R. Jayakrishnan , a Professor in the Department of Civil and Environmental Engineering at the Samueli School of Engineering , University of California, Irvine, is a leading researcher in transportation systems engineering. Ph.D., University of Texas, Austin, Civil Engineering, 1992 M.S., University of Texas, Austin, Civil Engineering, 1987 B.S., Indian Institute of Technology, Madras, India, 1985 His research focuses on dynamic traffic assignment , urban traffic simulation , and real-time information systems to improve congested traffic corridors. He is developing advanced dynamic simulation-assignment models for urban traffic networks. Recent publications highlight his contributions to: Crowdsourced delivery optimization using decomposition heuristics Eco-driving algorithms with V2I communication Multi-furniture placement applications via augmented reality Subscription mobility services cost-benefit analysis Agent-based lane-changing coordination systems These works demonstrate his interdisciplinary approach combining transportation engineering, optimization algorithms, and emerging technologies like AR and connected vehicles.
Filip Sondej is a Researcher at the Department of Cognitive Science within the Faculty of Philosophy at Jagiellonian University in Krakow, Poland. His work bridges cognitive science and artificial intelligence, focusing on critical safety aspects of modern language models and multi-agent systems. His primary research interests include AI safety, LLM unlearning techniques, Chain-of-Thought faithfulness, AI conflict resolution, and digital sentience. Sondej's work addresses fundamental challenges in ensuring that increasingly powerful language models behave safely and align with human values. Analysis of Sondej's publication record reveals a strong interdisciplinary focus combining cognitive neuroscience methodologies with AI safety research. His recent work demonstrates a clear trajectory from traditional cognitive neuroscience investigations toward cutting-edge AI safety mechanisms, particularly in developing methods for removing unsafe behaviors from language models while maintaining functionality. The publications show sophisticated integration of neural network analysis with human cognitive processes. Sondej collaborates extensively with researchers including Anna Grabowska and Magdalena Senderecka, appearing as co-author on multiple publications in high-impact journals such as NeuroImage, Cerebral Cortex, and Journal of Cognitive Neuroscience. His research program bridges theoretical cognitive science with practical AI safety applications.
Shie Mannor is a Professor at the Technion - Israel Institute of Technology in the Department of Electrical Engineering. He also holds a visiting professorship at Cornell-Tech in New York City and is affiliated with the Technion Machine Learning Center and the Grand Technion Energy Program . Key Research Interests: Machine Learning: Theory, algorithms, and applications to high-dimensional data and dynamics modeling. Reinforcement Learning and Markov Decision Processes: Adaptive control in large stochastic systems. Learning and control under uncertainty: Robust/stochastic optimization frameworks. Game Theory: Stochastic, dynamic, and network games applied to power markets and resource allocation. Multi-agent systems: Online learning and designing economic systems with optimal equilibria. Power Grid: Data-driven reliability, pricing, and decision-making in smart grids (e.g., EU-funded GARPUR project). Applications: Communication network optimization, mobile health, LDPC codes, and large-scale optimization problems. He actively seeks postdocs, graduate, and undergraduate students with strong mathematical or programming skills for projects in mobile phone programming and complex system optimization. Contact: shie.mannor@ee.technion.ac.il | Phone: ++972-4-829-3284
Dr. Ninghao Liu is an Assistant Professor of Computer Science in the School of Computing at the University of Georgia, part of the Franklin College of Arts & Sciences - Division of Physical & Mathematical Sciences. He holds a Ph.D. in Computer Science from Texas A&M University (2021) and an M.S. in Electrical and Computer Engineering from Georgia Institute of Technology (2015). His research focuses on Explainable AI (XAI), Graph Mining, Model Fairness, Recommender Systems, and Outlier Detection, with notable contributions to foundational AI techniques and their applications in education, healthcare, and environmental sciences. Dr. Liu has secured significant funding, including a three-year NSF grant (2022–2025) for 'Graph-Oriented Usable Interpretation' and a five-year $10 million grant from the U.S. Department of Education (2024–2029) for the GenAI Empowered National Initiative for STEM+C Education. He has also been honored with the Outstanding Paper Award at ICML 2022, Best Paper Award Shortlist at WWW 2019, and other distinctions. His work emphasizes interpretable machine learning, graph neural networks, and addressing algorithmic bias. He collaborates across disciplines, contributing to radiology AI, climate-smart forestry, and pandemic prediction through knowledge-enhanced deep learning. His lab is based at the Boyd Research and Education Center, where he advances research in trustworthy AI systems and data-centric solutions.
Andrea Appolloni is an Associate Professor at the Department of Management and Law, University of Rome Tor Vergata. His academic career focuses on Management with emphasis on Sustainable Supply Chain Management , Digital Transformation , and Circular Economy . His research explores the intersection of technological innovation and sustainability, particularly through topics like AI in Logistics , Green Procurement , and Policy Optimization . Publications span both theoretical frameworks and empirical studies in China, Italy, and Malaysia, with a strong focus on environmental impact and organizational performance. Recent work includes digital twin applications for human-AI collaboration, blockchain integration in sustainable supply chains, and analyzing barriers to circular economy adoption. His 15 most recent articles (2025-2022) demonstrate a trend toward combining Artificial Intelligence , Operations Management , and Environmental Governance .
Marco Picone is an Associate Professor at the Department of Sciences and Methods for Engineering (DISMI) of the University of Modena and Reggio Emilia. He leads the Distributed and Pervasive Intelligence (DIPI) Group and holds a PhD in Information Technology from the University of Parma. His postdoctoral research at the University of Parma (2012-2015) and a visiting scholar position at the University of Cambridge (2011) further enriched his expertise. He is nationally qualified as an Associate Professor by MIUR (2020). Education: PhD in Information Technology, University of Parma (Italy) M.Sc. (cum Laude) in Computer Engineering, University of Parma Visiting Researcher, NetOS Group, University of Cambridge (UK) His research focuses on Distributed Systems , IoT , Edge Computing , and Digital Twins , emphasizing their applications in smart industries and cities. He has supervised postdocs (e.g., Matteo Martinelli) and PhD students (e.g., Enrico Rossini) on topics like Digital Twin Continuum and Edge-Cloud Systems. Teaching spans courses on Intelligent IoT , Distributed IoT Software Architectures , and Edge Computing , with a focus on lab-based learning and industry-relevant projects. He actively contributes to open-source frameworks like the White Label Digital Twin (WLDT) and collaborates on initiatives such as the Web of Digital Twins (WoDT). Research collaborations include projects on smart city data fusion, livestock waste management, and Industry 5.0 human-centric systems. His work bridges theoretical advancements with practical implementations in cyber-physical environments.
Martin Bicher is a PostDoc Researcher at TU Wien, affiliated with the Department of Data Science under the Faculty of Informatics. He specializes in agent-based simulation, epidemiological modeling, and decision support systems for public health crises. His work focuses on optimizing resource allocation, vaccination strategies, and policy evaluation during pandemics. He teaches courses such as Modeling and Simulation (194.076), Modelling and Simulation in Health Technology Assessment (194.094), and Advanced Modeling and Simulation (194.056). His research is supported by projects like DynOptTestControl (2022–2026) and KLIPHA-COVID19 (2020–2021). Key research interests include agent-based modeling frameworks, integration of machine learning into simulation systems, and multi-criteria decision support for public health interventions. His publications analyze pandemic response strategies, vaccination prioritization, and the impact of environmental factors on disease spread. Recent work includes developing mathematical models for equitable disease testing, simulating vaccination strategies under supply uncertainties, and evaluating contact-tracing policies. He collaborates with interdisciplinary teams to address challenges in healthcare resource optimization and policy design. Advising two students, Bicher has mentored theses on railway simulation and delay modeling. His contributions to pandemic decision support have been featured in high-impact journals like Omega and PLoS ONE.
A.A.J. (Erjen) Lefeber is an Assistant Professor in the Department of Mechanical Engineering at Eindhoven University of Technology (TU/e). His research focuses on control systems, cooperative driving, vehicle dynamics, and autonomous systems. He is affiliated with the EAISI Mobility cluster and the ICMS Core group, emphasizing interdisciplinary collaboration. Dr. Lefeber has contributed to over 150 research outputs, including peer-reviewed articles, conference contributions, and datasets. His work addresses challenges in platooning systems, model predictive control (MPC), cybersecurity in cooperative vehicles, and multi-agent systems. Research interests include cooperative adaptive cruise control (CACC), decentralized control strategies, and robust control against adversarial attacks. His projects integrate theoretical analysis with experimental validation, such as testing heterogeneous platoons with actuation delays. Dr. Lefeber has received the AVEC '24 Best Paper Award for contributions to vehicle platooning control. He teaches courses on nonlinear control, manufacturing networks, and mechanical engineering fundamentals. Collaborations span academia and industry, focusing on sustainable transportation and automation. Current efforts explore online learning for interaction dynamics in multi-agent systems and high-performance MPC for aerial robotics. His research aligns with UN Sustainable Development Goals, particularly addressing safe and efficient mobility solutions.
Vahid Shahrezaei is a Professor of Biomathematics at Imperial College London's Department of Mathematics (Faculty of Natural Sciences). He holds affiliations with the Biomathematics Group, Centre for Synthetic Biology, and Mathematics in Medicine. His research focuses on Computational Molecular Systems Biology, studying cellular robustness under stochasticity and environmental noise using computational and analytical methods. Notable contributions include methods for single-cell RNA-sequencing analysis and simulation-based inference of biochemical networks. Education: PhD in Physics from Simon Fraser University (Canada), BSc/MSc in Physics from Sharif University (Iran). Career highlights include a sabbatical at the Crick Institute (2023-2024) and roles such as Diversity Champion for the Faculty of Natural Sciences. Awards include the Imperial College President Medal for Research Supervision (2017). He has led interdisciplinary grants, including a Leverhulme-funded study on noise in gene expression with Samuel Marguerat. Research Interests: Stochastic modeling, gene expression dynamics, systems biology applications Key Projects: Development of bayNorm for single-cell data normalization, studies on mycobacterial cell size control Professional Roles: BBSRC expert panel member, co-organizer of systems biology conferences His lab integrates mathematical modeling with experimental data, addressing questions in developmental biology, cancer metabolism, and microbial systems. Recent work includes agent-based modeling of environmental policy adoption and novel visualization techniques for multi-omics data.
Roberto Rojas-Cessa is a Professor in the Department of Electrical and Computer Engineering at New Jersey Institute of Technology (NJIT), affiliated with the School of Applied Engineering and Technology. His research focuses on networking, blockchain applications in smart cities, energy systems, wireless communications, and high-performance switching. He has led multiple National Science Foundation (NSF)-funded projects, including initiatives on controlled delivery power grids and next-generation network quality of service. Notably, his work explores blockchain for energy metering, sustainable environmental measures, and smart grid optimization. He is also a Senior Member of the National Academy of Inventors (2024). His research interests span network protocols, distributed systems, and IoT applications. Recent projects include AMI-Chain (a blockchain-based power metering system) and studies on indirect free-space optical communications for vehicular networks. He has contributed to advancements in medium access control for crowded networks and energy packet switches for digital microgrids. Rojas-Cessa’s work integrates machine learning for network management and flood impact analysis. He has developed tools for time-lapse analysis of urban data and agent-based models to evaluate electric vehicle adoption. His publications emphasize scalability, security, and efficiency in both traditional and emerging technologies. Grants: Collaborative Research on Power Grids (NSF, 2016–2018), NeTS-NR: Quality of Service Networks (NSF, 2004–2008) Awards: Senior Member of the National Academy of Inventors (2024) His lab activities include experimental evaluations of digital microgrids and blockchain implementations for carbon footprint tracking. He actively collaborates on projects addressing emergency communications and resilient energy distribution systems.
Ying-Cheng Lai is a Regents' Professor in the School of Electrical, Computer and Energy Engineering at Arizona State University (ASU), where he has been a full-time faculty member since 2005. He holds affiliations with the Center for Biodiversity Outcomes and the Center for Biological Physics. Previously, he served as the Sixth Century Chair in Electrical Engineering at the University of Aberdeen (2009–2017) and returned to ASU as the ISS Endowed Professor (2014–present). His academic journey includes a BS and MS in Optical Engineering from Zhejiang University (1982–1985), followed by MS and PhD in Physics from the University of Maryland, College Park (1989–1992). He completed a postdoctoral fellowship in Biomedical Engineering at Johns Hopkins University School of Medicine (1992–1994). His research focuses on Nonlinear Dynamics and Chaos , Machine Learning applied to complex systems, Relativistic Quantum Chaos , Complex Networks , Mathematical Biology , and Theoretical Ecology . He explores topics such as quantum scars in Dirac materials, synchronization control in networks, and early warning signals for ecological tipping points. His work integrates data analysis techniques with interdisciplinary applications in healthcare, climate science, and cybersecurity. His recent publications highlight advancements in machine learning-driven predictions for critical transitions, quantum transport modeling in graphene, and cybersecurity strategies for power grids. These trends reflect his commitment to bridging theoretical physics with applied engineering solutions. Awards: Regents Professor (ASU's highest faculty honor, 2021) Vannevar Bush Faculty Fellowship (DoD, 2016) Corresponding Fellow of the Royal Society of Edinburgh (2018) Foreign Member of Academia Europaea (2020) Fellow of AAAS (2020) Fellow of the American Physical Society (1999) Ying-Cheng Lai has advised 24 PhD and 20 MS students, supported 15 postdocs, and secured funding from agencies like DOD (AFOSR, ARO, Navy-ONR), NSF, and the National Academies. His grants include projects on quantum billiard systems, sensor applications, and network resilience in multilayer ecological frameworks. He runs a research group focused on advanced topics in electrical engineering and interdisciplinary physics.
Martin D. F. Wong is the Edward C. Jordan Professor of Electrical and Computer Engineering and Executive Associate Dean of the College of Engineering at the University of Illinois. A pioneer in Electronic Design Automation (EDA) and VLSI circuit design, his work has significantly advanced chip design methodologies through algorithmic innovations. He holds over 450 publications and has been recognized with prestigious awards, including the ASP-DAC Most Frequent Author Award and the inaugural EDA Research Award from Synopsys. Wong’s research focuses on EDA, computational lithography, and 3D integrated circuits. He has mentored 48 PhD students, many of whom have excelled in academia and industry. His contributions include foundational frameworks like OpenILT (Inverse Lithography Technique) and Xplace (global placement). He is an IEEE Fellow and has served as a Distinguished Lecturer for the IEEE Circuits and Systems Society. Key Achievements: Recipient of six best-paper awards in chip design and routing optimization Developed GPU-accelerated tools for static timing analysis and global routing Advances in machine learning applications for EDA, including congestion prediction and hotspot detection Wong’s legacy combines technical innovation with mentorship, shaping the future of semiconductor design and manufacturing.
Professor Matt Garratt is a faculty member at the University of New South Wales (UNSW Canberra), School of Engineering and IT, serving as AI theme lead for the Defence Trailblazer Universities initiative with over $200 million in funding. His primary research focuses on sensing, guidance, and control for autonomous systems within robotics and unmanned aerial vehicles. Garratt's research spans robotics, swarm intelligence, and autonomous systems with emphasis on bio-inspired navigation techniques and adaptive flight control. His work addresses critical challenges including terrain following using vision systems, landing UAVs on moving platforms, and developing self-organizing swarms. He integrates artificial intelligence, computer vision, and machine learning to advance unmanned systems capabilities in complex environments. Analysis of his recent publications reveals strong trends in bio-inspired UAV navigation (particularly honeybee behavior modeling) and swarm robotics applications. His work increasingly incorporates deep learning for perception tasks while addressing real-world challenges like gas plume detection and adversarial robustness in 3D vision systems. The research demonstrates consistent progression toward practical implementation of autonomous systems in dynamic environments. Professor Garratt has secured over $7.7 million in external research funding as Chief Investigator on 33 grants. He actively mentors graduate students with scholarships available for Masters and PhD research in robotics and AI, focusing on: UAV path planning and adaptive control systems Swarm robotics collective motion optimization Bio-inspired autonomous navigation techniques Computer vision for robotic perception He co-founded the UNSW Canberra AIR (AI and Robotics) Group (AIR Lab), which drives research in trusted autonomy, swarm intelligence, and AI integration for defense applications. The lab develops practical solutions for autonomous systems operating in complex, real-world environments while maintaining ethical AI frameworks.
Dr. Laura Maye is a Lecturer at the School of Computer Science and Information Technology, University College Cork. Her research specializes in Interactive Media and Human-Computer Interaction (HCI), focusing on participatory design methods and immersive technologies. Key research interests include virtual reality applications, co-design methodologies for cultural heritage, community-centered technology development, and sensory interaction studies. Her work emphasizes inclusive design and socio-ecological relationships in technology. Recent publications (2020-2025) demonstrate strong trends in VR-based sensory studies, community radio innovations, and rehabilitation technology. Articles frequently employ mixed-methods approaches and highlight cross-modal perception, participatory frameworks, and rural HCI challenges.
Cuo Zhang is a Lecturer of Power Engineering and ARC DECRA Fellow at the University of Sydney's School of Electrical & Computer Engineering. He holds a B.E. (Hons.) from the University of Sydney (2014) and a Ph.D. in Electrical Engineering from UNSW (2018). His research focuses on smart grids, renewable energy integration, voltage control, and optimization of power systems. Key interests include distributed generation planning, energy storage systems, and demand response mechanisms. Dr. Zhang leads research on enhancing distribution network resilience through advanced control strategies and participates in the Net Zero Institute. He has secured grants such as the 2024 ARC DECRA project on renewables hosting capacity. Supervised students include Yunqing Zhang, working on robust renewables integration. His recent publications emphasize data-driven approaches, decentralized energy trading, and adaptive control for unbalanced networks. He explores machine learning applications in energy management and stochastic optimization under uncertainty. Awards include the University Medal for academic excellence. Awards: ARC DECRA Fellow Grants: 2024 Robust Renewables Hosting Capacity Enhancement Labs/Teams: Member of the Net Zero Institute
