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Kim BerghausView profile

Research Fellow

• Theoretical Physics
• Particle Cosmology
• Dark Matter Detection
• +4 more

Kim Berghaus is a Sherman Fairchild Postdoctoral Scholar Research Associate in Theoretical Physics at the California Institute of Technology (Caltech), affiliated with the Department of Physics within the Division of Physics, Mathematics and Astronomy. Their research focuses on particle cosmology, dark matter detection, and beyond Standard Model physics, addressing topics like warm inflation, cosmological tensions, and sub-GeV dark matter detection through experimental collaborations such as IceCube and DESI. Research interests include theoretical frameworks for dark energy radiation, thermal friction effects in cosmology, and axion physics. Berghaus has contributed to developing novel detection methods like multi-modal Brillouin microscopy and high-resolution spectrometers for studying cellular forces and sub-GHz signals. Their work bridges particle physics, astrophysics, and cosmology to explore phenomena at the intersection of fundamental theory and observational data. Key research areas span: Warm inflation models and scalar field dynamics Dark matter signatures in ultra-high-energy neutrino observatories Experimental techniques for sub-GeV dark matter detection Cosmological parameter estimation using BAO measurements Axion-QCD interactions and open theoretical questions Publications emphasize resolving cosmological tensions through thermal friction mechanisms, analyzing relic particle decays, and advancing detector technologies. Berghaus has collaborated on Snowmass Cosmic Frontier reports outlining future dark matter detection strategies and contributed to high-impact experimental methodologies in both particle physics and biophysics.

• Biogeochemistry
• Climate Science
• Ecology
• +3 more

Siyuan Wang is a Doctoral Researcher at the Max Planck Institute for Biogeochemistry in Jena, Germany, affiliated with the Department Biogeochemical Integration (BGI) and the Model-Data Integration Research Group. As part of the International Max Planck Research School for Global Biogeochemical Cycles (IMPRS-gBGC), their work focuses on understanding biogeochemical cycles, disturbance regimes, and vegetation dynamics using advanced modeling and remote sensing techniques. Research interests include forest disturbance dynamics, carbon cycle modeling, climate impacts on ecosystems, and the application of remote sensing data to environmental monitoring. Their studies explore transient effects of vegetation changes on atmospheric CO2, spatial patterns of air pollutants like PM2.5 in China, and global patterns of tree wood density. Publications highlight contributions to robust parameterization in ecosystem models, high-resolution biomass observation analysis, and drought monitoring using satellite data. While no academic awards or grants are explicitly mentioned, their research contributes significantly to global environmental science through innovative methodological approaches. Wang collaborates within interdisciplinary teams at the Max Planck Institute, leveraging cutting-edge tools to address pressing questions in biogeochemistry and climate science. Their work bridges field observations with computational modeling, advancing our understanding of Earth system processes.

• Observational Cosmology
• Cosmic Expansion Rate
• Dark Energy and Dark Matter
• +7 more

Dr. Sherry H. Suyu is an Associate Professor at the Technical University of Munich (TUM) and a Max Planck Fellow at the Max Planck Institute for Astrophysics. Her research focuses on gravitational lensing applications for cosmology, particularly in measuring the cosmic expansion rate and studying dark energy/dark matter. She leads the HOLISMOKES program funded by an ERC Consolidator Grant. Her research group at TUM School of Natural Sciences Department of Physics investigates: Dark cosmos through gravitational lensing Supernova observations and Tidal Disruption Events Bayesian inference methods for astrophysical modeling Machine learning applications in lensed system analysis Scientific achievements include: 2021 Berkeley Prize from American Astronomical Society ERC Consolidator Grant for HOLISMOKES program Discovery of 330 high-quality lens candidates in Pan-STARRS survey She teaches courses including: Experimental Physics 1 (Winter 2024/5) Gravitational Lensing (Winter 2024/5) Introduction to Nuclear/Particle/Astrophysics (Summer 2025) The group includes 13 active members and 14 alumni across 7 institutions. Current work integrates HST and LSST imaging for next-generation lens discovery.

David HobbsView profile

Professor

• Space Astrometry
• Galactic Structure
• Exoplanet Detection
• +3 more

David Hobbs is a Professor in the Department of Physics at Lund University, specializing in astrometry and space missions. He is a key contributor to the European Space Agency's Gaia mission, focusing on astrometric data processing and fundamental physics applications. His research includes developing algorithms for the Astrometric Global Iterative Solution (AGIS) and analyzing Gaia's data to study galactic structure, exoplanets, and stellar dynamics. Hobbs has also proposed the GaiaNIR mission, aiming to expand astrometry into near-infrared wavelengths. Earlier work includes software development for XMM-Newton and Integral observatories, as well as spacecraft attitude control algorithms for Herschel and Planck missions. Research interests include: High-precision astrometry and mission design Galactic structure analysis using Gaia data Exoplanet detection via astrometric methods Near-infrared astrometry for obscured regions Data processing algorithms for large astronomical surveys Recent articles highlight advancements in Gaia DR3 data exploitation, including chemical cartography of the Milky Way and discovery of black holes via astrometric techniques. Future work focuses on GaiaNIR's development to enhance astrometric capabilities for 12 billion stars.

Kevin HuffenbergerView profile

Professor

• Astrophysics
• Cosmology
• Galactic Dust Polarization
• +7 more

Kevin Huffenberger is a Professor at Texas A&M University, leading research in astrophysics and cosmology with a primary focus on the Cosmic Microwave Background (CMB). He is actively involved in major experimental collaborations including the Atacama Cosmology Telescope (ACT), The Simons Observatory, and CMB-S4. His work has contributed to past missions like Planck and QUIET, and spans diverse topics such as galaxy cluster physics, dust polarization modeling, and gravitational lensing analysis. His research aims to unravel the universe’s large-scale structure, dark matter interactions, and cosmic evolution through precise observational techniques and advanced data analysis methods. Research Interests : Dr. Huffenberger’s expertise lies in CMB anisotropy studies, millimeter-wave astronomy, and cosmological parameter estimation. He develops sophisticated algorithms for foreground separation and transient detection, and his team produces high-resolution maps of the microwave sky. Key areas include understanding galactic dust emission via filament-based models, probing cosmic birefringence, and analyzing Sunyaev-Zel’dovich effects in galaxy clusters. His work bridges observational data with theoretical models to constrain dark matter properties and cosmic acceleration mechanisms. His research is supported by grants from NASA, the National Science Foundation, and the Department of Energy. While no formal advisees are listed, his collaborative projects involve large teams working on next-generation instrumentation and data analysis pipelines. He contributes to both the Atacama Cosmology Telescope and Simons Observatory initiatives, advancing scientific goals in cosmology, astrophysics, and fundamental physics.

Ullrich KötheView profile

Associate Professor

• Machine Learning
• Neural Networks
• Explainable AI
• +6 more

Prof. Dr. Ullrich Köthe is an Associate Professor and group leader in the Visual Learning Lab Heidelberg at the University of Heidelberg . He focuses on Explainable Machine Learning , leveraging Invertible Neural Networks to enhance transparency and utility in image analysis and medical applications . He also maintains the widely used VIGRA image analysis library. Education: PhD in Informatics, University of Hamburg, 2000 Habilitation in Informatics, University of Hamburg, 2008 His research interests center around machine learning , image analysis , and scientific computing , particularly the development of robust algorithms for medical imaging , computer vision , and life sciences . His work on invertible neural networks and parameter-free segmentation has led to significant advancements in the field. Recent publications highlight his contributions to Bayesian inference , neural network interpretability , and stochastic modeling , with applications ranging from disease outbreak dynamics to connectomics . Key trends include generative models , parameter-free segmentation , and likelihood-free inference . Scientific Awards: DAGM 2003 Main Prize DAGM Best Paper Award 2008 He has supervised numerous Master and Bachelor theses in machine learning and image analysis , with teaching roles in Advanced Machine Learning and Explainable AI . His collaborative research grants include funding from HARMAN International (2024). He leads the Explainable Machine Learning subgroup and has contributed to open-source software projects like ilastik and VIGRA , which are critical tools in bioimage analysis .

Devin CrichtonView profile

Professor

• Cosmology
• Astrophysics
• Galaxy Clusters
• +5 more

Dr. Devin Crichton holds the Professorship for Physics at ETH Zürich, focusing on observational cosmology and radio astronomy. His research centers on large-scale structure studies, galaxy cluster physics, and cosmic microwave background (CMB) analysis through major experiments like the Atacama Cosmology Telescope (ACT) and the Hydrogen Intensity Real-Time Analysis eXperiment (HIRAX). Key projects include developing SKA simulation frameworks and investigating 21cm intensity mapping for cosmological insights. His work spans telescope instrumentation (e.g., HIRAX antenna design), CMB power spectrum analysis, and SZ-selected cluster catalogs. Contributions include advancing understanding of cosmic string wakes and quasar feedback mechanisms through multi-wavelength observations. Dr. Crichton collaborates on international initiatives such as the Simons Observatory and LiteBIRD, addressing questions about dark energy, neutrino physics, and early universe cosmology. His technical expertise includes data pipeline development for real-time analysis and precision measurement systems for radio telescope noise characterization.

Kenny LauView profile

Researcher

• Cosmology
• Experimental Cosmology
• Microwave Background Radiation
• +3 more

Kenny Lau is a Postdoctoral Scholar and Research Associate in Physics at the California Institute of Technology, specializing in experimental cosmology. He focuses on measuring microwave signals from the early universe, including the Epoch of Reionization (EoR) and the Cosmic Microwave Background (CMB), through collaborations like BICEP/Keck, TIME, and CMB-S4. His work involves instrument development, data analysis, and telescope deployment at remote sites such as the South Pole. Education: Ph.D. in Physics from the University of Minnesota (2023), M.Sc. and B.Sc. from the Chinese University of Hong Kong. Research contributions include analyzing CMB B-mode polarization data to constrain inflationary models, developing the BICEP Array telescope mount, and deploying instruments at the Amundsen-Scott South Pole Station. His expertise spans cosmological parameter estimation, foreground separation, and experimental instrumentation for millimeter-wave astronomy. Key projects include the BICEP Array telescope mount assembly and deployment, contributing to first-light observations of CMB polarization at 40 GHz. His technical work includes detector module development, vacuum window design, and calibration techniques for high-sensitivity CMB experiments.

• Emergency Medicine Education
• Medical Education
• Physician Assistant Training
• +5 more

Thomas Morris is a Staff Affiliate in Emergency Medicine Education & Training at Yale University's Yale School of Medicine. He holds a Master of Science from Southern Connecticut State University (2000) and a Physician Assistant degree from Barry University (2002). He is board-certified as a Physician Assistant by the National Commission on Certification of Physician Assistants (2003). His work bridges medical education, particularly in emergency medicine and physician assistant training, with advanced research in astrophysics and cosmology. His technical expertise spans beamline alignment automation, machine learning tools for astronomical data processing, and cosmological parameter estimation using large-scale telescope data. He has contributed to projects like the Atacama Cosmology Telescope (ACT) and the development of simulators for millimeter/submillimeter observatories such as AtLAST. His research interests include optimizing observational techniques in astronomy, improving data fidelity through Bayesian algorithms, and advancing instrumentation for next-generation telescopes. Notable recent work focuses on atmospheric emission modeling at high-altitude sites, component-separated sky maps, and autonomous beamline alignment systems. His interdisciplinary approach combines medical education with cutting-edge astrophysical research, reflecting his dual role in academic and technical spheres. Education: MS (2000) Southern Connecticut State University; PA (2002) Barry University Affiliations: Yale School of Medicine, Emergency Medicine Department Technical Contributions: Machine learning for telescope data, beamline automation, cosmological parameter analysis Key Projects: Atacama Cosmology Telescope (ACT) DR6 data analysis, AtLAST telescope simulations

• Computational Imaging
• Compressed Sensing
• Signal Processing
• +5 more

Dr. Karen Eguiazarian is a Professor of Signal Processing at the Department of Computing Sciences , Tampere University . He leads the Computational Imaging research group and has served as head of the Signal Processing Research Community (SPRC) at Tampere University of Technology (2016-2018). Education: M.Sc. in Mathematics, Yerevan State University, Armenia (1981) Ph.D. in Physics and Mathematics, Moscow State University, Russia (1986) Doctor of Technology in Signal Processing, Tampere University of Technology, Finland (1994) His research focuses on Computational Imaging , Compressed Sensing , and Efficient Signal Processing Algorithms , with significant contributions to Image/Video Restoration and Compression . Recent work includes AI-driven phase imaging, hyperspectral reconstruction, and noise-robust algorithms for remote sensing and biomedical applications. Scientific Awards: Service Award from the Society for Imaging Science and Technology (IS&T) (2014) Honorary Doctoral Degree from Don State-Technical University, Russia (2015) Dr. Eguiazarian has supervised 25 doctoral theses and published over 650 papers. He serves as Editor-in-Chief of the Journal of Electronic Imaging and associate editor of the IEEE Transactions on Image Processing , while co-founding Noiseless Imaging Oy , a Tampere University spin-off.

Salvatore VitaleView profile

Associate Professor

• Gravitational Waves Data Analysis
• Astrophysics Observation
• Instrumentation and Experiment
• +3 more

Salvatore Vitale is an Associate Professor of Physics at the Massachusetts Institute of Technology (MIT), affiliated with the MIT LIGO Lab and the MIT Kavli Institute for Astrophysics and Space Research. His research focuses on gravitational wave data analysis, particularly using LIGO instruments to study compact objects like binary black holes and neutron stars. He has contributed to foundational work on parameter estimation, tests of general relativity, and next-generation observatory design like Cosmic Explorer. Education: BSc/MSc in Physics from University of Bologna, PhD from Pierre et Marie Curie University (Paris). Postdoctoral roles at Nikhef Amsterdam and MIT LIGO Lab before faculty appointment in 2017. Research emphasizes gravitational wave signal analysis, including unmodeled searches, compact object population studies, and calibration effects. Key publications address measuring the Hubble constant, binary black hole formation channels, and spin dynamics. Awards include the 2021 NSF CAREER Award and 2016 Gruber Cosmology Prize (LIGO Team). Lab affiliations: MIT LIGO Group and Advanced LIGO at MIT. Current group includes students (e.g., Cailin Plunkett) and postdocs (e.g., Matthew Mould). Active in open-source data releases and collaborations like the LIGO Scientific Collaboration.

Pengfei FanView profile

Lecturer

• Computational Imaging
• Low-level Vision
• Image Restoration and Enhancement
• +3 more

Dr. Pengfei Fan is a Lecturer in Data Science and AI at Queen Mary University of London (QMUL). He holds a PhD from QMUL (2021), focusing on learning-based imaging through dynamic scattering media. He previously served as an Assistant Professor at Xi'an Jiaotong-Liverpool University (2022–2024) and as a Research Associate at Nanjing University of Science and Technology (2021–2022). He also held a Visiting Researcher position at Tsinghua University (2020). His research interests span computational imaging, low-level vision, and medical imaging applications. He has secured grants such as the President and Principal’s Fund for Educational Excellence (2024) and collaborates with organizations like EPSRC, Royal Society, NSFC, and Jiangsu Science and Technology Programme. Pengfei is an active member of academic societies (IEEE, IET, BMVA, OSA, SPIE, IAPR) and holds Fellow status with the Higher Education Academy (FHEA). He contributes to educational initiatives via roles at QMUL-BUPT Joint Teaching and Learning Centre and the Centre for Excellence in AI in Education. His recent work emphasizes diffusion models, medical image segmentation, and competition-based learning in education. He welcomes PhD applications through CSC, CONACYT, and HEC.

Sara AlgeriView profile

Associate Professor

• Statistical Hypothesis Testing
• Goodness-of-Fit Tests
• Computationally Efficient Inference
• +7 more

Sara Algeri is an Associate Professor in the Department of Statistics at the University of Minnesota – Twin Cities, within the College of Science and Engineering. Her research bridges statistical theory and applications in astrophysics and medical sciences, supported by active funding from the National Science Foundation. Her research expertise lies in developing statistically rigorous and computationally efficient methods for hypothesis testing, particularly in the presence of nuisance parameters and unknown backgrounds. Key areas include goodness-of-fit tests, sequential testing, and inference in high-dimensional or uncertain settings. Her work enables discoveries in physics, such as searches for dark matter and axions, while also contributing to public health through improved screening risk assessments. The trends in her recent publications reveal a strong focus on solving inverse problems in high-energy astrophysics and particle physics with innovative statistical frameworks. She develops methods applicable to data from instruments like HAWC and axion haloscopes, often under high background noise. Simultaneously, she applies statistical rigor to clinical questions, such as surgical outcomes and screening test risks, demonstrating interdisciplinary impact. Dr. Algeri is actively involved in collaborative research, as evidenced by her role as Co-Investigator on the NSF-funded project Computationally Tractable Inference for Multi-Messenger Astrophysics (2022–2025), which aims to develop scalable statistical tools for next-generation astrophysical data. While no formal students are listed, her collaborative publications suggest mentoring and advising activities. She has not received any explicitly mentioned scientific awards in the provided text. Her work contributes to broader scientific goals, including the UN Sustainable Development Goals, by advancing methodological tools for scientific discovery and health improvement. She collaborates with researchers across institutions and disciplines, particularly in astrophysics and medical statistics.

• Environmental Risk Analysis
• Waste Management
• Environmental Pollution Monitoring
• +7 more

Jinmei Lu is a Professor in the Department of Technology and Security at UiT The Arctic University of Norway, actively contributing to research and teaching in environmental and technological safety domains. Her primary affiliation is with the Sustainable Technology and Safety (STS) research group and the nICE project, based in Tromsø. Her research interests focus on Environmental Risk Analysis and Assessment , Waste Management , Environmental Pollution Monitoring , and Environmental Pollution Prevention and Mitigation . Her work employs advanced modeling and statistical techniques to understand the environmental impacts of mining, shipping, and climate change in the Arctic. The 15 most recent publications reveal a strong trend in applying hydrological and reliability models to Arctic environmental challenges. Key themes include the leaching of heavy metals from mine tailings under varying temperatures, atmospheric emissions from Arctic shipping, hydrological modeling in permafrost regions, and reliability analysis of engineering systems. The research utilizes methods from machine learning, statistical modeling, and environmental simulation. Her scientific contributions are disseminated through high-impact journals in environmental science, engineering, and sustainability. Jinmei Lu is an active advisor and collaborator, with a long-standing research partnership with Fuqing Yuan and other colleagues. Her work is supported by institutional affiliations and research projects focused on Arctic sustainability. She teaches courses such as SVF-1201 (Environmental Vulnerability in the Arctic), SIK-2004 (HSE Risk Analysis and Management), and SVF-3207 (Resilience). She is a key member of the Sustainable Technology and Safety (STS) research group and the nICE project, which are central to her collaborative research on Arctic environmental and engineering challenges.

• Biostatistics
• Epidemiology
• Infectious Disease Modeling
• +3 more

Thomas Obadia is a biostatistician and research engineer at Institut Pasteur in Paris, France, where he has been working since June 2016. He is affiliated with the Bioinformatics and Biostatistics HUB and contributes to the Malaria: parasites and hosts research unit headed by Ivo Mueller. His work bridges statistical methodology development with practical applications in infectious disease epidemiology. Dr. Obadia holds a Ph.D. in biostatistics from Université Pierre et Marie Curie (Paris 6), with research focused on the spread of nosocomial pathogens using high-resolution contact network data. His doctoral work at INSERM involved developing statistical frameworks to correlate dynamic close-proximity interaction networks with biological carriage data of pathogens like S. aureus. He is the developer of the R0 package for R, which implements methods for estimating reproduction parameters for emerging transmissible diseases. His research interests span biostatistics, epidemiology, and mathematical modeling of infectious disease transmission. His recent publications demonstrate expertise in diverse areas including long COVID neurobiology, fungal infections, malaria epidemiology, and vector-borne diseases. His methodological contributions focus on contact network analysis, statistical modeling of transmission dynamics, and development of computational tools for epidemic analysis. As an educator, Dr. Obadia has been consistently involved in teaching bioinformatics and statistics to PhD students through the Bioinformatics program for PhD students at Institut Pasteur since 2018. This program covers statistics, bioinformatics, and image analysis, with a focus on reproducible research, R programming, and experimental design. His collaborative research spans multiple international projects, particularly in malaria-endemic regions like Cambodia and Brazil. He has contributed to studies on malaria risk exposure using GPS tracking, serological testing strategies for elimination, and mathematical modeling of transmission dynamics. His work demonstrates strong interdisciplinary collaboration across epidemiology, microbiology, and clinical research.