Stephen Goodsell is a researcher affiliated with the Department of Physics. He has led the GEMINI Project, focusing on astronomical instrumentation and adaptive optics systems. His research spans planetary science, exoplanet detection, and observational astronomy. His work includes pivotal contributions to the Gemini Planet Imager's calibration and performance, particularly in high-contrast imaging, polarimetry, and spectroscopy. Collaborative projects target debris disks, substellar companions, and disk-planet interactions, utilizing advanced optical engineering and data reduction techniques. Recent publications highlight his expertise in adaptive optics system integration, wavefront sensing, and real-time control platforms like SPARTA and DARTS. Articles emphasize instrumentation development for ground-based telescopes and the analysis of circumstellar environments using polarimetric and spectroscopic methods.
Peter Hamlington is a Professor and Vogel Faculty Fellow at the University of Colorado Boulder, serving as Department Chair of Mechanical Engineering. He holds a PhD in Aerospace Science from the University of Michigan Ann Arbor and a BA in Physics from the University of Chicago. His research focuses on large-scale numerical simulations using high-performance computing, with expertise in turbulent flows, wildland fires, ocean biogeochemistry, geophysical fluid dynamics, and renewable energy systems. He teaches courses such as Methods of Engineering Analysis (MCEN 5020) and Reacting Flows (MCEN 6001). His honors include the NSF CAREER Award (2019), Woodward Outstanding Faculty Award (2017), and Herb and Karen Vogel Fellowship (2013–present). Research interests span combustion, turbulence modeling, and oceanic carbon dynamics. Recent publications explore adaptive mesh refinement techniques for fire simulations, wind energy optimization, and submesoscale ocean turbulence impacts. Hamlington collaborates extensively with graduate students on projects like turbulent plume dynamics and wildfire behavior. Education: PhD (Aerospace Science, U Michigan), MS (Aerospace Science, U Michigan), BA (Physics, U Chicago) Research Themes: High-performance computing simulations, renewable energy systems, combustion physics Key Projects: Wildfire dynamics, ocean biogeochemical models, turbulence optimization His work bridges fundamental fluid mechanics and applied engineering challenges, with grants supporting combustion diagnostics and climate-related ocean studies. Hamlington’s lab utilizes advanced computational tools and experimental setups like inclinable wind tunnels to study fire propagation and fluid dynamics.
Christoph Leuenberger is a Lecturer at the University of Fribourg, holding positions in the Department of Informatics and the Department of Physics, while also serving in the Dean's Office of the Faculty of Science and Medicine. His work bridges computational methods, population genetics, and ecological modeling. He specializes in statistical inference techniques, particularly Bayesian methods applied to genomic data and evolutionary processes. His research focuses on analyzing population trends, genetic diversity, and evolutionary dynamics across species. Key research interests include computational biology, population genetics, and the development of statistical tools for analyzing large-scale genomic datasets. He has contributed to studies on lactase persistence evolution, ancient DNA analysis, and predator population dynamics. His interdisciplinary approach integrates methods from computer science, mathematics, and ecology to address complex biological questions. Recent articles highlight his work in ecological monitoring, Bayesian inference for sex chromosome analysis, and evolutionary jump modeling in phylogenies. These studies underscore his expertise in bridging theoretical frameworks with practical data analysis in genetics and environmental science. No scientific awards or grants are explicitly mentioned in the provided information. He advises no students listed here but actively contributes to teaching and academic administration within the Faculty of Science and Medicine. Labs and teams associated with his work are not specified in the available data. His location is at PER 21 bu. C321, Bd de Pérolles 90, 1700 Fribourg, with contact details via email and ORCID.
Nathan Whitehorn is an Associate Professor in the Department of Physics & Astronomy at Michigan State University, focusing on experimental particle astrophysics. His research integrates cosmology, neutrino physics, and instrumentation, particularly through projects at the South Pole Telescope and IceCube Neutrino Observatory. He holds a B.A. from the University of Chicago (2007) and a Ph.D. from the University of Wisconsin–Madison (2012). His work includes advancing the South Pole Telescope’s angular resolution for Cosmic Microwave Background (CMB) studies and improving IceCube’s sensitivity to high-energy neutrinos from cosmic sources. Key research themes include understanding dark matter/energy via CMB observations, detecting neutrinos from blazars and cosmic accelerators, and developing Antarctic-based instrumentation. His IceCube contributions led to the first detection of extraterrestrial neutrino sources, published in Science (2013, 2018). No formal advisees or grants are listed in the provided text, though his involvement with major observatories suggests collaborative projects. The IceCube Neutrino Observatory and South Pole Telescope remain central to his experimental work.
Christiaan van der Tol is an Associate Professor in the Department of Water Resources at the University of Twente's Faculty of Geo-Information Science and Earth Observation (ITC). He holds external roles as former Associate Editor of 'Remote Sensing of Environment' (2018–2021) and Member of the Mission Advisory Board for the FLEX satellite mission (2017–2023). His work contributes to UN Sustainable Development Goals focused on climate action, life on land, and responsible consumption. Education: MSc in Hydrology (Wageningen University, 2001) and PhD in Earth Sciences (Vrije Universiteit Amsterdam, 2007) with a thesis on climatic constraints of carbon assimilation and transpiration in sub-Mediterranean forests. His research interests include the Earth's energy and water budget, remote sensing of photosynthesis and evaporation, vegetation-thermodynamics interactions, and micro-climate control. He co-developed the SCOPE model, which integrates physical processes and satellite sensor simulations to enhance remote sensing data interpretation. His recent articles highlight advancements in evapotranspiration modeling, climate-vegetation interactions, and fluorescence-based crop monitoring. He collaborates extensively on global datasets (e.g., ESA CCI) and promotes open science through initiatives like SENSECO. No scientific awards are explicitly listed, but his impactful contributions are evident through the 8083 citations and h-index of 44. Advising and Grants: Supervised 11 academic works (no student names provided). Grants are not explicitly mentioned, but his collaborative projects suggest substantial funding engagement. He leads the SCOPE model development team and participates in missions like FLEX, emphasizing interdisciplinary approaches to ecosystem monitoring. Labs/Teams: Core contributor to the SCOPE model framework, with GitHub repositories showcasing its use in radiative transfer, crop growth simulations, and fluorescence retrieval. Collaborates with global institutions on datasets like 'Water and energy fluxes measurement in Speulderbos' (DANS, 2018) and 'Airborne HyPlant dataset' (University of Twente, 2019).
Ross Bannister is a Senior Research Fellow at the National Centre for Earth Observation (NCEO) and part of the Department of Meteorology at the University of Reading. His primary role involves advancing data assimilation techniques and their integration with machine learning, particularly in meteorological and environmental science contexts. He lectures on NERC/NCEO/DARC training courses focused on data assimilation and its applications. Affiliations: NCEO, University of Reading's Meteorology Department Research Interests: Data assimilation, inverse modeling, convective-scale dynamics, and background error covariance modeling His research spans projects like the Ocean Reanalysis Algorithms for Climate Studies (ORACS) and contributions to initiatives such as DIAMET and FFIR, improving weather prediction and flood forecasting. Recent publications highlight advancements in hybrid ensemble-variational methods, ecosystem connectivity analysis, and model development (e.g., the Hydro-ABC framework). He actively supervises PhD students in topics like satellite data assimilation and marine modeling. Bannister’s work emphasizes bridging observational data with computational models to enhance Earth system understanding, with a focus on high-resolution forecasting and reanalysis systems.
Yinlin Dong is an Associate Professor in the Department of Mathematics at the University of Central Arkansas. His research focuses on computational mathematics and numerical methods, with expertise in solving differential equations using high-order numerical techniques, vortex visualization, and finite element methods. He holds a Ph.D. from the University of Texas at Arlington, an M.A. from the University of Alabama, and a B.S. from Nankai University. Dr. Dong's work addresses complex fluid dynamics problems, including vortex generation mechanisms, shock-vortex interactions, and adaptive grid generation. His recent studies explore Liutex-based vortex identification, turbulence modeling, and high-resolution numerical schemes for flow simulation. He has contributed to advancing methods like the weak Galerkin finite element approach and matrix-based high-order derivative schemes. His research trends emphasize interdisciplinary applications of computational mathematics in fluid mechanics, with a focus on improving accuracy in turbulent flow simulations and vortex structure analysis. Despite no listed awards or grants, his publications reflect sustained engagement with core challenges in numerical analysis and fluid dynamics modeling.
Dr. Alex Amato is a researcher at the Paul Scherrer Institute (PSI), leading work in the PSI Center for Neutron and Muon Sciences. His research focuses on advanced materials characterization using muon spin spectroscopy (µSR), gravitational-wave detector technologies, and superconductivity in novel materials. He has contributed to mirror coating developments for LIGO/Virgo/KAGRA detectors and pioneered non-destructive analysis techniques like muon-induced X-ray emission (MIXE) for archaeological and industrial applications. Key domains include: Muon-based material studies Gravitational wave instrumentation Kagome superconductors Magnetic phase transitions His publications from 2022–2025 highlight work on charge-order phenomena in kagome superconductors, magnetic crossover behaviors in topological magnets, and novel detector designs for particle tracking. Collaborations include major initiatives like the LIGO Scientific Collaboration and KAGRA experiments. His work integrates cutting-edge muon beam applications with high-pressure material studies, advancing understanding of superconductivity, magnetism, and quantum phase transitions.
Pete Lally is a Lecturer in Magnetic Resonance (MR) Physics at the Department of Bioengineering, Imperial College London, affiliated with the Faculty of Engineering. His research focuses on developing advanced MRI techniques for rapid, high-resolution imaging, particularly in neonatal brain studies and degenerative diseases at 3T and 7T fields. He leads the London Collaborative Ultra-High Field System (LoCUS) facility and collaborates with the Computational, Cognitive and Clinical Neuroimaging Laboratory and the Centre for Paediatrics and Child Health. Education: PhD in Magnetic Resonance Physics (Imperial College London, 2015–2018) MSc in Physics and Engineering in Medicine (University College London, 2010–2012) MPhys in Physics (Durham University, 2006–2010) Research Interests: Lally specializes in MRI technology advancements, including quantitative imaging, neonatal brain injury biomarkers, and ultra-high-field (7T) imaging for peripheral nerves and degenerative diseases. His work emphasizes clinical translation of techniques like RAFO-4 MRI and QDESS for simultaneous morphology and quantitative mapping. Professional Roles: Current: Lecturer in Bioengineering (2024–present) Prior: Sir Henry Wellcome Postdoctoral Fellow (2020–2024), BRC MR Physics Fellow (2018–2020), and Senior MR Physicist (2018). Labs & Collaborations: He directs the LoCUS facility and collaborates with the Centre for Perinatal Neuroscience and Imperial MRI Physics Collective. His work spans clinical and academic settings, addressing challenges in neonatal care and high-field MRI applications.
Yudu Li is a Research Assistant Professor in the Department of Bioengineering at the University of Illinois at Urbana-Champaign (UIUC), affiliated with the Beckman Institute and the National Center for Supercomputing Applications. He holds a Ph.D. in Electrical and Computer Engineering from UIUC (2022), an M.S. from the same university (2017), and a B.S. in Electronic Engineering from Tsinghua University (2015). Primary Affiliation: Bioengineering Department, College of Engineering Secondary Affiliations: Beckman Institute (0%), National Center for Supercomputing Applications (0%) Research Interests His research focuses on computational methodologies for biomedical imaging, particularly Magnetic Resonance Imaging (MRI). He develops novel mathematical models and computational frameworks that integrate spin physics, signal modeling, statistical inference, and machine learning to overcome challenges in high-dimensional imaging problems. Key areas include: MRI-based metabolic imaging Quantitative MRI Multimodal neuroimaging MRI reconstruction techniques (e.g., SPICE, subspace learning) Publications & Trends His work emphasizes high-resolution MRI advancements, particularly in: Dynamic metabolic imaging of brain tumors using deuterium MRSI Machine learning-driven image reconstruction for faster/safer scans Integration of subspace models with deep learning Applications in stroke prediction and Alzheimer’s disease Awards & Honors Summa Cum Laude Paper Award (ISMRM, 2023) W.S. Moore Award (ISMRM, 2023) Young Investigator Award (OCSMRM, 2023) Thomas and Margaret Huang Award (UIUC, 2022) Grants & Labs Active in collaborative projects at the Beckman Institute, focusing on MRI innovation. His lab develops cutting-edge techniques for brain imaging and metabolic analysis. Notably, he has contributed to fast high-resolution 3D MR spectroscopic imaging and machine learning-enhanced subspace reconstruction methods.
Greg Buzzard is Professor of Mathematics and Director of the Center for Computational & Applied Mathematics (CCAM) at Purdue University's College of Science. His research develops mathematical foundations for computational imaging systems through collaborative work with electrical engineering and materials science researchers. Dr. Buzzard leads a substantial research group focusing on inverse problems in imaging science. His work combines theoretical advances in optimization with practical applications in electron microscopy, neutron tomography, and medical imaging. Key research areas include: Consensus Equilibrium frameworks for model integration Dynamic sampling algorithms for computational imaging Plug-and-play methods for image reconstruction Multi-agent systems for large-scale inverse problems Recent publications demonstrate leadership in computational tomography innovations, particularly neutron imaging techniques for materials science and spectral CT reconstruction. His team's methods enable faster, higher-resolution imaging across scientific domains from biomedical applications to aerospace materials characterization. The 2020 SIAM Imaging Sciences Best Paper Prize recognized fundamental contributions to Plug-and-Play reconstruction methods. Professor Buzzard maintains an active mentoring program with 9 current graduate students and postdocs. His interdisciplinary collaborations include sustained partnerships with the Purdue School of Electrical and Computer Engineering and materials science research groups.
William McNally is a Professor at Wilfrid Laurier University's Lazaridis School of Business and Economics. His research focuses on insider trading, financial disclosure, and stock repurchases. He teaches finance pedagogy and explores interdisciplinary applications of machine learning in sports science and geophysics. McNally's recent work integrates physics-based models with deep learning for sports simulations (golf, hockey) and environmental systems. His articles emphasize practical computational solutions, from optimizing golf equipment to climate model calibration, reflecting a strong cross-disciplinary approach.
Dr. Chang Liu is a Senior Lecturer in Electronic Engineering at the University of Edinburgh's School of Engineering. He received his B.Sc. in Automation from Tianjin University (2010) and Ph.D. in Testing, Measurement Technology and Instrument from Beihang University (2016). Following postdoctoral research at Empa-Swiss Federal Laboratories, he joined the Agile Tomography Group at Edinburgh. His research focuses on laser spectroscopy, laser imaging, and data-driven imaging techniques for applications in reacting flow-field diagnostics and environmental monitoring. Key specialties include design of near/mid-infrared LAS sensing systems, development of high-sensitivity imaging methodologies, spectroscopic modeling, inverse problem solving, and embedded system design. His publications demonstrate a consistent focus on advancing tomographic imaging techniques, with recent work emphasizing machine learning integration, hardware acceleration, and industrial applications in aero-engine monitoring. Research consistently addresses challenges in spatial/temporal resolution enhancement and real-time system implementation. Dr. Liu teaches courses in Digital System Design, Analogue Circuits, and Embedded Systems. He leads multiple research projects including EPSRC-funded initiatives on laser imaging of turbine engine combustion species. His team collaborates with industrial partners to develop cutting-edge laser-based sensing solutions.
Dr. Ivan Stoianov is a Professor (Reader) in Water Systems Engineering at Imperial College London's Department of Civil and Environmental Engineering, part of the Faculty of Engineering. He holds a prestigious five-year EPSRC Fellowship (2017-2021) focused on designing resilient, dynamically adaptive water supply networks that integrate computational control with physical infrastructure to enhance resilience, efficiency, and sustainability. His affiliations include the Environmental and Water Resource Engineering group, the Grantham Institute, and InfraSense Labs. Dr. Stoianov earned his PhD and worked as a Research Associate at MIT (2000s), collaborating with Intel Research on wireless sensor networks for water infrastructure monitoring. His research has been supported by major water utilities (e.g., Bristol Water, Severn Trent) and tech firms (e.g., NEC, SAP). He co-founded InfraSense Labs, a cross-disciplinary group with a research portfolio exceeding £2.5M, and Inflowmatix Ltd, a venture-backed startup (£4M) focused on optimizing water network operations. His research interests span smart infrastructure, hydraulic modeling, leak localization, and adaptive control systems for water networks. Notable contributions include the 2010 Telford Gold Medal from the Institution of Civil Engineers for work on wireless sensor networks in infrastructure. He teaches courses on water engineering, hydroinformatics, and computational methods, having supervised over 50 MSc projects on topics like chlorine decay modeling and hydraulic control optimization. Dr. Stoianov’s work integrates advanced optimization, AI, and sensor technologies to address challenges in water network resilience, energy efficiency, and contamination management. His recent publications focus on adaptive control systems, leak localization algorithms, and high-resolution pressure monitoring for infrastructure longevity.
Siawoosh Mohammadi serves as a Senior Postdoctoral Research Fellow at the Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf. His research focuses on advanced MRI methodologies with expertise spanning diffusion, structural, and reconstruction techniques. His primary research interests include: Diffusion MRI: Modeling and correction of artefacts, sequence optimization, bio-physical modeling from macro- to micro-structure Structural MRI: Artefact correction and bio-physical modeling of proton density, myelin, and iron content MRI reconstruction: K-space to image-space artefact correction and complex data modeling Current projects on high-resolution spinal-cord DTI, Kurtosis Diffusion Imaging, and multi-parameter mapping integration Mohammadi holds significant recognition through a DFG (German Research Council) Research Fellowship and is the author of the ACID toolbox for diffusion MRI pre-processing within the SPM package. His professional trajectory includes postdoctoral roles at UCL's WTCN and University of Münster, with doctoral training in quantitative Diffusion Tensor Imaging. DFG Research Fellowship As an enabling activity, he developed the ACID toolbox – a model-based academic software toolkit for diffusion MRI artefact correction, DTI index estimation, and spatial normalization, fully integrated into SPM's batch system. His academic foundation includes a PhD in quantitative DTI from University of Münster (2009) and an MSc in theoretical physics from University of Hamburg (2002).
