جست‌وجوی استاد و پژوهشگر

Jonathan Blazekمشاهده پروفایل

استادیار

• Cosmology
• Astrophysics
• Galaxy Clustering
• +۵ مورد دیگر

Jonathan Blazek is an Assistant Professor of Physics at Northeastern University's College of Science, specializing in observational and theoretical cosmology. His research focuses on large-scale astronomical surveys to understand cosmic structure and dark energy, particularly through galaxy clustering and weak gravitational lensing. He is a key member of the Dark Energy Survey and Vera C. Rubin Observatory collaborations, leading efforts to combine multi-wavelength datasets for cosmological insights. Blazek earned his Ph.D. from UC Berkeley and completed postdoctoral fellowships at EPFL (Switzerland) and Ohio State University. Education: Ph.D. in Physics, University of California, Berkeley Postdoctoral Fellowships: EPFL (Switzerland), Ohio State University Research Interests: His work centers on cosmological modeling using galaxy surveys, particularly refining analytic and numerical methods to connect observations with theoretical frameworks. Key areas include: Weak gravitational lensing and galaxy clustering Combined-probe cosmology (integrating datasets across wavelengths) Dark matter and dark energy dynamics Large-scale structure formation Publications & Grants: Blazek has authored over 50 peer-reviewed articles, including foundational work on intrinsic alignment modeling and cosmic shear analysis. He leads the NSF CAREER grant project exploring dark sector physics with galaxy surveys. His recent publications address baryonic feedback effects, CMB lensing cross-correlations, and next-generation survey methodologies. Labs & Collaborations: He contributes to the Northeastern Cosmology Group and the Dark Energy Science Collaboration, advancing projects like the Legacy Survey of Space and Time (LSST) at Vera Rubin Observatory.

Vicente Rodriguez-Gomezمشاهده پروفایل

دانشیار

• Galaxy Formation and Evolution
• Cosmological Simulations
• Hydrodynamics
• +۳ مورد دیگر

Vicente Rodriguez-Gomez is an Associate Professor at the Instituto de Radioastronomía y Astrofísica (IRyA) of the Universidad Nacional Autónoma de México (UNAM) in Morelia. He previously served as a Postdoctoral Fellow at Johns Hopkins University and the Harvard-Smithsonian Center for Astrophysics. His research focuses on galaxy formation and evolution, cosmological simulations, and data analysis. PhD in Astronomy & Astrophysics from Harvard University (2016), with a Secondary Field in Computer Science and Engineering. AM in Astronomy from Harvard University (2012). Bachelor's in Physics from UNAM (2009). His key research interests include galaxy mergers, hydrodynamics, and morphological diagnostics. He has developed software tools like statmorph for galaxy morphology analysis and SubLink for merger tree analysis in simulations. Rodriguez-Gomez has authored/co-authored 68 refereed publications and holds grants from NASA, XSEDE, and UNAM. Recent work emphasizes cosmological simulations (e.g., IllustrisTNG), exploring merger dynamics, black hole impacts, and galaxy cluster evolution. He has advised three graduate students and serves as an academic referee for journals like the Astrophysical Journal and Monthly Notices of the Royal Astronomical Society. Outreach activities include popular media features in Scientific American and The Atlantic, and public talks on cosmological simulations and galaxy evolution.

Thomas M Braunمشاهده پروفایل

استاد

• Adaptive clinical trials
• Bayesian methods
• Longitudinal data
• +۴ مورد دیگر

Thomas M Braun, PhD is a Professor in the Department of Biostatistics at the University of Michigan School of Public Health. He serves as a Member of the Senate Advisory Committee on University Affairs (SACUA) and teaches graduate-level biostatistics courses. Dr. Braun is an internationally recognized expert in Bayesian adaptive clinical trial design, with primary focus on cancer treatment and prevention research. Dr. Braun's educational background includes: PhD in Biostatistics from the University of Washington, Seattle (1999) MS in Biostatistics from the University of Washington, Seattle (1996) BBA from the University of Wisconsin, Madison (1990) His research expertise spans adaptive clinical trials, Bayesian statistical methods, longitudinal data analysis, oncology, rare diseases, dyadic analyses, and mobile health technology. Dr. Braun has developed novel Bayesian clinical trial designs for discovering safe and effective chemotherapy and immunotherapy regimens for cancer treatment. He is also pioneering methods to incorporate patient preferences into randomized clinical trials and sequential multiple assignment randomized trials (SMARTs). Analysis of Dr. Braun's recent publications (2023-2025) reveals a strong concentration on small n sequential multiple assignment randomized trials (snSMARTs), Bayesian methodology for rare disease research, mobile health interventions for cancer patients and caregivers, and innovative approaches to merging electronic health records with clinical trial data. His work consistently bridges theoretical statistical methodology with practical clinical applications in oncology and transplantation medicine. Dr. Braun has secured significant research funding from: The Food and Drug Administration (FDA) for developing Bayesian clinical trial designs for rare diseases The Patient Centered Outcomes Research Institute (PCORI) for developing Bayesian methods incorporating patient preference in SMARTs He actively collaborates on multiple research projects examining cancer treatment disparities, immunotherapy regimens for melanoma and other cancers, and the Roadmap mobile health platform for improving outcomes in stem cell transplant recipients and their caregivers. His work often focuses on dyadic patient-caregiver relationships and developing innovative statistical methodologies for complex clinical trial designs.

Carolin Liefkeمشاهده پروفایل

پژوهشگر

• Stellar Activity
• X-ray Astronomy
• Astronomy Education
• +۲ مورد دیگر

Dr. Carolin Liefke serves as Deputy Head of the Haus der Astronomie (House of Astronomy) and Deputy Director of the IAU Office of Astronomy for Education at Heidelberg University. With over 20 years of experience in astronomy education and public outreach, she has been a research associate at the House of Astronomy since March 2010. She also serves as the National Outreach Coordinator for Germany of the International Astronomical Union. Dr. Liefke's research interests span stellar activity, X-ray astronomy, and innovative approaches to astronomy education. Her work focuses on developing educational resources that make astronomy accessible to diverse audiences, including the "telescope license" program for schools, the HdA partner school network, and the Pan-STARRS asteroid search project for students. She has taught numerous seminars at Heidelberg University covering astronomy topics from multi-wavelength observations to solar system studies. Her publications demonstrate consistent focus on translating complex astronomical concepts into classroom-appropriate materials. She maintains an active presence in science communication through her long-running blog "Astronomers do it at Night" and on social media as @astrophysikerin. Her approach emphasizes hands-on learning and connecting cutting-edge astronomical discoveries with school curricula. Deputy Head, Haus der Astronomie (HdA) Deputy Director, IAU Office of Astronomy for Education National Outreach Coordinator for Germany, International Astronomical Union Research Associate, House of Astronomy since 2010 Active blogger since 2009 on astronomy education

• Human-Computer Interaction
• Educational Technology
• Computer Science Education
• +۳ مورد دیگر

Francisco Javier Oliver Bernal is a Lecturer at the University of Deusto in Bilbao, Spain, within the Faculty of Education and Sport and the Department of Physical Activity and Sports Sciences. He teaches across Computer Engineering, Physical Activity and Sports Sciences, and Primary Education bachelor's programs, as well as the Master's in Secondary Education. He earned his Doctor of Medicine and Surgery from the University of the Basque Country. His research spans Human-Computer Interaction, Educational Technology, and Science Education, with a strong emphasis on accessibility for visually impaired users. He has developed tools for e-learning, digital resource centers, and innovative teaching methodologies in computer science and natural sciences, aiming to enhance educational experiences through technology. His scholarly output, spanning from the 1990s to 2023, demonstrates a consistent focus on technology-enhanced learning, evolving from early work in 3D interfaces and computer graphics to recent applications in health, music, and interdisciplinary educational contexts. Key trends include the integration of accessibility features, the development of domain-specific educational tools (e.g., for biology and astronomy), and responses to contemporary challenges like the COVID-19 pandemic. He has supervised multiple theses on digital accessibility and cooperative systems, though student names were not listed. Details on research grants were not provided in the available text. As a member of the eVida research group (officially recognized by the Basque Government), he contributes to projects advancing accessible educational technologies, including the ACCE project for audiovisual accessibility and READIS digital resource centers for visually impaired users.

Kerstin Bunteمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• XAI (Explainable Artificial Intelligence)
• +۷ مورد دیگر

Kerstin Bunte is a Professor of Machine Learning for interdisciplinary data analysis at the University of Groningen, affiliated with the Faculty of Science and Engineering and the Bernoulli Institute's Intelligent Systems Group. She holds an Honorary Fellowship at the University of Birmingham and leads the Intelligent Systems Group. Her research focuses on interpretable machine learning, interdisciplinary applications (e.g., astrophysics and biomedical data), and visualization techniques. Research Interests: - Machine Learning - Artificial Intelligence - Explainable AI (XAI) - Interpretable Models - Dimensionality Reduction - Data Visualization - Astrophysical Data Analysis - Medical Imaging Awards & Grants: - DSSC XS funding (2023) - NWO VIDI grant (2020) - Rosalind Franklin Fellowship (2016–present) Advising & Students: Supervised PhD students include Elisa Oostwal, Janis Norden, Matteo Marcantoni, and Petra Awad. Research spans topics like tumor segmentation in medical imaging, astrophysical structure detection, and autonomous navigation systems. Labs & Collaborations: Leads the Intelligent Systems Group, collaborating with institutions like the University of Birmingham and the University of Warwick. Work involves interdisciplinary projects combining machine learning with astronomy, biomedical sciences, and robotics.

Giuseppe Vinciمشاهده پروفایل

استادیار

• Astrostatistics
• Bayesian Statistics
• Computational Neuroscience
• +۸ مورد دیگر

Giuseppe Vinci is an Assistant Professor at the Department of Applied and Computational Mathematics and Statistics (ACMS) at the University of Notre Dame, within the College of Science. His research focuses on probabilistic graphical models, particularly in neuroscience and genomics applications. He holds a Ph.D. in Statistics from Carnegie Mellon University (2017), and completed postdoctoral research and lecturing at Rice University (2017–2020). Vinci’s expertise spans astrostatistics, forensic science, and geometric data analysis. Education: Ph.D. in Statistics, Carnegie Mellon University (2017) M.Sc. in Statistics, Carnegie Mellon University (2013) M.Sc. in Economics and Social Sciences, Bocconi University (2012) B.Sc. in Economics, University of Catania (2009) Research Interests: High-dimensional statistical theory of graphical models, matrix completion, astrostatistics, neuroscience, genomics, forensic science, and geometric data analysis. His work addresses challenges in neuronal functional connectivity, genomic networks, and climate science. Awards: NeuroNex Postdoctoral Trainee (NSF) Rice Academy Postdoctoral Fellow Three-minute thesis competition (Top-10, Carnegie Mellon University) Advising & Grants: Vinci mentors multiple Ph.D., MSc, and undergraduate students in projects spanning forensic statistics, genomics, and astrostatistics. He has secured funding for undergraduate research programs and participated in NIH-funded interdisciplinary training. Labs/Teams: Involved in collaborative projects with institutions like Baylor College of Medicine and Rice University, focusing on neurotheory and statistical methods in neuroscience.

• Artificial Intelligence
• Curriculum and Pedagogy
• Control Engineering
• +۶ مورد دیگر

Dr. Anthony Bartel is an Adjunct Industry Fellow at the School of Social Sciences, Media, Film and Education, Swinburne University of Technology. He teaches Physics, Electronic Instrumentation, and Multimedia Technology to undergraduate and postgraduate students across science, engineering, games, and multimedia disciplines. He has coordinated multiple subjects and courses in these areas. His educational background includes an undergraduate major in Physics, a Master's involving instrumentation for balloon-borne x-ray astronomy, and a PhD in neutron physics instrumentation development. Undergraduate: Physics Master's: Instrumentation for Balloon-Borne Astronomical X-Ray Observatory PhD: Instrument Development for Neutron Physics Experiments Dr. Bartel's research spans Artificial Intelligence, Curriculum and Pedagogy, Control Engineering, Mechatronics, Robotics, and Music Education Technology. His work emphasizes the use of AI and serious games to enhance deliberate music practice and self-regulated learning. He explores feedback models, incentive systems, and progression design in mobile music games to improve educational outcomes. His recent publications (2010–2021) demonstrate a strong trend toward integrating AI with music education, particularly in generating adaptive sight-reading exercises and analyzing percussive sounds. The research bridges computer science, education, and acoustics, focusing on intelligent tools for music practice and systematic sound description. He is available to supervise PhD students and has previously supervised doctoral research in intelligent music tools and percussive audio lexicons. Intelligent Tools for Deliberate Music Practice: Evolving Targeted Sight Reading Exercises (2015–2019) The Percussive Audio Lexicon (2014–2015) Dr. Bartel has not received any publicly listed scientific awards. His teaching and research are supported through institutional roles and collaborative projects in educational technology and instrumentation. He is involved in interdisciplinary teams working on serious games for music education and AI-driven learning systems, contributing expertise in instrumentation, signal processing, and pedagogical design.

Salvatore Ramponeمشاهده پروفایل

دانشیار

• Machine Learning
• Cybersecurity
• Internet of Things (IoT)
• +۷ مورد دیگر

Salvatore Rampone is an Associate Professor at the Department of Law, Economics, Management and Quantitative Methods (DEMM) of Università degli Studi del Sannio (Unisannio). His research focuses on Machine Learning , Internet of Things (IoT) , and Cybersecurity , with applications in intrusion detection, bioinformatics, and financial systems. Research Interests : Privacy-preserving Machine Learning Hybrid Cloud-IoT Architectures Genetic Programming Neural Network Applications Data Processing in Distributed Environments Recent Publication Trends : His work spans federated learning for IoT security ( 2025 ), DNA splicing site prediction ( 2020 ), meningitis classification ( 2019 ), RFID-based traceability systems ( 2015 ), and geological data analysis using neural networks ( 1995-1996 ). Key technologies include Apache Spark , MLlib , and Wavelet Transform . Teaching Activities : He teaches courses like Applied Machine Learning and Principles of Computer Sciences across multiple degree programs including Statistical and Actuarial Sciences and Economics and Management .

Veronique Petitمشاهده پروفایل

دانشیار

• Stellar Astrophysics
• Structure and Evolution of Massive Stars
• Stellar Magnetic Fields
• +۶ مورد دیگر

Veronique Petit is an Associate Professor in the Department of Physics & Astronomy at the University of Delaware , affiliated with the College of Arts & Sciences and the Bartol Research Institute . She holds a Ph.D. from Université Laval and has been at UD since 2017. Her research focuses on magnetism in massive stars , including magnetic field detection, stellar evolution, and magnetospheric interactions. She leads the Magnetism in Massive Stars (MiMeS) research group, which uses spectropolarimetry and simulations to study stellar magnetic fields' impact on star evolution. Education: Ph.D. in Astrophysics, Université Laval Research Interests: Her work explores the origins and effects of magnetic fields in massive stars, including how these fields influence stellar structure, mass loss, and eventual supernova/black hole outcomes. Key areas include: Magnetic field detection via spectropolarimetry Simulating magnetospheric dynamics Observations of binary star systems (e.g., ϵ Lupi) Development of software tools like SpecpolFlow Research Trends: Recent articles highlight advancements in magnetic field measurement techniques, discoveries of magnetospheric interactions in binary systems, and computational models predicting magnetic star evolution. Her work bridges observational astronomy and theoretical modeling to address fundamental questions about stellar magnetism. Advising & Teams: Mentors a robust group of graduate students (e.g., Shaquann Seadrow, Victor Ramirez Delgado) and postdoctoral researchers. Alumni include professionals in academia and industry, such as Dr. Cori Fletcher (NASA) and Dr. Kyle Johnston (Prespecta). Labs & Collaborations: Leads the MiMeS group, collaborating with facilities like the Mount Cuba Astronomical Observatory and leveraging instruments like the Chandra X-ray Observatory and SPectroPolarimetric High-contrast Exoplanet REsearch (SPHERE) .

Joshua Speagleمشاهده پروفایل

استادیار

• Astrostatistics
• Data Science
• Machine Learning
• +۷ مورد دیگر

Joshua Speagle is an Assistant Professor jointly appointed in the Department of Statistical Sciences and the David A. Dunlap Department of Astronomy & Astrophysics at the University of Toronto. He is also an Associate Member of the Dunlap Institute for Astronomy & Astrophysics and a Member of the Data Sciences Institute. His research lies at the intersection of statistics, astronomy, and computer science, focusing on astrostatistics and data-intensive astrophysics. His research interests include astrostatistics, data science, machine learning, statistical inference, and Bayesian methods. He develops novel statistical learning techniques to extract insights from large, complex datasets, particularly from astronomical surveys. His work emphasizes interpretability, robust inference, and computational efficiency, with applications to galaxy formation, stellar photometry, and 3D dust mapping. The trends in his recent publications reflect a strong focus on interdisciplinary methodologies, particularly in Bayesian inference, nested sampling, and machine learning applied to astrophysical problems. His work consistently bridges theoretical statistics with practical applications in astronomy, emphasizing open-source software and reproducible research. Banting Postdoctoral Fellowship Dunlap Fellowship Joshua Speagle is deeply committed to mentorship and collaboration. He co-leads the Astrostatistics Research Team (ART) with Gwen Eadie, mentoring students and postdocs across disciplines. He is involved in graduate and undergraduate research programs, including the Astronomy & Astrophysics Summer Undergraduate Research Program (SURP). He teaches courses in statistics and astronomy and serves on committees within the University of Toronto and professional societies such as the AAS, ASA-AIG, and SSC-DSA. He co-leads the interdisciplinary Astrostatistics Research Team (ART), which fosters a collaborative, inclusive environment focused on cutting-edge research at the intersection of statistics and AI. The team emphasizes open and accessible science, releasing open-source tools like dynesty and brutus , and mentoring the next generation of data scientists.

Xan Guilian Morice-Atkinsonمشاهده پروفایل

پژوهشگر

• Machine Learning
• Astrophysics
• Cosmology
• +۵ مورد دیگر

Xan Guilian Morice-Atkinson is a Research Software Engineer at the University of Portsmouth's Faculty of Technology, working within the Institute of Cosmology & Gravitation and the Portsmouth AI and Data Science Centre. With a PhD in Astrophysics and Cosmology completed in 2018, Xan applies machine learning methods to astrophysical research problems, developing computational tools that advance cosmological understanding. Xan earned their PhD from the Institute of Cosmology and Gravitation in 2018, focusing on machine learning applications in astrophysics and cosmology. After completing their doctorate, they worked for several years as a Data Scientist in Air Traffic Services before returning to the ICG as a Research Software Engineer where they program computers to perform astrophysics-related tasks. Research interests span machine learning applications in astrophysics, gravitational wave analysis, cosmological simulations, and galaxy formation studies. Xan specializes in developing computational tools for astrophysical data analysis, with particular expertise in applying machine learning algorithms to classify astronomical objects and model cosmological phenomena, as evidenced by their first-author publication on machine learning interpretation for source classification. Xan's publication record shows a strong focus on applying computational methods to fundamental astrophysical problems, with recent work on gravitational wave alert systems, modified gravity emulators, and galaxy classification. Their research bridges the gap between advanced computational techniques and observational cosmology, contributing significantly to large collaborative projects like the LIGO-Virgo-KAGRA observing run and the Dark Energy Survey. Xan is currently involved as a team member in the "DDME: Data discovery made easy" project funded by the Economic and Social Research Council (June 2024 to September 2025), applying machine learning to social science databases. They have also participated in interdisciplinary work, presenting at the AI in Orthopaedics conference in September 2022, demonstrating the transferability of their computational expertise across scientific domains.

Jalal Fadiliمشاهده پروفایل

استاد

• Signal Processing
• Image Processing
• Inverse Problems
• +۷ مورد دیگر

Jalal Fadili is a Professor at the National School of Engineers of Caen (ENSICAEN) and conducts research at the Research Group in Computer Science, Image, Automation and Instrumentation of Caen (GREYC - CNRS/ENSICAEN/Université Caen Normandie). He serves as director of the AISSAI center and was appointed as a junior member of the Institut Universitaire de France from 2013-2018. His educational background includes: Engineering Degree in Signal Processing from ENSI Caen (1996) MSc in Signal and Image processing from the University of Caen (1996) PhD in Signal and Image processing from the University of Caen (1999) Habilitation in Signal and Image processing from the University of Caen (2010) Professor Fadili's research focuses on signal and image processing, with particular emphasis on inverse problems, variational approaches, and optimization techniques. His work has significant applications in medical and astronomical imaging, including fMRI analysis where he developed wavelet-based methods for structural noise modeling and hypothesis testing. He has created numerous software tools for image processing tasks such as decomposition, inpainting, denoising, and deconvolution. His professional progression shows steady advancement from Assistant Professor at the University of Caen (2000-2001), to Associate Professor at ENSICAEN (2001-2011), and currently Full Professor at ENSICAEN since 2011. His research has resulted in innovative methodologies including MCALab for morphological component analysis and various image restoration techniques. His laboratory work centers around the GREYC research group, where he leads projects related to sparse representation-based image processing techniques and their applications across scientific domains.

• Astronomy
• Astrophysics
• Science Education
• +۳ مورد دیگر

Karen Knierman is an Associate Teaching Professor at Arizona State University's School of Earth and Space Exploration. She specializes in astronomy and astrophysics, focusing on star formation in colliding galaxies using telescopes like the Hubble Space Telescope. Her work also emphasizes astronomy and physics education, particularly for minority students and their families. Knierman holds a Ph.D. from ASU, an M.S. from the University of Arizona, and a B.S. from Penn State. She previously served as faculty at Mesa and Chandler-Gilbert Community Colleges. Her research interests span observational astronomy, galaxy dynamics, and educational outreach. Notably, she leads the development of ASU's online BS in Astronomical and Planetary Sciences, which emphasizes accessible STEM education. Her recent studies explore student outcomes in online astronomy programs and strategies for inclusive astronomy education. Education: Ph.D., Arizona State University; M.S., University of Arizona; B.S., Penn State University Awards: National Science Foundation Astronomy & Astrophysics Postdoctoral Fellowship Outreach: Multicultural Milky Way project, disability justice advocacy, and K-12 astronomy outreach Knierman’s articles address both astrophysical phenomena (e.g., star formation in tidal tails) and pedagogical innovations (e.g., online labs and course-based research experiences). Her work bridges cutting-edge astronomy research with efforts to diversify and democratize access to STEM education.

• Geoinformatics
• Computer Graphics
• Computer Vision
• +۵ مورد دیگر

Prof. Dr. rer. nat. habil. Stephan Kopf is a faculty member at the Faculty of Spatial Information, where he holds the Professorship in Informatics and Geoinformatics. He actively contributes to research in geographic information systems (GIS), computer graphics, computer vision, and multimedia technologies. His academic leadership roles include serving as Dean and as a member of the Faculty Council, with advisory roles in the Senate. His research spans crowd simulation , image/video retargeting , augmented reality platforms , and interactive learning systems . He focuses on algorithm development for GIS, temporal effects in re-captured video, and scalable solutions for classroom interactivity. His work frequently integrates machine learning, mobile computing, and geospatial data. For teaching, he oversees courses such as Internet Technologies, Informatics, and Programming in degree programs like Geoinformatics/Management and Surveying. He supervises theses involving programming-centric topics like mobile app development , VR applications , and astronomical analysis of geospatial data .