Gauri Joshi is an Associate Professor in the Electrical and Computer Engineering (ECE) department at Carnegie Mellon University, with affiliate appointments in the Machine Learning Department and Robotics Institute. Her work focuses on system-aware algorithms for distributed machine learning, combining optimization, probability, and coding theory to address communication and computational constraints in edge networks. MIT Ph.D. in EECS (2016) IIT Bombay B.Tech/M.Tech in Electrical Engineering (2010) Research themes include federated learning with communication efficiency, erasure coding for non-linear computations, and reinforcement learning for heterogeneous queueing systems. She leads the Optimization, Probability and Learning (OPAL) lab , affiliated with the Parallel Data Lab (PDL), CyLab, and FLAME Center. Recent publications highlight advances in: federated fine-tuning with low-rank adaptation, robust PCA for model aggregation, privacy-preserving prediction mechanisms, and adaptive reinforcement learning for job dispatching systems. Her group has received 15+ paper awards across SIGMETRICS, MobiHoc, and NeurIPS workshops. Scientific recognition includes: IEEE Goldsmith Lecturer (2025), MIT Technology Review '35 Innovators Under 35' (2022), ONR Young Investigator Award (2023), NSF CAREER (2021), and ACM SIGMETRICS Best Paper (2020). She has advised 20+ graduate students, many now at tech giants like Google, Apple, and Meta. Service contributions span program co-chair roles (MLSys 2025), associate editorships (IEEE/ACM Transactions on Networking), and workshop organization (ICML, NeurIPS). Her NSF AI-EDGE Institute leadership (2021-present) drives next-generation intelligent edge networks for robotics and aerospace applications.
Dr. Liang Cheng is the Department Chair and Professor in the Department of Electrical Engineering and Computer Science at the University of Toledo. He leads a department with ~700 students across CS, CSE, and EE programs. His research focuses on Cyber-Physical Systems (CPS), IoT, AI/ML, and intelligent infrastructure, supported by over $30M in funding from NSF, DOE, DOT, and industry. Notable projects include CPS Breakthrough initiatives and underground sensing systems. He co-edited a multidisciplinary book on Underground Sensing and contributed to smart grid cybersecurity. Dr. Cheng has held leadership roles at Lehigh University, shaping faculty governance and equity policies. His 100+ publications span networking, real-time systems, and sensor networks. He advises on funded projects totaling $30M+ and has pioneered pedagogical approaches in computer science education. Research Interests: Cyber-Physical Systems (CPS): Focuses on autonomous drones, energy systems, and real-time infrastructure Networking: Expertise in TSN, DTN, and wireless protocols Cybersecurity: SCADA systems, PLC attack detection, and blockchain energy modeling Underground Sensing: Geo-sensing via wireless signals and subsurface tomography Grants & Projects: Over 20 sponsored projects including NSF CPS Breakthrough (2018-2023), ABB smart grid projects, and DARPA-funded EDIFY systems. Key contributions include reconfigurable wireless architectures and network calculus tools for real-time systems. Teaching: Courses span senior design, compiler design, parallel computing, and wireless sensor networks. Developed pedagogical patterns for non-CS programming education. Awards: Recognized for leadership in academic governance and interdisciplinary research collaboration.
Dr. Mo Adda is a Principal Lecturer at the University of Portsmouth's School of Computing, part of the Faculty of Technology. He holds a PhD in Distributed Systems and Parallel Processing from the University of Surrey. His research focuses on network security, distributed systems, wireless networks, and cybercrime prevention. He leads projects in the Centre for Cybercrime and Economic Crime, exploring fault management in networks, blockchain applications, and IoT forensics. With 16 supervised theses, he advises on topics like energy-efficient cloud systems and machine learning for environmental modeling. His work bridges academia and industry, addressing challenges in software-defined networks, traffic control, and secure data sharing in social networks. Education: PhD in Distributed Systems (University of Surrey) Affiliations: Centre of Excellence in Defence, Risk & Resilience; Portsmouth Centre for Advanced Materials and Manufacturing Research Interests Dr. Adda's research spans: Network security and fault detection mechanisms Blockchain applications in IoT and forensics Energy-efficient cloud data center optimization Machine learning for climate modeling Self-organizing network protocols Grants & Collaborations His projects include collaborations with industry partners on secure data leakage detection in cloud systems and resilient wireless protocols for harsh environments. He has pioneered fault classification systems using clustering algorithms and fuzzy logic. Labs & Teams He contributes to the Centre for Cybercrime and Economic Crime, focusing on digital forensics and network intrusion analysis. His team develops frameworks for privacy management in social networks and proactive routing in software-defined networks.
Patrick McDaniel is a Professor in the School of Computer, Data & Information Sciences at the University of Wisconsin-Madison. He is a Fellow of IEEE, ACM, and AAAS, and leads the MadS&P (UW-Madison Security and Privacy) group. His research spans computer and network security, adversarial machine learning, and technical public policy. Academic Affiliation : Tsun-Ming Shih Professor of Computer Sciences Key Research Areas : Mobile/IoT security, election systems security, AI policy Research Trends : His recent publications and funded projects focus on adversarial machine learning, SDN security, and robustness of AI systems. Keywords include cybersecurity, machine learning, network security, and privacy. Grants and Leadership : He has secured over $5M in NSF funding for end-to-end trustworthiness of ML systems and leads collaborative projects with Army Research and UW-Madison. Awards : Multiple best/most influential paper awards (ICSE 2015, PLDI 2014, ACSAC 2024, EthiCS 2023) Advising : Mentored 13+ PhD students and postdocs now at top institutions like Google, Purdue, and University of Toronto
Michele Zorzi is a Professor of Telecommunications at the School of Engineering, University of Padova, Italy, where he has held a faculty position since 2003. He leads the SIGNET (Signal processing and Networking) Research Group, focusing on cutting-edge wireless networking challenges including mmWave communications and underwater networks. His extensive publication record exceeds 600 papers in top-tier journals and conferences, reflecting significant contributions to the field through both theoretical and experimental work. He received his Laurea Degree (1990) and Ph.D. (1994) in Electrical Engineering from the University of Padova. Prior academic appointments include Politecnico di Milano (1993-1996), University of California San Diego (1995-1998), and University of Ferrara (1998-2003), where he progressed from Associate Professor to full Professor. His educational trajectory demonstrates deep roots in Italian academia with international exposure. Professor Zorzi's research spans wireless communications and networking, with current emphases on mmWave networking for vehicular systems, underwater acoustic/optical communications, non-terrestrial networks, and AI-driven networking solutions. His group conducts experimental validations including at-sea trials for underwater systems and testbeds for vehicular networks. Key projects include PRATA for predictive QoS in autonomous driving and IoT-based environmental monitoring of the Venice Lagoon, demonstrating practical applications of theoretical work. Analysis of his 2022-2025 publications reveals strong trends in applying artificial intelligence to networking challenges across diverse environments. There is significant emphasis on vehicular networks (predictive QoS, teleoperated driving), underwater systems (acoustic/optical communications, AUV swarms), and satellite networks (Starlink integration, NTN security). Experimental validation in real-world scenarios like the Venice Lagoon monitoring project and underwater sea trials characterizes his applied research approach. His scientific accolades include: IEEE Fellow (2007) IEEE Communications Society Best Tutorial Paper Award (2008, 2019) Stephen O. Rice Best Paper Award (2018) Multiple best paper awards at IEEE conferences (2005-2020) As principal investigator for numerous European and US research projects plus 20+ industry-funded initiatives, Professor Zorzi has mentored over 35 PhD students and post-docs. Graduates now hold prominent positions at institutions including Stanford, UCSD, CTTC, and Huawei. His SIGNET group maintains active international collaborations and contributes to open-source networking tools via GitHub, demonstrating commitment to community engagement. The SIGNET Research Group, housed within the Department of Information Engineering, operates specialized experimental facilities for mmWave and underwater communications. Current initiatives include AI-based predictive QoS frameworks for vehicular networks, underwater optical communication systems using ultraviolet light, and large-scale IoT deployments for environmental monitoring. The group's GitHub presence indicates strong open-science practices, while recent sea trials confirm hands-on experimental capabilities beyond theoretical work.
Ming-Syan Chen is a distinguished academic holding dual roles as a Distinguished Research Fellow and Director of the Research Center for Information Technology Innovation (CITI) at Academia Sinica, Taiwan, and a Distinguished Professor jointly appointed across multiple departments at National Taiwan University (NTU), including Electrical Engineering (EE), Computer Science and Information Engineering (CSIE), and the Graduate Institute of Communication Engineering (GICE). His career spans academia and industry, with prior roles as a research staff member at IBM Watson Research Center and leadership positions in Taiwan's technology sector. Education: He earned a B.S. in Electrical Engineering from National Taiwan University, followed by M.S. and Ph.D. degrees in Computer, Information, and Control Engineering from the University of Michigan, Ann Arbor. Research Interests: Chen's work focuses on databases, data mining, machine learning, multimedia networking, and cloud computing. He has authored over 350 papers and holds numerous patents, contributing to foundational advancements in query processing, data management, and networked systems. Award Highlights: Recipient of ACM and IEEE Fellowships, National Chair Professorship (lifetime honor), Teco Award, Pan Wen Yuan Distinguished Research Award, and IBM's Outstanding Innovation Award. His contributions span research, teaching, and technology commercialization. Leadership & Service: Former Dean of NTU's College of Electrical Engineering and Computer Science, CEO of Taiwan's Networked Communication Program, and Editor-in-Chief of the International Journal of Electrical Engineering. He has chaired international conferences and served on editorial boards of journals like IEEE TKDE and VLDB. Labs & Teams: Leads the Network Database Laboratory and collaborates on national initiatives in information and communication technologies. His research groups focus on data science, distributed systems, and social network analysis.
Qi Alfred Chen is an Assistant Professor in the Department of Computer Science at the University of California, Irvine (UCI), within the Donald Bren School of Information and Computer Sciences. He also holds affiliations with the Department of Electrical Engineering and Computer Science (EECS), the Institute of Transportation Studies at UC Irvine (ITS-Irvine), the Center for Embedded and Cyber-physical Systems (CECS), the Institute for Software Research (ISR), and the UC Irvine Cybersecurity Policy & Research Institute (CPRI). His research focuses on network and systems security, with particular emphasis on autonomous vehicle and IoT security. Dr. Chen received his Ph.D. from the University of Michigan in 2018. His educational background has prepared him for his current research in security of critical computer systems. His work bridges theoretical security principles with practical implementations in real-world systems. Chen's research interests center on network and systems security , with a major focus on smart systems and IoT security , particularly in transportation and autonomous vehicle systems . His work addresses security challenges through systematic problem analysis and mitigation, discovering and mitigating security problems in next-generation transportation systems, smartphone OSes, network protocols, DNS, GUI systems, and access control systems. His research has high impact in both academic and industry contexts, with over 10 top-tier conference papers, a DHS US-CERT alert, multiple CVEs, and coverage in major news media like Fortune and BBC News. His publication record shows a clear evolution from foundational work on network protocols and smartphone security toward increasingly sophisticated security analyses of autonomous vehicle systems and AI-powered transportation technologies. The research trajectory demonstrates growing technical sophistication and real-world impact, with recent work focusing on physical-world adversarial attacks against autonomous driving perception systems, LiDAR spoofing, and security of multi-sensor fusion in autonomous vehicles. NSF CAREER Award (2022) on securing the AI stack in emerging autonomous and connected CPSs Chancellor's Award for Excellence in Undergraduate Research Mentorship, UC Irvine (2021) 5th place nation-wide at National CCDC competition (2021, as faculty advisor) 1st place (Gold Medal) at CCDC Western Regional competition (2021) ProQuest Distinguished Dissertation Award, University of Michigan (2019) Dr. Chen has mentored numerous successful students, including PhD candidates and undergraduates who have gone on to positions at major tech companies like Meta, Uber, Amazon, and Intel. His research group has secured significant funding, including an NSF CAREER award, and has made substantial contributions to the field through high-impact publications and vulnerability disclosures. He is also the co-founder of the ISOC VehicleSec Symposium and has organized the AutoDriving CTF contest at DEF CON. Chen leads the AV & IoAT Security Research Group at UCI, focusing on security challenges in autonomous vehicles and the broader Internet of Autonomous Things. His team has developed numerous attack demonstrations and security analyses that have received significant media attention and influenced industry practices. The group maintains an active YouTube channel showcasing their security research.
Grant Ho is an Assistant Professor in the Computer Science Department at the University of Chicago. His research focuses on computer security, particularly at the intersection of data and security. Prior roles include a CSE Postdoctoral Fellowship at UC San Diego, a Visiting Researcher position at Corelight Labs, and a PhD in Computer Science from UC Berkeley (advised by Vern Paxson and David Wagner). He holds a B.S. in Computer Science from Stanford University. **Research Interests**: Enterprise security, AI/ML applications in security, large-scale threat analysis, and data-driven cybersecurity practices. His work spans detecting attacks (e.g., phishing, ransomware), improving security measures, and evaluating the efficacy of security policies. **Awards**: 2023 IEEE Euro S&P Best Paper Award, 2019 and 2017 USENIX Security Distinguished Papers, 2017 Internet Defense Prize, NSF and Facebook Fellowships, and the 2015 IEEE S&P Distinguished Practical Paper Award. **Advising & Teaching**: Current advisees include Aniket Anand (PhD), Christiana Marchese (PhD), and Robert Liu (B.S.). Taught courses include Introduction to Computer Security (CMSC 23200) and seminars on AI/ML and NLP in cybersecurity. Collaborates with industry partners like Barracuda Networks and Dropbox. **Labs & Teams**: Leads a research group focused on enterprise security challenges, emphasizing practical impact and interdisciplinary approaches.
Roles and Affiliations: Professor David Levinson is a Professor of Transport in the School of Civil Engineering at the University of Sydney since 2017. He holds adjunct faculty status at the University of Minnesota's Department of Civil, Environmental, and Geo-Engineering (since 1999–2016). He previously directed the Accessibility Observatory and the Networks, Economics, and Urban Systems (NEXUS) research group. He is a member of The Net Zero Institute and leads the TransportLab research group. Education: PhD 1998 in Urban and Regional Planning from the University of California, Berkeley. Research Interests: Focuses on transportation networks, urban systems, accessibility analysis, and policy. Key themes include spatial equity, transport economics, and the co-evolution of land use and transport infrastructure. His work emphasizes data-driven approaches to urban mobility challenges, including modeling transport networks and evaluating infrastructure impacts on accessibility and equity. Grants and Projects: Recent grants include Design of micro-decisions in automated transport (2022, ARC) and Reducing pedestrian crashes through better intersection design (2021, Australian Road Safety Innovation). Collaborates internationally on projects like the Accessibility Observatory. Labs and Teams: Leads the TransportLab and collaborates with the NEXUS group. Active in interdisciplinary research, including urban analytics and transport policy.
Navid Azizan is the Alfred H. (1929) and Jean M. Hayes Career Development Assistant Professor at Massachusetts Institute of Technology (MIT), holding dual appointments in the Department of Mechanical Engineering (in Control, Instrumentation & Robotics) and the Schwarzman College of Computing's Institute for Data, Systems & Society (IDSS). He is also a Principal Investigator in the Laboratory for Information & Decision Systems (LIDS), and a faculty member of the MIT Statistics and Data Science Center, the Center for Computational Science and Engineering, and the Operations Research Center. Dr. Azizan received his PhD in Computing and Mathematical Sciences from the California Institute of Technology (Caltech) in 2020, his MSc in Electrical Engineering from the University of Southern California in 2015, and his BSc in Electrical Engineering with a minor in Physics from Sharif University of Technology in 2013. Prior to joining MIT, he completed a postdoc at Stanford University's Autonomous Systems Laboratory and was a research scientist intern at Google DeepMind. His research spans the intersection of machine learning, systems and control, mathematical optimization, and network science. Dr. Azizan's work focuses on developing principled learning and optimization algorithms for reliable intelligent systems, with applications to autonomy and sociotechnical systems. His research has significant implications for creating trustworthy AI systems that can operate effectively in complex, uncertain environments. Dr. Azizan's recent publications demonstrate a strong focus on uncertainty quantification, reliable AI systems, constrained optimization, and control-oriented learning. His work bridges theoretical foundations with practical applications, particularly in autonomous systems where safety and reliability are paramount. His research group has made notable contributions to areas including neural network verification, multi-agent reinforcement learning, and adaptive inference techniques for large language models, with several papers featured on MIT News and selected for oral presentations at top conferences. Alfred H. (1929) and Jean M. Hayes Career Development Professorship (2025-present) Frank E. Perkins Award for Excellence in Graduate Advising (2025) List of Outstanding Academic Leaders in Data from the CDO Magazine (2024, 2023) Amazon Science Hub Research Award (2023) Outstanding UROP Faculty Mentor (2023) Esther and Harold E. Edgerton (1927) Career Development Chair (2022-2025) Information Theory and Applications (ITA) Gold Graduation Award (2020) Dr. Azizan has been recognized for his excellence in graduate advising, receiving the Frank E. Perkins Award for Excellence in Graduate Advising in 2025. During the pandemic, he founded and co-organized the 'Control meets Learning' virtual seminar series, connecting researchers across disciplines. His work has attracted significant research funding from industry partners including Google, Amazon, and MathWorks, supporting both fundamental research and practical applications in reliable intelligent systems. The Azizan Lab at MIT brings together researchers from mechanical engineering, computer science, and applied mathematics to tackle challenges at the intersection of learning and control. The lab emphasizes both theoretical foundations and practical implementations, with a particular focus on developing algorithms that provide guarantees of performance and safety. Current research directions include uncertainty quantification in AI systems, constrained optimization for neural networks, and control-oriented learning for autonomous systems, with applications spanning robotics, transportation, and complex sociotechnical systems.
Prof. Eleni Chatzi is a Full Professor and Chair of Structural Mechanics at ETH Zurich's Department of Civil, Environmental and Geomatic Engineering. She holds a PhD from Columbia University (2010) and has held roles from Assistant to Full Professor at ETH since 2010. Her research focuses on intelligent structural monitoring and data-driven asset management, emphasizing nonlinear dynamics and sensor integration. Affiliations : Institute of Structural Engineering, European Academy of Wind Energy (EAWE President), Swiss Community for Computational Methods (SWICCOMAS Chair) Research interests include Structural Health Monitoring (SHM), system identification, and advanced simulation tools. She pioneered work on data-driven diagnostics and self-aware infrastructure, supported by grants like the ERC Starting Grant (2015). Awards include the 2020 Walter L. Huber Prize and 2024 SHM Person of the Year Award. Her work spans wind energy infrastructure, metamaterials for vibration control, and AI-driven structural analytics. Over 600 publications and 200k+ citations highlight her impact. She teaches computational science and structural dynamics in ETH's programs and collaborates globally on sustainable infrastructure projects.
Raissa M. D'Souza is a Professor and Associate Dean of Research in the College of Engineering at the University of California, Davis. She holds joint appointments in the Department of Computer Science, Department of Mechanical and Aerospace Engineering, Graduate Group in Applied Mathematics, and Graduate Group in Physics. Her research focuses on complex networks, percolation theory, cascades, and interdependent systems, with applications to social, biological, and technological networks. She leads the Networks and Control research group and is a Founding Lead Editor of Physical Review Research . Her work spans interdisciplinary collaborations, including with the Santa Fe Institute and the Complexity Sciences Hub Vienna. Key awards include Fellowships from AAAS (2024), APS (2016), and the Network Science Society (2019), as well as the Euler Award for her contributions to network science. Dr. D'Souza has advised over 10 PhD students and has secured grants from agencies including the NSF. Her lab explores topics like explosive percolation, synchronization in oscillator networks, and network controllability. Notable recent work includes studies on exotic synchronization patterns and strategies for mitigating cascading failures in interdependent systems.
Professor Ernest Foo is a distinguished academic at Griffith University's School of Information and Communication Technology, specializing in cybersecurity with a focus on industrial control systems and cryptographic protocols. With over 15 years of experience in computer networking, he has established himself as a leading expert in SCADA security and smart grid cybersecurity. His research has significant practical applications in critical infrastructure protection, and he has developed hands-on security training programs that have trained professionals from major Australian utilities and government agencies. Professor Foo's educational background includes: Bachelor of Engineering with Honours in Electronic and Computer Engineering from University of Queensland Doctor of Philosophy from Queensland University of Technology Professor Foo's research interests center around secure cryptographic protocols with specific applications in industrial control system security and cyber physical systems. His work spans SCADA security, smart grid protection, wireless sensor network security, and post-quantum cryptography. He has made significant contributions to understanding vulnerabilities in industrial protocols like Modbus and DNP3, and has pioneered the application of process mining and data mining techniques for attack detection in critical infrastructure systems. His research bridges theoretical security concepts with practical implementations in real-world industrial environments. Professor Foo's recent publications demonstrate a clear trajectory toward increasingly sophisticated security frameworks for critical infrastructure. His work shows a progression from foundational SCADA security research to advanced applications of artificial intelligence, machine learning, and formal methods in cybersecurity. A notable trend is the integration of zero trust principles with industrial control systems, alongside growing emphasis on post-quantum cryptographic solutions. His publications span high-impact journals and conferences in cybersecurity, with increasing focus on anomaly detection in cyber-physical systems and the application of graph-based machine learning techniques to network security challenges. Professor Foo's notable scientific achievements include: Best paper award at the 2nd International Cyber Resilience Conference for "Gap analysis of Intrusion Detection in Smart Grids" Professor Foo has secured significant research funding including an ARC Linkage grant with Powerlink Queensland focused on cyber security for electricity sub-stations. He currently leads multiple research projects including Westpac Micro-Credentials in Financial Crime Investigation, Digital Banking Micro-credentials with ANZ, and research on quantum-safe cryptography. As a dedicated educator, he serves as Program Director for multiple cybersecurity programs including the Master of Cyber Security, and has supervised numerous doctoral and masters students. His Cyber Security: Industrial Control System course, conducted annually from 2013-2018, featured innovative hands-on training with real-world participants from major Australian utilities. Professor Foo has been instrumental in establishing the SCADA security research laboratory with multiple vendor system miniatures running industrial PLCs. His work bridges theoretical security concepts with practical implementations, making significant contributions to the security of industrial control systems and critical infrastructure worldwide.
Albert Lau is an Associate Professor of Civil and Environmental Engineering at the Norwegian University of Science and Technology (NTNU), located in Trondheim, Norway. He specializes in railway engineering, structural dynamics, and transportation systems. Lau holds leadership roles as the Study Program Leader for the MSc in Road, Railway, and Transportation Engineering, overseeing curriculum development and program coordination. His research focuses on railway track design, dynamic modeling of train-track interactions, and infrastructure maintenance, with projects such as the MeTinT initiative (Measurement with Train in Regular Traffic). He has extensive experience supervising master’s and PhD students, and his work emphasizes innovation in rail infrastructure and sustainable transportation solutions. Education and Professional Background: Lau earned his PhD from NTNU in 2018, focusing on numerical simulations of railway turnouts. Prior roles include Postdoc (2018–2020) and Assistant Professor (2017–2018) at NTNU, and teaching at Oslo Metropolitan University (2020). His industry experience includes roles as a Design Engineer (2010–2012) and Project Engineer (2013–2014) in Malaysia, where he managed construction projects and structural design. Research Interests: Lau’s work spans railway track dynamics, infrastructure health monitoring, and machine learning applications in transportation. Key projects include developing digital twins for railway test sites and analyzing ground displacement impacts on track anomalies. His contributions to the Road, Railway and Transport Group at NTNU aim to advance rail safety and efficiency through interdisciplinary approaches. Teaching and Outreach: Lau coordinates courses such as TBA4225 (Railway Engineering) and BA6012 (Fundamental Railway Technology). His outreach includes expert commentary on railway incidents, such as an interview on NRK (2024) discussing potential causes of a train accident. Current initiatives focus on revitalizing regional rail services and optimizing train positioning systems.
Baris Fidan is a Professor in Mechanical & Mechatronics Engineering at the University of Waterloo, with cross appointments in System Design Engineering and Electrical & Computer Engineering. He is a senior member of IEEE and AIAA. His research focuses on cooperative/adaptive control, autonomous systems, multi-agent networks, and vehicular control applications. He leads the Cooperative & Adaptive Mechatronic Systems (CAMS) Lab, which develops control strategies for autonomous vehicles, robotic systems, and intelligent transportation. Education: PhD in Electrical Engineering, University of Southern California (2003) Masters in Electrical & Electronic Engineering, Bilkent University (1998) Bachelor's in Electrical & Electronic Engineering & Mathematics, Middle East Technical University (1996) Research Interests: His work spans adaptive control theory, sensor networks, multi-agent coordination, autonomous vehicle networks, and biomedical systems control. He emphasizes practical applications in intelligent transportation, robotic navigation, and distributed system optimization. Grants & Projects: He has led major grants including NSERC Discovery Programs on cooperative mechatronic systems and 3D autonomous vehicle coordination. Industrial projects include autonomous driving strategies, vehicle control optimization, and high-precision gear manufacturing technologies. Labs/Teams: Directs the CAMS Lab, which collaborates on projects involving distributed motion planning, sensor localization, and autonomous vehicle networks. Current projects address challenges in urban autonomous driving, cooperative robotic systems, and resilient sensor networks.
