James McLaughlin is a Professor of Physics at Northumbria University, specializing in solar physics and magnetohydrodynamics. He holds a PhD from the University of St Andrews and previously worked at NASA Goddard Space Flight Center and the University of St Andrews as a Research Fellow. His research focuses on magnetic reconnection, solar coronal dynamics, and MHD wave behavior. He leads the Solar and Space Physics Group and secured a £1.29M STFC grant (2023–2026). McLaughlin supervises PhD students exploring oscillatory reconnection dynamics and has authored over 50 peer-reviewed papers. He is a Fellow of the Royal Astronomical Society and a Member of the Institute of Physics. Education: MSci (Mathematics & Physics), Durham University, 2002 PhD (Applied Mathematics & Solar Physics), University of St Andrews, 2002–2006 Research Interests: Magnetic reconnection mechanisms, solar flare dynamics, coronal heating, MHD wave propagation, and plasma diagnostics in extreme astrophysical environments. His work bridges theoretical modeling, numerical simulations, and observational data from instruments like SDO/AIA and DKIST. Recent Projects: STFC Consolidated Grant: Solar and Space Physics Group (£1.29M, 2023–2026) Awards: Fellow of the Royal Astronomical Society (2002) Member of the Institute of Physics (1998) Advising & Grants: Supervises PhD students Ryan Smith and Jordan Talbot. His research explores oscillatory reconnection’s role in generating solar waves and energy release. He collaborates internationally on space physics missions and heliophysics studies.
Andrea Meilán-Vila is an Assistant Professor in the Department of Statistics at Universidad Carlos III de Madrid since 2021, holding a Juan de la Cierva Fellowship since 2023. She earned her PhD in Statistics from Universidade da Coruña (2021) and previously served as a Postdoctoral Fellow at Universidade de Santiago de Compostela's Department of Statistics, Mathematical Analysis and Optimisation. Her research focuses on nonparametric methods for analyzing complex data types, including directional, spatial, and functional data. Key areas include kernel smoothing techniques, goodness-of-fit testing for regression models, and spatial trend estimation. She serves as an Associate Editor for the Journal of Nonparametric Statistics . Recent work emphasizes applications in climate science (temperature curve modeling), fluid dynamics (wake flow control), and biomedical imaging (hippocampus shape analysis). Her methodologies address challenges like sparse data estimation and spatial correlation in regression frameworks. Key Projects: STENED (Stein-based goodness-of-fit tests for non-Euclidean data) Awards: Juan de la Cierva Fellowship (2023) Publications span journals like Journal of Fluid Mechanics , Statistical Papers , and TEST , with a focus on methodological advancements in statistical modeling and computational validation.
Benjamin Lev is a Professor in the Department of Decision Sciences and Management Information Systems (DS&MIS) at the LeBow College of Business, Drexel University. He previously served as Trustee Professor (2014–2021) and Department Head (2009–2014) at Drexel. His academic leadership extends to prior roles as Professor, Department Head, and Dean at the University of Michigan-Dearborn (1990–2009), Professor and Department Head at Worcester Polytechnic Institute (1987–1990), and Professor and Department Head at Temple University (1970–1987). He has held short appointments at institutions in China and the U.S., including the Wharton School and Tel Aviv University. Lev’s research spans Operations Research, Management Science, and Decision Sciences , with expertise in mathematical programming, operations planning, inventory control, supply chain management, and optimization under uncertainty. His recent work focuses on applications in disaster management, sustainable supply chains, AI in operations, water resource allocation, and emergency logistics. He has published over 150 journal articles and authored or edited 18 books, with a strong emphasis on real-world problem-solving using quantitative methods. The trends in his recent publications (2022–2025) reflect a focus on complex optimization under uncertainty , particularly in humanitarian logistics, environmental sustainability, and digital commerce. His work frequently employs advanced methodologies such as bi-level programming, stochastic optimization, fuzzy logic, and data envelopment analysis (DEA), often applied to critical societal challenges like disaster response, air pollution, and resource scarcity. Lev is an INFORMS Fellow (2003) and has received several honors, including the 2023 Top Cited Article award in Naval Research Logistics and the 2023 First Prize from the Jiangxi Province Social Science Outstanding Achievement Award. His editorial leadership is most notably demonstrated by his 23-year tenure as Editor-in-Chief (2002–2025) of OMEGA – The International Journal of Management Science , one of the premier journals in the field. He has advised numerous scholars and presented his work globally, particularly on his experience as EiC of OMEGA . He has been actively involved in academic collaborations, especially in China, serving on advisory boards and as an external reviewer for institutions like Sichuan University. He has also received significant grant funding from the U.S. National Institutes of Health, U.S. Public Health Service, and U.S. Air Force for research in medical information systems and operations research applications. Lev has been instrumental in organizing major international conferences and has served on the editorial boards of over 20 journals, including Interfaces, IIE Transactions, OR Journal, and Financial Innovation . His role as Vice President of TIMS and INFORMS further underscores his leadership in the global operations research community.
Luyang Zhao is an incoming tenure-track Assistant Professor in the Department of Electrical and Computer Engineering at Clemson University (starting August 2025). He earned his PhD in Computer Science and double undergraduate degrees in Computer Science and Mathematics from Dartmouth College and the University of Minnesota respectively. Academic Affiliation : Clemson University (Assistant Professor) Education : PhD in Computer Science (Dartmouth College), BS in Computer Science & Mathematics (University of Minnesota) His research focuses on Robotics , particularly soft robotics, modular systems, and bio-inspired designs. Key areas include: Large Language Models for robotic design automation Modular tensegrity systems for self-assembling structures Swarm coordination strategies Multi-environment adaptability (land/aquatic/aerial) Simulation tool integration for design optimization Recent publications highlight his work on SoftSnap modular platforms, LLM-driven swarm intelligence, and bioinspired dolphin robots. He received the Neukom Outstanding Graduate Research Prize for his contributions. Industry Experience : Research internships at Amazon Robotics and TuSimple Mentorship : Advised 6+ graduate/undergraduate researchers Open-Source Contributions : Developed SoftSnap platform for rapid prototyping Academic Service : Workshop co-organization (IROS 2023), peer reviewing (RA-L, ICRA, IROS, RoboSoft, BioRob)
Professor Alexandros Taflanidis holds a concurrent faculty position as Professor in the Department of Civil and Environmental Engineering and Earth Sciences and the Department of Aerospace and Mechanical Engineering at the University of Notre Dame's College of Engineering. He serves as the Director of Graduate Studies for CEEES. His research focuses on uncertainty quantification, disaster risk reduction, Bayesian model updating, and enhancing the sustainability and resilience of civil infrastructure systems, particularly in natural hazard contexts like hurricanes and earthquakes. His work integrates computational statistics and surrogate modeling to improve real-time emergency response and long-term risk mitigation strategies. Prof. Taflanidis earned a Ph.D. from the California Institute of Technology (2007), and M.S. and B.S. degrees in Civil and Environmental Engineering from Aristotle University of Thessaloniki (2003 and 2002). He leads projects such as the Coastal Hazards System (CHS) for Louisiana and Puerto Rico, advancing probabilistic coastal hazard analysis frameworks. His research also explores storm surge emulation, seismic response estimation, and innovative protective device designs for structures. He won the ASCE Huber Prize for his contributions to community resilience through scientific computing. His collaborative efforts include advancing machine learning for data imputation in coastal hazards and developing lifecycle assessment workflows for resilient buildings. Current research trends in his publications emphasize computational efficiency, multi-fidelity modeling, and adaptive strategies for real-time predictions. Prof. Taflanidis's work bridges academic and practical domains, addressing challenges such as climate change impacts on coastal regions and earthquake early warning systems. His lab focuses on integrating interdisciplinary approaches to create actionable solutions for infrastructure resilience.
Tania Cerquitelli is a Full Professor in the Department of Control and Computer Science (DAUIN) at Politecnico di Torino, where she leads research in data science, concept-drift management, and inclusive AI technologies. She is a member of SmartData@PoliTO, the GEDI Observatory for Gender Equality, and serves in leadership roles related to social affairs and community policies at the university level. She also acts as a scientific advisor for the partnership with Accenture. Her research interests span Data Science , Concept-Drift Management , Database Systems , Conversational Data Science , and Industry 4.0 . She applies AI and machine learning to industrial, societal, and ethical challenges, particularly in promoting inclusive communication and gender equality in research. The most recent publications highlight her work in explainable AI, concept drift detection, multimodal diagnostics, and AI for social good. Her research integrates machine learning, natural language processing, and computer vision to address real-world problems in manufacturing, healthcare, agriculture, and education. She is an Associate Editor for several prestigious journals including Expert Systems with Applications , Computer Networks , Future Generation Computer Systems , and Knowledge and Information Systems . She has served on the program committees of major conferences such as ECML PKDD, EDBT/ICDT, and ACM KDD, and has been a reviewer and selection committee member for ETH Zurich and EMPA. She actively supervises PhD students and teaches a wide range of courses including Data Science and Database Technologies, Business Intelligence for Big Data, and Gender and Diversity in Research. She is involved in multiple national and international research projects such as E-MIMIC, WEBFARE, and EnABLES, focusing on inclusive AI, smart data, and industrial applications. Her lab affiliations include the DBDM - Database and Data Mining Group (DAUIN) and the Interdepartmental Center SmartData@PoliTO - Big Data and Data Science Laboratory , where she contributes to advancing data science methodologies and their societal impact.
Etienne Mémin is a Research Director (Full Professor status) at Inria and leads the Odyssey research group, which is affiliated with multiple institutions including University of Rennes, IRMAR, Ifremer, LOPS, UBO, IMT Atlantique, and Lab-STICC. He serves as a Visiting Professor at the Department of Mathematics, Imperial College London (2020–2026) and is the Principal Investigator of the ERC STUOD grant. His research spans the intersection of geophysical sciences, fluid mechanics, computational sciences, and applied mathematics, focusing on stochastic modeling of fluid flows, data assimilation, and uncertainty quantification. He has developed frameworks for stochastic geophysical flows, coarse-scale simulations, and robust motion estimation techniques. Recent publications highlight his work on stochastic Navier-Stokes equations, ensemble forecasting, and data assimilation for ocean and atmospheric models. He has applied these methods to numerical weather prediction, turbulence analysis, and real-time flow reconstruction using sparse measurements. Scientific Awards: ERC STUOD grant PhD Students: Francesco Tucciarone (ERC STUOD, NEMO code) Benjamin Dufée (Ensemble Kalman filters, ATER position) Berenger Hug (Stochastic Navier-Stokes analysis, teaching) Antoine Moneyron (Stochastic ocean models) Collaborations: Imperial College London (D. Crisan, S. Laizet), Zhejiang University (S. Cai, C. Xu), MétéoFrance (P. Arbogast, O. Pannekoucke), Ifremer (B. Chapron), IRSTEA Lyon (L. Pénard), IRMAR (R. Lewandovsky), University of Buenos Aires (G. Artana), ISSI Beijing (T. Corpetti).
Diego Patiño is an Assistant Professor in the Department of Computer Science and Engineering at the University of Texas at Arlington (UTA), a position he began in September 2024. He earned his Ph.D. in Computer Engineering from the National University of Colombia in 2020, following M.S. and B.S. degrees from the same institution. Prior to joining UTA, he served as a Postdoctoral Fellow at Drexel University and a Postdoctoral Researcher at the GRASP Laboratory, University of Pennsylvania. B.S. in Computer Engineering, National University of Colombia, 2010 M.S. in Computer Engineering, National University of Colombia, 2012 Ph.D. in Computer Engineering, National University of Colombia, 2020 Dr. Patiño's research centers on geometric computer vision and machine learning, with applications in robotics and 3D vision. His primary interests include 3D reconstruction, graph neural networks, symmetry detection, physics-informed machine learning, and reinforcement learning. He develops algorithms that integrate geometric priors and physical constraints into deep learning models to improve robustness and generalization in real-world robotic systems. His recent publications demonstrate a strong trend in leveraging implicit neural representations for 3D shape reconstruction, applying graph neural networks to swarm robotics, and enhancing computer vision tasks with self-supervised and physics-informed learning. Work spans high-impact venues such as IEEE RA-L, ICRA, ICPR, and MICCAI, showing a consistent focus on geometric reasoning, robotic perception, and medical imaging applications. His scientific contributions have been recognized with awards from the UTA Division of Student Affairs for exceptional dedication and positive impact (2024 and 2025). He is actively involved in securing research funding, with multiple grants under review from NSF, Air Force SBIR, and industry partners like Sony. Exceptional dedication and positive impact recognition, UTA Division of Student Affairs (December 9, 2024) Exceptional dedication and positive impact recognition, UTA Division of Student Affairs (April 30, 2025) Dr. Patiño advises and serves on committees for multiple graduate students in computer science and engineering, including doctoral and master’s candidates. He is also leading or co-leading several research grants under review, covering topics such as aerial swarm navigation, neuromorphic sensing, and industrial computer vision. He teaches graduate courses in computer vision and is involved in service roles including PhD admissions and faculty appointments committees. He is affiliated with research initiatives at UTA, including the UTARI Research Institute, where he has presented on geometric modeling and physics-informed learning. His lab focuses on developing next-generation computer vision algorithms for robotics, industrial inspection, and safety-critical systems.
Associate Professor Erica Southgate is a leading researcher at the University of Newcastle , specializing in emerging educational technologies with a focus on virtual reality and AI ethics . Her work bridges digital learning and sociology of education , emphasizing equity in education and technology accessibility for all learners. PhD and BEd from University of Newcastle 2016 Equity Fellow for national research 2017 ASCILITE Innovation Award Southgate's research explores how immersive technologies can transform education through student content creation , ethical frameworks , and curriculum redesign . Recent studies focus on generative AI policy , VR learning environments , and digital footprint management . Her VR School Study demonstrates practical implementation strategies. Key publications include the first VR education book and AI ethics guidelines for Australian Government. She leads the Digital Identity, Curation and Education (DICE) research network and advocates for human rights in educational technology .
Dr. Clark N. Taylor is an Associate Professor of Computer Engineering and Director of the ANT Center at the Air Force Institute of Technology (AFIT), located at Wright-Patterson Air Force Base, Ohio. He is actively engaged in research and education within the Graduate School of Engineering and Management, focusing on advanced navigation and sensor fusion technologies for autonomous systems. Ph.D., Electrical and Computer Engineering (Computer Engineering), University of California, San Diego, 2004 M.S., Electrical and Computer Engineering, Brigham Young University, 1999 B.S., Electrical and Computer Engineering, Brigham Young University, 1995 Dr. Taylor's research spans computer engineering, navigation systems, and autonomous robotics, with a strong emphasis on sensor fusion, state estimation, and robust uncertainty modeling. His work integrates vision, inertial, magnetic, and pressure sensors for navigation in GPS-denied environments, particularly for unmanned aerial vehicles (UAVs). He is a leading expert in factor graph-based estimation, visual-inertial odometry, cooperative localization, and magnetic navigation. His publications demonstrate a consistent trend toward robust, uncertainty-aware estimation frameworks. Over the past decade, his research has evolved from early work on visual stabilization and pose estimation to advanced topics such as conservative covariance estimation, invariant filtering, and machine learning for spacecraft pose estimation. His recent articles focus on factor graphs, multi-agent fusion, and deep learning, indicating a trajectory toward intelligent, resilient navigation systems for defense and aerospace applications. Scientific awards include a Best Presentation in Session award at the ION GNSS+ conference in 2021. His research is supported by the U.S. Air Force and related defense agencies, with applications in surveillance, autonomous refueling, and on-orbit inspection. Dr. Taylor has advised numerous MS and PhD students, particularly in the areas of UAV navigation, sensor fusion, and cooperative localization. His lab, the ANT Center, focuses on advanced navigation and tracking, bringing together students and researchers to develop cutting-edge solutions for real-world operational challenges. The team conducts both simulation and experimental work, often integrating novel sensor modalities and estimation algorithms for improved system performance.
Luca D'Acci is an Associate Professor in Sustainable Urban Forms and Evaluations at the Polytechnic of Turin, affiliated with the Interuniversity Department of Territorial Sciences, Planning and Policies (DIST). He holds additional affiliations as a Senior Research Fellow at the University of Portsmouth and as a member of research networks at the University of Birmingham and Erasmus University Rotterdam. His academic journey includes international roles such as Head of Urban Environment at Erasmus University Rotterdam and visiting researcher positions at the University of Oxford, University of Cambridge, and ETH Zurich. Education: MSc in Architecture-Science of Cities, Polytechnic of Turin (2003, cum laude) PhD in Economic Assessments, Polytechnic of Turin (2007) BSc in Mathematics, University of Turin (2007) BSc in Construction Engineering, Polytechnic of Turin (2009, cum laude) Post-PhD in Urbanism, University of Campinas (2010) Anthropology, University of Oxford (2020, 20 credits) Luca D'Acci’s research focuses on urban morphology, urban allometry, isobenefit urbanism, and the socio-economic-environmental impacts of urban form. His work bridges humanistic and quantitative approaches, integrating engineering, architecture, economics, and anthropology. He investigates how urbanicity, urban form, and spatial configuration influence well-being, sustainability, and resilience. His recent publications (2023–2025) reveal a strong trend toward computational modeling and simulation of urban growth, particularly through the lens of isobenefit urbanism —a concept he has pioneered. These works combine cellular automata, agent-based modeling, and morphogenetic frameworks to simulate sustainable urban futures. He also explores fractal patterns in housing markets, the psychology of urban living, and the mental costs of urbanicity. His research spans disciplines including urban science, environmental psychology, urban economics, and complex systems. Scientific Awards and Honors: Fellow, Erasmus Happiness Economics Research Organisation (EHERO), Erasmus University Rotterdam (2022–) Senior Research Fellow, University of Portsmouth (2017–) Fellow, Cluster for Sustainable Cities, University of Portsmouth (2017–2020) Fellow, Urban Morphology Research Group, University of Birmingham (2016–2021) Member, Cambridge Networks Network, University of Cambridge (2016–) Honorary Fellow, University of Birmingham (2016–) Luca D'Acci actively advises PhD students as a member of the Doctoral Collegium for Urban and Regional Development at Politecnico di Torino (2020–2024). He has secured and contributed to significant research grants, including projects funded by the World Bank, Asian Development Bank, European Commission, EPSRC, Lincoln Institute of Land Policy, and University College London (Future Urban Growth Lab). His editorial roles include membership on the boards of PLOS ONE , PLOS Mental Health , and Humanities & Social Sciences Communications . Labs and Research Networks: Future Urban Growth Lab (UCL, 2019–) LEUr Urban Ecology Lab (UFSC, 2022–) URban Evolution Morphology (UReM, 2024–) Erasmus Universiteit Rotterdam (EHERO, 2022–2024) Spatial Intelligence Unit (SPIN Unit), Estonia (2013–) International Society of Biourbanism (2013–)
Marilena Cardu is an Associate Professor in the Department of Environmental, Land, and Infrastructure Engineering (DIATI) at the Polytechnic University of Turin. She is actively involved in teaching, research, and supervision within the fields of excavation engineering, rock blasting, tunneling, and sustainable mining. She serves as a course instructor for key programs including Civil and Environmental Engineering and Georesources and Geoenergy Engineering. Academic Rank: Associate Professor Institution: Polytechnic University of Turin Department: DIATI (Environmental, Land, and Infrastructure Engineering) Email: marilena.cardu@polito.it Her research interests center on excavation engineering and safety, with strong interdisciplinary engagement in geotechnics, environmental engineering, and mineral resource management. She specializes in rock blasting, demolition techniques, TBM (Tunnel Boring Machine) performance, and mining safety. Her work aligns with UN SDGs such as Industry and Innovation, Sustainable Cities, and Responsible Production. The recent publications reflect a strong trend in tunneling technology, rock fragmentation, blast safety, and sustainable resource development. Her work combines experimental, numerical, and field-based approaches, often applied to real-world industrial and infrastructure challenges. Scientific Projects and Responsibilities: Scientific Director for collaborative research in excavation engineering (2023–2026) Scientific Manager for gas network compliance testing (2022–2025) Lead on multiple commercial consultancy projects in rock characterization, blast design, and vibration monitoring She supervises PhD students such as Nestor David Mejia Almeida and Oveis Farzay, focusing on TBM modeling and geomechanics. She is a key member of the Geomechanics and Geotechnologies Laboratory at DIATI, contributing to both academic and applied research in underground construction and mining safety.
Professor Stefan Thor Smith is a distinguished academic at the University of Reading , serving as a Professor in the Department of Energy and Environmental Engineering . His work bridges energy systems with urban sustainability , focusing on the integration of social and technical aspects of energy demand , urban energy system modeling , and climate change resilience . Academic Qualifications Postgraduate Certificate in Academic Practice (University of Reading, 2016) PhD in Built Environment (University of Nottingham, 2009) MSc in Computer Science (University of Glasgow, 2002) BSc in Physics (University of Nottingham, 2001) His research interests span the dynamics of energy demand in socio-technical systems, urban heat fluxes, pollution exposure modeling, and climate adaptation strategies. He has developed novel models for energy demand-side management , building environmental control , and urban climate interactions . Recent publications highlight his expertise in areas such as EV charging infrastructure , urban tree radiative performance , phase change material storage , and anthropogenic heat emissions . His work often involves interdisciplinary collaborations with institutions like the Centre for Research into Energy Demand Solutions and the Institute of Physics . Smith supervises a diverse group of postgraduate students and contributes extensively to teaching modules including Numerical Modelling and Programming and Urban Sustainability . His professional affiliations include the Institute of Physics , International Association of Urban Climatology , and the Higher Education Association .
Jina Kang is an Assistant Professor in the Department of Curriculum & Instruction at the University of Illinois Urbana-Champaign , with an affiliate appointment at the Siebel Center for Design . Her research focuses on immersive technology-supported learning environments , collaborative problem-solving dynamics , and educational data mining for understanding multimodal engagement in science education. Recent publications examine embodied cognition in STEM through gesture-based learning simulations, joint attention dynamics in astronomy VR environments, and systematic reviews of immersive technology applications in collaborative education. Her work integrates XR platforms , Bayesian knowledge tracing , and multimodal behavioral analysis to enhance science learning outcomes. She teaches graduate courses including CI 539: Introduction to Educational Data Mining and CI 489: Educational Technology Capstone Course , where students develop technology-supported learning activities using studio-based approaches.
Guillermo Gallego is a Professor of Robotic Interactive Perception at the Faculty of Electrical Engineering and Computer Science , Technische Universität Berlin , holding the Einstein Center Digital Future (ECDF) Professorship since 2019. His research bridges robotics , computer vision , and applied mathematics , focusing on optimization methods for interdisciplinary imaging and control problems. Education : PhD in Electrical and Computer Engineering (Georgia Tech, 2011), MS in Mathematics (Georgia Tech, 2009), MS in Electrical Engineering (Georgia Tech, 2007), MS in Mathematical Engineering (Universidad Complutense de Madrid, 2005). Gallego's work explores event-based vision to enhance robot perception through low-latency sensing and real-time 3D reconstruction . He previously held postdoctoral positions at the Institute of Neuroinformatics (University of Zurich/ETH Zurich) and Technical University of Madrid (Marie Curie Experienced Researcher). His interdisciplinary projects span applications in ocean remote sensing , autonomous driving , and space exploration . Key scientific awards include the Fulbright Fellowship (2005-2010) and Marie Curie Experienced Researcher (2011-2014). His recent publications focus on event camera algorithms for optical flow , SLAM , and noise estimation , reflecting his leadership in event-based vision research. Collaborations include institutions like University of Zurich , Georgia Tech , and University of Pennsylvania . Research Grants : Funded through ECDF and Marie Curie programs. Labs : Affiliated with the Einstein Center Digital Future and Institute of Neuroinformatics (Zurich/ETH Zurich).
