Thomas Grenier is an Associate Professor in the Department of Electrical Engineering at INSA Lyon and a member of the CREATIS laboratory (CNRS UMR 5220, INSERM U1294). He obtained his HDR (Habilitation à Diriger des Recherches) in 2023 and his Ph.D. in Image Processing from INSA Lyon in 2005. His research focuses on medical image segmentation, clustering, and filtering using feature space, scale-space, and deep learning approaches. Doctoral School: EEA (Electronics, Energy, and Automatics) Research Affiliation: CREATIS Lab (CNRS/INSERM/INSA Lyon/Université Lyon 1/Université Jean Monnet Saint-Etienne) He has contributed to 20 papers and co-supervised 5 PhD students, including Léo Dumortier and Florent Guépin. Grenier leads the annual Deep Learning for Medical Imaging (DLMI) school, which he co-founded, and has organized five editions across Lyon and Montreal since 2019. The school emphasizes practical deep learning applications in medical imaging for participants of all expertise levels. His work spans interdisciplinary domains such as medical imaging , deep learning , and image processing , with recent publications on generative AI for MRI synthesis, explainable networks, and segmentation of neurological pathologies in preclinical models. He manages pedagogical platforms, coordinates LabEx PRIMES project activities, and oversees lab room infrastructure for 200 hours/year across 10 training programs. Grenier also leads the MUSIC transversal project on Multiple Sclerosis since 2019.
David A. Muller serves as the Samuel B. Eckert Professor of Engineering in the School of Applied and Engineering Physics at Cornell University and co-directs the Kavli Institute at Cornell for Nanoscale Science. His research group focuses on developing quantitative electron microscopy methods to understand materials properties at the atomic scale, with particular emphasis on sustainable energy applications and quantum materials. Muller's laboratory utilizes some of the world's highest resolution electron microscopes housed in specially designed, environmentally isolated rooms. Muller received his undergraduate education at the University of Sydney and earned his Ph.D. in Physics from Cornell University in 1996. Between 1997 and 2003, he was a member of the technical staff at Bell Laboratories, where he applied his expertise in imaging single atoms and atomic-scale spectroscopy to determine the physical limits of transistor miniaturization. In 2003, he returned to Cornell as a faculty member, where he has since established himself as a leader in advanced electron microscopy techniques. Muller's research spans multiple frontiers in materials science, with particular focus on understanding how electronic-structure changes at the atomic scale control macroscopic behavior in diverse systems like turbine blades, fuel cells, and transistors. His current work emphasizes the physics of renewable energy materials, atomic-scale control of materials to create electronic phases that cannot exist in bulk, and developing hardware and algorithms for 'big data' acquisition from high-bandwidth pixelated electron microscope detectors. His group's work bridges theoretical physics and experimental techniques, requiring researchers who can think in both real and reciprocal space while considering both fundamental principles and practical applications. Analysis of Muller's recent publications reveals a strong trend toward advancing electron ptychography and 4D-STEM techniques for atomic-scale imaging. His group has pioneered methods for 3D atomic-scale metrology, strain mapping, and imaging of radiation-sensitive materials. The research spans applications from semiconductor technology to quantum materials and energy storage systems, demonstrating the versatility of his microscopy approaches across multiple scientific domains. Top 100 Young Innovator by Tech Review Magazine (2003) Burton Medal from Microscopy Society of America (2006) Ernst Ruska Prize of German Society for Electron Microscopy (2021) John Cowley Medal from International Federation of Societies for Microscopy (2023) Fellow of American Physical Society Fellow of American Association for the Advancement of Science Fellow of Microscopy Society of America Muller has mentored an extensive group of students and postdocs who have gone on to successful careers in academia and industry. His former students hold faculty positions at institutions including Rice University, University of Southern California, Seoul National University, Colorado School of Mines, and the University of Michigan, among others. His research has been supported by substantial grants, including a $22.5M NSF grant that accelerates materials discovery. The Muller lab maintains close collaborations with the Kavli Institute at Cornell and PARADIM (Platform for the Accelerated Realization, Analysis, and Discovery of Interface Materials). The Muller lab operates at the forefront of electron microscopy, housing specialized instrumentation including high-resolution transmission electron microscopes in environmentally isolated rooms. The group collaborates extensively with other research teams at Cornell and worldwide, focusing on understanding materials atom by atom. Current research directions include applying machine learning to electron microscopy data analysis, developing cryogenic techniques for studying low-melting-point materials, and exploring quantum phenomena in engineered materials systems.
Chun Liu is Chair and Professor of Applied Mathematics at the Department of Applied Mathematics, Illinois Institute of Technology (IIT), within the College of Computing. His research focuses on Nonlinear Partial Differential Equations , Complex Fluids , and Multiscale Modeling , with applications in electrophysiology and materials science. He earned a Ph.D. from New York University’s Courant Institute, an M.S. from Duke University, and a B.S. from Fudan University. Prof. Liu leads projects on General Diffusion Systems , Ion Channel Dynamics , and Viscoelastic Fluids . He has secured grants from NSF, BSF, and DAAD for research in energetic variational approaches, multiscale materials modeling, and biomolecular systems. Key contributions include the development of Poisson-Boltzmann models , coarse-grained dynamics , and energetically stable numerical methods . He serves on editorial boards for Communications in Mathematical Sciences , SIAM Journal on Mathematical Analysis , and others. His work bridges applied mathematics with engineering and biophysics, addressing challenges in fluid mechanics, ion transport, and nonlinear systems.
Dr. Mark Bissett is Reader in Nanomaterials at the University of Manchester's Department of Materials Engineering. He holds a PhD in Nanotechnology from Flinders University and was Research Assistant Professor at Kyushu University before joining Manchester. He directs the Advanced Nanomaterials Group focusing on 2D material applications. His research integrates graphene, carbon nanotubes, and transition metal dichalcogenides into electrochemical energy storage devices and polymer nanocomposites. Key areas include: Supercapacitor and battery electrode design Tribological coatings for industrial applications Multifunctional structural composites Publications span electrodeposition techniques, composite reinforcement strategies, and nanomaterial synthesis. Recent work shows strong emphasis on graphene-enhanced composites, MXene applications, and energy storage innovations. He teaches nanotechnology and composites courses, and leads the Nanotechnology unit coordination. Industrial collaborations include his role as CSO at MOLYMEM Limited. Laboratory facilities at the National Graphene Institute support his group's experimental work.
Jaime Peraire is the H.N. Slater Professor of Aeronautics and Astronautics at MIT, affiliated with the School of Engineering. He leads research in computational mechanics, aerodynamics, and numerical methods for partial differential equations, with key roles as former Department Head (2011-2018) and Director of the Aerospace Computational Design Lab (1993-2011). His expertise spans finite element methods, shock capturing algorithms, and high-order numerical techniques applied to hypersonic flows, space weather, and metamaterials. Education includes a Ph.D. from the University of Wales (1986) and engineering degrees from the University of Barcelona (1983, 1987). He holds prestigious awards like the T.J. Hughes Medal (2015) and the Ildefons Cerdá Medal (2015). His work bridges computational science and engineering, with contributions to discontinuous Galerkin methods, mesh adaptivity, and GPU-accelerated simulations. Research interests emphasize high-fidelity modeling of compressible flows, plasma dynamics, and terahertz spectroscopy. Notable projects include MIT’s space weather modeling initiative and metamaterial fabrication using atomic layer lithography. His labs collaborate across MIT’s Schwarzman College of Computing, IDSS, and CCSE to advance computational tools for aerospace and environmental systems. Awards: Over 10 major prizes, including NASA Exceptional Achievement (1997) and IACM Young Researchers Award (1998). Grants/Advising: Led NSF-funded space weather projects and advised numerous PhD students in computational engineering. Labs: Aerospace Computational Design Lab, MIT Schwarzman College of Computing collaborations.
Stephane Cotin is a Research Director at Inria and leader of the MIMESIS team, specializing in real-time physics-based medical simulations. His work focuses on surgical training, planning, and image-guided therapy, with over 200 scientific articles and the development of the open-source SOFA framework. He co-founded InSimo, Twinical, and EVE, and previously held roles at Harvard Medical School and Mitsubishi Electric Research Lab. Cotin’s research bridges imaging, robotics, and medicine to improve healthcare outcomes, emphasizing patient-specific biophysical modeling and real-time computation. His awards include the Academy of Sciences Award (2018) and Dirk Bartz Medical Prize (2015). He has advised numerous PhD students and led projects like MediTwin and PREMYOM, advancing digital twin technologies for precision medicine.
Professor Chongmin Song is a faculty member at the University of New South Wales (UNSW), affiliated with the School of Civil and Environmental Engineering. His academic rank is Professor, and he specializes in computational mechanics with a focus on innovative numerical methods. He holds a BE and ME from Tsinghua University and a DEng from the University of Tokyo. His research explores computational mechanics, fracture analysis, wave propagation, and soil-structure interactions. Key methodologies include the Scaled Boundary Finite Element Method (SBFEM), image-based modeling, and dynamic simulations of infrastructure systems. He leads significant ARC-funded projects like 'A scaled boundary framework for nonlinear dynamic analysis of structures' (DP250100955) and 'Developing sustainable graded porous cementitious structures' (LP240100123), totaling over $1M in recent grants. Recent publications emphasize adaptive modeling techniques, multiphysics simulations, and high-performance computing applications. Trends include topology optimization for structural dynamics, phase-field fracture modeling for brittle materials, and GPU-accelerated elastodynamics. His work integrates computational efficiency with real-world engineering challenges, particularly in geomechanics and material failure analysis. Professor Song collaborates extensively on projects involving computational fracture mechanics and maintains laboratories focused on numerical simulation advancements. Future work targets scalable algorithms for 3D crack propagation and multiphysics coupling in infrastructure systems.
David Simmons-Duffin is a Professor of Theoretical Physics at the California Institute of Technology (Caltech), where he has held positions since 2016. He is part of the Division of Physics, Mathematics and Astronomy, contributing to the Physics Department. His career progression includes roles as Visiting Associate (2016–17), Assistant Professor (2017–20), and Associate Professor (2020–21) before becoming full Professor in 2021. Education: A.B. and A.M. from Harvard University (2006), CASM from the University of Cambridge (2007), and Ph.D. from Harvard University (2012). His research focuses on conformal field theory (CFT), bootstrap methods, quantum field theory, and AdS/CFT correspondence. Key areas include precision computations in strongly coupled systems, critical phenomena, and applications to holography and quantum gravity. Research highlights include advancing the conformal bootstrap program, analyzing CFT data in 3D Ising models, and exploring connections between CFTs and gravitational theories. His work often bridges theoretical frameworks with numerical methods, yielding insights into operator product expansions (OPE), spectral gaps, and causality constraints. Affiliations include the Institute for Quantum Information and Matter (IQIM) and other Caltech research centers. His contributions have shaped modern approaches to understanding universality in critical systems and the geometric aspects of quantum field theories. Notable collaborations involve high-precision calculations, bootstrap island techniques, and studies of thermal QFT and light-ray operators. His work emphasizes interdisciplinary methods, combining analytic tools with computational advancements to tackle complex theoretical problems.
Carl Henrik Ek is a Professor of Statistical Learning at the Department of Computer Science and Technology (Computer Laboratory) at the University of Cambridge. He is also a fellow and Director of Studies at Pembroke College, and holds visiting positions at Karolinska Institute in Stockholm and the Royal Institute of Technology. He serves as co-Director for the UKRI AI Centre for Doctoral Training in Decision Making for Complex Systems, a collaboration between Cambridge and Manchester universities, and is involved with the Accelerate Program in the Computer Laboratory. Dr. Ek's educational background includes a MEng degree in Vehicle Engineering from the Royal Institute of Technology in Stockholm, followed by a PhD from Oxford Brookes University. During his PhD, he spent time at the University of Manchester and the University of Sheffield. His PhD supervisors were Professor Neil Lawrence and Professor Phil Torr, and his postdoctoral research was conducted at UC Berkeley with Professor Trevor Darrell and Professor Raquel Urtasun. Professor Ek's research focuses on statistical learning, particularly on developing data-efficient and interpretable machine learning methods. His work spans modeling and inference in machine learning, with special emphasis on Bayesian non-parametric methods and Gaussian processes. He explores how to specify assumptions that allow learning from small amounts of data, bridging theoretical foundations with practical applications in various domains. His recent publications demonstrate a strong trend toward applying machine learning to healthcare, drug discovery, and engineering design. There's significant work on Gaussian processes, reinforcement learning, and generative models, with applications ranging from medical diagnostics to structural engineering. His research shows an increasing interdisciplinary focus, connecting machine learning with fields like cardiology, pharmacology, and computational geometry. Professor Ek has received numerous teaching awards throughout his career: Pilkington Price for Teaching Excellence (2024) Teacher of the year in Computer Science at University of Bristol (2016) Docent in Machine Learning at Royal Institute of Technology (2016) Teacher of the year at Royal Institute of Technology, Sweden (2015) Teacher of the year from Student chapter in Industrial Economics at Royal Institute of Technology (2015) Teacher of the year in Computer Science at Royal Institute of Technology (2012) Professor Ek teaches Advanced Data Science, Advanced topics in machine learning, and Machine Learning and the Physical World. He has supervised PhD students throughout his career but is not currently accepting new PhD students for 2025/26 or 2026/27. His research is supported by various grants, including his role as co-Director of the UKRI AI Centre for Doctoral Training. He is an active member of the ml@cl research group at Cambridge and has previously been involved with research groups at University of Bristol and Royal Institute of Technology. His work connects with several interdisciplinary initiatives, particularly in healthcare AI and engineering applications of machine learning.
Dr. Hossein Alizadeh Otorabad is a Research Fellow at the Department of Engineering, School of Computing and Engineering, University of Huddersfield. He joined the Institute of Railway Research (IRR) in 2019 and was promoted to Research Fellow in 2022. His work focuses on finite element analysis, railway engineering, and thermal dynamics in wheel-rail interactions. BSc in Solid Mechanics, Tehran Polytechnic University MSc in Applied Mechanics, Khajeh Nasir Toosi University (2002) PhD in Railway Engineering (2018), focusing on wheel-flat fatigue crack initiation His research expertise spans Railway Engineering , Finite Element Analysis , and Thermal Modeling , with a particular focus on wheel-flat dynamics and fatigue analysis. He has contributed to studies on dynamic load effects in railway crossings, temperature evolution during wheel flat formation, and elasto-plastic behavior in railway wheels. Recent publications show a strong emphasis on Railway Systems (2018-2024), covering topics like: Dynamic load prediction in crossings Thermal analysis of wheel-rail sliding Contact mechanics in flatted wheels Fatigue life evaluation under transient loads His work aligns with UN Sustainable Development Goals for sustainable infrastructure and transportation systems. Scientific Recognition: h-index of 31 (Scopus metrics) 16+ citations for elasto-plastic wheel analysis Contributions to key railway engineering conferences At IRR, he conducts FE analysis, laboratory/field testing of railway assets, hammer testing, and signal processing. He previously received funding from Iran's Ministry of Science for sabbatical research at TU Delft's Material Science and Engineering department.
Dr. Martin Rohde is a Professor and Group Leader at the Radiation Science & Technology department within the Faculty of Applied Sciences at Delft University of Technology (TU Delft) in the Netherlands. He leads the Transport Phenomena & Nuclear Applications research group, focusing on advanced nuclear reactor technologies, particularly molten salt reactors, and their associated transport phenomena. Professor Rohde's research interests span across several critical areas in nuclear engineering and fluid dynamics. His work primarily focuses on understanding transport phenomena in nuclear applications, with particular emphasis on molten salt reactors for sustainable and safe nuclear power generation, innovative production techniques of medical isotopes, and advanced energy storage systems like flow batteries. His research group actively investigates complex physical phenomena occurring under extreme conditions such as high pressures, high temperatures, and interactions with radioactive processes. His publication record demonstrates a strong focus on computational methods for nuclear applications, particularly the Lattice Boltzmann Method (LBM), which is used to model fluid flow, heat transfer, and phase change phenomena in nuclear systems. Recent work has concentrated on freezing and melting processes in molten salt reactors, microfluidic separation techniques for medical isotopes, and advanced modeling of flow batteries. His research shows a clear progression toward increasingly sophisticated numerical methods applied to real-world nuclear engineering challenges. Professor Rohde has secured significant funding through multiple European Commission projects including ENDURANCE, MIMOSA, and ReZilient, demonstrating the international recognition of his research. He has supervised numerous PhD and MSc students, many of whom have gone on to complete theses on topics related to molten salt reactors, microfluidics, and flow battery technology. His research group includes several technicians, post-doctoral researchers, and PhD candidates working collaboratively on cutting-edge nuclear technology. The Transport Phenomena & Nuclear Applications laboratory operates several specialized facilities including the ESPRESSO facility for measuring melting and solidification under convective boundaries, and experimental setups for studying molten salt behavior, microfluidic purification, and flow battery technology. The group maintains strong collaborations with international partners including TRIUMF (Canada), NRG, and URENCO (The Netherlands).
Leon Shpanin is a Senior Lecturer in Electronic and Electrical Engineering at Sheffield Hallam University, where he serves as Course Leader for MSc Automation Control and Robotics. He holds an MSc in Radio Frequency Engineering and a PhD in Electrical Engineering from the University of Liverpool. His research focuses on electrical engineering applications including renewable energy systems, HVDC circuit interruptions, and electromagnetic techniques for current interruption. He received the RAEng Award (2020) for developing next-generation circuit breakers for rail networks. Key projects: Development of Novel Energy Efficient Magnetic Scroll Air Motor (EPSRC) Smart control of multirotor drone propellers for vibration energy harvesting
Prof. David Ham is a Professor of Computational Mathematics at the Department of Mathematics, Faculty of Natural Sciences, Imperial College London. His research focuses on high-level abstractions for scientific computation, particularly in geophysical fluids and numerical software. He leads the Firedrake project and co-developed the dolfin-adjoint framework, which received the 2015 Wilkinson Prize for Numerical Software. Ham holds a BSc (Mathematics) and LLB from The Australian National University, and a PhD from TU Delft. His career includes roles as a NERC Independent Research Fellow and Grantham Research Fellow at Imperial College. He is affiliated with the Grantham Institute, Mathematics of Planet Earth, and Software Performance Optimisation groups. His research spans computational science, including finite element methods, adjoint-based inversion, and parallel computing. Recent work emphasizes differentiable programming integration with machine learning and geophysical modeling. Ham has contributed to numerous grants and projects, including EPSRC and NERC-funded initiatives. He leads development of software tools like Firedrake and Thetis, advancing computational methods for oceanography and geodynamics.
Houman BOROUCHAKI is a Professor at the University of Technology of Troyes (UTT), France, with over 20 years of academic leadership. He has served as Head of the Automatic Mesh Generation and Advanced Methods (GAMMA3) project team since 2008 and previously led the Laboratory of Mechanical Systems and Concurrent Engineering (LASMIS) (2005-2007). His work bridges academic research and industrial applications through collaborations with INRIA , French Petroleum Institute (IFPEN) , Dassault Aviation , and others. Research Interests: A pioneer in adaptive meshing , he focuses on finite element methods , geometric modeling , and numerical simulations . His innovations underpin mesh generation algorithms , 3D triangulation software , and industrial applications in metal forming, composite simulation, and subterranean modeling. Scientific Trends: His recent work emphasizes metric-based meshing , high-order geometric validity , and parallel processing for mesh generation , with applications in petroleum reservoirs, aviation surfaces, and nanomaterials. His Google Scholar profile reflects 25+ years of contributions to meshing and simulation. Teaching: With 22 years of experience, he teaches courses on meshing , numerical analysis , geometric modeling , and computer graphics at UTT, covering undergraduate to PhD levels. Labs & Teams: He leads the interdisciplinary GAMMA3 team and has contributed to LASMIS (mechanical engineering), L2n (CNRS-UMR 7076) (nanomaterials), and LIST3N (computer science).
Satish C. Boregowda is a Senior Lecturer at the School of Mechanical Engineering, Purdue University in West Lafayette, Indiana. His work focuses on thermodynamics-based analysis of human physiological systems, energy systems engineering, and renewable energy integration. He is affiliated with Purdue's Mechanical Engineering department and maintains an office in POTR 322A. Education & Professional Background : While specific educational details are not provided, his long-term research contributions since 1992 indicate advanced expertise in thermodynamics, biomedical engineering, and energy systems. His career spans over three decades with continuous publication activity. Research Interests : Dr. Boregowda’s core research combines thermodynamics with human physiology, developing metrics like the Objective Stress Index (OSI) to quantify stress responses. His work also addresses energy security through renewable integration, entropy analysis in biological systems, and thermal comfort modeling. He applies constructal theory, fractional calculus, and finite element methods to model human thermal regulation and environmental interactions. Publications Trends : His articles (1992–2025) show sustained focus on: 1) Thermodynamic modeling of human stress and thermal comfort, 2) Renewable energy grid integration strategies, and 3) Advanced computational methods for physiological systems. Recent works emphasize decarbonization pathways and energy policy implications. Grants & Advising : No specific grants or advisees are listed in the provided data. His research likely involves collaborations with aerospace and environmental engineering groups given his work on thermal systems in microgravity and HVAC applications. Labs & Teams : While no specific lab affiliations are mentioned, his research aligns with Purdue’s mechanical engineering initiatives in renewable energy, biomedical engineering, and thermal systems design.
