineering Computer Science and Mathematics of the University of L'Aquila (Italy), where he leads research in Model-Driven Engineering and Software Engineering. His work spans domain-specific modeling languages, model transformations, and recommender systems applied to open source software and autonomous systems. His research interests focus on Model Driven Engineering , Model evolution , Open Source Software , and Recommender Systems , with recent work exploring LLM applications in code analysis and fairness engineering. Key application domains include service-based systems, autonomous systems, and hybrid polystore systems. Di Ruscio actively contributes to the software engineering community through leadership roles in major conferences and journals. He serves on the steering committees of ICMT, SLE, SATTOSE, MiSE, and RoSE, and is on the editorial boards of SoSyM, IEEE Software, Journal of Object Technology, and IET Software. His work has been published in over 200 papers across top-tier venues. He has contributed to numerous European and Italian research projects since 2006, applying MDE concepts to real-world systems. Current teaching includes Software Engineering for Autonomous Systems and Software Engineering for the Internet of Things, with office hours on Tuesdays and Wednesdays from 11:30-13:30 at Edificio Alan Turing, Room 208.

Xiaofei Xieمشاهده پروفایل

استادیار

• Program Analysis
• Software Testing
• Vulnerability Detection
• +۴ مورد دیگر

Xiaofei Xie is an Assistant Professor in the School of Computing and Information Systems at Singapore Management University (SMU), where he has been employed since 2022. Prior to this position, he was a postdoctoral researcher at Nanyang Technological University in Singapore from 2018 to 2021. His research primarily focuses on program analysis, software testing, vulnerability detection, and quality assurance of AI systems. SMU is ranked No. 9 (No. 5 in Asia) in the Software Engineering category according to CSRankings. Dr. Xie's research interests span multiple critical areas in software engineering and AI systems. His work on program analysis includes detecting non-termination bugs and developing practical methods like EndWatch for real-world software. In software testing, he has made significant contributions to deep learning systems testing, autonomous driving systems testing, and smart contract security. His research on vulnerability detection encompasses various aspects of AI security, including backdoor attacks, adversarial examples, and security testing for web-based deep learning frameworks. His quality assurance work for AI systems includes developing metrics for robustness evaluation and creating testing methodologies for diverse AI applications. Dr. Xie's publication record shows a strong trend toward integrating large language models with traditional software engineering techniques. His recent work demonstrates increasing focus on testing autonomous systems, securing AI models, and applying advanced machine learning techniques to traditional software engineering problems. The research spans multiple domains including deep learning frameworks, smart contracts, autonomous driving systems, and federated learning environments. Among his notable achievements are multiple ACM SIGSOFT Distinguished Paper Awards (ASE 2019, ASE 2023, ISSTA 2022), the ACM Tianjin Doctoral Dissertation Award 2019, and the Best Paper Award at APSEC 2020. His work has been accepted to top-tier conferences including ICSE, FSE, ASE, ISSTA, and security venues like USENIX Security. Dr. Xie actively serves the academic community as a PC co-chair for ICECCS 2025 and as a program committee member for numerous prestigious conferences including ICSE, FSE, ASE, ISSTA, and AAAI. He has also organized workshops such as the Workshop on AI and Software Testing/Analysis (AISTA) and served as Guest Editor for special issues on AI security. His service demonstrates leadership in bridging software engineering with AI and security research communities.

Zhi Jinمشاهده پروفایل

استاد

• Requirements Engineering
• Self-Adaptive Systems
• Learning from Source Code
• +۵ مورد دیگر

Zhi Jin is a Professor in the Department of Computer Science and Technology at Peking University, where he has been employed since 2009. Previously, he served as a professor at the Academy of Mathematics and System Sciences, Chinese Academy of Sciences from 1994-2009. He received his BS from Zhejiang University in 1984 and MS/PhD from National University of Defense Technology in 1984 and 1992 respectively. He progressed from assistant professor (1992) to associate professor (1995) to full professor (2001). His research focuses on knowledge engineering and software engineering, with special interests in knowledge graphs, self-adaptive systems, and deep learning applications. Current research directions include Self-Adaptive Software in Human-Cyber-Physical Systems, Crowd-based Requirements Engineering, and Learning from both Natural Language and Programming Language. His work bridges theoretical knowledge engineering with practical software development challenges. His recent publications demonstrate a strong trend toward applying large language models and AI techniques to traditional software engineering problems, particularly in requirements engineering, code generation, and vulnerability detection. The articles span multiple high-impact venues including ASE, ICSE, and RE, with significant focus on aerospace applications and multi-agent collaboration approaches. Scientific honors include: Winner of National Science Fund for Distinguished Young Scholars (2006) Project 973 project lead scientist (2014) Member of Discipline Appraisal Group of the Academic Degree Commission (2015) Multiple ACM Distinguished Paper Awards He serves in numerous editorial roles including Associate Editor for IEEE Transactions on Software Engineering (2018-present) and IEEE Transactions on Reliability (2019-present). He is also an Editorial Board Member for Empirical Software Engineering and Requirements Engineering Journal, and holds leadership positions in the China Computer Federation. His extensive conference service includes PC membership for ICSE, FSE, RE, and other major software engineering venues.