جست‌وجوی استاد و پژوهشگر

Yun Pengمشاهده پروفایل

استادیار

• Intelligent Code Analysis
• Automatic Program Repair
• Software Ecosystem
• +۳ مورد دیگر

Dr. Yun Peng is an Assistant Professor in the Department of Computer Science and Engineering at The Chinese University of Hong Kong, specializing in software engineering with a focus on intelligent code analysis, automatic program repair, and software ecosystems. With an active research profile, Dr. Peng serves on program committees for major conferences including ASE and ESEC/FSE. Dr. Peng's educational background includes doctoral training focused on programming languages and software engineering, though specific institutions aren't detailed in the available information. Their research primarily addresses challenges in type inference, code analysis, and the application of large language models to software engineering tasks. Research interests center on intelligent code analysis techniques, particularly in type inference systems for dynamic languages like Python. Dr. Peng has pioneered hybrid approaches combining static analysis with deep learning, as demonstrated in their influential ICSE 2022 paper on HiTyper. Recent work has shifted toward leveraging large language models for code review, program repair, and API recommendation, reflecting the field's evolution. Current projects examine LLM applications in secure code review, code efficiency optimization, and vulnerability detection in smart contracts. Dr. Peng's publication record shows a clear progression from foundational type inference research toward cutting-edge LLM applications in software engineering. The work spans multiple dimensions of code quality including security, performance, and maintainability, with tools like HiTyper, TypeGen, and APIBench making significant contributions to the research community. As a program committee member for top-tier conferences, Dr. Peng actively contributes to the software engineering research community. Their work has been recognized through publication in premier venues including ICSE, ASE, and ESEC/FSE, demonstrating consistent high-impact contributions to the field. Dr. Peng maintains an active research group focused on advancing the state of the art in intelligent code analysis, with current projects exploring the intersection of large language models and traditional program analysis techniques. The research has practical applications in developer productivity tools, security analysis systems, and software maintenance automation.

Prateek SAXENAمشاهده پروفایل

دانشیار

• Computer Security
• Machine Learning Security
• Trustworthy AI
• +۴ مورد دیگر

Prateek SAXENA is an Associate Professor in the Computer Science Department at the School of Computing, National University of Singapore. He serves as Co-Director of the CRYSTAL Centre and teaches courses including CS3235 Computer Security and CS5562 Trustworthy Machine Learning. His research spans multiple domains within computer security and systems. Dr. SAXENA earned his Ph.D. in Computer Science from the University of California, Berkeley (2012), an M.S. in Computer Science from Stony Brook University (2007), and a B.E. in Computer Engineering from the University of Pune, India (2004). His research focuses on building better security and privacy in practical systems through principled approaches combining formal reasoning, tools and ideas from several computer science domains. Current research thrusts include machine learning security, decentralized systems security, security processors, and automatic program translation. His work has resulted in several practical artifacts powering real-world systems, including spinoffs like Zilliqa and Kyber Network. His publication record demonstrates consistent high-impact research across security domains including web security, blockchain, trusted execution environments, and machine learning security. His work shows a progression from foundational web security research to cutting-edge work on blockchain systems and machine learning security. MIT Technical Review, Top 10 Innovators under 35, Asia - 2017 Security and Privacy Research Award, 2018 Young Research Award, NUS, 2017 David J. Sakrison Memorial Prize for outstanding doctoral work, UC Berkeley, 2012 AT&T Best Applied Security Research Paper Award 2010 Dr. SAXENA has advised numerous successful PhD students who have gone on to prominent positions at Microsoft Research, Georgia Tech, ETH Zurich, and industry leaders like Zilliqa and Kyber. His research has been generously supported by CISCO Research, Google, Intel, Symantec, MoE-Singapore, DSO Labs, and NRF-Singapore. He leads the KISP Lab (Keep It Secure and Private), which has produced influential research in blockchain, trusted execution environments, and security tools.

Tevfik Bultanمشاهده پروفایل

استاد

• Software Verification
• Static Analysis
• Model Checking
• +۹ مورد دیگر

Tevfik Bultan is a Professor in the Department of Computer Science at the University of California, Santa Barbara, where he leads the Verification Laboratory (VLab). He has been actively teaching undergraduate and graduate courses including CS 160 (Translation of Programming Languages), CS 267 (Automated Verification), and CS 272 (Software Engineering), along with specialized seminars on topics like Neural Network Verification and Quantitative Verification. Ph.D. in Computer Science, University of Maryland, College Park (1998) M.S. in Computer Engineering and Information Science, Bilkent University (1992) B.S. in Electrical and Electronics Engineering, Middle East Technical University Professor Bultan's research focuses on software verification, static analysis, model checking, and security, with particular emphasis on quantitative information flow, side channel analysis, and string analysis. His work bridges theoretical foundations with practical applications in web software, service-oriented computing, and concurrency. He has pioneered techniques for detecting bugs in identity and access management policies and quantifying information leakages in crypto libraries, which earned him Amazon Research Awards in 2017 and 2023. His recent publications and student dissertations reveal a strong trend toward quantitative approaches to security analysis, with increasing focus on automated techniques for detecting and mitigating side channels in various contexts including network communications, cryptographic libraries, and access control systems. His work combines formal methods with practical security applications, often developing novel constraint solving and model counting techniques. Amazon Research Award (2017): Automatically Detecting Bugs in Identity and Access Management Policies Amazon Research Award (2023): Detecting and Quantifying Information Leakages in Crypto Libraries Professor Bultan has advised numerous PhD students who have gone on to academic positions at institutions like Stevens Institute of Technology, Harvey Mudd College, and King Saud University, as well as industry positions at companies including Amazon, Google, Microsoft, and Intel. His laboratory, the Verification Laboratory, has received funding from various sources to support research in software verification and security analysis. The lab focuses on developing practical verification techniques that can be applied to real-world software systems.

Christopher Timperleyمشاهده پروفایل

پژوهشگر

• Search-based software engineering
• Robotics software testing
• Program analysis
• +۵ مورد دیگر

Christopher Timperley is a Senior Systems Scientist in the Software and Societal Systems Department at Carnegie Mellon University, with additional appointments in the Robotics Institute and National Robotics Engineering Center within the School of Computer Science. His work is primarily conducted in the context of squaresLab. Timperley's research focuses on the intersection of software engineering, robotics, and program analysis, with particular emphasis on developing techniques for automatically detecting, locating, and repairing faults in large-scale software systems. His recent work has applied these techniques specifically to robotics systems, particularly autonomous vehicles. His research interests span search-based software engineering and testing, with applications in security and privacy, embedded systems security, formal methods for security, intrusion and anomaly detection, and security of AI and ML systems. His recent publications demonstrate a strong focus on robotics software systems, particularly those built using ROS (Robot Operating System). His work addresses critical challenges in robotics software development including architecture misconfigurations, behavioral modeling, test automation through simulation, and empirical studies of the robotics ecosystem. The publications reveal a consistent theme of applying software engineering principles to improve the reliability and security of robotics systems. Timperley received his Ph.D. in computer science from the University of York, UK, with a dissertation on search-based program repair under the supervision of Professor Susan Stepney. His work bridges theoretical advances in software engineering with practical applications in robotics, contributing significantly to the development of more robust and reliable robotics software systems.

Lina Ochoa Venegasمشاهده پروفایل

استادیار

• Software Engineering
• Cloud Computing
• Software Architecture
• +۳ مورد دیگر

Lina Ochoa Venegas is an Assistant Professor in the Department of Software Engineering and Technology at Eindhoven University of Technology (TU/e), affiliated with the EAISI High Tech Systems initiative. Her research focuses on software architecture, API design, software evolution, and dependency analysis, with a particular emphasis on improving software quality, maintainability, and developer productivity. She holds a Ph.D. in Computer Science and has taught courses such as Programming, Programming for Applied Physics, and Software Evolution. Her work bridges theoretical advancements and practical applications in software engineering, including studies on breaking changes, architecture recovery, and educational technology for young learners. Her research interests span software architecture communication, API security, multi-lingual system dependencies, and the impact of breaking changes in open-source ecosystems. Notable contributions include the BreakBot tool for analyzing breaking changes and systematic reviews on harmful API uses and repair techniques. Awards: Best Teacher Award (Bachelor Data Science 2024), VERSEN PhD Award (2024). Grants & Projects: Supervision of 12 academic works and contributions to datasets on software evolution challenges. Labs & Teams: Active in TU/e’s EAISI initiative, collaborating on high-tech systems research.

• Human-Computer Interaction (HCI)
• Usability and User Experience (UX)
• Technology-enhanced Learning (TEL)
• +۴ مورد دیگر

Professor Effie Lai-Chong Law is a full Professor in the Department of Computer Science at Durham University. She specializes in Human-Computer Interaction (HCI), focusing on Usability and User Experience (UX) methodologies applied to Technology-enhanced Learning (TEL), Affective Computing, and Trustworthy Autonomous Systems (TAS). She earned her PhD (summa cum laude) in psychology (minor: computer science) from the University of Munich with a DAAD scholarship and holds a Bachelor's and Master's in Social Science from the University of Hong Kong. Her research spans multidimensional UX measurement, automatic emotion recognition, conversational AI, and mixed reality. Recent publications reveal strong emphasis on chatbot trust dynamics, educational applications (e.g., traditional Chinese painting critique), healthcare empathy systems, sustainable travel behavior, and pandemic impacts on child well-being in Majority World contexts. Scientific awards: DAAD four-year full scholarship Professor Law supervises postgraduate students (Caitlin Brown, Jinyu Liu, Kubra Kaymakci Ustuner, Yasemin Bozdemir) and leads major projects including EU-funded ARETE (H2020 augmented learning) and UKRI TAS Verifiability. She serves on editorial boards for Interacting with Computers (OUP), International Journal of Human-Computer Studies (Elsevier), and Quality and User Experience (Springer), with extensive conference leadership at CHI and INTERACT.

Todd Mowryمشاهده پروفایل

استاد

• Computer Architecture
• Systems
• Databases
• +۳ مورد دیگر

Todd Mowry is a Professor in the Computer Science Department at Carnegie Mellon University, affiliated with the School of Computer Science. His research focuses on enhancing microprocessor-based system performance through parallelism exploitation, including single-chip multiprocessing and latency mitigation strategies. He leads initiatives like the STAMPede project, addressing challenges in compiler-driven parallelization and hardware-software co-design. His work spans computer architecture, systems, and databases, with a strong emphasis on compiler optimization, memory management, and scalable computing. Mowry has advised students including Sam Arch, Patrick Coppock, Hongyi Jin, Ruihang Lai, and Eliot Solomon. He teaches advanced courses such as 15745 (Fall 2025) and 15418 (Spring 2025), contributing to graduate-level education in computer systems. Recent research highlights include innovations in machine learning optimization (e.g., Relax framework), efficient LLM microservices, and architectural solutions for thread-safe metadata management. His publications address topics like UDF optimization, auto-batching in neural networks, and energy-minimal dataflow systems. Mowry's projects often involve collaboration across academic and industrial partners, driving advancements in both theoretical and applied computer systems. He has contributed to frameworks like CORA for tensor compilation and NVOverlay for non-volatile memory systems, emphasizing practical scalability and efficiency.

• Online Privacy
• Secure Software Engineering
• Cybersecurity
• +۵ مورد دیگر

Federica Maria Francesca Paci is an Associate Professor in the Computer Science Department at the University of Verona, Italy. She holds academic position INFO-01/A in Informatics with research focus spanning computer architecture, security, software engineering, and web information systems. Her educational background includes a Ph.D. in Information Technology from the University of Milan. Prior to joining the University of Verona, she served as a Lecturer in Cyber Security at the University of Southampton, UK, where she was a member of the Southampton Centres of Excellence in Cyber Security Research recognized by the UK Government Communications Headquarters (GCHQ). She also completed post-doctoral fellowships at Purdue University's Department of Computer Science (USA) and the University of Trento's Department of Information Engineering and Computer Science (Italy). Dr. Paci's research focuses on online privacy and secure software engineering, with emphasis on developing security methodologies for software systems and Internet applications. She has proposed technologies for secure and privacy-preserving data sharing across web services, social networks, cloud services, and Industrial IoT. Her current research examines passwordless authentication protocols, automatic vulnerability detection and code repair tools, and compliance measurement with EU Data Protection Law. She teaches multiple courses including Cyber Security and Data Protection, Data Security & Privacy, and Malware across Master's programs in Computer Science and Engineering and Data Science. Her teaching spans both theoretical and laboratory components across multiple academic years through 2025/2026. Dr. Paci is an active researcher with more than 80 publications in international conferences and journals. She serves on program committees for international conferences in data security and privacy and is a member of the IFIP WG 11.14 Working Group on Secure Engineering. Her current research projects include: Securing Decentralized Finance and Remote Healthcare Systems (SHIELD) Securing sOftware Platforms In search of evidence of stealth cyber threats (COVERT) Novel Methodologies and Tools for Next Generation Cyber Ranges (NOMEN) Administratively, she serves as Communication Officer for Computer Science, is a member of multiple teaching committees, and participates in various departmental governance bodies including the Computer Science Department Council and the Commissione Paritetica Docenti-Studenti.

Alessandra Gorlaمشاهده پروفایل

استاد پژوهشی

• Software Engineering
• Software Testing and Analysis
• Mobile Security
• +۳ مورد دیگر

Alessandra Gorla is an Associate Research Professor at the IMDEA Software Institute in Madrid, Spain. She holds a Ph.D. in Informatics from the Università della Svizzera Italiana (University of Lugano) in Switzerland, where her doctoral thesis on automatic workarounds for software failures earned the Fritz Kutter Award (2011). Prior to IMDEA, she was a postdoctoral researcher at Saarland University’s Software Engineering Chair in Germany and a visiting researcher at Google. Education: Ph.D. in Informatics, Università della Svizzera Italiana (2011) Research Interests: Her work focuses on software engineering, with emphasis on mobile and web security, software testing/analysis, and automatic software repair. She has pioneered techniques like intrinsic redundancy for self-healing systems and malware detection in mobile applications. Awards: Fritz Kutter Award (2011) Best Student Poster Award (2008) Best Paper Award (2014) Service: She has served as SBST program co-chair (2018), tool demo co-chair at FSE 2016, and artifact evaluation co-chair at ISSTA and ESSoS 2016. Her work spans organizing committees for major software engineering conferences. Teaching: She has taught courses on mobile testing, self-healing systems, and software engineering at Saarland University and the University of Lugano. Her lab focuses on advancing software reliability through automated techniques, with ongoing projects analyzing app behaviors, third-party libraries in iOS, and lazy initialization optimizations.

Dejice Jacobمشاهده پروفایل

پژوهشگر ارشد

• Compiler Design
• Heterogeneous Computing
• Virtual Machines
• +۳ مورد دیگر

Dr. Dejice Jacob is a Research Fellow at the University of Glasgow's School of Computing Science, working on the EPSRC-funded CapableVMs project. His research focuses on enhancing virtual machine security through capability-enhanced CPU architectures. Previously, he completed his PhD at Glasgow developing automatic parallelization techniques for Python in CPU/GPU systems. Jacob's technical expertise spans compiler design for heterogeneous systems, memory management security, and performance optimization of dynamic language runtimes. His current work explores hardware-assisted security mechanisms for memory safety and efficient garbage collection in capability-based systems.

Saemundur Haraldssonمشاهده پروفایل

مدرس

• Artificial Intelligence
• Intelligent Systems
• Search-Based Software Engineering
• +۴ مورد دیگر

Dr. Saemundur Haraldsson is a Lecturer in Computing Science at the University of Stirling, specializing in AI and intelligent systems applied to healthcare and search-based software engineering. He holds a PhD from the University of Stirling and degrees from the University of Iceland. His research focuses on genetic improvement of software, automatic program repair, and predictive modeling in vocational rehabilitation. Key contributions include pioneering work on genetic improvement frameworks for industry, co-organizing major tutorials on the topic, and co-authoring the first comprehensive survey on genetic improvement. Education: PhD in Computer Science, University of Stirling MSc in Industrial Engineering, University of Iceland BSc in Industrial Engineering, University of Iceland Research interests span AI-driven software optimization, genetic programming applications in real-world systems, and healthcare informatics. His work bridges academia and industry, with notable projects in energy usage optimization and rehabilitation pathway prediction. Articles emphasize practical applications of evolutionary algorithms and program repair techniques, reflecting trends toward developer-centric automated solutions and sustainable computing. Awards: N/A Advising & Grants: Supervised research assistants on projects like the Rehabilitation Pathway Generator (2024) Managed grants supporting personalized ADHD screening systems Labs/Teams: Active contributor to the university's Data Science and Intelligent Systems research group, exploring contextual learning and digital society initiatives.

Bernd Fischerمشاهده پروفایل

استاد

• Automated software engineering
• Logic-based techniques
• Program verification
• +۷ مورد دیگر

Bernd Fischer is a Professor and current Head of Division in the Division of Computer Science at Stellenbosch University, South Africa. He has previously worked at TU Braunschweig, NASA Ames Research Center, and University of Southampton. His academic journey includes a PhD from Universität Passau (defended June 1, 2001). Professor Fischer's research focuses on automated software engineering with particular emphasis on logic-based techniques. His work spans specification-based component reuse, program synthesis, program verification (including annotation inference, software model checking, and human-oriented presentation of verification results), and innovative visualization approaches like tag clouds over concept lattices. His research has led to significant contributions in concurrent program verification, fault localization, and software repository analysis. Analysis of his recent publications reveals strong trends in software verification, particularly in concurrent systems and model checking. His work has consistently focused on developing practical verification tools (CSeq, ESBMC) that have achieved top rankings in international verification competitions. He has also pioneered visualization techniques for software repositories and academic data exploration using concept lattices and tag clouds. His scientific achievements have been recognized with multiple awards including Best Paper Award at ASE 1998, ASE 2012 Most Influential Paper Award, ACM Distinguished Paper Award (2011), Best Presentation Award at SAFECOMP 2010, and multiple Gold and Silver medals in the International Competition on Software Verification (SV-COMP) in 2014, 2015, and 2016. Professor Fischer has successfully supervised numerous PhD and Master's students, both at Stellenbosch University and previously at University of Southampton. His service to the academic community includes committee memberships in IFIP TC-2 Working Group on Program Generation (since 2004), ASE steering committee (since 2007), and GPCE steering committee (since 2011). He has served as General Chair for SPIN 2015 and GPCE'16, and as Program Committee Chair for GPCE 2009 and ASE 2007. His research group has developed several notable tools including AutoBayes (for automatic generation of data analysis programs), ConceptCloud (for browsing software archives), CSeq (a concurrency pre-processor for sequential C verification), and ESBMC (a model checker for C and C++ programs).

Fabio Patriziمشاهده پروفایل

دانشیار

• Formal Methods
• Knowledge Representation
• Machine Learning
• +۷ مورد دیگر

Fabio Patrizi is an Associate Professor in Computer Science at the Department of Computer, Control and Management Engineering (DIAG) at Sapienza University of Rome. He serves as Coordinator of the Bachelor in Information Engineering program at the Latina site. His academic career includes significant service roles such as Editorial Board Member for the prestigious Artificial Intelligence Journal (AIJ) since 2023, Area Chair for ICSOC 2023, and co-chair for the KR 2025 track on Knowledge Representation & Reasoning and Planning & Scheduling. Patrizi's educational background led to National Scientific Habilitation for Full Professorship for sector SC 09/H1 (now GSD 09/IINF-05). His research spans Formal Methods, Knowledge Representation, Machine Learning, Reasoning about Action, Planning in AI, Service-oriented Computing, and Business Processes. His work focuses on theoretical, methodological, and practical aspects including Behavior and Service composition, Planning programs, MAS Verification, Reasoning About Actions, Infinite-plan synthesis, Data abstraction techniques, and Reinforcement Learning with non-Markovian Rewards. His publication record includes over 70 scientific papers in top-level international journals and conferences. Recent research trends show a strong focus on temporal reasoning in reinforcement learning, planning frameworks, process mining, and verification techniques. His work bridges theoretical foundations with practical applications in AI planning and verification. ICDT Test of Time Award on Automatic verification of data-centric business processes (2009) RMIT Visiting Researcher's Award (2011) ICAPS 2024 Outstanding SPC Award Patrizi has supervised 2 PhD students and actively serves on program committees for major AI conferences including AAAI, IJCAI, ICAPS, AAMAS, KR, and ECAI. His research projects include MARLeN (Principal Investigator, 2023-2025), AIPlan4EU (Task Leader, 2021-2023), DRAPE (Principal Investigator, 2019-2022), WhiteMech (Participant, 2019-2024), TAILOR (Participant, 2020-2023), VerySynCopated (Principal Investigator, 2014-2016), ACSI (Participant, 2010-2013), and SM4All (Participant, 2008-2011). He is a member of the Data Management & Service-Oriented Computing and Artificial Intelligence & Knowledge Representation research groups at DIAG, contributing to collaborative efforts in advanced AI research and applications.

Mahsa Varshosazمشاهده پروفایل

دانشیار

• Software Testing
• Formal Verification
• Software Safety Analysis
• +۶ مورد دیگر

Dr. Mahsa Varshosaz is an Associate Professor in the Software Quality Research group at the IT University of Copenhagen , Denmark. Her work bridges theoretical and practical aspects of software quality assurance, with a focus on model-based testing , formal verification , and automatic program repair for complex systems. Her research spans software product lines , autonomous systems , and cyber-physical systems , including projects like REMARO (testing of underwater robotic systems) and Linux kernel program repair. She employs formal methods to address challenges in system safety, reliability, and variability. Selected publications reveal a trajectory in hybrid testing techniques combining symbolic execution and reinforcement learning, safety analysis of autonomous underwater vehicles, and formal verification of probabilistic systems. Her work intersects software testing formal methods AI-based verification robotics safety product line engineering concurrent system analysis . She actively contributes to academia as Co-Chair of Doctoral Symposium in SPLC conferences Editorial Board member of Science of Computer Programming Program Committee member across testing/verification workshops like A-MOST, ITEQS, and ECOOP . Her 2023 invited talk series at Trustworthy Autonomous Systems Verifiability Node and participation in Dagstuhl/Shonan seminars highlight her influence in unifying formal methods with AI-based autonomous systems . Projects include REMARO (co-coordinator) and INSIGHT.

Xiang Lingمشاهده پروفایل

دانشیار

• Software Security
• Data-driven Security
• AI Security
• +۴ مورد دیگر

Xiang Ling is an Associate Professor at the Institute of Software, Chinese Academy of Sciences (ISCAS) in Beijing, specializing in software security, data-driven security, AI security, and network/web security. His research bridges theoretical computer science with practical security applications, focusing on malware analysis, vulnerability detection, and adversarial machine learning. His research interests span multiple security domains with emphasis on applying data-driven approaches to security challenges. He investigates how machine learning techniques can be both applied to security problems and themselves secured against adversarial manipulation. His work particularly focuses on Windows and Android security ecosystems, with significant contributions to malware detection systems and vulnerability analysis tools. His publication record shows consistent contributions to top software engineering and security venues including ICSE, USENIX Security, IEEE S&P, and Black Hat. His research demonstrates strong trends toward integrating deep learning with security analysis while addressing practical challenges in real-world security systems. Recent work increasingly incorporates large language models for security applications. ACM SIGSOFT Distinguished Paper Award at ICSE 2025 for 'FUTURE' paper Multiple papers accepted at premier venues including ESEM 2025, EASE 2025, and ICSME 2025 Research funded through collaborations with major academic institutions Dr. Ling actively mentors students and collaborates with researchers globally. He maintains an active research group at ISCAS focusing on code analysis, system security, deep learning, and fuzz testing. His team regularly publishes in top-tier conferences and journals while developing practical security tools that address real-world vulnerabilities. He is currently recruiting interns and graduate students through platforms like 实习僧 (intern recruitment website).