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He XuView profile

Visiting Professor

• Biomaterials
• Tissue Engineering
• Biomechanics
• +7 more

Dr. He Xu is a Visiting Professor in the Department of Engineering Science at the University of Oxford, with a focus on Biomaterials , Tissue Engineering , and Biomechanics . She previously worked at Shanghai Normal University, rising from lecturer (2014) to associate professor (2018) and full professor (2024). Education: BEng in Materials Science and Engineering (China University of Geosciences), DPhil in Biomedical Engineering (Shanghai Jiao Tong University, 2014) Her research explores: Biomaterials : Smart hydrogels, piezoelectric systems, and nanogenerators for therapeutic applications. Tissue Engineering : Innovations in intervertebral disc and tendon regeneration. Drug Delivery : Targeted activation, nitric oxide therapy, and bioelectronic systems. Her publications span 2021–2025 , combining Biomaterials , Nanotechnology , and Medical Imaging to address challenges in Diabetes , Cancer , and Cardiovascular Disease . Key collaborations include the 3DMed Interreg 2 Seas Consortium and work on rapid Covid-19 testing .

Michael WinikoffView profile

Professor

• Autonomous Agents and Multiagent Systems
• Explainable AI (XAI)
• Agent-Oriented Software Engineering
• +5 more

Professor Michael Winikoff is a prominent academic at Victoria University of Wellington's School of Information Management, where he joined in June 2019 and served as Head of School from early 2021 to early 2025. Previously, he was Head of Department at University of Otago's Department of Information Science (February 2011 to December 2016) and Associate Professor at RMIT University's School of Computer Science and IT. His career spans over two decades of research in autonomous systems and agent-oriented software engineering. His educational background includes a PhD from the University of Melbourne (1994-1997). His research focuses on improving software creation methodologies, particularly for intelligent agents that exhibit robust and flexible behavior. He is best known for developing the Prometheus methodology for agent-based systems design. More recently, his work has expanded to address societal consequences of autonomous systems and trust issues, with significant contributions to explainable AI (XAI). His publication record demonstrates consistent contributions to autonomous systems research, with recent work focusing on explainability frameworks, trust calibration, and certification of reliable autonomous systems. His 15 most recent publications (2018-2025) show a clear evolution from technical agent design toward broader societal implications, particularly the intersection of technical design and human trust in autonomous systems. The publications span journals like Artificial Intelligence and IEEE Internet Computing, as well as major conferences including AAMAS. Past President of International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS) Co-Editor-in-Chief of Journal of Autonomous Agents and Multi-Agent Systems (JAAMAS) Chair of ICORE rankings management committee Editor-in-Chief for International Journal of Agent-Oriented Software Engineering (IJAOSE) Vice President of Computing Research and Education Association of Australasia (CORE) Professor Winikoff has led significant research projects funded by the Australian Research Council, including work on adaptive personae for interactive toys, service-oriented negotiation in multi-agent systems, and advanced software engineering for intelligent agent systems. His current research continues this trajectory, focusing on making autonomous systems more explainable and trustworthy through engineering approaches that consider both technical and human factors. He maintains active collaborations across institutions, with recent work involving researchers from RMIT University and other international partners.

Jinqiu YangView profile

Associate Professor

• software reliability
• quality assurance of machine learning systems
• autonomous vehicles
• +5 more

Jinqiu Yang is an Associate Professor in the Department of Computer Science and Software Engineering at Concordia University in Montreal, Canada. Her research focuses on improving software reliability and quality assurance, particularly in the context of machine learning systems and autonomous vehicles. She leads active research projects in software testing, automated program repair, and mining software repositories, with strong connections to both academic and industrial applications. Her research interests span software reliability, quality assurance of machine learning systems including autonomous vehicles, software testing, automated program repair, text analytics of software artifacts, and mining software repositories. She has developed novel approaches for testing deep learning libraries, evaluating robustness in autonomous driving systems, and tracking the evolution of static code warnings. Her work bridges traditional software engineering with emerging challenges in AI systems, addressing critical issues of reliability and safety in complex software environments. Yang's recent publications (2021-2025) demonstrate a clear trajectory toward AI/ML system reliability, with increasing focus on autonomous vehicles, concept drift detection, and security aspects of large language models. Her work spans both theoretical foundations and practical applications, often involving empirical studies of real-world systems and development of practical tools to address identified challenges. ACM SIGSOFT Distinguished Paper Award Dr. Yang actively mentors graduate students and is currently recruiting Master's and PhD candidates. She has secured significant research funding including NSERC Discovery Grants (2019-2025), Gina Cody Research and Innovation Fellowship (2024-2026), and participation in the NSERC CREATE Program SE4AI (2021-2026). Her research is supported by multiple grants including NOVA – FRQNT-NSERC PROGRAM (2024-2027) and Volt-Age Seed Grant (2024-2026). She leads research in the O-RISA Lab at Concordia University, focusing on reliability and security aspects of intelligent software systems. Her team collaborates with industry partners including IBM, where she previously worked at IBM Watson Research Lab and IBM CAS, bringing practical experience to her academic research.

Jingling XueView profile

Professor

• Programming Languages
• Compilers
• Program Analysis
• +5 more

Jingling Xue is a Scientia Professor at the School of Computer Science and Engineering at the University of New South Wales (UNSW) in Sydney, Australia. As an IEEE Fellow of the Computer Society, he leads the Programming Languages and Compilers research group, focusing on practical applications of compiler optimization and program analysis techniques. His work bridges theoretical foundations with real-world software systems, particularly in developing open-source tools for large-scale program analysis. Professor Xue received his B.Eng and M.Eng degrees from Tsinghua University in 1984 and 1987, respectively, followed by a PhD from the University of Edinburgh in 1992. His academic journey has established him as a leading figure in programming languages and compiler technology. Xue's research spans programming languages, compiler technology, and program analysis with emphasis on practical relevance. His current projects include compiler techniques for improving parallelism and locality, pointer/alias analysis for million-line-scale programs, and static/dynamic analysis for detecting bugs and security vulnerabilities in real-world applications like web browsers and Android apps. His group actively develops open-source tools to support scientific replicability and reproducibility in these areas. His recent publications demonstrate a strong focus on applying program analysis techniques to modern challenges including AI compilers, homomorphic encryption, security vulnerability detection, and graph processing systems. The work shows evolution from traditional compiler optimization to addressing emerging domains like privacy-preserving computation and deep learning systems while maintaining rigorous theoretical foundations. Scientific Awards: Best Paper Award at CGO'13 Best Paper Award at CGO'16 Distinguished Paper Award at ECOOP'16 Distinguished Paper Award at ICSE'18 Distinguished Paper Award at ISSTA'19 Distinguished Paper Award at ASE'19 Distinguished Artifact Award at ISSTA'23 Best Artifact Award at FSE'23 Distinguished Paper Award at ASE'23 Test-of-Time Award at CGO'21 Professor Xue has successfully supervised 30 PhD students to completion, many of whom now work as professors or researchers in academia and industry. He has served as Program Chair for major conferences including LCTES'13, CC'18, CGO'20, and General Chair for LCTES'20. His group currently focuses on memory safety in Rust, smart contract analysis, AI compilers, compilation for privacy-preserving computation, and adversarial attacks in deep learning. The Programming Languages and Compilers group maintains strong connections with industry partners, translating theoretical advances into practical tools for real-world software development challenges. Their work on pointer analysis, memory safety, and compiler optimizations continues to influence both academic research and industrial practice.

Shaohua LiView profile

Assistant Professor

• Software Engineering
• Programming Languages
• Security
• +3 more

Shaohua Li is an Assistant Professor at The Chinese University of Hong Kong (CUHK), specializing in the correctness and security of critical software systems with emphasis on compilers. His research spans Software Engineering , Programming Languages , and Security , focusing on innovative compiler testing methodologies. Key areas include leveraging large language models for test generation, optimizing fuzzing techniques through prefix-guided execution, and decoupling sanitization mechanisms to reduce overhead in vulnerability detection. His work addresses fundamental challenges in ensuring reliability of systems programming infrastructure. Recent publications demonstrate a cohesive trajectory toward practical compiler validation: from empirical rustc bug analysis to SAND's low-overhead sanitization framework. The research consistently bridges theoretical formal methods with real-world implementation challenges in security-critical systems, showing particular strength in adapting AI techniques for traditional software testing problems.

Kimin LeeView profile

Assistant Professor

• Safe and capable decision-making agents
• Physical AI
• Reinforcement Learning from Human Feedback
• +9 more

Kimin Lee is an assistant professor at the Graduate School of AI at Korea Advanced Institute of Science and Technology (KAIST), where he focuses on developing safe and capable decision-making agents. His research spans multiple aspects of artificial intelligence with a strong emphasis on safety and reliability. Dr. Lee completed his educational journey at KAIST, earning a Ph.D. in Electrical Engineering with a focus on Machine/Deep Learning (2015-2020), advised by Professor Jinwoo Shin. He also holds a Master's degree in Electrical Engineering (Wireless Communication Networks, 2013-2015) and a Bachelor's degree in Electrical Engineering (2009-2013), both from KAIST. His primary research interests include: Physical AI - developing AI systems that can interact safely and effectively with the physical world Alignment - particularly reinforcement learning from human feedback (RLHF) and scalable oversight techniques Monitoring - safety evaluation frameworks and benchmarking for AI systems LLM Agents - enhancing the capabilities and safety of large language model-based agents Dr. Lee's recent publications reveal a strong trajectory toward addressing critical challenges in AI safety. His work consistently bridges theoretical advances with practical applications, particularly in the areas of reinforcement learning, computer vision, and natural language processing. A notable trend in his research is the development of methods to evaluate and enhance the safety of AI systems, especially large language models and diffusion models, while maintaining or improving their capabilities. As an active member of the academic community, Dr. Lee serves as an area chair for major conferences including NeurIPS, ICLR, and ICML, and regularly reviews for top-tier AI venues. He has also organized workshops focused on safe and trustworthy AI agents. Dr. Lee's research group at KAIST appears to focus on AI safety and decision-making, with research projects spanning from theoretical foundations to practical implementations of safe AI systems. His collaborative work with institutions like UC Berkeley and Google Research demonstrates the interdisciplinary nature of his research approach.

• Virtual Reality
• Augmented Reality
• Human-Computer Interaction
• +3 more

Matteo Bosco is a PreDoc Researcher at the Department of Software Technology and Interactive Systems, TU Wien, within the Virtual and Augmented Reality group. His work focuses on advancing human-computer interaction in immersive environments, particularly through conversational agents, neurotechnology integration, and circular economy frameworks. He contributes to the Circular Twin research project, addressing sustainable building design through digital twins and generative algorithms. Education & Roles: PreDoc Researcher (PhD candidate) at TU Wien Research Interests: His primary areas include: VR/AR interfaces enhanced by large language models BCI-driven human-robot interaction systems Procedural building design using conversational AI Sustainable architecture via circular twin frameworks He explores how immersive technologies can bridge human-centric design with computational efficiency, emphasizing accessibility and environmental sustainability. Key Projects: Circular Twin (2022–2025): Developing digital ecosystems for circular buildings using BIM and VR. Neurotag Visualization Media: Comparing AR/VR/monitor-based BCI interfaces. Labs & Teams: Active in the Mixed Reality Lab and collaborates with the Virtual Meeting Room initiative at TU Wien.

Mehdi DastaniView profile

Professor

• Artificial Intelligence
• Multi-Agent Systems
• Formal Methods in AI
• +5 more

Prof. Mehdi Dastani is a Professor and chair of the Intelligent Systems group within the Department of Information and Computing Sciences at Utrecht University's Faculty of Science. He leads the Master's program in Artificial Intelligence and focuses on formal and computational models in AI, particularly multi-agent systems. His research integrates insights from philosophy, psychology, and law to develop autonomous agents that reason about social and cognitive concepts like norms, emotions, and responsibility. Dastani has held academic roles at Utrecht University since 2001, including postdoctoral research and faculty positions. Education: M.Sc. Computer Science (University of Amsterdam, 1991), M.Sc. Philosophy (University of Amsterdam, 1992), Ph.D. in Humanities (University of Amsterdam, 1998). His work spans theoretical and applied projects, including grants for initiatives like Golden Agents (simulating Golden Age creative industries) and traffic control systems using virtual organizations. He is actively involved in academic committees, editorial boards, and organizing international conferences like AAMAS and PRIMA. Research Interests: Multi-Agent Programming, Normative Systems, Autonomous Agents, Cognitive Robotics, and Human-Centered AI. His projects address challenges like norm enforcement, decision-making in complex systems, and ethical AI integration with societal needs. Advising & Grants: Supervised numerous PhD students (e.g., Birna van Riemsdijk, Bas Testerink) and secured grants for projects such as 'Controllable AI: Human-Centered Approach'. His work includes collaborations on urban governance, autonomous driving, and AI tools for literacy support in children. Labs & Teams: Leads the Intelligent Systems group, contributing to agent-based simulations, ethical AI frameworks, and interdisciplinary collaborations with social scientists and urban planners.

Pieter SimoensView profile

Assistant Professor

• Distributed Artificial Intelligence
• Edge Computing
• Algorithms for Neuromorphic Computing
• +2 more

Pieter Simoens is an Assistant Professor at Ghent University and affiliated with the imec research institute. He works at the intersection of distributed artificial intelligence, edge computing, and collective intelligence, with a focus on AI applications for resource-constrained environments and robotic systems. His research explores innovative approaches to machine learning deployment in heterogeneous infrastructures, task planning for IoT-integrated robotics, and modeling collective decision-making processes. He has contributed to frameworks like DIANNE for distributed deep learning and developed methods for cognitive modeling in reinforcement learning scenarios. With over 100 publications, his recent work spans adaptive neural networks, privacy-preserving surveillance, UAV hyperspectral data analysis, and computational fairness in AI systems. He leads research initiatives within the Internet Technology and Data Science Lab (IDLab) and contributes to educational programs in software engineering and applied machine learning. Responsible for courses on software engineering, mobile development, system design, and applied machine learning Active in edge computing and neuromorphic algorithms research Develops AI solutions for robotics, surveillance, and industrial IoT applications

Patrick LamView profile

Associate Professor

• Static Program Analysis
• Verifiable Software Specifications
• Software Engineering
• +8 more

Patrick Lam is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Waterloo, with a cross-appointment to the Cheriton School of Computer Science. His research focuses on applications of programming languages and static analysis to software engineering challenges, emphasizing verifiable software specifications and program understanding. Dr. Lam has held significant grants from NSERC and is recognized for his impactful work, including the First Decade High Impact Paper award for his Soot framework. Education: Doctorate in Computer Science, Massachusetts Institute of Technology, 2007 Master's in Computer Science, McGill University, 2000 Bachelor's in Joint Honours Mathematics and Computer Science, McGill University, 1999 Research Interests: His primary areas include static program analysis, verifiable software specifications, and compiler design, with a focus on linking high-level software designs to low-level implementations. He explores techniques like lightweight specifications and domain-specific languages to enhance software reliability and efficiency. Recent work also addresses empirical studies of programming practices and security through modularization. Publications: Dr. Lam's recent publications span advancements in static analysis tools (e.g., WasmWalker for WebAssembly), formal verification of code generated by AI tools like GitHub Copilot, and empirical studies on C++ immutability usage. His work bridges theoretical programming language research with practical software engineering applications, emphasizing tools for developer productivity and code reliability. Awards and Recognition: First Decade High Impact Paper recognition for "Soot – A Java Optimization Framework" (2010) Teaching and Grants: He has taught courses such as CS 447, ECE 453, and ECE 459 on software testing and performance programming. Active in grant-funded research, he secured NSERC Engage Grant (2013) and an ongoing NSERC Discovery Grant (2013–2018). Lam has also advised graduate students and contributed to the Software Engineering Program at Waterloo as its Director (2016–2019). Labs and Teams: His research group explores topics in program analysis and software engineering, with collaborations on projects like abstract debugging tools (GobPie) and static analysis frameworks (Soot). He maintains an open-source repository on GitHub, contributing to educational materials and research tools.

• Artificial Intelligence
• Computer Vision
• Natural Language Processing
• +5 more

Gwenn Englebienne is an Assistant Professor at the Digital Society Institute and Human Media Interaction group of Utrecht University. Their research focuses on Artificial Intelligence, Computer Vision, and Human-AI Interaction, with applications in robotics, health, and social computing. They have contributed to over 80 research outputs since 2007, emphasizing embodied AI, social robotics, and explainable machine learning. Research interests span activity recognition, teleoperation systems, and ethical AI design. Notable work includes developing GNN-based group detection algorithms and evaluating chatbot reliability through automated question-answering frameworks. Their studies often bridge technical innovation with human-centered design, such as measuring embodiment via pupil dilation or addressing asymmetry in video-conferencing interactions. Key collaborations include work on social robotics, telepresence systems, and health monitoring using ambient sensors. Publications span conferences like IDA, CogMI, and LREC-COLING, reflecting interdisciplinary impact. A dataset on robot social positioning behavior is publicly accessible via 4TU.Centre for Research Data. Current work explores semi-supervised domain adaptation, spiking neural networks, and the psychological dimensions of AI trustworthiness. They lead initiatives in the Digital Society Institute to align technological advancements with societal needs.

Eugene BagdasarianView profile

Assistant Professor

• Artificial Intelligence Security
• Privacy-Preserving Technologies
• Federated Learning
• +4 more

Eugene Bagdasarian is an Assistant Professor in the Department of Computer Science at the University of Massachusetts Amherst. His research focuses on security and privacy in AI systems, particularly adversarial machine learning, federated learning vulnerabilities, and contextual integrity frameworks. He holds a PhD from Cornell Tech and a prior engineering degree from Bauman University, with industry experience at Cisco as a software engineer. Research interests include backdoor attacks in federated learning (developing frameworks like Backdoors101 and Mithridates), privacy-preserving technologies such as AirGapAgent and Ancile, and mitigating vulnerabilities in multi-modal systems. His work has addressed instruction injections, adversarial illusions, and bias in generative models, with coverage in media like The Economist and VentureBeat. Notable achievements include the Usenix Security Distinguished Paper Award, Apple Scholars in AI/ML Fellowship, and Digital Life Initiative Fellowship. His contributions span technical domains like differential privacy, secure aggregation, and policy-based federated learning frameworks. Advising and grants: No specific advising/grants listed. However, his research collaborations include developing systems like Ancile (use-based privacy enforcement) and Mithridates (backdoor auditing). Current work trends focus on advancing security in LLM agents, dynamic network firewalls, and ethical AI governance.

Xuan ZhangView profile

Associate Professor

• Artificial Intelligence Hardware
• Machine Vision Sensors
• Electronic Design Automation
• +3 more

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.

Pingbo TangView profile

Associate Professor

• Civil Engineering
• Remote Sensing
• Human Systems Engineering
• +5 more

Pingbo Tang is an Associate Professor in the Department of Civil and Environmental Engineering at Carnegie Mellon University, affiliated with the College of Engineering. He leads the Spatiotemporal Workflows and Resilient Management Laboratory (SWARM Lab). Tang holds a Ph.D. from Carnegie Mellon University (2009), a Master’s in Bridge Engineering from Tongji University (2005), and a Bachelor’s in Civil Engineering from Tongji University (2002). His research focuses on civil infrastructure operations, human systems engineering, and AI-driven solutions for predictive management. Key areas include remote sensing, data analytics, and Human-Cyber-Physical-Systems (H-CPS) in construction and infrastructure (e.g., airports, nuclear plants). He has published over 100 peer-reviewed articles and secured funding from NSF, DOE, NASA, and industry partners. Tang is a leader in professional organizations, including ASCE (Computing Division Chair), TRB (Bridge Management Committee), and ASTM International. He has received prestigious awards, including the NSF CAREER Award (2015), ASCE Halpin Award (2020), and multiple best-paper honors. His work integrates AI, digital twins, and safety innovation, with applications in aviation scheduling (e.g., Alaska Airlines collaborations) and nuclear power plant operations. Tang’s research emphasizes sustainable infrastructure, safety, and interdisciplinary solutions for complex systems.

• Labor Law
• Civil Law
• European Labor Law
• +3 more

Prof. Dr. Felix Hartmann is a full professor of Civil Law and Labor Law at the Free University of Berlin since October 2016. His academic journey includes studies at the Universities of Tübingen and Fribourg (Switzerland), a legal state examination (2001), an LL.M. from Harvard Law School (2007), and a habilitation in Heidelberg (2012). He has held professorships at EBS Law School Wiesbaden (2013–2014) and the University of Hamburg (2014–2016) before his current position. His research focuses on labor law, civil law, and European legal frameworks, particularly addressing issues like collective bargaining, discrimination, and digitalization impacts. His work often bridges contractual freedom and social policy, with notable contributions on tariff contract validity, employment discrimination, and labor rights in the digital age. Recent publications explore fairness in cognitive testing (2025), transnational labor law (2024), and wage discrimination (2023). He advises on EU legal challenges and chairs the Institute for Labor Law at FU Berlin. His teaching emphasizes labor law digitalization and individual/collective labor rights.