Dr. Nicolas Francois is an Associate Professor in the Department of Materials Physics at Australian National University (ANU), specializing in experimental geomaterials physics, soft matter, and fluid hydrodynamics. He leads the X-ray Tomography and Applications Research Group, combining curiosity-driven and applied research in out-of-equilibrium systems. ARC Industry Fellow (2024-2030): Improving Australian iron ore comminution for green steel production ARC DECRA Fellow (2016-2018): Biofilms in two-dimensional turbulent flows His research spans fundamental questions in: Fragmentation of solid materials Autonomous devices powered by chaotic flows Hydrodynamic waves Stochastic thermodynamics Granular matter Polymer rheology and applied areas in: Comminution of geomaterials Mechanics of fractured rocks Wave-energy conversion Environmental fluid mechanics Publications reveal a trajectory focused on X-ray tomography applications, granular dynamics, and turbulence-driven systems. He utilizes advanced imaging techniques to study material failure mechanisms and fluid-structure interactions, contributing to fields ranging from green steel production to biofilm dynamics. Current student projects and grants emphasize sustainable resource processing and fundamental fluid physics.
Lili Qiu is a Professor in the Department of Computer Science at The University of Texas at Austin, where she has been a faculty member since January 2005. She is an active member of the Wireless Networking and Communications Group (WNCG) and has made significant contributions to the field of networking research. Dr. Qiu previously spent 2001-2004 as a researcher at Microsoft Research in Redmond, WA, before joining UT Austin. Dr. Qiu's research spans Internet and wireless networking with a current focus on wireless network management and content distribution in mobile networks. Her work extends into diverse applications including acoustic imaging, metasurface applications, healthcare sensing technologies, and AI systems. She has pioneered research in areas such as acoustic motion tracking, passive RFID sensing, and wireless network optimization. Her research demonstrates a consistent pattern of innovation that bridges theoretical networking concepts with practical real-world applications, particularly in mobile and wireless systems. Her extensive publication record shows a clear evolution from fundamental networking research to increasingly interdisciplinary work that combines wireless systems with healthcare applications, AI, and novel sensing technologies. Recent publications demonstrate growing integration of machine learning techniques with traditional networking problems, as well as expansion into healthcare applications like Parkinson's disease modeling and non-invasive glucose monitoring. ACM Fellow IEEE Fellow National Academy of Inventors (NAI) Fellow ACM Distinguished Scientist NSF CAREER award Google Faculty Research Award Best paper award at ACM MobiSys'18 Best paper award at IEEE ICNP'17 Dr. Qiu has supervised numerous students, including a PhD dissertation that won the SIGMOBILE best dissertation award in 2020. She has served in significant leadership roles including chair of ACM SIGMOBILE, General co-chair for ACM MobiCom 2025, and various conference chair positions for IEEE ICNP, ACM CoNEXT, and other major networking conferences. Her research has been supported by substantial grants from NSF, Google, and other organizations, enabling her to lead cross-disciplinary research teams. As a member of the Wireless Networking and Communications Group at UT Austin, Dr. Qiu leads research efforts that combine networking expertise with innovations in sensing technologies, metasurfaces, and AI systems. Her lab has produced numerous influential results in mobile networking, wireless sensing, and network management, with applications spanning healthcare, consumer electronics, and communication infrastructure.
Travis L. Nicholson is an Assistant Professor of Physics at Duke University with a secondary appointment in Electrical and Computer Engineering and membership in the Duke Quantum Center. His research pioneers quantum science experiments with ultracold neutral atoms, particularly Group III elements. Education: M.S. in Physics, University of Colorado, Boulder (2011) Ph.D. in Physics, University of Colorado, Boulder (2015) Dr. Nicholson's work focuses on cooling atoms near absolute zero, optical trapping, and quantum state manipulation. His team achieved the first laser cooling of Group III atoms (indium), enabling novel quantum many-body states, quantum computing architectures, and atomic clocks with unprecedented accuracy. He also develops quantum sensors and explores theoretical proposals for novel lasers. Analysis of his 15 most recent publications (2022-2010) reveals three dominant themes: advancing optical lattice clock precision to 10 -18 levels, pioneering quantum simulation with triel atoms, and developing superradiant lasers for quantum metrology. His Group III atom research has established new quantum control paradigms. Scientific Awards: 2022 NUS Physics Breakthrough Prize for realizing the first indium magneto-optical trap Dr. Nicholson actively mentors graduate students including PhD candidates Connor Bowerman and Jinchao (recently defended), and Master's graduate Desiree Lim (now at PASQAL). His research is supported by Duke Quantum Center collaborations and industry partnerships, notably as scientific advisor to PlanQC, a leading neutral atom quantum computing company. Nicholson Labs operates within the Duke Quantum Center's Chesterfield Building, focusing on quantum computing, simulation, and metrology. The group recently relocated from NUS and maintains an active culture including university events like Duke basketball games, with ongoing recruitment for new research members.
Luis Merino Cabañas is a Professor at the Universidad Pablo de Olavide , affiliated with the Deporte e Informática department and leading the SRL Service Robotics Laboratory . His research focuses on robotics, systems engineering, and automation, with a specialization in human-robot interaction and path planning. Education : PhD in Systems Engineering from the Universidad de Sevilla (2007), where his thesis explored cooperative perception techniques for multiple unmanned aerial vehicles in forest fire detection. Research Trends : Recent work (2023–2025) emphasizes 3D path planning, sensor fusion (LiDAR, radar, inertial systems), neural distance fields for safe navigation, and socially aware robotics. His studies integrate AI, genetic programming, and multi-modal perception for applications in construction, healthcare, and GNSS-denied environments. Labs & Teams : He leads the SRL Service Robotics Laboratory , contributing to projects like the Skyeye team and BIM2ROS integration for construction robotics.
Dr. Ramsey Faragher is a Senior Research Associate at the Computer Laboratory , University of Cambridge, and a Bye-Fellow at Queens' College. His work focuses on infrastructure-free indoor positioning systems, sensor fusion, and improvements to smartphone sensing capabilities. Academic Affiliation : University of Cambridge (Computer Laboratory) Professional Roles : Bye-Fellow at Queens' College, Senior Research Associate His research spans multiple disciplines within computer science and engineering, emphasizing innovative navigation solutions and signal processing techniques. Key areas include GNSS robustness, wireless security, and machine learning applications for positioning systems. Recent publications highlight advancements in supercorrelation for automotive GNSS, sensor data calibration, and motion-compensated signal processing. Articles frequently address challenges such as spoofing mitigation, urban navigation, and infrastructure-free localization. Scientific Recognition Fellow of the Royal Institute of Navigation Chartered Physicist (CPhys)
Ricardo Zednik is a Professor at the Department of Mechanical Engineering, École de Technologie Supérieure (ÉTS) in Montreal. Holding degrees from Rice University (BA, BS) and Stanford University (MS, PhD), he specializes in piezoelectric materials, fracture mechanics, and microelectronic systems. His research focuses on sensors, innovative materials, and health technologies. Fields of Interest: Piezoelectricity, Fracture Mechanics, MEMS, Smart Materials, Crystallography With over 36 peer-reviewed publications and extensive supervision of graduate research (including 15+ co-directed theses and projects since 2016), Zednik contributes to applied research in materials science and biomedical engineering. He collaborates with LaCIME and PULÉTS laboratories on cutting-edge projects involving ultrasonic transducers, flexible sensors, and high-temperature material characterization. Current courses include Materials Technology (MEC200) and advanced research topics in Functional and Smart Materials (SYS877). His students explore applications like terahertz quality control, piezoelectric earcanal sensors, and Kirigami techniques for wearable electronics.
Samuel Herrmann is a Professor of Applied Mathematics at the University of Burgundy, France. He is a member of the Statistics, Probability, Optimization and Control team and an external member of the TOSCA project team at INRIA. His research focuses on stochastic processes, particularly asymptotic analysis of non-linear stochastic processes, large deviations, and stochastic resonance phenomena, with applications in climatology, biology, and financial modeling. Education: PhD in Mathematics (2001) - University of Burgundy Habilitation (2009) - Asymptotic analysis related to stochastic processes Research Interests: Stochastic differential equations and their numerical simulation Large deviation phenomena in stochastic processes Self-stabilizing diffusions and stochastic resonance First-passage and exit time problems for diffusions Applications in climatology, biology, and finance Scientific Contributions: Professor Herrmann has published extensively on stochastic processes, with over 50 peer-reviewed articles and a monograph on stochastic resonance. His work includes exact simulation methods for diffusion processes, studies on self-stabilizing systems, and theoretical contributions to large deviations theory. He has collaborated with leading researchers such as Peter Imkeller and David Peithmann. Awards and Recognition: Contributed to the encyclopedia of mathematical physics Co-authored the book "Stochastic Resonance: A Mathematical Approach in the Small Noise Limit" (2014) Teaching and Supervision: He teaches courses on stochastic processes and their simulation at both undergraduate and master's levels, including the Master in Turin program. He has supervised numerous PhD and master's students in stochastic processes and related fields.
Professor Stuart C. Althorpe is a Professor of Theoretical Chemistry at the Department of Chemistry, University of Cambridge, where he leads the Althorpe Research Group. His work focuses on quantum dynamics and the application of quantum mechanics to chemical reactions, particularly examining how quantum effects influence atomic and molecular motion in systems ranging from isolated reactions to liquid water and ice. Professor Althorpe's research interests center on quantum dynamics , with specific focus on quantum tunneling , conical intersections , and path-integral methods . His group develops computational techniques that bridge quantum statistics and classical dynamics, creating methods like Matsubara dynamics to model quantum effects in complex systems. Key research areas include visualizing complete wave functions of chemical reactions, calculating tunneling splittings in water clusters, and investigating quantum interference effects at electronic degeneracies. The group employs both pen-and-paper theoretical derivations and high-performance computational algorithms for parallel CPUs and GPUs to tackle these challenging problems. The trends in Professor Althorpe's recent publications (2018-2024) reveal an evolution from foundational quantum dynamics methods toward increasingly sophisticated applications in complex, dissipative environments. His work consistently bridges computational chemistry and quantum physics, with growing emphasis on connections between quantum dynamics, chaos theory, and quantum information concepts. The publications demonstrate methodological innovation in path-integral approaches, particularly in handling quantum effects in condensed-phase systems like water and ice, while maintaining strong connections to experimental observations. Professor Althorpe has mentored over two dozen PhD students and postdoctoral researchers who have secured positions at leading institutions including ETH Zürich, UCL, EPFL, and various industry roles. His research group maintains active international collaborations, most notably with Professor D.J. Wales at Cambridge (on water clusters) and Professor Richard N. Zare at Stanford University, where theoretical calculations interpret detailed experimental measurements of reaction dynamics. The Althorpe Group operates within the Department of Chemistry at the University of Cambridge as part of the Theoretical Research Interest Group. The group's work contributes significantly to understanding quantum mechanical phenomena in chemical processes, with implications for fields ranging from atmospheric chemistry to biochemistry. Professor Althorpe organized the 2019 Faraday Discussion on "Quantum effects in complex systems," demonstrating his leadership in advancing theoretical frameworks for quantum phenomena in chemistry.
Alex Holcombe is a Professor in the School of Psychology at the University of Sydney . His research spans two major domains: perception and attention (investigating visual information bottlenecks and temporal processing) and meta-science (advancing open science and reproducibility through tools like tenzing.club and editorial roles in open-access journals). Education: PhD in Psychology (Harvard), BA in Psychology and Cognitive Science (University of Virginia) Editorial Roles: Associate editor for WikiJournal of Science , Collabra: Psychology , and Meta-psychology His experimental psychology work uses behavioral studies to reveal limits in visual processing speed, feature binding, and attentional resource allocation. Key findings include the role of naps in learning, parietal lobe function in rapid letter processing, and temporal binding dynamics in perception. Recent publications focus on peer review reform (2025 PNAS ), contributorship attribution (2024 Accountability in Research ), and visual cognition (2024 Journal of Vision ). These works reflect his dual commitment to understanding perceptual mechanisms and improving scientific practices. Advising: Mentors PhD students including De-wei DAI, Jye MARCHANT, and Rasmus PEDERSEN Labs: Leads research on visual perception and attentional bottlenecks Grants: 2024 Core Funding Grant for object tracking research
Peter W. Glynn is the Thomas Ford Professor in the Department of Management Science and Engineering (MS&E) at Stanford University's School of Engineering, and also holds a courtesy appointment in the Department of Electrical Engineering. Additionally, he serves as a Senior Fellow of the Hong Kong Institute for Advanced Study at City University of Hong Kong. His distinguished career spans over four decades, with significant contributions to the fields of simulation, computational probability, and stochastic modeling. Professor Glynn received his Ph.D. in Operations Research from Stanford University in 1982 and his B.S. with Honors in Mathematics from Carleton University in 1978. His academic journey began at the University of Wisconsin at Madison (1982-1987) before returning to Stanford, where he has held various leadership positions including Deputy Chair of MS&E (1999-2005), Director of Stanford's Institute for Computational and Mathematical Engineering (2006-2010), and Chair of MS&E (2011-2015). His research interests focus on simulation , computational probability , queueing theory , statistical inference for stochastic processes , and stochastic modeling . Professor Glynn's work has developed algorithms widely used across the field of Monte Carlo simulation, with applications in financial risk management, service systems engineering, logistics, and retail operations. His recent publications demonstrate continued innovation in areas such as numerical methods for stochastic systems, rare-event simulation, and analysis of queueing systems under various traffic conditions, showing a strong trajectory of advancing both theoretical foundations and practical applications. Professor Glynn's scholarly contributions have been recognized with numerous prestigious awards, including: Fellow of INFORMS (2007) Fellow of the Institute of Mathematical Statistics (1998) John von Neumann Theory Prize from INFORMS (2010) Member of the US National Academy of Engineering (2012) Lifetime Professional Achievement Award, INFORMS Simulation Society (2021) Philip McCord Morse Lecturer, INFORMS (2020) Throughout his career, Professor Glynn has mentored numerous doctoral students whose research has made significant contributions to operations research and related fields. His editorial service has been extensive, including founding Editor-in-Chief of Stochastic Systems and service on the editorial boards of leading journals in operations research, probability, and statistics. His professional service extends to numerous advisory boards and committees at national and international levels, reflecting his standing as a leader in his field.
Taein Kwon is a postdoctoral research fellow at the Visual Geometry Group (VGG) within the Department of Engineering Science at the University of Oxford, working under Prof. Andrew Zisserman. Previously, he completed his PhD at ETH Zurich under Prof. Marc Pollefeys and earned master's and bachelor's degrees from UCLA and Yonsei University, respectively. His educational background includes: Bachelor's in Electrical Engineering from Yonsei University, Seoul, Korea Master's degree from UCLA PhD from ETH Zurich (defended July 2024) His research spans Egocentric Vision, Action Recognition, Hand-object Interaction, Video Understanding, AR/VR, and Multi-modal Learning, with emphasis on first-person perspective analysis for AI assistants and human-computer interaction. His work integrates 3D reconstruction, pose estimation, and multimodal signals to model complex human activities and physical interactions. Analysis of his 2021-2025 publications reveals a consistent focus on egocentric vision datasets (H2O, HoloAssist, EgoPressure) and novel frameworks for hand-object interaction, action recognition, and gesture understanding. His research demonstrates strong interdisciplinary connections between computer vision, robotics, and human-centered AI, with increasing emphasis on pressure sensing, co-speech gestures, and cross-modal alignment. His scientific recognition includes: CVPR Egovis 2022/2023 Distinguished Paper Award for HoloAssist (July 2024) SNSF Postdoc.Mobility fellowship (May 2024) He actively mentors students on egocentric vision projects, supervising master's theses, semester projects, and collaboration initiatives leading to publications at top conferences. His research is supported by the SNSF fellowship and industry collaborations with Meta Reality Labs and Microsoft Research. As part of Oxford's Visual Geometry Group, he contributes to cutting-edge computer vision research while maintaining strong ties with ETH Zurich's computer vision community through ongoing collaborations and dataset development efforts.
David Mount is a Professor in the Department of Computer Science at the University of Maryland, with an additional appointment at the University of Maryland Institute for Advanced Computer Studies (UMIACS). His primary research focus is Computational Geometry, particularly in designing, analyzing, and implementing data structures and algorithms for geometric problems. Applications of his work span image processing , pattern recognition , information retrieval , and computer graphics . He is a Fellow of the ACM and has received the ACM Recognition of Service Award twice. A member of the Algorithms and Theory Group, Mount has authored over 200 publications, many of which are available on Google Scholar, DBLP, and ArXiV. Research Focus Computational Geometry Algorithm Design and Analysis Geometric Data Structures Nearest Neighbor and Range Searching Clustering Algorithms Recent Publications Mount's recent publications (2023-2025) emphasize non-Euclidean geometry (e.g., Hilbert metric), dynamic geometric structures , and approximation algorithms for polytopes, Voronoi diagrams, and Delaunay triangulations. Collaborative works with students and researchers address challenges in kinetic data compression , label tracking , and geometric software development (e.g., Ipelets for polygonal geometry). Professional Activities Editorial Board Member, TheoretiCS (2021-present) Senior Associate Editor, ACM Trans. on Spatial Algorithms and Systems (2013-2020) Program Committee Member, FOCS , ESA , SODA , and other major conferences Awards ACM Fellow ACM Recognition of Service Award (twice)
Max Wardetzky is a Professor at the Institute for Numerical and Applied Mathematics within the Faculty of Mathematics and Computer Science at the University of Göttingen, Germany. His office is located at Lotzestraße 16-18, 37083 Göttingen, and he can be reached via email at wardetzky@math.uni-goettingen.de or by phone at +49 551 39 26778. Professor Wardetzky leads the Discrete Differential Geometry Lab at the University of Göttingen, where he conducts research at the intersection of mathematics, computer science, and geometry processing. His work bridges theoretical foundations with practical applications in computer graphics and scientific computing. His primary research interests include: Applied Geometry Discrete Differential Geometry Numerical Analysis Geometry Processing Physical Simulation Computer Graphics Professor Wardetzky's extensive publication record demonstrates significant contributions to the field of discrete differential geometry and its applications. His work shows a consistent focus on developing mathematically rigorous yet computationally efficient methods for geometric problems. Key trends in his research include the development of discrete analogues of smooth geometric objects, the study of convergence properties between discrete and continuous models, and the application of these methods to problems in computer graphics and physical simulation. Professor Wardetzky has made substantial contributions to the theoretical foundations of discrete differential geometry while maintaining strong connections to practical applications. His work on discrete Laplacians, curvature approximations, and geometric flows has influenced both theoretical mathematics and practical geometry processing algorithms.
Christa Cuchiero is a Professor at the Department of Statistics and Operations Research , University of Vienna , and an elected member of the Austrian Young Academy (Junge Akademie) since 2020. Her research bridges rigorous mathematics and cutting-edge applications in finance, machine learning, and stochastic analysis. Education: Christa earned her M.Sc. in 2006 from TU Wien with a thesis on affine interest-rate models, her Ph.D. in 2011 from ETH Zürich on affine and polynomial processes, and completed her Habilitation at the University of Vienna in 2018 on high-dimensional finance beyond classical paradigms. Research Interests: Her work centers on affine and polynomial processes , stochastic portfolio theory , signature methods , and infinite-dimensional stochastic analysis . Recent projects explore signature-based neural SDEs for option calibration, measure-valued diffusions for energy markets, and universal approximation properties of signature transforms. Awards & Recognition: Among her accolades are the FWF START Award 2019 , the Bruti-Liberati Visiting Fellowship 2018 , the ETH Medal 2012 for an outstanding Ph.D. dissertation, and the Prix de l’Institut Europlace de Finance 2017 for the best paper in finance. Contact: christa.cuchiero@univie.ac.at , Kolingasse 14-16, 05.47, 1090 Wien, Austria.
Kevin Chetty is a Professor of Wireless Sensing at University College London (UCL), leading the Urban Wireless Sensing Lab within the Department of Security and Crime Science. His work bridges radar technology, machine learning, and healthcare applications, with a focus on passive sensing systems. Education: PhD in Medical Ultrasound Physics (Imperial College London, 2004-2007), MRes in Image and X-Ray Physics (King's College London, 2003), BSc in Physics (King's College London, 1999) Research spans radar micro-Doppler signature analysis for human behavior classification, software-defined radar development, and integrated communication-sensing systems, with applications in security, healthcare, and smart environments. Recent work emphasizes privacy-preserving technologies and edge processing for real-time operations. Scientific awards include the 2022 IET Radar Systems Best Paper Runner-Up, 2022 IEEE Radar Conference 2nd Place, and 2015 National Instruments Engineering Impact Award. He has received funding from government and industry sectors in telecommunications, IoT, security, and healthcare. Teaching roles: Programme Convener for MSc Crime Science and IEP Minor in Crime and Security Engineering; Module Convener for Security Technologies and Crime Mapping & Spatial Analysis Consultancy: Huawei Technologies (2020-2022), Metropolitan Police Service (2019)
