Sebastijan Dumancic is an Assistant Professor at Delft University of Technology, focusing on neuro-symbolic AI through program synthesis and probabilistic programming. He leads the RAIL lab and collaborates with institutions like Harvard, MIT, and CNRS. His research bridges symbolic AI and machine learning, applying program synthesis to scientific discovery, transportation, and robotics. He holds an FWO-funded PhD from KU Leuven and has participated in initiatives like ELLIS and the Symbolic Computation and Machine Learning Initiative. Program synthesis Probabilistic programming Neuro-symbolic AI Constraint-based learning His recent articles highlight advancements in program synthesis, neuro-symbolic integration, and constraint satisfaction. Projects like Find2Fix and Intelligent Greenhouse Horticulture (funded by NWO) demonstrate practical applications. ELLIS Membership University Teaching Qualification He supervises numerous MSc and PhD students in projects involving logic programming, program synthesis, and probabilistic modeling. Active in workshops and symposia, he contributes to neuro-symbolic AI and scientific discovery.
Daniel J McAllister is an Associate Professor at the National University of Singapore Business School , Department of Management and Organisation. His research explores interpersonal relationships in organizations , with particular emphasis on social emotions , trust dynamics , and their implications for organizational citizenship behavior and ethical leadership . He has published extensively in top-tier journals such as Academy of Management Review, Journal of Applied Psychology, and Academy of Management Journal. Academic Focus: Organizational Behavior, Trust/Distrust, Workplace Emotions Teaching Interests: Technical Knowledge, Practical Application, Ethical Decision-Making Key Courses: MNO2007 (Undergraduate), BMA5004A (MBA), MNO6012A (PhD) McAllister's research spans workplace underdog trajectories, awe in leadership, abusive supervision, and cross-cultural management in China. His work examines how emotions like contempt, envy, and schadenfreude influence organizational outcomes. He emphasizes creating a safe learning environment that integrates theoretical knowledge ( technical ), real-world application ( practical ), and ethical judgment ( wisdom ). McAllister has received consistently positive student feedback for his engaging teaching style , with recent evaluations highlighting improvements in time management and practical relevance. He distributes course materials post-class and avoids rote memorization, prioritizing conceptual understanding. His 2025 work on workplace underdogs and awe-driven leadership continues to shape contemporary organizational theory.
Terese Johannessen serves as an Associate Professor in the Department of Health and Nursing Sciences at the University of Agder (UiA), Norway. Her academic work focuses on quality improvement, leadership development, and implementation science within healthcare settings, particularly in nursing homes and home care services. Dr. Johannessen's research primarily centers on understanding quality and safety in long-term care environments. Her work investigates contextual factors influencing healthcare quality, leadership interventions for improvement, and the conceptualization of 'quality' among healthcare providers. She employs qualitative methodologies to explore managers' experiences, implementation challenges, and the development of practical tools for frontline staff. Her research has significant implications for improving care delivery in nursing homes and home care services through evidence-based leadership approaches. Analysis of Dr. Johannessen's publication record reveals a strong thematic focus on the SAFE-LEAD project, a comprehensive research initiative examining leadership interventions for quality and safety in nursing homes and home care. Her work spans multiple dimensions including intervention design, implementation challenges, contextual mapping, and evaluation of leadership approaches across different healthcare settings. The research demonstrates a methodological preference for qualitative and mixed-methods approaches to capture the complex realities of healthcare improvement work. Dr. Johannessen collaborates extensively with researchers including Siri Wiig, Eline Ree, and Ingunn Aase, primarily through the SHARE Centre for Resilience in Healthcare at the University of Stavanger. Her work bridges academic research and practical application in healthcare settings, with significant contributions to understanding how quality improvement efforts can be effectively implemented in real-world contexts. Her research portfolio demonstrates consistent productivity with multiple publications annually, showing progression from foundational studies on contextual factors to the development and testing of specific leadership interventions. The SAFE-LEAD project represents a cohesive research program addressing critical challenges in long-term care quality and safety through systematic investigation and practical tool development.
Adrian Weller is a Director of Research in Machine Learning at the University of Cambridge and Head of Safe and Ethical AI at The Alan Turing Institute, where he also serves as a Turing Fellow. He additionally directs the Trust and Society programme at the Leverhulme Centre for the Future of Intelligence. His career includes senior roles in finance and advisory positions for governmental AI ethics bodies. His research integrates technical and societal dimensions of artificial intelligence, with major foci including: Foundational ML : Statistical methods, high-dimensional inference, causality, and Monte Carlo techniques Trustworthy AI : Fairness, privacy, bias mitigation, and algorithmic transparency Applied Domains : Computer vision, reinforcement learning, and bio-applications of ML Socio-technical Systems : Policy frameworks, human perceptions of algorithms, and ethical deployment Notable recognition includes an MBE (2022) for pioneering contributions to digital innovation. His work actively informs UK and EU AI policy discussions.
Inseok Hwang is the Paul Stanley Professor of Aeronautics and Astronautics at Purdue University's School of Aeronautics and Astronautics. He earned his Ph.D. from Stanford University, specializing in multiple-vehicle control systems. His research focuses on hybrid systems, air traffic control, unmanned systems, and cybersecurity of cyber-physical systems. He leads the Flight Dynamics and Control/Hybrid Systems Laboratory and has received numerous awards, including the NSF CAREER Award and AIAA Associate Fellow designation. His work spans theoretical advancements in control theory and practical applications in aerospace systems. He has over 150 peer-reviewed publications and actively collaborates with industry and government agencies like NASA and the FAA. Education: B.S. (Seoul National University, 1992), M.S. (KAIST, 1994), Ph.D. (Stanford, 2004). Professional memberships include AIAA and IEEE. Research Interests: Hybrid systems analysis, air traffic surveillance and control, fault detection and isolation, spacecraft control, and cybersecurity for autonomous systems. His lab develops algorithms for safe and efficient operation of networked systems, including UAS traffic management and resilient control protocols against cyberattacks. Awards: NSF CAREER (2008), AIAA Associate Fellow (2012), University Faculty Scholar (2017), C.T. Sun Award (2019), multiple Seed for Success Awards (2020–2024), and Paul Stanley Professorship (2024). Grants and Collaborations: Active projects funded by NSF, NASA, FAA, and industry partners. Focus areas include resilient navigation, anomaly detection in air traffic systems, and cyberattack mitigation for autonomous vehicles.
Mahnoosh Alizadeh is an Associate Professor in the Department of Electrical and Computer Engineering at the University of California, Santa Barbara (UCSB), affiliated with the Institute for Energy Efficiency and the Center for Control, Dynamical Systems and Computation (CCDC). She directs the Smart Infrastructure Systems laboratory and focuses on scalable control frameworks, data analytics, and market mechanisms for sustainable cyber-physical systems in smart grids and electric transportation. PhD in Electrical and Computer Engineering from UC Davis (2014) Recipient of the National Science Foundation CAREER award (2019) Associate Editor for IEEE Transactions on Control of Network Systems and IEEE Open Journal of Control Systems Her research spans theoretical work in networks, optimization, and AI, with applications in smart grids , electric transportation , and resilient infrastructure . She has contributed to safe optimization algorithms, decentralized learning, and game-theoretic approaches in resource allocation. Recent publications highlight advancements in safe optimization (safe linear bandits, conservative linear bandits), decentralized learning (robust federated learning), game theory (General Lotto games, resource allocation), and smart charging (mobility-aware EV scheduling). These works emphasize real-time decision-making under constraints, security, and robustness in cyber-physical systems. NSF Early CAREER Award Northrop Grumman Excellence in Teaching Award Her research group includes PhD students Spencer Hutchinson, Arghavan Zibaei, Nanfei Jiang, and Sajjad Ghiasvand, with alumni placed at institutions like Apple, Toyota, and the University of Colorado.
Byron Boots is the Amazon Professor of Machine Learning in the Paul G. Allen School of Computer Science and Engineering at the University of Washington, where he directs the UW Robot Learning Laboratory. He also serves as a Principal Research Scientist in the Seattle Robotics Lab at NVIDIA Research and co-chairs the IEEE Robotics and Automation Society Technical Committee on Robot Learning. Dr. Boots received his Ph.D. from the Machine Learning Department in the School of Computer Science at Carnegie Mellon University, where he was a member of the Sense, Learn, Act (SELECT) Lab co-directed by Carlos Guestrin and his advisor Geoff Gordon. Prior to joining the University of Washington faculty, he was an Assistant Professor in the School of Interactive Computing within the College of Computing at Georgia Tech, and before that, he completed a post-doc in the Robotics and State Estimation Lab directed by Dieter Fox at the University of Washington. Professor Boots' research focuses on the intersection of machine learning, artificial intelligence, and robotics, with particular emphasis on developing theory and systems that tightly integrate perception, learning, and control. His work spans computer vision, state estimation, localization and mapping, high-speed navigation, motion planning, and robotic manipulation. His group develops algorithms drawing from deep learning and neural networks, nonparametric statistics, graphical models, nonconvex optimization, quantum physics, online learning, reinforcement learning, and optimal control. The research demonstrates a strong theoretical foundation while maintaining practical relevance to real-world robotic systems. His recent publications reveal a clear trend toward integrating advanced machine learning techniques with robotics, particularly in model predictive control, motion planning, and learning-based approaches to robot control. His work shows increasing focus on developing theoretically grounded methods that can handle the complex, nonlinear dynamics of real-world robotic systems while maintaining computational efficiency. The publications span top venues including ICRA, CoRL, IROS, and NeurIPS, demonstrating broad impact across multiple subfields of robotics and AI. Finalist for Best Systems Paper at Conference on Robot Learning (CoRL-2021) Multiple papers selected for oral presentations at top robotics conferences Work recognized for theoretical contributions and practical applications in robot learning As director of the UW Robot Learning Laboratory, Boots leads a vibrant research group focused on fundamental and applied research in robot learning. The lab maintains strong collaborations with NVIDIA Research and has produced numerous high-impact publications that bridge theory and practice. Professor Boots teaches courses in autonomous robotics, machine learning, and reinforcement learning, contributing to both undergraduate and graduate education at the University of Washington.
Wenhao Ding is a Research Scientist at NVIDIA's Autonomous Vehicle Group, focusing on enhancing the safety and robustness of physical autonomous systems, particularly autonomous vehicles. His research integrates multi-modal large language models, reinforcement learning, and causal discovery to improve model reasoning capabilities. He holds a Ph.D. from Tsinghua University's Department of Electronic Engineering, with a thesis on 'Generative AI for Critical Digital Twins.' Key research interests include safety-critical scenario generation, causal representation learning, and offline reinforcement learning. His work emphasizes closed-loop simulation for autonomous systems and has led to contributions like the SafeBench benchmarking platform and the RealGen scenario generation framework. He has received the 2022 Qualcomm Innovation Fellowship. Notable collaborations include projects with Prof. Marco Pavone at Stanford and internships at Amazon Lab126 (Astro team) and Bosch Center for AI. He actively reviews for top conferences (ICML, NeurIPS, CVPR) and journals (IEEE T-ITS, RA-L). His recent focus on privacy risks in robotics and causal-aware driving models underscores his commitment to trustworthy AI systems. He organizes conferences like the 2024 IEEE International Automated Vehicle Validation Conference and co-hosted the Secure and Safe Autonomous Driving (SSAD) Workshop at CVPR 2023. His interdisciplinary work bridges theory and practice, addressing critical challenges in autonomous systems' safety and generalization.
Guodong Shi is Associate Professor at the University of Sydney's Australian Centre for Robotics, heading the Centre for Robotics and Intelligent Systems. His research develops theoretical frameworks for multi-agent coordination, distributed optimization, and networked control systems. Current projects investigate collective decision-making under information constraints, privacy-preserving optimization, and game-theoretic formulations for social and robotic networks. His group develops algorithms for distributed solution of linear equations, Boolean networks, and equilibrium seeking. Doctoral supervision includes projects on acrobatic legged robots, reinforcement learning for robotic stability, and safe control under dynamic environments. Laboratory capabilities support theoretical and experimental validation. Research has applications in autonomous swarm robotics, smart grid optimization, and social network analysis. Teaching includes graduate courses on networked systems and optimization.
Leandro Parada Pradenas is an Honorary Research Associate in the Department of Civil and Environmental Engineering at Imperial College London's Faculty of Engineering. He is also a PhD Candidate in AI and Transportation, focusing on advancing safe and efficient autonomous vehicles through Deep Reinforcement Learning (DRL) integrated with Vehicle-to-Vehicle (V2V) communication. His research addresses scalability in Multi-Agent Systems, real-world deployment of robotics, and resilience against adversarial AI threats. Education: PhD Candidate in AI and Transportation (Imperial College London) Research Interests: His work spans cooperative planning among autonomous vehicles, simulation-to-real-world transitions in robotics, and enhancing system security against adversarial AI. He aims to bridge theoretical advancements with practical applications in mobility and security. Professional Background: Previously, he served as a Management Consultant for four years, developing Decision Support Systems for Logistics, Healthcare, and Energy sectors. This experience informs his interdisciplinary approach to solving complex systems challenges. Lab/Teams: While specific lab affiliations are not detailed, his research likely intersects with Imperial's robotics and AI groups focused on autonomous systems and cybersecurity.
Dongsheng Yang is an Assistant Professor with the Electrical Energy Systems Group at the Department of Electrical Engineering of Eindhoven University of Technology (TU/e). He has been working at TU/e since 2019, focusing on power electronics and renewable energy integration, and previously served as Assistant Professor at Aalborg University's Department of Energy Technology (2018-2019). Dr. Yang received his B.S., M.S., and Ph.D. degrees in electrical engineering from Nanjing University of Aeronautics and Astronautics, Nanjing, China, in 2008, 2011, and 2016, respectively. His academic journey progressed from postdoctoral researcher at Aalborg University (2016) to faculty positions at both institutions. Dr. Yang's research focuses on the modeling, analysis, control, and design of power electronics dominated power systems , with the goal of safely accommodating high-penetrations of renewable energy sources and energy-efficient end-uses. His work spans several critical areas in modern power systems: Power electronics dominated grid stability and control Renewable energy integration and grid synchronization EV fast-charging infrastructure development Hydrogen production systems Medium-frequency transformer design and modeling AI applications in power electronics Analysis of Dr. Yang's recent publications reveals a strategic research trajectory toward developing advanced control strategies for power converters, improving modeling techniques through AI approaches, and addressing practical implementation challenges for renewable energy systems. His work spans both theoretical developments and practical applications, with increasing emphasis on neural network frameworks for magnetic modeling, safety boundaries for EV charging architectures, and enhanced fault ride-through capabilities for grid-connected systems. This progression demonstrates his commitment to solving real-world engineering challenges in the transition to renewable energy. Dr. Yang has received professional recognition including: Senior Member of IEEE Corresponding Member of CIGRE Working Group C4.56 Topic chair, technical committee member, and reviewer for top-level conferences and journals in power electronics Dr. Yang actively supervises doctoral candidates and postdoctoral researchers, including Xiao Yang (working on AI for power electronics), Saizhao Yang (postdoc), and L.A. Vlaar. He serves as project manager for multiple significant research initiatives totaling over €5 million in funding: REDCON (2023-2028) - Reconfigurable power electronics testbench Flexible Offshore Wind Hydrogen Power Plant Module (2022-2026) Sectorplan-DCES-Y.D.inv.: Reconfigurable power electronics testbench (2021-2029) E2GO-RDC Cost-reduction of EV fast-charging station (2021-2026) CW620863 System impact analysis for large scale renewable hydrogen production (2022-2023) Dr. Yang leads research within the Electrical Energy Systems group at TU/e's High Tech Systems Center, focusing on power conversion technologies. His work connects with multiple research teams across Europe through collaborative projects focused on renewable energy integration, EV infrastructure, and hydrogen production systems. He also teaches the course 'Dynamic control of power conversion in renewable energy systems' and contributes to the UN Sustainable Development Goals related to affordable and clean energy.
Dr. Jennifer Adams is a Professor at the University of Calgary with dual appointments in the Werklund School of Education and the Faculty of Science’s Department of Chemistry. She holds a Tier II Canada Research Chair in Creativity, Equity, and STEMM. Her work focuses on equity in STEM education, postsecondary faculty education, and transdisciplinary approaches. Dr. Adams earned her PhD in Urban Education from The Graduate Center, CUNY, and has prior roles including Associate Professor at Brooklyn College and leadership in informal science institutions like the American Museum of Natural History. Education: PhD Urban Education (2006), MS Nutrition (1996), MA Education (1995), BA (not specified) Her research emphasizes anti-deficit and justice-oriented pedagogies, with a focus on marginalized communities. Key areas include racial equity in STEM, sociocultural theory, and critical transdisciplinary methods. Dr. Adams leads the Creativity, Equity, and STEM Lab, advancing equity through community-based and arts-infused approaches. She edits academic journals such as the International Journal of Informal Science and Environmental Learning and has collaborated on projects like the Resilient Schools Consortium (RiSC). Recent publications explore belonging in STEM, anti-racist education frameworks, and transdisciplinary methodologies. Her work bridges formal and informal learning environments to address systemic inequities. Dr. Adams actively engages in policy advocacy and community partnerships to foster inclusive STEM ecosystems globally.
Dr. Edoardo Bertone is a Senior Lecturer at Griffith University's School of Engineering and Built Environment - Architecture and Design. He holds a PhD in Water Resources Engineering from Griffith University and Bachelor/Master degrees in Civil Engineering from the Polytechnic University of Turin. His research focuses on data-driven modeling, Bayesian Networks, and System Dynamics applied to water resources management, climate change adaptation, and the water-energy nexus. He is affiliated with Griffith's Cities Research Institute and Australian Rivers Institute, collaborating on projects with water utilities, governments, and private entities. Dr. Bertone has received awards such as the 2024 PVC Science Excellence in Teaching and the JSPS Fellowship (2022). He supervises doctoral and master's students in areas like water quality management and climate change impacts. Education: PhD in Engineering (Griffith University, 2015); MEng and BEng in Civil Engineering (Polytechnic University of Turin, 2009-2011). Research Interests: Water quality modeling, drinking water optimization, data-driven prediction, climate change adaptation, and sustainable development goals. He leads over 25 funded research projects, including initiatives on reservoir water quality management in Thailand, real-time nutrient monitoring, and cyanobacteria bloom modeling. Dr. Bertone’s work integrates advanced sensors, machine learning, and Bayesian networks to address environmental challenges. Awards: Listing includes PVC Excellence Awards (2024, 2017), JSPS Fellowship, and recognition as a Rising Star in Queensland Science (2015). Grants & Supervision: Principal supervisor for 10+ doctoral candidates and collaborator on projects funded by Seqwater, CSIRO, and the Ian Potter Foundation. Key grants include $745k for biofertilizer combatting eutrophication and $269k for coagulation optimization models. Dr. Bertone’s contributions extend to urban sustainability, co-editing the book *SeaCities: Urban Tactics for Sea-Level Rise* and developing frameworks for integrating SDGs into architectural education.
Kwan-Wu Chin is a Professor in the School of Electrical, Computer and Telecommunications Engineering at the University of Wollongong, where he also serves as Head of Postgraduate Studies (HPS) and co-directs the Wireless Technologies Lab (WTL). His research focuses on resource allocation problems in Internet of Things (IoT) systems, maritime networks, edge computing platforms, and integrated sensing-communication systems. Chin leads an active research group currently supervising five PhD students working on UAV networks, edge computing, maritime systems, and metaverse resource allocation. He has graduated over 20 PhD students who now hold positions in academia and industry. Chin serves as editor for Elsevier Computer Communications and IEEE Internet of Things Journal. His work develops optimization techniques using graph theory, stochastic processes, and machine learning for next-generation wireless systems.
Nicola Paltrinieri is a Professor of Risk Assessment at the Department of Mechanical and Industrial Engineering, NTNU (Norway), and an Adjunct Professor at the University of Bologna (Italy). His expertise spans risk assessment, hydrogen technologies, process safety, and data-driven safety management. He holds Chartered Engineer and Chartered Scientist certifications and has served on editorial boards for journals like Safety Science and Journal of Risk Research . Education: PhD in Environmental, Safety and Chemical Engineering (University of Bologna, 2012) Master’s in Chemical and Process Engineering (University of Bologna, 2008) Research Interests: Focuses on hydrogen infrastructure safety, Natech accident analysis, risk-based inspection strategies, and AI integration in safety systems. His work emphasizes sustainable energy transitions and mitigating risks in emerging technologies like hydrogen. Key Projects (2022-2026): H2Glass : Decarbonizing glass and aluminum sectors via hydrogen HyInHeat : Hydrogen technologies for industrial heating HYDROGENi : Norwegian research center for hydrogen/ammonia Awards: Onsager Fellowship (2016–2021) Frank Lees Medal (2012) for safety-related publications Grants & Leadership: Head of NTNU Energy Team Hydrogen, coordinator for EU-funded projects like SUSHy , and active in international risk committees (e.g., EFCE, ESRA). His work bridges academia and industry, with over 8 PhD examinations supervised. Labs/Teams: Leads the NTNU Energy Team Hydrogen and collaborates on initiatives like SH2IFT-2 for safe hydrogen fuel handling. His research group focuses on AI-driven risk analysis and hydrogen infrastructure resilience.
