جست‌وجوی استاد و پژوهشگر

Aaron Tohuvavohuمشاهده پروفایل

پژوهشگر ارشد

• Astrophysics
• High Energy Astrophysics
• Gravitational Wave Astronomy
• +۳ مورد دیگر

Aaron Tohuvavohu is a Research Fellow in the Division of Physics, Mathematics, and Astronomy at the California Institute of Technology. His work focuses on high-energy astrophysics, particularly gamma-ray bursts (GRBs) and multi-messenger astronomy. He is deeply involved in the Neil Gehrels Swift Observatory mission, specializing in real-time localization of transient events using the BAT-GUANO pipeline and collaborating with gravitational-wave detectors like LIGO/Virgo/KAGRA. His research emphasizes rapid-response observations of GRBs and gravitational-wave events, leveraging the Interplanetary Network (IPN) for precise localization. He has contributed to studies of short-hard GRBs associated with compact object mergers and long-duration GRBs linked to hypernovae. Notable projects include the CASTOR mission concept for UV photometry and detector characterization for next-generation astronomical instruments. Aaron's recent work includes analyzing Swift/XRT and UVOT observations of GRB afterglows, setting upper limits for electromagnetic counterparts to gravitational-wave triggers, and improving IPN triangulation algorithms. His publications reflect a systematic approach to transient astronomy, integrating data from multiple observatories for comprehensive event characterization.

Tamás Budaváriمشاهده پروفایل

دانشیار

• Computational Statistics
• Bayesian Inference
• Astronomy
• +۶ مورد دیگر

Tamás Budavári is an Associate Professor in the Department of Applied Mathematics and Statistics at Johns Hopkins University (JHU), with joint appointments in Physics and Astronomy and a secondary appointment in Computer Science. He is affiliated with the Whiting School of Engineering and the Institute for Data-Intensive Engineering and Science (IDIES). His research focuses on computational and statistical methods for big data in astronomy and interdisciplinary applications such as urban blight analysis. Education: PhD in Astrophysics (2001), Eötvös Loránd University, Budapest Master’s in Theoretical Physics (1997), Eötvös Loránd University Research Interests: Budavári develops algorithms for handling large astronomical datasets, including Bayesian inference, streaming algorithms, and GPU-accelerated processing. His work includes SkyQuery (an online astronomy data tool), photometric redshift estimation, and cross-matching catalogs. He also applies computational methods to urban planning, such as optimizing strategies to address vacant housing in Baltimore City. Publications & Tools: Budavári’s recent work spans topics like deep learning for astronomical image restoration, combinatorial optimization for urban policy, and probabilistic catalog matching. His tools, such as CUDAHM and NWAY, enable scalable analysis of multi-epoch survey data and N-way catalog cross-identification. Awards & Grants: Recipient of the Gordon and Betty Moore Fellowship and SAMSI Research Fellowship Funded by NSF, STScI, NIH, and others Leadership & Outreach: He serves on the Steering Committee of the 21st Centuries Cities Initiative and is a founding editor of the Journal of Astronomy and Computing. His interdisciplinary work bridges astrophysics, data science, and urban systems.

Allison Kirkpatrickمشاهده پروفایل

دانشیار

• Astronomy
• Astrophysics
• Supermassive Black Holes
• +۵ مورد دیگر

Allison Kirkpatrick is an Associate Professor, Associate Chair, and Director of Undergraduate Studies in the Department of Physics & Astronomy at the University of Kansas. Her research focuses on understanding the relationship between supermassive black holes and galaxy evolution through observational astronomy. Dr. Kirkpatrick earned her PhD in Astronomy from the University of Massachusetts in 2016 and her BS in Mathematics from the University of Florida in 2007. Prior to joining KU in 2018, she was a Postdoctoral Fellow at the Yale Center for Astronomy and Astrophysics. Her research centers on observational studies of supermassive black holes and their effect on galaxy evolution. She utilizes data from major space telescopes including Spitzer, Herschel, Hubble, Chandra, and the James Webb Space Telescope. She is particularly known for coining the term "cold quasar" in 2019 to describe galaxies hosting luminous unobscured AGN while simultaneously maintaining prodigious star formation. Dr. Kirkpatrick serves as Principal Investigator for the MIRI EGS Galaxy and AGN (MEGA) survey, a 67-hour Cycle 2 JWST observing campaign that represents the largest area MIRI survey in more than 3 filters. Her work has been featured extensively in the press, with coverage in NewScientist, The Atlantic, Space.com, and other major media outlets from 2019-2024. She actively contributes to diversity and inclusion efforts in astronomy as the inaugural chair of her department's Diversity, Equity, and Inclusion Committee, faculty liaison for the Diversity in Physics group, and former Co-Director of the university's Multicultural Scholars Program.

Jay Straderمشاهده پروفایل

استاد

• Compact Objects
• Black Holes
• Neutron Stars
• +۵ مورد دیگر

Jay Strader is a Professor in the Department of Physics and Astronomy at Michigan State University, where he serves as Graduate Director for the astronomy PhD program and Associate Chair for astronomy. His research focuses on compact objects, particularly black holes and neutron stars in globular clusters, neutron star binaries in Fermi gamma-ray sources, and intermediate-mass black holes. He has received a Packard Fellowship for Science and Engineering and grants from NSF and NASA. His research group includes postdoc Ryan Urquhart, graduate students Thomas Do and Rebecca Kyer, and several undergraduates, with past students like Teresa Panurach (now director of NoVEL Consortium) and Samuel Swihart (NRC fellow at Naval Research Lab). Education: PhD in Astronomy, UC-Santa Cruz/Lick Observatory Awards: Packard Fellowship Collaborations: Member of Rubin Observatory's Stars, Milky Way, and Local Volume science collaboration since 2008 Previous Positions: Hubble Fellow and Menzel Fellow at Harvard-Smithsonian Center for Astrophysics (2007-2012) Program Initiatives: Co-founder of PAREDS program for early research opportunities at MSU His work has been supported by NSF and NASA grants, and he has contributed to studies on black holes in M22, hypervelocity globular clusters around M87, and transitional millisecond pulsars. His group collaborates with Laura Chomiuk and contributes to data catalogs like the M31 globular cluster velocity dispersion database.

August Evrardمشاهده پروفایل

استاد

• Cosmology
• Astrophysics
• Galaxy Clusters
• +۲ مورد دیگر

Professor August Evrard is a distinguished academic at the University of Michigan, holding the Arthur F. Thurnau Professorship in Physics and Astronomy. He is affiliated with the Department of Physics within the College of Literature, Science, and the Arts. Known for his contributions to cosmology and astrophysics, he pioneered the Problem Roulette tool, recognized with the Provost's Teaching Innovation Prize. His research focuses on galaxy clusters, dark matter, and cosmological surveys like the Dark Energy Survey (DES) and XXL Survey. He has been honored as an AAS Fellow (2025) and has contributed to advancements in physics education through innovative teaching methods and technologies. In research, Prof. Evrard explores topics such as dark matter halo dynamics, galaxy cluster properties, and weak lensing analyses. His work spans observational cosmology, computational modeling, and multi-wavelength astronomy. Notable projects include studies on galaxy cluster mass distributions, the relationship between X-ray emissions and velocity dispersions, and the application of machine learning to astrophysical data analysis. His contributions to education highlight the integration of AI-driven tools to enhance learning, as seen in initiatives like the Problem Roulette and course recommendation systems. Prof. Evrard's awards include the Provost's Teaching Innovation Prize for Problem Roulette and his AAS Fellowship. His academic leadership and innovative approaches to both research and education solidify his role as a pivotal figure in astrophysics and STEM pedagogy.

• Active Galactic Nuclei
• Black Holes
• Galaxy Formation and Evolution

Professor David Alexander is a distinguished academic at Durham University's Department of Physics, where he serves as Professor and Chair of Board of Examiners. His responsibilities include Head of Section for Astronomy and Director of Postgraduate Research, demonstrating his significant leadership role within the department. Alexander is also responsible for teaching the Stars Lecturer component of the Level 2 Stars and Galaxies course. His primary research interests focus on Active Galactic Nuclei, black holes, and galaxy formation and evolution, representing core areas in modern astrophysics. Alexander's work spans observational and theoretical aspects of high-energy astrophysics, with particular emphasis on understanding the connections between supermassive black holes and their host galaxies across cosmic time. His research leverages data from major international facilities including NuSTAR, ALMA, DESI, and XMM-Newton, conducting multi-wavelength studies that combine X-ray, infrared, optical, and radio observations. Analysis of his most recent publications reveals a strong focus on AGN demographics, obscuration mechanisms, quasar environments, and the role of AGN in galaxy evolution. His work frequently explores the connection between AGN activity and galaxy properties, with particular attention to the cosmic evolution of these relationships. Recent papers demonstrate increasing emphasis on large-scale surveys and statistical approaches to understanding AGN populations. Leverhulme Research Fellowship (2000-2012) Philip Leverhulme Prize (2000-2008) Royal Society University Research Fellowship (2000-2003) Thomson Reuters ESI highly cited researcher over 2002-2012 Professor Alexander has been instrumental in numerous major survey projects including the NuSTAR extragalactic surveys, DESI quasar studies, and the VST ATLAS Quasar Survey. His collaborative work spans international teams and leverages cutting-edge observational facilities across the electromagnetic spectrum. His research group focuses on understanding the physical processes that govern AGN activity and their connection to galaxy evolution, with particular emphasis on obscured AGN populations and their role in the cosmic black hole growth history.

Olga Vitekمشاهده پروفایل

استاد

• Statistical Science
• Machine Learning
• Mass Spectrometry
• +۵ مورد دیگر

Olga Vitek is a Professor at Northeastern University's Khoury College of Computer Sciences, with affiliated faculty status in the Department of Chemistry and Chemical Biology. Her research bridges statistical science and machine learning with mass spectrometry-based proteomics and systems biology, focusing on developing open-source software tools like MSstats and Cardinal for quantitative proteomic analyses and imaging. Education: PhD in Statistics (Purdue University), Postdoc at the Ruedi Aebersold Lab (Institute for Systems Biology) Leadership: Director of the Barnett Institute for Chemical and Biological Analysis Her work emphasizes: Statistical experimental design Signal detection in complex mass spectrometry data Causal inference in biomolecular networks Reproducible computational infrastructure Recent publications highlight advancements in quantitative proteomics , mass spectrometry imaging , and causal modeling , with applications spanning cancer research, immunology, and clinical diagnostics. Notable trends include deep learning integration for image analysis and open-source tool development for scalable, transparent workflows. Scientific accolades: Elected Fellow of the American Statistical Association 2021 Gilbert S. Omenn Computational Proteomics Award NSF CAREER award Chan-Zuckerberg Essential Open-source Software award Senior Member, International Society for Computational Biology

Robert J Brunnerمشاهده پروفایل

استاد

• Data Science
• Machine Learning
• Statistical Analysis
• +۳ مورد دیگر

Robert J. Brunner is a Professor at the University of Illinois with primary appointments in the Gies College of Business (Department of Accountancy) and the School of Information Sciences. He holds affiliate roles across multiple departments including Astronomy, Computer Science, and Statistics, as well as research centers like the Beckman Institute and NCSA. His research focuses on applying statistical/machine learning to solve complex problems in astronomy, finance, and large-scale data science. Education: Ph.D. in Astrophysics from Johns Hopkins University (advisor: Alex Szalay). Postdoctoral work at Caltech on the Digital Sky project. Research Interests: Machine learning applications, computational techniques, data management/visualization, and observational cosmology. His work bridges astrophysical data analysis with modern data science methodologies. Recent work includes developing spatio-temporal neural networks for forecasting, evaluating AI-driven financial analysis tools, and planning for the Vera C. Rubin Observatory. He collaborates internationally on large-scale surveys like the Dark Energy Survey and SDSS. Labs/Teams: Leads data science initiatives at the University of Illinois Research Park. Active in interdisciplinary teams at NCSA and Beckman Institute focusing on algorithm optimization and data-intensive research.

• Gamma-ray Bursts
• Gravitational Wave Counterparts
• Multi-messenger Astrophysics
• +۳ مورد دیگر

Dr Samantha Oates is a Lecturer in Astrophysics within the Department of Physics at Lancaster University's Faculty of Science and Technology. Her office is located in C035, C-Floor, Physics Building. Her research focuses on Gamma-ray Bursts (GRBs) in the gravitational wave era, investigating environments of GRB explosions, central engine mechanisms, jet structures, cosmological evolution of GRBs, and electromagnetic counterparts to gravitational wave events. She actively participates in international collaborations including Swift, LSST, STARGATE, and ENGRAVE . Her work addresses critical questions about optical/UV contaminants in gravitational wave counterpart searches and the cosmological utility of GRB correlations. Dr Oates supervises PhD students including Samuel Shilling in Observational Astrophysics. She contributes to the Astrophysics research group at Lancaster University, delivering specialized lectures and supervising projects on multi-messenger astrophysics. Recent publications demonstrate her expertise in gravitational wave follow-up campaigns, supernova classification, and nuclear transient phenomena. Her research group engages in multiwavelength observations from gamma-ray to radio wavelengths, utilizing facilities for real-time transient detection and characterization. Current projects involve analyzing data from gravitational wave events and developing methods to distinguish true counterparts from serendipitous transients.

Christopher Kochanekمشاهده پروفایل

استاد

• Cosmology
• Gravitational Lensing
• Supernovae
• +۳ مورد دیگر

Christopher Kochanek is a Professor and Ohio Eminent Scholar in the Department of Astronomy at The Ohio State University, affiliated with the College of Arts and Sciences. His expertise lies in cosmology, gravitational lensing, and supernovae. He earned his Ph.D. from the California Institute of Technology (1989) and a B.A. from Cornell University (1985). His research focuses on using gravitational lensing to study dark energy, dark matter substructures, and quasar accretion disks. He pioneered time-domain astronomy, exploring variability in massive stars and quasars, and co-led the ASAS-SN project for all-sky supernova detection. Key achievements include the Dannie Heineman Prize for Astrophysics (2020) and the AAS Beatrice M. Tinsley Prize (shared 2020). His work spans binary neutron star mergers, Milky Way mass estimation, and dust-obscured stellar explosions. Recent studies analyze supernova progenitors and long-term variability trends in transient events. Awards: Heineman Prize (2020), Tinsley Prize (2020) Grants & Projects: ASAS-SN collaboration with Prof. Stanek, dark energy constraints via lensing, and Milky Way dynamics. Labs/Teams: Active in the Ohio State Astronomy Instrumentation Group and ASAS-SN observatory network.

• Astrophysical Fluid Dynamics
• Star Formation
• Protostellar Disks
• +۷ مورد دیگر

Chris Matzner is a Professor and Associate Graduate Chair at the University of Toronto's Department of Astronomy and Astrophysics, affiliated with the Dunlap Institute for Astronomy & Astrophysics. He earned his Ph.D. from UC Berkeley in 1999. His research focuses on astrophysical fluid dynamics, particularly star formation processes (protostellar disks, molecular clouds, energy feedback) and stellar explosions (supernovae, gamma-ray bursts), employing analytical, numerical, and observational approaches. His research encompasses: Dynamics of protostellar outflows and molecular cloud interactions Models for supernova shocks and gamma-ray burst mechanisms Fragmentation in star and planet formation Massive black hole accretion processes Evolution of giant molecular clouds Stellar feedback in galactic environments Analysis of his 15 most recent publications reveals strong emphasis on supernova dynamics (particularly Type Ia explosions), star formation mechanisms in clusters and molecular clouds, shock wave physics in astrophysical contexts, and the development of astronomical instrumentation. The works demonstrate consistent focus on explosive transients, fluid dynamics in cosmic environments, and observational constraints on theoretical models. As Associate Graduate Chair, he oversees academic programs and student development. His laboratory affiliations include the Dunlap Institute's computational astrophysics and instrumentation groups. Current work involves modeling star cluster-galaxy interactions, tidal disruption events, and developing next-generation UV/IR detectors.

• Astrophysics
• Early Universe Studies
• Gamma-Ray Bursts
• +۳ مورد دیگر

Nathaniel Butler is an Associate Professor at Arizona State University's School of Earth and Space Exploration. His research focuses on astrophysical explosions and their role in probing the early universe, with emphasis on gamma-ray bursts (GRBs) and their optical counterparts. He holds a Ph.D. in Physics from MIT (2003) and a B.A. in Physics (magna cum laude) from Princeton University (1998). Education: Ph.D. Physics, Massachusetts Institute of Technology, 2003 B.A. Physics (magna cum laude), Princeton University, 1998 Research Interests: Dr. Butler's work centers on understanding the properties and evolution of the early universe through observations of GRBs and associated phenomena. His projects involve collaborations with advanced facilities like the COLIBRÍ robotic telescope and the SVOM mission, focusing on optical counterpart detection, afterglow analysis, and multi-messenger astronomy. His expertise in data mining and transient event analysis supports large-scale sky surveys and rapid-response observation protocols. Grants & Advising: No student advisees or grant details listed. His research is supported through institutional affiliations and collaborative projects. Labs/Teams: Active in the COLIBRÍ telescope initiative and projects like the Exoplanet Climate Infrared Telescope (EXCITE) and Star-Planet Activity Research CubeSat (SPARCS).

Adín Ramírez Riveraمشاهده پروفایل

استاد

• Computer Vision
• Machine Learning
• Representation Learning
• +۶ مورد دیگر

Adín Ramírez Rivera is a Professor in the Digital Signal Processing and Image Analysis (DSB) group at the Department of Informatics, University of Oslo. His research focuses on representation learning and computer vision, particularly exploring machine learning methods to describe and understand visual data. He is a Senior Member of the IEEE and a member of the ELLIS Society. Education : PhD from Kyung Hee University's Image Processing Lab, South Korea; Bachelor's degree in Engineering from Universidad de San Carlos de Guatemala, majoring in Computer Science and Systems Engineering. Ramírez Rivera's research spans diverse computer vision tasks including facial analysis, object detection, image enhancement, and vision transformers. His work emphasizes self-supervised learning, fair representation learning, and novel neural network architectures for image segmentation and classification. Recent publications highlight trends in vision transformers, crowd counting, facial expression recognition, and fair representation learning. His articles frequently address statistical modeling, feature extraction, and deep learning techniques for visual tasks. Scientific Awards : Senior Member of the IEEE, Member of the ELLIS Society. He collaborates with researchers across institutions, contributing to projects involving anomaly detection, multilingual translation, and astrophysical modeling. His lab affiliations include the Digital Signal Processing and Image Analysis group and the Section for Machine Learning at the University of Oslo.

Kobus Barnardمشاهده پروفایل

استاد

• Computer Vision
• Machine Learning
• Scientific Applications
• +۳ مورد دیگر

Kobus Barnard is a Professor in the Department of Computer Science at the University of Arizona, with his office located in GS 708. His research bridges computer vision, machine learning, and interdisciplinary scientific applications across diverse domains. Education: Ph.D. from Simon Fraser University (1999) His research interests focus on extracting meaningful insights from complex data through computer vision and probabilistic modeling. Key areas include machine learning for environmental monitoring (flood detection, plant disease analysis), social dynamics (interpersonal coordination, emotional coregulation), astronomy (transient classification), and multimodal learning (visual-linguistic integration). His work consistently applies deep learning to real-world problems requiring high-resolution data interpretation. Analysis of his 2022-2025 publications reveals three dominant trends: (1) Environmental applications using satellite imagery for flood mapping and agricultural monitoring, (2) Cognitive modeling of human teams and emotional dynamics through probabilistic frameworks, and (3) Astronomical data analysis leveraging host galaxy properties for transient classification. These threads demonstrate his commitment to solving practical scientific challenges through computational innovation. While scientific awards aren't documented in available sources, his leadership in projects like FloodPlanet and ToMCAT indicates significant contributions to data infrastructure. His advising and grant activities remain unreported in the source material, though his extensive interdisciplinary collaborations suggest substantial mentorship impact. Barnard's work operates at the intersection of multiple scientific communities, evidenced by applications spanning neuroscience, agriculture, astronomy, and social science. His current focus on high-resolution data fusion and multimodal modeling positions him at the forefront of real-world AI deployment.

Daniel M. Scolnicمشاهده پروفایل

دانشیار

• Cosmology
• Supernovae
• Dark Energy
• +۳ مورد دیگر

Daniel M. Scolnic is an Associate Professor of Physics at Duke University's Trinity College of Arts & Sciences and holds a joint appointment in the Department of Electrical and Computer Engineering at the Pratt School of Engineering. His research focuses on cosmology, particularly using Type Ia supernovae and near-infrared observations to probe dark energy and resolve the Hubble tension. Ph.D. (2013), Johns Hopkins University B.S. (2007), Massachusetts Institute of Technology As a leading figure in supernova cosmology, Scolnic works on refining the cosmic distance ladder, studying time-evolving dark energy, and analyzing systematic uncertainties in cosmological measurements. His work leverages data from the Dark Energy Survey (DES), Pantheon+ collaboration, and James Webb Space Telescope (JWST) to address discrepancies in the Hubble constant (H₀) derived from early- and late-universe observations. His recent publications highlight advancements in inverse distance ladder techniques, host galaxy dust modeling, and the role of photometric redshifts in cosmological analyses. Notably, his team's JAGB 2.0 study improves Hubble constant constraints using JWST. Defense Science Study Group (DSSG) Clarivate Most Highly Cited Scientists Fred Kavli Plenary Lectureship Sloan Research Fellowship in Physics Department of Energy Early Career Award Packard Fellowship Scolnic leads major grants from NASA, the Packard Foundation, and the Department of Energy, including a NASA Roman Project Infrastructure Team grant (2023-2028) and a Packard Fellowship (2019-2027) to investigate cosmological tensions. He actively collaborates with the Duke Cosmology Group and contributes to the Nancy Grace Roman Space Telescope's High-Latitude Time-Domain Survey.