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Dongyan XuView profile

Professor

• Cybersecurity
• Cyber-Physical Security
• Cloud Computing
• +5 more

Roles and Affiliations: Dongyan Xu is the Samuel D. Conte Professor of Computer Science at Purdue University and Director of CERIAS (Cybersecurity Center). He leads the Purdue System Security Lab (PurSec) and focuses on cyber and cyber-physical security research. His work extends to cloud computing and embedded systems security. Education: BS in Computer Science from Zhongshan University (1994), PhD in Computer Science from University of Illinois at Urbana-Champaign (2001). Research Interests: Cybersecurity, cyber-physical systems security, embedded systems security, malware analysis, reverse engineering, and cloud infrastructure optimization. Key projects include virtualization-based malware defense, Bluetooth protocol vulnerability analysis, and secure UAV control systems. Grants and Funding: Principal Investigator of over $36 million in grants, with total research funding exceeding $132 million from sponsors like AFOSR, DARPA, NSA, NSF, and industry partners. Projects address automotive cybersecurity, IoT security, and critical infrastructure protection. Awards and Recognition: NSF CAREER Award (2006), multiple Best Paper Awards at RAID, SoCC, ASE, and USENIX Security. Named University Faculty Scholar (2012). Honored with College of Science awards for advising and leadership. Labs and Teams: Directs CERIAS and leads PurSec, collaborating with interdisciplinary teams on cyber-physical security, embedded systems, and cloud computing innovations. Active in policy-guided vulnerability discovery and automated patching for robotic vehicles.

• Uncertainty Propagation
• Inverse Problems
• Optimal Learning
• +3 more

Dr. Ilias Bilionis is a Professor of Mechanical Engineering at Purdue University's West Lafayette campus, leading the Predictive Science Lab. His research focuses on uncertainty quantification, inverse problems, optimal learning, and materials design, with applications in computational engineering, human-machine interaction, and high-performance buildings. He holds a strong record of awards, including the 2008-2009 Olin Fellowship and multiple academic excellence awards from his early career. His work integrates machine learning, Bayesian methods, and physics-informed models to address complex engineering challenges. Notable areas include 4D flow MRI analysis, extraterrestrial habitat systems, and energy-efficient building technologies. He has contributed to advancing methodologies in stochastic processes, optimal transport theory, and computational fluid dynamics. Awards: Extensive recognition for academic excellence and research innovation, including awards from Cornell University and the National Technical University of Athens. Labs/Teams: Heads the Predictive Science Lab, focusing on data-driven modeling and uncertainty quantification. Grants/Projects: Leads initiatives in smart energy systems, space habitat resilience, and advanced manufacturing.

Na LiView profile

Professor

• Control Theory
• Artificial Intelligence
• Applied Mathematics
• +7 more

Na Li is the Winokur Family Professor of Electrical Engineering and Applied Mathematics at Harvard University's John A. Paulson School of Engineering and Applied Sciences (SEAS). Her research focuses on control, learning, and optimization of networked systems with applications to energy systems, robotics, and cyber-physical infrastructure. She is also a visiting researcher at Mitsubishi Electric Research Laboratories and co-founder of Singularity Energy, Inc., specializing in decarbonization solutions. Li has been recognized with prestigious awards including the IFAC Manfred Thoma Medal and IEEE Ruberti Young Researcher Prize. Her work integrates mathematical, computational, and interdisciplinary tools to advance reliable and efficient system operation. Research Interests Key areas include control theory, artificial intelligence, climate technology, robotics, and energy systems. Her lab develops foundational methods for large-scale optimization and decision-making in smart grids, buildings, and biomedical systems. Awards Manfred Thoma Medal (2023) Antonio Ruberti Young Researcher Prize (2024) NSF CAREER Award (2020) ONR/AFOSR Young Investigator Awards Advising & Grants Guided students like Dr. Xin Chen (Texas A&M) and Dr. Yingying Li (UIUC) to tenure-track roles. Active in NSF-funded workshops on reinforcement learning and climate tech initiatives. Leads projects funded by Harvard Climate Funds and federal grants. Labs & Teams Directs the Control, Learning, and Optimization (CLO) Lab at SEAS, collaborating with industry partners like MERL. Engages in interdisciplinary teams addressing energy transition and sustainable infrastructure challenges through AI-driven solutions.

Stephen XiaView profile

Assistant Professor

• Embedded AI
• Mobile and Embedded Systems
• IoT and Wearables
• +7 more

Stephen Xia is an Assistant Professor in the Department of Electrical and Computer Engineering and by courtesy in Computer Science at Northwestern University. He directs the Intelligent Mobile and Embedded Computing (IMEC) Lab. His research focuses on embedded AI, mobile systems, IoT, and digital health, aiming to create smarter, safer environments through resource-efficient technologies. Xia holds a Ph.D. from Columbia University and a B.S. from Rice University. Education: Ph.D., Electrical Engineering, Columbia University B.S., Electrical Engineering, Rice University Research Interests: Embedded AI, mobile systems, IoT, wearables, edge computing, smart cities, cyber-physical systems, energy-efficient buildings, and digital health. His work spans applications like urban safety, healthcare monitoring, and energy optimization in commercial buildings. Labs/Teams: Director of the IMEC Lab, focusing on embedded intelligence, health-aware systems, and smarter environments. Grants/Projects: Includes projects on drone-based smart homes, low-cost fever screening, construction worker safety wearables, and energy footprint tracking in commercial buildings. Notable platforms include LegoSENSE and Anemoi.

• Electrical Engineering
• Energy Systems
• Optimization Methods
• +5 more

Dr. Gang Lei is a Senior Lecturer at the University of Technology Sydney (UTS), School of Electrical and Data Engineering. He holds a Ph.D. in Electrical Engineering (2009) from Huazhong University of Science and Technology, with postdoctoral research at UTS (2012–2016). His research focuses on AI-driven design optimization of electrical machines, electric vehicles, energy management systems, and control methodologies using digital twins and large language models. He has published over 300 papers (200+ journals, 140+ IEEE Transactions) with an h-index of 55. His awards include Best Paper Award (IEEE TEC 2024) and inclusion in Stanford’s Top 2% Scientists (2020–2024). He leads editorial roles for 6 Q1 journals including IEEE Transactions on Transportation Electrification and Industrial Electronics. His funded research includes advanced motor design for space robotics, battery management systems, and microgrid optimization. He collaborates internationally and serves as an assessor for national research projects in multiple countries. Teaching responsibilities include postgraduate courses on energy systems and optimization. Dr. Lei’s research group actively pursues multidisciplinary projects, emphasizing physics-informed AI and sustainable energy solutions. He currently supervises PhD candidates in electrical engineering and mathematics, focusing on emerging technologies like wireless power transfer and machine learning integration.

Michael ShortView profile

Professor

• Control Engineering
• Sustainable Systems
• Energy Engineering
• +5 more

Michael Short is a Professor and Acting Associate Dean (Research & Innovation) at Teesside University, leading the Centre for Sustainable Engineering within the School of Computing, Engineering and Digital Technologies (SCEDT). He holds visiting professorships at VIT Chennai, India, and serves as an External Examiner at Robert Gordon University (RGU), Scotland. With a BEng in Electronic and Electrical Engineering (1999) and a PhD in real-time robot control (2003), both from the University of Sunderland, he has over 20 years of academic and industrial experience. His roles include research leadership, teaching in embedded systems, and international collaborations. Michael’s research focuses on Control Engineering and Systems Informatics applied to sustainable systems, addressing challenges in energy efficiency, smart grids, and battery recycling. His work spans projects funded by FP7, H2020, and Innovate UK, totaling over £3.0M. He has authored/co-authored over 200 publications, with an H-index of 27 and 55 i10-index citations. Notable achievements include eight Best Paper Awards at international conferences and supervising 11 PhD completions. His consultancy expertise spans industrial technology applications, including marine systems, smart energy, and demand response. Awards and recognitions include being named one of the UK’s top 10 influencers in the Net Zero agenda (2022). He serves on editorial boards for journals like International Journal of Energies and actively reviews for IEEE, IET, and ACM conferences. Michael’s current projects include hydrogen infrastructure development, EV battery recycling, and smart port energy systems. His research themes emphasize sustainable engineering solutions, digital trade innovation, and decarbonization strategies for industry and cities.

• Process and System Design
• Water Resources Management
• Energy Systems Management
• +3 more

George Arampatzis is an Associate Professor at the School of Production Engineering and Management, Technical University of Crete. His expertise spans Process and System Design, Water/Energy/Environmental Management, ICT Systems, and Decision Support Systems. He holds a Ph.D. and Diploma in Chemical Engineering from NTUA. He has authored 5 academic books and contributed to numerous research projects, including the TREEADS and SWAN initiatives. His work emphasizes sustainable technologies, circular economy practices, and disaster management solutions using digital twins and GIS tools. Education: Diploma in Chemical Engineering (1991), Doctor Engineer (2000), both from NTUA. Research Focus: Hybrid renewable energy systems, wastewater treatment optimization, wildfire management, and decision support frameworks. Key Projects: Led the SWAN platform for industrial waste reuse and contributed to Mediterranean wildfire resilience via the TREEADS project. Publications: Over 100 articles in peer-reviewed journals and conference proceedings, focusing on energy autonomy, water efficiency, and digital twin applications. Labs/Teams: Involved in interdisciplinary teams developing cognitive digital twins and smart water management systems. His research bridges computational modeling, environmental policy, and industrial innovation, addressing global challenges like climate adaptation and resource efficiency.

Cindy BethelView profile

Professor

• Human-Robot Interaction
• Human-Computer Interaction
• Affective Computing
• +3 more

Dr. Cindy L. Bethel is the Billie J. Ball Endowed Professor in Engineering and Director of the Social, Therapeutic, and Robotic Systems (STaRS) Lab in the Department of Computer Science and Engineering at Mississippi State University. Currently serving as NSF Program Director (CISE/IIS Human-Centered Computing), she was previously a Fulbright Senior Scholar in Australia. Research applications focus on: Robotic therapy support (PTSD, trauma victims) Information gathering from children Law enforcement/military support SWAT team integration She has secured $9.7M in research funding from NSF, DoD, and industry partners. Recent publications address socially assistive robotics, HRI privacy concerns, and therapeutic robot design. Her STaRS Lab develops technologies like Therabot™ for mental health support and interfaces for tactical robots. Education includes Ph.D. from University of South Florida (2009), NSF Postdoctoral Fellowship at Yale, and specialized training in child-robot interaction. She has received numerous honors including IEEE Senior Membership and selection as one of the 'World's 50 Most Renowned Women in Robotics'.

• Cybersecurity
• Federated Learning
• IoT Security
• +2 more

Dr. Aaisha Makkar is a Lecturer in Computer Science at the University of Derby's College of Science and Engineering. Her research develops AI-driven security solutions for next-generation systems including IoT networks and medical data infrastructures. Makkar's work bridges cybersecurity and machine learning, with emphasis on federated learning architectures that preserve privacy while enabling collaborative threat detection. Recent innovations include adaptive models for spam classification and intrusion detection in resource-constrained environments. Her publications demonstrate consistent focus on practical security frameworks for industrial applications. Current research explores quantum-resistant cyber-physical systems and federated analytics for healthcare informatics. Makkar collaborates internationally on projects involving network traffic classification, malware detection, and secure edge computing paradigms.

• Green and Sustainable Computing
• High-Performance Computing
• AI-Driven Assistive Technologies
• +9 more

Dr. Ishfaq Ahmad is a Full Professor of Computer Science and Engineering at The University of Texas at Arlington (UTA) since 2002, with prior roles as Associate Professor and Director of the Multimedia Technology Research Center at the Hong Kong University of Science and Technology. He leads the Institute of Research in Security (IRIS) at UTA and has extensive industry collaboration, overseeing over $15M in research funding. His research focuses on sustainable computing, AI-driven assistive technologies, IoT, and high-performance computing. Dr. Ahmad has authored over 290 publications and holds prestigious awards including IEEE Fellow and IDES Distinguished Life Fellow. Education: PhD in Computer Science (Syracuse University, 1992), M.S. in Computer Engineering (Syracuse University, 1987), B.S. in Electrical Engineering (University of Engineering and Technology, Lahore, 1985). Research Interests: Green Computing, Exascale Systems, Assistive Technologies, Smart Grids, and STEM Education. His work emphasizes sustainability and interdisciplinary innovation, with contributions to video compression, parallel algorithms, and technology transfer. Awards & Grants: Over 40 international awards, including Google Scholar top rankings in multiple fields. Key grants include NSF-funded projects on assistive technologies and sustainable computing, along with industry partnerships like those with Adobe and Sun Microsystems. Labs & Leadership: Founded Journal of Sustainable Computing and the International Green and Sustainable Computing (IGSC) conference. Serves as Editor-in-Chief of Springer’s Discover Internet-of-Things and on editorial boards of IEEE journals.

Xiaowei ShiView profile

Assistant Professor

• Connected and Automated Vehicles
• Transportation Systems
• Vehicle-to-Everything Communications
• +2 more

Dr. Xiaowei (Tom) Shi is an Assistant Professor in Civil and Environmental Engineering at the University of Wisconsin-Milwaukee's College of Engineering & Applied Science and serves as the Founder and Director of the Automated, Connected & Electric Mobility Systems (ACES) Lab. He holds a PhD in Transportation Engineering from the University of South Florida with additional graduate degrees from Beijing Jiaotong University. His research specializes in evaluating and developing technologies for connected/automated vehicles (CAVs), utilizing hardware-in-the-loop methodologies to advance cooperative driving automation systems. His work integrates transportation engineering, control systems, optimization, and data science. Publications demonstrate consistent focus on CAV technologies with evolving emphasis on human factors and sustainability (2021-2024). Early work centered on fundamental traffic modeling and vehicle operations, while recent publications increasingly address user acceptance, energy efficiency, and neural network applications in transportation systems. He teaches courses covering transportation systems analysis, intelligent transportation technologies, and traffic operations modeling. He maintains active participation in IEEE technical committees and TRB's Emerging Technologies in Network Modeling panel.

Muhammad Zahid IqbalView profile

Lecturer

• Human-Computer Interaction
• Augmented Reality
• Mixed Reality
• +6 more

Dr. Muhammad Zahid Iqbal is a Lecturer in Immersive Technologies at Teesside University, with affiliations as associate faculty at the University of Glasgow and National College of Ireland. He holds a PhD in Computer Science from University College Dublin, specializing in Immersive Technologies and Intelligent Agents. His research focuses on Augmented Reality, Virtual Reality, Digital Twins, and the Metaverse, emphasizing their applications in education, healthcare, and industry. He leads projects funded by Innovate UK and collaborates on security, ethics, and immersive training initiatives. Zahid is actively involved in academic service, including roles as a reviewer for ACM SIGCHI and organizer of iLRN2023. He mentors PhD students in immersive tech, XR & AI, and digital transformation. His work bridges cutting-edge technologies with societal impact, addressing challenges in education, healthcare, and environmental sustainability. Education: PhD in Computer Science, University College Dublin (2018–2022) MS in Computer Science, Information Technology University (2014–2016) Advance HE Diploma/Fellowship (2024) Research Interests: Human-Computer Interaction, Digital Twins, Metaverse, AI in Education, and Ethical XR. Zahid explores how immersive technologies can transform sectors like healthcare, manufacturing, and rural education, with a focus on digital twin frameworks and ethical implications of emerging tech. Grants & Projects: Innovate UK AI Feasibility Study (£50K) Real-time Hand Interaction & Machine Learning Agents (Immersive Learning) Metaverse & Emerging Opportunities in Immersive Tech Generative AI & XR for Menopause Awareness (2024–2025) Publications: Over 30 peer-reviewed articles in journals like Multimodal Technologies and Interaction and conferences such as ACM IUI. Recent work addresses predictive maintenance, AR gamification, and security in extended reality. Lab/Teams: Part of Teesside University's Immersive Technologies group, collaborating on interdisciplinary projects with industry partners. Active in ACM Metaverse Chapter committees and iLRN organizing committees.

Qun WangView profile

Assistant Professor

• Cybersecurity
• Machine Learning
• Edge Computing
• +5 more

Dr. Qun Wang is an Assistant Professor and Graduate Advisor in the Department of Computer Science at San Francisco State University (SFSU), part of the College of Science & Engineering. Their research focuses on cybersecurity, machine learning applications in edge computing, IoT security, federated learning, and energy-efficient wireless networks. Dr. Wang’s work addresses challenges in mobile edge computing (MEC), UAV-assisted systems, and secure resource allocation in distributed networks. Key research areas include intelligent IoT attack detection, phishing URL identification using graph-based ML, and energy efficiency in MEC-IoT systems. Recent publications emphasize secure trajectory scheduling for UAVs, robust cooperative jamming in MEC networks, and optimization of NOMA-enabled architectures. Their contributions span theoretical frameworks and practical implementations in edge computing and wireless communication. Dr. Wang’s articles highlight trends in integrating machine learning with cybersecurity protocols, optimizing energy-delay tradeoffs in mobile networks, and advancing federated learning techniques. No specific grants or awards are listed, but their research demonstrates significant engagement with emerging technologies in distributed systems and IoT. Office hours are held weekly on Thursdays via Zoom, and they can be reached at qunwang@sfsu.edu .

• Computational Intelligence
• Eldercare Technology
• Geospatial Intelligence
• +2 more

James Keller is a Curators Professor and R. L. Tatum Professor in the Electrical Engineering and Computer Science Department at the University of Missouri. His research in computational intelligence develops innovative applications across diverse domains including eldercare technology, bioinformatics, geospatial intelligence, and landmine detection. Keller's recent work focuses on explainable AI systems, autonomous drone navigation, advanced materials characterization, and biomedical signal processing. His laboratory employs cutting-edge techniques in deep learning, fuzzy logic, and computer vision to solve complex real-world problems. The research portfolio demonstrates strong interdisciplinary collaboration across engineering, materials science, and healthcare domains.

Michael JenkinView profile

Professor

• Mobile Robotics
• Computer Vision
• Immersive Displays
• +3 more

Michael Jenkin is a Professor of Computer Science and Engineering at York University's Lassonde School of Engineering, affiliated with the Centre for Vision Research. His research focuses on visually guided robotics, immersive displays, and human-machine interaction. He has contributed over 150 publications and co-authored seminal works in mobile robotics and human/machine vision. Current projects include the amphibious AQUA robot collaboration with Dalhousie/McGill, crime scene investigation tools, and microgravity perception studies. His work bridges robotics, computer vision, and neuroscience with applications in healthcare robotics, virtual reality therapy, and environmental monitoring. Research emphasizes multimodal sensing (visual-tactile integration), autonomous navigation systems, and energy-efficient communication networks. He leads development of haptic interfaces for medical training, robotic teleoperation systems, and perception models for challenging environments like underwater/microgravity conditions. Notable contributions include the Ansel photobot, visuotactile sensor frameworks, and reinforcement learning approaches for network optimization. His lab explores both theoretical advancements and practical implementations across robotics, VR/AR, and biomedical engineering domains.