جست‌وجوی استاد و پژوهشگر

Thomas Vogelمشاهده پروفایل

پژوهشگر

• Software Engineering
• Search-Based Software Engineering
• Model-Driven Engineering
• +۵ مورد دیگر

Thomas Vogel is a postdoctoral researcher in the Software Engineering Group at Humboldt-Universität zu Berlin, where he teaches courses including Adaptive Systems, Software Engineering, and Methods and Models of System Design. Previously, from October 2021 to September 2022, he served as stand-in professor for Empirical Software Engineering at Paderborn University. His academic journey includes a Ph.D. with summa cum laude from the Hasso Plattner Institute at University of Potsdam (2018) and undergraduate studies in Information Systems at University of Bamberg with distinction. Dr. Vogel's research focuses on the intersection of software engineering with self-adaptive and autonomous systems. His work bridges search-based and model-driven software engineering with control theory to create robust adaptive systems. He has made significant contributions to quality assurance of self-adaptive software, particularly in the areas of uncertainty reduction, runtime verification, and formal synthesis of controllers. His research has practical applications in mobile robot navigation, server infrastructure management, and scientific computing domains. His publications reveal a strong emphasis on formal methods for adaptive systems, with a notable trend toward integrating machine learning techniques with traditional control theory approaches. Recent work demonstrates increased focus on industrial applicability of self-adaptation research, uncertainty management, and evaluation methodologies for adaptive systems. His 2015 paper "Software Engineering meets Control Theory" received the SEAMS 10-Year Most Influential Paper Award in 2025, highlighting the lasting impact of his contributions to the field. Among his notable recognitions are the Facebook Testing and Verification Research Award, the Karsten Schwan Best Paper Award at ICAC 2017, and the Best Paper Award at SEAMS 2024 for "Formal Synthesis of Uncertainty Reduction Controllers." He has served on program committees for major conferences including ASE, ICSE, ICST, and SEAMS, and was Program Committee Co-Chair for the Research Track at SEAMS 2025. Dr. Vogel actively mentors PhD students, with recent successful defenses by Arut Prakash Kaleeswaran ("Explanation of the Model Checker Verification Results") and Sona Ghahremani ("Incremental Self-Adaptation of Dynamic Architectures Attaining Optimality and Scalability"). His research has been supported by the Deutsche Forschungsgemeinschaft (DFG), including the "Controlling Search-Based Test Generation and Program Repair" project. He is also involved in the collaborative research center "FONDA: Foundations of Workflows for Large-Scale Scientific Data Analysis."

Yingfei Xiongمشاهده پروفایل

دانشیار

• Software Engineering
• Programming Languages
• Program Synthesis
• +۴ مورد دیگر

Yingfei Xiong is an Associate Professor at Peking University specializing in Software Engineering and Programming Languages. His research focuses on program synthesis, automated program repair, and software analysis techniques with extensive contributions to top-tier conferences including ASE, ICSE, PLDI, and SPLASH/OOPSLA. Dr. Xiong's primary research interests include: Program Synthesis and Inductive Programming Automated Program Repair and Bug Fixing Static and Dynamic Program Analysis Machine Learning Applications in Software Engineering Compiler and Language Implementation Techniques Software Testing and Verification His publication trends show a strong shift toward neural approaches in program repair, with significant integration of large language models in synthesis tasks since 2023. His work bridges theoretical foundations with practical applications, evidenced by industrial collaborations such as the Alibaba case study on static taint analysis. Recent contributions include GrammarT5 for code generation, Tare for type-aware neural repair, and Equality Saturation techniques guided by LLMs. At Peking University, Dr. Xiong is affiliated with the School of Electronics Engineering and Computer Science (SEI) as indicated by his institutional website domain. He actively contributes to the academic community through program committee service for major software engineering conferences and mentoring student researchers.

Weisong Sunمشاهده پروفایل

پژوهشگر ارشد

• Trustworthy Intelligent Software Engineering
• AI Model Security
• Software Security
• +۲ مورد دیگر

Weisong Sun is a Research Fellow at the Cyber Security Lab (CSL), School of Computer Science and Engineering, Nanyang Technological University, Singapore. His work bridges software engineering with artificial intelligence, focusing on developing reliable and secure AI-powered systems. His research centers on Trustworthy Intelligent Software Engineering and AI Model Security , with significant contributions in requirements engineering, code generation, and AI security. Dr. Sun employs multi-agent approaches to solve complex software engineering challenges, particularly in formalizing requirements for reliable code generation and securing AI models against watermarking attacks. His work addresses critical ethical concerns in software systems, examining the trade-offs in bias mitigation approaches. Dr. Sun's publication record shows a clear trend toward integrating knowledge representation with multi-agent systems to enhance software development processes while addressing security and fairness concerns. His recent work spans from practical type inference frameworks for Python to building generalizable testing scenarios for autonomous vehicles. As an active contributor to the software engineering community, Dr. Sun serves as a Program Committee member for multiple tracks at ASE 2025 and other major conferences including ICSE, FSE, and ISSTA, demonstrating his standing as a respected researcher in the field.

• Artificial Intelligence
• Reinforcement Learning
• Machine Learning
• +۳ مورد دیگر

Stuart Russell is a Professor at the University of California, Berkeley , affiliated with the Department of Electrical Engineering and Computer Sciences in the College of Engineering. His research spans Artificial Intelligence , Reinforcement Learning , and AI Ethics , focusing on safety, alignment, and theoretical foundations. Research Interests : Provably beneficial AI systems Human-compatible AI Reinforcement learning theory Language model interpretability AI safety and control mechanisms Recent Work Trends : 2025-2024: Safety frameworks (off-switch games, reversal curse analysis) 2024: Reward learning with partial observability, program synthesis via diffusion 2023: Societal risk taxonomy, human feedback coordination Collaborations include leading researchers like Anca D. Dragan (UC Berkeley), Yoshua Bengio (Mila), and Michael I. Jordan (UC Berkeley), with publications in venues such as NeurIPS , ICLR , and ICML .

• Software Engineering
• Artificial Intelligence
• Requirements Engineering
• +۶ مورد دیگر

Timo Poranen is a Lecturer at Tampere University's Faculty of Information Technology and Communication Sciences in the Department of Computing Sciences. He focuses on software engineering practices, artificial intelligence applications, and educational technology development. His research spans: AI integration in software development workflows Accessibility and usability in digital platforms Requirements engineering methodologies Collaborative software project management Algorithmic graph theory DevOps and cloud-native systems Recent work examines AI-driven assessment tools, accessibility testing frameworks, and networked educational practices in Finland. He contributes to capstone project guidance and teaches software development fundamentals. Key trends in his 2023-2025 publications: AI-assisted coding patterns Accessibility evaluation metrics Service desk optimization Open-source project dynamics Cloud system monitoring

• System and Software Security
• Embedded Systems Security
• Firmware Analysis
• +۴ مورد دیگر

Aravind Machiry is an Assistant Professor at the Elmore Family School of Electrical and Computer Engineering at Purdue University. His research focuses on system and software security, particularly in securing embedded systems, firmware, and leveraging modern tools like static analysis and fuzzing to mitigate vulnerabilities. He leads the Purdue Systems and Software Security Lab (PurS3) , which emphasizes principled yet practical security solutions. Research interests include embedded systems security (e.g., Rust adoption in embedded contexts), vulnerability detection in firmware and network stacks, and securing continuous integration workflows. He has explored topics like language model integration in Android apps, fault injection in API error handling, and mitigating data poisoning in federated learning. His work bridges hardware and software domains, with contributions to secure execution environments (e.g., ARM TrustZone via Tarnhelm) and binary analysis tools like BinTrimmer. While no formal awards are listed, his grant-funded projects (e.g., CAREER award) reflect recognition of his research impact. Advising focuses on graduate students in cybersecurity and embedded systems domains. Key projects include rehosting embedded applications as Linux apps (LEMIX), automated firmware analysis (KARONTE), and feedback-driven binary generation (Cornucopia). His research often addresses scalability challenges in vulnerability detection across large codebases.

Tom Van Cutsemمشاهده پروفایل

دانشیار

• Distributed Systems
• Software Security
• Blockchain
• +۵ مورد دیگر

Tom Van Cutsem is an Associate Professor of computer science at KU Leuven in Belgium, where he is affiliated with the Distributed and Secure Software (DistriNet) research group within the Faculty of Engineering Science and Department of Computer Science. He also maintains an affiliation with Nokia Bell Labs, where he previously served as a Research Department Head with research interests in decentralized systems, IoT, stream processing and machine learning for software engineering. His academic career includes a previous position as Assistant Professor at Vrije Universiteit Brussel from 2010 to 2014. Dr. Van Cutsem holds a PhD in Computer Science from Vrije Universiteit Brussel. His educational background has provided the foundation for his expertise in distributed systems, programming languages, and software security. His primary research interests span Distributed Systems , Software Security , Blockchain , and Programming Language Design . With the DistriNet research group, he pursues work on decentralized systems, IoT, stream processing, and machine learning for software engineering. His research focuses on making distributed systems more secure, reliable, and accessible, with particular attention to blockchain technology and Web3 infrastructure. He is a founding member of IFIP WG 2.16 on Programming Language Design. His recent publications demonstrate a strong focus on blockchain security and infrastructure, with numerous papers on Web3, smart contracts, and decentralized systems from 2023-2025. His work addresses critical challenges in blockchain validation, smart contract security, and Web3 infrastructure, reflecting his commitment to making decentralized systems more secure and accessible to low-resource participants. Several of his current projects focus on secure middleware for smart contracts and scalable infrastructure for self-sovereign applications. Dr. Van Cutsem supervises PhD students including Weihong Wang, with whom he has co-authored multiple recent publications. He is currently leading several research projects including SODISA (Scalable Software Development and Infrastructure for Self-sovereign Applications), Secure Sandboxing for WebAssembly-based Smart Contracts, and Programming languages for privacy-preserving and verifiable computing, with funding extending through 2028-2029. He leads research within the Distributed and Secure Software (DistriNet) group at KU Leuven, which focuses on creating secure and dependable distributed systems. His team works on cutting-edge challenges in blockchain infrastructure, smart contract security, and decentralized application development, with connections to both academic and industrial partners including Nokia Bell Labs.

• Cybersecurity
• Artificial Intelligence in Education
• Information Systems Security
• +۳ مورد دیگر

Dr. Bernard Vukelić is a Professor of Professional Studies at Veleučilište u Zagrebu (Collegium Carolinum), specializing in cybersecurity, AI applications in education, and information systems security. He holds a Doctorate in Sociology (Dr. Sc.) and focuses on topics like digital transformation, phishing detection, and lifelong learning strategies. His research bridges theoretical frameworks with practical applications in office automation, web application security, and AI-driven solutions for educational assessment. Education: PhD in Sociology (Dr. Sc.) His research interests include: Cybersecurity measures for critical infrastructure AI ethics and implementation in higher education Automated office processes using ChatGPT Phishing attack recognition and mitigation Content management system vulnerabilities Indoor positioning system analysis Recent Research Trends: His work from 2024 emphasizes AI's role in office automation and cybersecurity resilience, while 2023 papers address AI-driven examination proctoring and phishing detection methodologies. Earlier studies explored backend web frameworks and biometric algorithms. He teaches courses like Tools for Digitalization of Business and Security of Information Systems , reflecting his practical focus on industry-relevant technical skills.

• Network Security
• Decentralized Systems
• Privacy-Preserving Technologies
• +۷ مورد دیگر

Prof. Björn Scheuermann is a Professor in the Department of Computer Science at Technische Universität Kaiserslautern, Germany. His research focuses on network security, decentralized systems, privacy-preserving technologies, and distributed algorithms. He has contributed to foundational work on anonymization protocols (e.g., Tor improvements), blockchain systems, and vehicular networks. His work often bridges theoretical computer science with practical implementations in domains like satellite communication and industrial IoT. Key areas of research include: Decentralized Systems: Analysis of IPFS, Bitcoin, and blockchain architectures. Anonymity Networks: Improving Tor's fairness and privacy guarantees. Network Security: Collaborative intrusion detection in SDN, firewall acceleration via FPGA. Wireless & Satellite: Optimizing data transmission for low-earth-orbit satellites and industrial sensors. Recent trends in his publications emphasize: Integration of machine learning for adaptive network security (e.g., COML-IDS framework). Dynamic routing protocols for fault tolerance (e.g., OD³R). Privacy in emerging technologies like mobile data donation apps and decentralized storage. His work has been published in top-tier venues like IEEE/ACM Transactions, IFIP Networking, and ACM SIGCOMM workshops. Collaborations span academia and industry, focusing on practical applications of secure and efficient network architectures.

Azalia Mirhoseiniمشاهده پروفایل

استادیار

• AI Systems
• Chip Design Automation
• Deep Reinforcement Learning
• +۳ مورد دیگر

Azalia Mirhoseini is an Assistant Professor of Computer Science at Stanford University and founder of the Scaling Intelligence Lab. She also serves as a Senior Staff Scientist at Google DeepMind. Her research focuses on scalable and self-improving AI systems, including advancements in chip design automation, large language models, and reinforcement learning applications. Prior roles include co-founding the ML for Systems team at Google Brain. Education: BSc in Electrical Engineering from Sharif University of Technology; PhD in Electrical and Computer Engineering from Rice University. Awards include MIT Technology Review’s 35 Under 35 and Best ECE Thesis Award. Research highlights include AlphaChip (deep RL for chip layout optimization), Mixture-of-Experts (MoE) architectures, and Constitutional AI frameworks for alignment. Her work bridges AI and systems, addressing real-world challenges in hardware design and scalable AI. Key contributions span over 50 publications in top venues like Nature , NeurIPS , and ICLR . Media coverage includes MIT Tech Review, IEEE Spectrum, and WIRED. Teaching includes CS229s: Systems for Machine Learning. Labs/Teams: Scaling Intelligence Lab (Stanford), collaborations with Google Research/DeepMind. Advising/Grants: Mentors students through Stanford; her work is funded via industry-academic partnerships.

• Theoretical Computer Science
• Complexity Theory
• Machine Learning Theory
• +۲ مورد دیگر

Li-Yang Tan is an Assistant Professor of Computer Science at Stanford University. His research focuses on theoretical computer science, particularly complexity theory and its intersections with machine learning and algorithm design. He holds a Ph.D. from Columbia University, advised by Rocco Servedio, and has been recognized with prestigious awards including the Sloan Fellowship and best paper awards at FOCS, CCC, and SAT conferences. His work bridges foundational theoretical contributions with practical algorithmic innovations, emphasizing topics like decision tree learning, computational hardness, and pseudorandomness. Notable achievements include polynomial-time decision tree learning algorithms, hardness results for learning problems, and contributions to coding theory and property testing. He collaborates extensively with researchers such as Guy Blanc, Caleb Koch, and Carmen Strassle on projects addressing core challenges in computational complexity. Tan's publications span top venues like FOCS, STOC, and JACM, reflecting his impact on areas such as query complexity, certification algorithms, and parallel computation lower bounds. His research also extends to cryptographic primitives and information-theoretic methods, showcasing a broad yet deeply technical approach to theoretical computer science.

Vuk Marojevicمشاهده پروفایل

دانشیار

• Wireless Communications
• Software-Defined Radio
• Open Radio Access Networks (O-RAN)
• +۷ مورد دیگر

Dr. Vuk Marojevic is an Associate Professor in the Department of Electrical and Computer Engineering at Mississippi State University (MSU), holding the Paul B. Jacob Chair. He leads research in wireless communications, focusing on open radio access networks (O-RAN), AI-driven security, and UAV (drone) communications. His work emphasizes software-defined radio (SDR), spectrum sharing, and testbed development for 5G/6G networks. Education: Ph.D. and M.S. in Electrical Engineering from Barcelona Tech–UPC (Spain) and University of Hannover (Germany), respectively. Key projects include leading NSF-funded initiatives like AERPAW and Open Artificial Intelligence Cellular (OAIC), which prototype AI-enabled control systems for cellular networks. He also co-designed the AERPAW testbed for UAV experimentation. Research interests span O-RAN security, AI at the physical layer, wireless security protocols, and vehicular communications. His contributions include open-source implementations of 4G/5G protocols and frameworks for secure slicing and intrusion detection in O-RAN deployments. He actively collaborates with industry and academic partners to advance standards for unmanned aircraft systems (UAS) in cellular networks. Publications highlight innovations in reinforcement learning for resource allocation, RIS (reconfigurable intelligent surfaces) applications, and secure UAV communications. He serves as an associate editor for IEEE Transactions on Vehicular Technology and IEEE Vehicular Technology Magazine. Current research includes prototyping zero-trust security architectures (ZTRAN) and evaluating interference mitigation in drone corridors. He mentors students in MS and Ph.D. programs focusing on wireless systems, security, and AI integration in next-gen networks.

Gwan Choiمشاهده پروفایل

دانشیار

• Fault-tolerance
• Verification simulation
• High-performance VLSI circuits
• +۷ مورد دیگر

Gwan Choi is an Associate Professor in the Department of Electrical and Computer Engineering at Texas A&M University, affiliated with the College of Engineering. His research focuses on fault-tolerance, verification simulation, high-performance VLSI circuits, radiation testing, design for dependability, and software engineering. He holds a Ph.D., M.S., and B.S. in Electrical and Computer Engineering from the University of Illinois at Urbana-Champaign (1988–1994). Education: Ph.D., Electrical and Computer Engineering, University of Illinois at Urbana-Champaign (1994) M.S., Electrical and Computer Engineering, University of Illinois at Urbana-Champaign (1989) B.S., Electrical and Computer Engineering, University of Illinois at Urbana-Champaign (1988) Research Interests: Dr. Choi’s work spans fault-tolerant computing, radiation-hard electronics, and high-performance VLSI design. He has contributed to LDPC decoding architectures, MIMO communication systems, and energy-efficient circuit designs. His research emphasizes practical implementations in aerospace and embedded systems. Awards: National Science Foundation (NSF) Career Award (1997) Labs & Activities: He has collaborated with NASA Langley Research Center as a visiting scientist and contributed to industry through roles at Cray Research and Tandem Computers. His work bridges academic research with real-world applications in fault tolerance and low-power systems.

• Computer Software
• Computation Theory and Mathematics
• Cognitive Sciences
• +۳ مورد دیگر

Professor Alastair Donaldson is a faculty member in the Department of Computing at Imperial College London. He leads the Multicore Programming research group and teaches Object-Oriented Programming. His work focuses on programming languages, compiler verification, and GPU computing. Research Areas: Compiler fuzzing and testing Formal verification of GPU programs Memory and cache coherence protocols Concurrency and synchronization Language design for parallel computing Software reliability and bug detection Recent Contributions: His publications highlight advancements in fuzzing techniques for compilers, formal analysis of hardware-software interfaces, and GPU concurrency challenges. Key areas include zero-knowledge proofs, LLM code testing, and multi-level compiler verification. Groups & Labs: He directs the Multicore Programming research group, focusing on scalable concurrency solutions and programming language semantics.

S. Shankar Sastryمشاهده پروفایل

استاد

• Artificial Intelligence
• Control Systems
• Intelligent Robotics
• +۵ مورد دیگر

S. Shankar Sastry is a distinguished academic and researcher holding the title of Professor in the Departments of Electrical Engineering and Computer Sciences, Bioengineering, and Mechanical Engineering at the University of California, Berkeley. He served as Dean of the College of Engineering from 2007 to 2018 and previously directed CITRIS (Center for Information Technology Research in the Interest of Society), the EECS Department, and the DARPA Information Technology Office. His academic journey includes a B.Tech. from IIT Bombay (1977) and advanced degrees from UC Berkeley (M.S. EECS 1979, M.A. Mathematics 1980, Ph.D. EECS 1981). Research interests span artificial intelligence, control systems, robotics, cyber-physical systems, and security. He leads initiatives at the Berkeley Artificial Intelligence Research Lab (BAIR), Berkeley Center for New Media, and the Blum Center for Developing Economies. Notable contributions include work on sparse representation for face recognition, hybrid system analysis, and biomimetic robotics. His publications (15+ highlighted here) showcase expertise in dynamic games, networked control, and quantum computing. Awards include the Rufus Oldenburger Medal (2021), Berkeley Citation (2018), and multiple fellowships. Sastry has advised numerous students and oversees labs focusing on autonomous systems and human-robot interaction. Teaching includes courses on feedback control and linear system theory.