جست‌وجوی استاد و پژوهشگر

• Validation Intelligence
• Software Testing
• Software Validation and Verification
• +۴ مورد دیگر

Arnaud Gotlieb serves as a Research Professor and Chief Research Scientist at Simula Research Laboratory in Norway, where he heads the Department Validation Intelligence for Autonomous Software Systems. His academic career spans over two decades with significant contributions to software testing and artificial intelligence integration. Gotlieb's research focuses on Validation Intelligence of Autonomous Software Systems , Software Testing , Software Validation and Verification , and the application of Constraint Programming and Machine Learning to safety-critical systems. His innovative work bridges theoretical computer science with practical industrial applications, particularly in industrial robotics and autonomous systems. His publication record shows a consistent trajectory of high-impact research with approximately 15 recent publications (2024-2025) focused on metamorphic testing, constraint acquisition, explainable AI for autonomous driving, and verification of AI systems. The research demonstrates a clear evolution from foundational constraint programming work toward contemporary challenges in trustworthy AI and autonomous systems validation. Gotlieb has developed several influential tools including ITE (Stratified Disjunctive Reasoning), TITAN / FLOWER (Test suite optimization), EUCLIDE (constraint-based platform for critical C programs), and FPSE (Floating Point Symbolic Execution), which have advanced the field of automated software testing.

• Software Engineering
• Human-Computer Interaction
• Artificial Intelligence
• +۳ مورد دیگر

Jialong Li is an Assistant Professor (non-tenure-track) at the Waseda Institute for Advanced Study. His research focuses on dynamic responsibility allocation in human-software collaboration, with applications in autonomous driving, robotics, and adaptive systems. He employs techniques such as discrete controller synthesis and large language models to address runtime environmental changes. Key research themes: human-robot collaboration, self-adaptive software, autonomous systems Technical approaches: Discrete Controller Synthesis, LLM integration, reinforcement learning Recent publications emphasize multimodal human-AI collaboration, prompt engineering for software development, and ethical considerations in adaptive systems. His work bridges theoretical modeling (e.g., requirements analysis, stochastic control) with real-world applications like warehouse robotics and color-blind accessibility tools. While no formal awards are listed, his research has been presented at top conferences in software engineering and autonomous systems.

Mahdi Farnaghiمشاهده پروفایل

استادیار

• Artificial Intelligence
• Machine Learning
• Data Mining
• +۹ مورد دیگر

Mahdi Farnaghi is an Assistant Professor at the University of Twente , specializing in the Department of Geo-Information Processing . His research spans Artificial Intelligence , Machine Learning , and Geospatial Analysis , with applications in Environmental Monitoring , Public Health , and Urban Mobility . He leads the NWO-NGF IntelliGeo project (2024-2025), integrating Large Language Models into GIS workflows. Focus areas: Wastewater-based epidemiology , AI-powered environmental modeling , and spatiotemporal analysis Recent work: Machine learning frameworks for pollutant mapping , crop water needs , and infectious disease distribution The 2025 TU Delft Water for Impact Best Paper Award recipient for collaborative research on wastewater modeling. His projects include EO AFRICA R&D Innovation Lab and software tools for reproducible geospatial research .

Gang Wangمشاهده پروفایل

دانشیار

• Security and Privacy
• Machine Learning
• Internet Measurement
• +۴ مورد دیگر

Gang Wang is an Associate Professor in the Department of Computer Science at the Siebel School of Computing and Data Science at the University of Illinois Urbana-Champaign (UIUC) . He serves as the Associate Director of the Capital One Illinois Center for Generative AI Safety and holds courtesy affiliations with the Department of Electrical and Computer Engineering , Security and Privacy Research at Illinois (SPRAI) , and the Coordinated Science Laboratory (CSL) . His research focuses on developing explainable and robust machine learning systems to enhance internet security and privacy. Key areas include adversarial machine learning , deepfake detection , phishing prevention , and security of social computing platforms . He actively contributes to major conferences like USENIX Security , CCS , NDSS , and ICML . Recent publications highlight his work on LLM benchmark contamination , VLM jailbreaks , and deepfake profile detection . His research team has produced award-winning papers at CHI and IEEE SP , with grants from NSF , Amazon , and Google . Notable students include Qingying Hao and Limin Yang , who co-authored multiple high-impact papers.

Guy Katzمشاهده پروفایل

دانشیار

• Formal Methods
• Software Engineering
• Machine Learning
• +۳ مورد دیگر

Guy Katz is an associate professor at the School of Computer Science and Engineering, The Hebrew University of Jerusalem, specializing in formal verification of AI-integrated systems. His work bridges Formal Methods and Software Engineering to ensure reliability in software incorporating machine learning components. He received his Ph.D. from the Weizmann Institute of Science in 2015. His research focuses on verifying neural networks and large language models (LLMs), developing frameworks for safety-critical AI applications. Key methodologies include abstraction techniques, reachability analysis, and certified verification tools. Recent publications reveal a concentrated effort on neural network verification, with increasing emphasis on LLM safety and reinforcement learning systems. His work consistently targets practical verification challenges in deep learning architectures. Dr. Katz secures major research funding through: European Research Council (ERC Starting Grant) European Commission (RobustifAI consortium) Israeli Science Foundation (ISF) Israel Innovation Authority He actively recruits graduate students and postdocs for funded positions. Leading the Katz Lab, he develops verification frameworks for machine learning systems while co-chairing TACAS 2026 to advance the formal methods community.

Jinhan Kimمشاهده پروفایل

پژوهشگر

• Software Engineering
• Artificial Intelligence
• Testing of AI Systems
• +۵ مورد دیگر

Jinhan Kim is a Postdoctoral Researcher at the Software Institute of USI University of Lugano, Switzerland, working in the TAU lab under the guidance of Prof. Paolo Tonella. He completed his Ph.D. in Software Engineering at KAIST, South Korea, under the supervision of Prof. Shin Yoo, where his research focused on mutation testing and the intersection of artificial intelligence and software engineering. His educational background includes: Ph.D. in Software Engineering, KAIST, South Korea (completed February 2023) Kim's research spans software engineering and artificial intelligence, with a focus on mutation testing, testing of deep learning systems, and security of AI models. He investigates techniques for improving the reliability and robustness of AI systems, particularly in safety-critical domains like autonomous driving. His work bridges traditional software engineering practices with modern AI systems, leading to novel approaches in fault localization, program repair, and adversarial testing. His recent publications reveal a strong trend toward testing and securing deep learning models in autonomous systems. He has developed taxonomies for attacks, frameworks for testing autonomous agents, and empirical studies on fault localization for neural networks. His work increasingly addresses securing AI systems against adversarial attacks and improving robustness of security detectors generated by large language models. Kim has received notable recognition including: Best Paper Award at the 18th International Workshop on Mutation Analysis (Mutation 2023) As an advisor, Kim supervises two PhD students: Masoud Jamshidiyan Tehrani and Samuele Pasini, working on security of deep learning models and robustness of security attack detectors. He actively serves the research community through program committees for ASE, ICSE, ISSTA, and ICST, and as organizer of DeepTest and SBFT workshops. His service includes being a Distinguished Reviewer for TOSEM. Kim is a core member of the TAU (Testing: Analysis and Understanding) lab at USI, which pioneers innovative approaches to software testing and analysis for modern AI-based systems.

Konstantinos Vergidisمشاهده پروفایل

استاد

• Business Process Management
• Enterprise Architecture
• BPMN Modeling
• +۷ مورد دیگر

Konstantinos Vergidis is a Professor at the Department of Applied Informatics, School of Information Sciences, University of Macedonia. With a PhD in Business Process Optimisation from Cranfield University (2008), he has established himself as a leading expert in business process management, enterprise architecture, and process modeling. His academic career spans over a decade at the University of Macedonia, where he has held various administrative roles including Director of the Doctoral Studies Coordination Committee and member of the University's Research Committee. PhD in Business Process Optimisation (2008), Cranfield University MSc in IT for Product Realisation (2005), Cranfield University Degree in Applied Informatics (2003), University of Macedonia Professor Vergidis' research focuses on Business Process Intelligence (BPI), with particular expertise in BPMN modeling, process optimization, and decision modeling with DMN. His work bridges theoretical frameworks with practical applications in both public and private sectors, examining process quality metrics, resilience, and the integration of emerging technologies like low-code platforms and AI. He has pioneered research on process modeling in the public sector and the impact of Large Language Models on BPMN-based process modeling. His publication record includes 21 scientific journal articles, 22 conference papers, and 8 books, demonstrating consistent scholarly output across multiple venues. Recent work shows a clear trajectory toward integrating AI technologies with traditional business process management, particularly examining how LLMs can transform process modeling practices and exploring low-code development platforms for process-driven application development. Teaching Excellence Award (2022-2023) Research Award 2022 (14th cycle) Research Award 2021 (13th cycle) Teaching Excellence Award (2020-2021) Thatcher Bros Prize (2008/09) Professor Vergidis has secured multiple research grants, including projects with Expertise France/European Commission on digital public services and with the National Research Foundation on process modeling for public administration. He leads the Business Process Intelligence research group and has coordinated significant projects including MODA (Multi-Omics Data Analysis) and Science Data and Analytics Platform. His administrative contributions include directing doctoral studies and serving on numerous university committees related to research, curriculum development, and digital transformation initiatives. His work with the National Process Registry (mitos.gov.gr) demonstrates his commitment to applying academic research to real-world public sector challenges, developing tools and methodologies that enhance process transparency and service delivery for Greek citizens.

• Formal Methods
• Artificial Intelligence Safety
• Software Verification
• +۷ مورد دیگر

Corina Pasareanu is a Principal Scientist at Carnegie Mellon University's CyLab Security and Privacy Institute and serves as a Technical Professional Leader for Data Science at NASA Ames Research Center through KBR. She holds a PhD in Computer Science from Kansas State University (2001), an MS (1995) and BS (1994) from the University Politehnica of Bucharest. Her research focuses on formal methods for trustworthy AI , including model checking, symbolic execution, compositional verification, and probabilistic software analysis. She pioneers techniques for verifying autonomous systems, neural networks, and cryptographic applications, with emphasis on safety-critical domains like autonomous vehicles and aerospace systems. Recent publications demonstrate strong focus on AI safety verification , including adversarial robustness of large language models, vision-based autonomous systems, and neural network interpretability. Her work integrates formal methods with machine learning to address security challenges in emerging AI technologies. Awards and honors: ACM Fellow (2023) IEEE ASE Fellow ETAPS Test of Time Award (2021) ASE Most Influential Paper Award (2018) ESEC/FSE Test of Time Award (2018) ISSTA Retrospective Impact Paper Award (2018) She leads major projects funded by DARPA, NSF, AWS, and NASA including: Trinity: Neurosymbolic Learning and Reasoning (DARPA) Proving Timing Side Channel Absence (AWS) Safety of Shared Control in Autonomous Driving (AAIP) Verifiable Federated Learning (CyLab) She advises PhD students at CMU and co-leads the CyLab Security and Privacy Institute's research initiatives.

• Computer Security
• Hardware Design
• Embedded Systems
• +۳ مورد دیگر

Daniel Große serves as a full Professor at Johannes Kepler University Linz, holding primary affiliation with the Institute for Symbolic Artificial Intelligence and secondary appointments at the Institute of Complex Systems and LIT Secure and Correct Systems Lab. He currently leads 3 major research projects including Modular Real-Time Control (2023-2026) and the ENGEL Austria GmbH industry collaboration (2019-2027), while maintaining active roles in 113 professional activities through 2025. His research integrates Computer Security , Hardware Design , and Artificial Intelligence to address critical challenges in embedded systems. Key focus areas include cryptographic instruction chaining for control flow protection, RISC-V vector workload simulation, and LLM-assisted metamorphic testing of graphics libraries, with strong emphasis on practical applications for safety-critical systems. Recent publications (2025) reveal a cohesive research trajectory toward secure computing foundations, combining formal verification techniques with AI-driven development tools. His work consistently bridges hardware security primitives and software engineering innovations, particularly targeting vulnerabilities in instruction set architectures and embedded graphics pipelines. Prof. Große has supervised 4 graduate students and secured significant funding through both public grants (VerA project) and industry partnerships. His project portfolio demonstrates strategic balance between theoretical advances in arithmetic circuit verification and applied industrial solutions for real-time control systems. As a core member of the LIT Secure and Correct Systems Lab, he contributes to interdisciplinary research initiatives developing formally verified secure computing platforms, with active collaborations spanning hardware security validation, virtual prototyping frameworks, and AI-enhanced software testing methodologies.

Yuan Zhangمشاهده پروفایل

استاد

• program analysis
• software testing
• system security
• +۹ مورد دیگر

Yuan Zhang is a Professor in the School of Computer Science at Fudan University, where he co-directs the System Software and Security Laboratory. He is also the co-founder and coach of Fudan University's CTF team, Whitzard, which has participated in numerous international competitions. His academic journey includes earning a Ph.D. from Fudan University (2009-2014) and a B.Eng. from Nanjing University (2005-2009), progressing from Assistant Professor to Associate Professor and finally to Professor at Fudan University. Dr. Zhang's research focuses on system security for widely-deployed critical targets, with current emphasis on open-source software, kernels, Android/Web platforms, firmware, LLM-based agents, and autonomous driving systems. His work spans vulnerability discovery/exploitation/mitigation, malware/attack detection, and privacy protection, utilizing interdisciplinary techniques such as Program Analysis, LLMs, and Machine/Deep Learning. His research demonstrates a consistent trajectory from traditional software security toward emerging AI-driven security challenges. His recent publications (2024-2025) reveal a strong focus on novel security challenges in emerging technologies, particularly LLM-based systems and autonomous driving security, while maintaining expertise in traditional software security domains like Android, web applications, and firmware. His work combines practical vulnerability discovery with innovative detection methodologies, often achieving recognition through distinguished paper awards. Honorable Mention Award at USENIX Security 2025 Distinguished Paper Award at IEEE S&P 2025 ACM SIGSOFT Distinguished Paper Award Dr. Zhang actively contributes to the academic community through teaching courses including Principles of Reverse Engineering, System Security: Attacks & Defenses, and Emerging Attack & Defense Techniques. He serves on editorial boards for ACM Transactions on Security and Privacy and Cybersecurity, and participates in numerous top security conference program committees including USENIX Security, IEEE S&P, and ACM CCS. His laboratory, the System Software and Security Laboratory at Fudan University, focuses on practical security research with real-world impact, particularly in the rapidly evolving landscape of AI security.

Junjie Chenمشاهده پروفایل

استاد

• Software Testing
• Deep Learning
• Automated Debugging
• +۴ مورد دیگر

Junjie Chen is a Professor at the College of Intelligence and Computing, Tianjin University, where he leads the Software Engineering Team. He has been a Professor since January 2024, after serving as an Associate Professor from July 2019 to January 2024. Prior to his position at Tianjin University, he completed his PhD in Computer Science at Peking University under the supervision of Prof. Bing Xie, Prof. Lu Zhang, Prof. Dan Hao, and Prof. Yingfei Xiong. During his PhD studies, he was also a visiting PhD student at The University of Texas at Dallas under Prof. Lingming Zhang. His educational background includes a Bachelor's degree in Software Engineering from Beihang University (2010-2014). Professor Chen's research focuses on four main areas: Software Fuzzing : Focusing on fundamental software testing for compilers, operating systems, chip design systems, and AI infrastructure. Intelligent Software Engineering : Applying LLMs and deep learning to solve software engineering challenges like code generation, test generation, and code review. Trusted AI : Improving AI security, fairness, robustness, and performance through adversarial attacks, data optimization, and software engineering methodologies. Software Maintenance : Researching bug localization and fixing, as well as AIOps for anomaly detection and diagnosis. His recent publications (2024-2025) demonstrate a strong focus on the intersection of software testing, compiler infrastructure, and large language models. His work spans from practical compiler testing techniques to advanced applications of AI in software engineering. His research shows a clear trajectory toward addressing the challenges of modern software systems, particularly those involving AI and deep learning components. Professor Chen has received numerous prestigious awards including: National Key R&D Program Young Scientist (2024) Huawei Spark Award (2024) National Excellent Young Scientists Fund recipient (2023) China Institute of Electronics Natural Science First Prize (2023) CAST Young Elite Scientists Sponsorship Program (2022) Multiple ACM SIGSOFT Distinguished Paper Awards He is actively involved in academic service, serving on editorial boards for JCST and ASEJ, and as a program committee member for major conferences including ICSE, ASE, FSE, and ISSTA. He has also co-organized workshops and seminars, including the Compiler Technology Seminar under the CCF System Software Committee. Professor Chen leads a research team at Tianjin University that is actively recruiting PhD and Master's students with strong programming skills and interests in Software Engineering, LLMs, Security, and Program Analysis.

Rrezarta Krasniqiمشاهده پروفایل

استادیار

• Software Engineering
• Requirement Engineering
• Software Quality
• +۵ مورد دیگر

Rrezarta Krasniqi is an Assistant Professor of Software Engineering in the Department of Software and Information Systems at the University of North Carolina at Charlotte. Her work focuses on improving software quality through innovative approaches to bug detection and system-wide quality issue analysis. Her educational background includes: B.S. in Mathematics and Computer Science from the University of Prishtina M.S. in Computer Science from Midwestern State University M.S. in Computer Science and Engineering from the University of Notre Dame Ph.D. in Computer Science and Engineering from the University of North Texas Dr. Krasniqi's research centers on quality-related bug detection problems, with particular emphasis on security, usability, and reliability issues that emerge from long-term software maintenance. She develops tools and techniques to enhance understanding of complex system-wide quality issues using code analysis, program comprehension, and AI-driven approaches. Her work combines technical depth with empirical research methodologies to identify root causes of software quality problems and develop more reliable software systems. Her recent publications demonstrate a consistent research trajectory focused on software quality concerns, with increasing integration of NLP and machine learning techniques. Her work spans from foundational bug detection methods to more recent applications of large language models in code translation benchmarking. Dr. Krasniqi actively contributes to the software engineering research community through service on numerous program committees including ASE, ICSE, ICSME, and EASE conferences. In 2025, she served as Tool and Demo Track co-chair for SANER and presented her work on code translation with LLMs at ASE. Before joining UNC Charlotte, she taught computer science courses at various institutions and worked for over three years as a senior Java developer in industry, contributing to web-based application development and maintenance.

Ashutosh Trivediمشاهده پروفایل

دانشیار

• Formal Methods
• Reinforcement Learning
• Software Engineering
• +۴ مورد دیگر

Ashutosh Trivedi is an Associate Professor of Computer Science at the University of Colorado Boulder, specializing in formal methods for safety-critical learning-enabled systems. His research bridges computer science, control theory, and machine learning with a focus on trustworthy AI systems that operate safely, fairly, and responsibly. His research interests encompass Formal Methods , Reinforcement Learning , Software Fairness , and Software Accountability . Trivedi develops mathematical approaches to bring precision to AI system design, using formal languages, automata, and logic to transform vague natural-language instructions into clear specifications. His work emphasizes principled interaction with AI, creating explainable and reliable systems through techniques like reinforcement learning algorithms for cardiac pacemaker design, SAT solvers for grounding LLM outputs, and formal logic for capturing legal obligations. His recent publications reveal a strong trend toward integrating formal verification with machine learning systems, particularly focusing on fairness testing, safety constraints in reinforcement learning, and neurosymbolic approaches. The research spans applications from legal-critical software to medical devices, with consistent emphasis on accountability and ethical considerations in AI systems. 2022 NSF CAREER award Liverpool Fellowship Distinguished Paper Award at CAV for Regular Reinforcement Learning Senior Member of the ACM Royal Society Wolfson Visiting Fellowship NeuS 2025 Disruptive Idea Award Trivedi actively mentors PhD students, having supervised multiple successful thesis defenses including Shadi Tasdighi Kalat on multi-agent games, Mateo Perez on formal languages for reinforcement learning, John Komp on pacemaker therapy, Vishnu Murali on functional proofs, and Taylor Dohmen on sequential optimization. His group, the Programming Languages and Verification (CUPLV) at CU Boulder, focuses on making AI systems more trustworthy through mathematical precision and formal verification techniques. Currently on sabbatical at the University of Liverpool, Trivedi continues to bridge the gap between machine learning and responsible system design, helping AI systems earn the trust we increasingly place in them for critical applications.

• Software Engineering
• Green Computing
• Machine Learning Applications
• +۵ مورد دیگر

Clément Quinton is a researcher at the University of Lille, France, specializing in software engineering with significant contributions to international conferences including ASE and ICSE. His work bridges theoretical research and practical applications in modern software development paradigms. His research focuses on the critical intersection of sustainability and software engineering, particularly examining environmental impacts of AI-driven development. Key areas include green computing in LLM-based code optimization, variability-aware systems for edge/fog environments, and configuration management for highly configurable software. He investigates fundamental trade-offs between performance gains and ecological costs in emerging technologies. Analysis of his publication trajectory reveals a strategic progression from core software product line research toward urgent sustainability challenges. His recent work demonstrates increasing emphasis on quantifying hidden environmental costs in AI-assisted development while maintaining strong foundations in configuration management for distributed systems. No scientific awards or student advising information is documented in available sources. Similarly, no details regarding research grants, laboratory affiliations, or team structures are present in the current professional footprint.

Lingming Zhangمشاهده پروفایل

دانشیار

• Software Engineering
• Programming Languages
• Machine Learning
• +۵ مورد دیگر

Lingming Zhang is an Associate Professor in the Department of Computer Science at the Grainger College of Engineering, University of Illinois Urbana-Champaign. He maintains an active research program focused on the intersection of Software Engineering, Programming Languages, and Machine Learning, with particular emphasis on LLM-based software testing, repair, and synthesis. His research has resulted in over 100 publications with an h-index exceeding 50. His work has practical impact, having helped detect over 1,000 bugs and vulnerabilities in open-source projects from Apache and GitHub, as well as software systems from major technology companies including eBay, Google, Meta/Facebook, Microsoft, NVIDIA, OctoML, Oracle, and Yahoo!. Zhang's research interests span several key areas in software engineering and programming languages: LLM-based software testing, debugging, and repair Code generation and understanding with large language models Automated program repair Software testing and fuzzing techniques Deep learning for software engineering His recent publications demonstrate a strong focus on leveraging large language models for software engineering tasks, with notable contributions including Agentless, TitanFuzz, AlphaRepair, and ChatRepair. His group has also released influential open-source code language models including StarCoder2 and Magicoder, which have garnered over 1 million downloads worldwide. Zhang has received numerous prestigious awards and recognitions for his work: ACM Distinguished Member ACM SIGSOFT Early Career Researcher Award NSF CAREER Award UIUC Dean's Award for Excellence in Research Multiple ACM SIGSOFT Distinguished Paper Awards He has successfully mentored numerous PhD and MS students, many of whom have gone on to positions at top technology companies and academic institutions. His research is supported by grants from major technology companies including Alibaba, Amazon, Google, Kwai Inc, Meta/Facebook, NVIDIA, and Samsung. Currently, he serves as program co-chair for ASE 2025 and LLM4Code 2025, demonstrating his leadership in the software engineering research community.