جست‌وجوی استاد و پژوهشگر

Matthew Grahamمشاهده پروفایل

استاد پژوهشی

• Astronomy
• Astrophysics
• Machine Learning in Astronomy
• +۴ مورد دیگر

Matthew Graham is a Research Professor of Astronomy at the California Institute of Technology (Caltech) and Project Scientist for the Zwicky Transient Facility (ZTF). He holds a B.A. from the University of Oxford (Trinity College, 1993) and a Ph.D. from the University of Central Lancashire (1996). His research focuses on applying machine learning and advanced statistical methods to astrophysical problems, particularly in time-domain astronomy and large-scale surveys. Key contributions include leadership in the ZTF, CRTS (Catalina Real-time Transient Survey), NOAO DataLab, and Virtual Observatory initiatives. Graham’s work emphasizes transient phenomena, active galactic nuclei (AGN), and variable stars, with a focus on uncovering insights through large-scale survey data analysis. He has pioneered methodologies for automated discovery and classification of astrophysical transients, including tidal disruption events, supernovae, and eclipsing binaries. His role in ZTF has enabled real-time monitoring of the sky, producing hundreds of thousands of public transient alerts nightly. Teaching roles include co-instructing advanced courses like Astroinformatics (Astrophysics 119), emphasizing data science techniques in astronomy. Graham’s research also intersects with gravitational-wave follow-up studies and multi-messenger astronomy, leveraging ZTF’s rapid response capabilities. His projects, such as Cryoscope (a proposed Antarctic infrared telescope), aim to expand observational horizons in transient astronomy. Collaborations span institutions globally, reflecting his commitment to open science and collaborative data-driven discovery.

Chris Millerمشاهده پروفایل

استاد

• Astroinformatics
• Theoretical Cosmology
• Galaxy Clusters
• +۴ مورد دیگر

Chris Miller is a Professor of Physics and Astronomy at the University of Michigan, affiliated with the Department of Physics within the College of Literature, Science, and the Arts. He specializes in astroinformatics, leveraging computer science, statistics, and data mining to address cosmological questions. His research focuses on galaxy clusters, dark matter distribution, and large-scale surveys like the Dark Energy Survey (DES) and the upcoming Large Synoptic Survey Telescope (LSST). Miller pioneered methods to trace cosmic structure by analyzing massive datasets from surveys like the Sloan Digital Sky Survey (SDSS), and his work aligns observations from opposing cosmological epochs to support the Hot Big Bang theory. Education: BS from Penn State, PhD from the University of Maine. Postdoctoral work at Carnegie-Mellon and the National Optical Astronomy Observatory in Chile. Appointed at U-M through a presidential initiative for data mining in science. Research Interests: Galaxy cluster dynamics and mass estimation Dark energy and matter studies via large-scale surveys Combining observational data with cosmic microwave background findings Acoustic oscillation signatures in the early universe Notable Achievements: First to observe early universe sound wave signatures frozen in matter distribution. Demonstrated synergy between big-data analysis and observational astronomy. Key contributor to LSST development, a top-priority project in the National Research Council’s decadal survey. Lab Affiliations: Randall Lab and Homer A. Neal Lab, University of Michigan.

Eric B. Fordمشاهده پروفایل

استاد

• Exoplanets
• Planet Formation
• Orbital Dynamics
• +۶ مورد دیگر

Eric B. Ford is a Professor in the Department of Astronomy & Astrophysics at Pennsylvania State University, with affiliations at the Institute for Computational & Data Sciences, Center for Exoplanets & Habitable Worlds, Center for Astrostatistics, and Astrobiology Research Center. Dr. Ford received his BSc in Physics and Mathematics from MIT in 1999, followed by a PhD in Astrophysical Sciences from Princeton University in 2003. His postdoctoral work included a Miller Research Fellowship at UC Berkeley (2003-2006) and a Hubble Fellowship at Harvard-Smithsonian Center for Astrophysics (2006-2007). Dr. Ford's research centers around exoplanets, with long-term goals of improving our understanding of planet formation and the dynamical evolution of planetary systems. His work emphasizes the interface between theory and observation, including techniques for characterizing extrasolar planets, statistical analysis of exoplanet observations, methodology for exoplanet demographics, and the efficient design of extrasolar planet surveys. He collaborates with leading planet surveys, including NASA's Kepler mission, the Habitable Zone Planet Finder and NEID teams. His research encompasses several key areas: planetary populations (Kepler data, hierarchical Bayesian models, machine learning), radial velocity surveys (extremely precise RV measurements), transit surveys (characterizing planets using transit data), orbital dynamics of planetary systems, planet formation theory, astrostatistics & astroinformatics (Bayesian computing, MCMC), high-performance scientific computing, and astrobiology & future missions. His recent publications focus on overcoming stellar activity challenges in radial velocity measurements, exoplanet population statistics, and developing advanced statistical methods for exoplanet detection. Hubble Fellow (2006-2007) Miller Research Fellow (2003-2006) Dr. Ford leads or contributes to several major exoplanet surveys and initiatives, including the NEID Earth Twin Survey, which aims to detect Earth-like planets around nearby stars using the NEID spectrograph. His research group develops sophisticated statistical and computational tools to extract planetary signals from noisy data, with a particular focus on overcoming challenges posed by stellar activity. He has secured significant research funding for instrumentation development and exoplanet surveys, supporting both graduate students and postdoctoral researchers in his group.

Maarit Korpi-Laggمشاهده پروفایل

دانشیار

• High-Performance Computing
• Numerical Modelling
• Astroinformatics
• +۳ مورد دیگر

Dr. Maarit Korpi-Lagg is an Associate Professor in the Department of Computer Science at Aalto University's School of Science. She specializes in High-Performance Computing (HPC), numerical modelling, and astroinformatics, with a focus on solar magnetic activity and dynamo theory. Her work bridges computational methods with astrophysical phenomena. External Positions: Corresponding Fellow, Nordic Institute for Theoretical Physics (2020–2025) Independent Max Planck Research Group Leader, Max Planck Institute for Solar System Research (2016–) Research interests include: High-Performance Computing for astrophysical simulations Solar magnetic field dynamics and sunspot analysis GPU-accelerated code development Stellar convection zones and atmospheric modeling Her recent publications highlight advancements in exascale astrophysics data analysis, solar dynamo simulations, and cross-disciplinary applications like epidemic modeling via computational methods. Key collaborations span Europe and global institutions. Scientific Awards: Grand Challenge Award (2018) for galactic dynamo research Nordita Corresponding Fellowship (2020) She leads multiple projects, including ERC UniSDyn and NEOSC, focusing on cosmic magnetism and GPU-based stencil computations. Her work contributes to UN Sustainable Development Goals, particularly quality education and planetary science.

Mike Gowanlockمشاهده پروفایل

دانشیار

• High-Performance Computing
• GPU Acceleration
• Astroinformatics
• +۳ مورد دیگر

Dr. Mike Gowanlock is an Associate Professor in the School of Informatics, Computing, and Cyber Systems at Northern Arizona University. His research develops parallel algorithms and data-intensive computing frameworks for scientific applications in astronomy, ecology, and cybersecurity. Key projects include the Solar System Notification Alert Processing System (SNAPS) for asteroid detection using Rubin Observatory data, GPU-accelerated outlier detection algorithms, and high-throughput cryptographic systems. His NSF CAREER award supports research enabling time-domain astronomy through heterogeneous computing architectures. Dr. Gowanlock teaches courses in parallel programming, GPU computing, and high-performance computing. He advises multiple graduate and undergraduate students and has received several best paper awards for his work on parallel clustering and similarity joins.

Gwendolyn Eadieمشاهده پروفایل

استادیار

• Astrostatistics
• Bayesian hierarchical modelling
• Milky Way structure and dynamics
• +۹ مورد دیگر

Gwendolyn Eadie is an Assistant Professor jointly appointed between the Department of Astronomy & Astrophysics and the Department of Statistical Sciences at the University of Toronto. She leads the Astrostatistics Research Team (ART) and chairs the American Astronomical Society's Working Group on Astroinformatics & Astrostatistics and the American Statistical Association's Astrostatistics Interest Group. Her research focuses on astrostatistics, applying advanced statistical methods to astronomical problems such as Milky Way dynamics, globular clusters, and Fast Radio Bursts (FRBs). She earned her PhD in Physics & Astronomy from McMaster University (2017), an MSc from Queen's University, and a BSc from Simon Fraser University. Education: PhD in Physics & Astronomy, McMaster University (2017) MSc in Physics, Queen's University (2013) BSc in Physics, Simon Fraser University (2010) Her research interests span astrostatistics, Bayesian hierarchical modeling, and time series analysis. Notable works include studies on Milky Way mass estimation using globular clusters and developing methods for FRB localization and analysis. She has received the 2018 J.S. Plasket Medal for her doctoral research on Milky Way dynamics. Advising and Grants: Supervised students include Amanda Cook (McGill Banting Fellow), Aarya Patil (Max Planck Institute postdoc), and Jeff Shen (Princeton PhD candidate) Directed the ART group's interdisciplinary collaborations Labs/Teams: Co-leads the Astrostatistics Research Team (ART) at UofT, and collaborates with CHIME/FRB and the eScience Institute.

• Artificial Intelligence
• Machine Learning
• Probabilistic Circuits
• +۳ مورد دیگر

Devendra Singh Dhami is an Assistant Professor in Mathematics and Computer Science at Eindhoven University of Technology, specializing in uncertainty in artificial intelligence. His research develops advanced methods in probabilistic circuits, neural-symbolic learning, and causal inference for applications in computer vision and bioinformatics. Key research areas include machine learning scalability, graph-based biological network analysis, and satellite image processing for infrastructure risk detection. His work frequently integrates deep learning with symbolic reasoning. Recent publications focus on neuro-symbolic systems, temporal causality, and efficient algorithms for large-scale data. Trends show strong emphasis on interpretable AI and cross-disciplinary applications. No awards, students, or lab details are documented.

• Artificial Intelligence
• Machine Learning
• Electronic Materials
• +۴ مورد دیگر

Tess Smidt is an Assistant Professor in the Department of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology (MIT). Her research focuses on Artificial Intelligence, Machine Learning, and applications in materials science and quantum devices. She leads innovative work on equivariant neural networks for physical system modeling. Her primary research interests include developing symmetry-preserving machine learning models for understanding complex physical systems, creating high-performance computational methods for materials discovery, and advancing quantum material design through computational approaches. The research combines fundamental computer science principles with practical applications in physics and chemistry. Analysis of recent publications reveals strong trends in developing novel equivariant neural network architectures for scientific applications. Key focus areas include molecular dynamics simulations, materials property prediction, and symmetry-aware learning frameworks. The work consistently bridges theoretical machine learning advances with experimental validation in physical sciences. While no awards are mentioned in available information, the publications demonstrate significant contributions to machine learning for science. The research involves collaboration across disciplines, particularly in computational physics and materials science. Professor Smidt leads a research group at MIT developing computational tools for scientific discovery. Current projects explore applications of equivariant models across quantum materials, protein dynamics, and cosmological data analysis, demonstrating the breadth of these computational approaches.

Peter Freemanمشاهده پروفایل

عضو هیئت علمی

• Astrostatistics
• Astronomical Data Analysis
• Machine Learning
• +۳ مورد دیگر

Peter Freeman is an Associate Teaching Professor and Director of the Undergraduate Program in the Department of Statistics & Data Science at Carnegie Mellon University's Dietrich College of Humanities and Social Sciences. He holds a B.A. in Physics (1989, University of California) and a Ph.D. in Astronomy and Astrophysics (1997, University of Chicago). Before joining CMU in 2004, he worked as a scientific programmer for the Chandra X-ray Telescope mission, specializing in data analysis methodologies. His research focuses on astrostatistics, developing advanced statistical techniques for analyzing complex astronomical datasets. Key areas include source detection algorithms, cosmic microwave background mapping, photometric redshift estimation, and galaxy morphology analysis. He has collaborated with faculty and students from CMU and the University of Pittsburgh on interdisciplinary projects, integrating machine learning and computational methods into astrophysical research. Freeman is actively involved in educational initiatives such as the STAMPS (Statistical Pedagogy & Educational Research) group and the Carnegie Mellon Sports Analytics Camp (CMSAC). His work emphasizes authentic, community-engaged learning experiences, particularly in undergraduate laboratory courses and data science curricula. His recent publications span astrostatistics, ecological studies (e.g., Coqui frog acoustic analysis), and pedagogical innovations in STEM education. He has contributed to major projects like the Rubin Observatory Legacy Survey of Space and Time (LSST) and the CANDELS galaxy structure classification effort.

Nikos Gianniotisمشاهده پروفایل

پژوهشگر

• probabilistic machine learning
• dimensionality reduction
• Bayesian inference
• +۱ مورد دیگر

Nikos Gianniotis serves as a Staff Scientist in the Astroinformatics group at the Heidelberg Institute of Theoretical Studies (HITS), leveraging his computer science expertise to develop advanced machine learning solutions for astronomical data analysis. His role focuses on probabilistic modeling and algorithmic innovation within a dedicated research environment. His academic credentials include: PhD in Computer Science from the University of Birmingham (2007) MSc in Natural Computation (with Distinction) from the University of Birmingham (2003) BSc in Computing Science (First class) from the University of Aberdeen (2002) Dr. Gianniotis specializes in probabilistic machine learning , Bayesian inference , and dimensionality reduction techniques applied to astronomical challenges. His research bridges computational statistics and astrophysics, with emphasis on interpretable models for complex observational data. Recent work demonstrates sophisticated integration of Gaussian processes with time-series analysis for cosmic phenomena. Analysis of his 15 most recent publications (2016-2024) reveals consistent innovation in machine learning for astronomy, particularly in time-delay estimation for active galactic nuclei, spectral analysis, and time-series dimensionality reduction. His methodology frequently combines Gaussian processes, variational inference, and neural architectures like autoencoders to address astronomical data challenges. No scientific awards were documented in the provided materials. While no student advisement or grant details were specified, his publications indicate active collaboration within the astroinformatics community. The Astroinformatics group at HITS provides his primary research environment, focusing on scalable computational frameworks for next-generation astronomical datasets from missions like Kepler.

Risa Wechslerمشاهده پروفایل

استاد

• Cosmology
• Galaxy Formation
• Dark Matter
• +۴ مورد دیگر

Risa Wechsler is the Director of the Kavli Institute for Particle Astrophysics and Cosmology (KIPAC) and Professor of Physics and Particle Physics & Astrophysics at Stanford University. She holds a PhD from UC Santa Cruz and leads research on galaxy formation, dark matter, and dark energy using cosmological simulations and sky survey data. Her research investigates cosmic structure formation, dark matter properties, and the accelerating universe through projects like DESI and Rubin Observatory's LSST. Recent work combines AI/ML with astrophysical simulations to decode cosmological patterns. Publications focus on cosmological constraints from DESI and DES surveys, galaxy evolution simulations, Milky Way structure, and dark matter detection methods. Articles showcase advances in multi-probe cosmology combining cluster abundances, weak lensing, and galaxy clustering. Honors include election to National Academy of Sciences and American Academy of Arts & Sciences. She directs the Center for Decoding the Universe and leads teams developing analysis frameworks for next-generation sky surveys.

Min Longمشاهده پروفایل

دانشیار

• Computational Hydrodynamics
• Astrophysics
• High-Performance Computing
• +۶ مورد دیگر

Dr. Min Long is an Associate Professor in the Department of Computer Science at Boise State University, affiliated with the College of Engineering. He holds a Ph.D. in Astrophysics from Cornell University and completed postdoctoral research at the University of Illinois and the Flash Center for Computational Sciences at the University of Chicago. His research focuses on interdisciplinary applications of computing and AI, including computational hydrodynamics, high-energy astrophysics, plasma dynamics, astroinformatics (AI-driven spectral analysis), X-ray emission studies, and multi-scale modeling of nuclear/irradiated materials. He is also affiliated with the Center for Advanced Energy Studies (CAES) at Idaho National Laboratory (INL). His expertise spans computational methods, high-performance computing, and AI algorithms such as evolutionary and neuroevolution approaches. His work bridges astrophysics, materials science, and computer science, emphasizing automated data analysis and large-scale simulations. For more details, visit his personal website .

Kai Polstererمشاهده پروفایل

استاد پژوهشی

• Astroinformatics
• Machine Learning
• Data Science
• +۴ مورد دیگر

Dr. Kai Polsterer is the Scientific Director of the Heidelberg Institute for Theoretical Studies (HITS) since January 2025, succeeding Tilmann Gneiting. He has been a leading researcher at HITS since 2013, heading Europe's first astroinformatics research group. His work focuses on developing machine learning methods to handle exponential data growth in astrophysics, including projects like the ERC Synergy Grant for universe mapping and exascale computing initiatives (SPACE). He collaborates across disciplines with statisticians and physicians to advance weather forecasting and cardiological analysis. Polsterer is actively involved in international organizations such as the International Astroinformatics Association (IAIA, President since 2023), IEEE Task Force on Mining Complex Astronomical Data, and the German Physical Society's AI working group. His research spans astroinformatics, data science, and interdisciplinary applications of AI. His expertise includes photometric redshift estimation, galaxy classification, and unsupervised learning techniques. Polsterer has pioneered tools like PINK (Parallelized Kohonen Maps) and contributed to projects like the LUCIFER instrument for near-infrared spectroscopy. He emphasizes open science and collaborative frameworks, such as the HiPS ecosystem for astronomical data visualization. His current role as Scientific Director reflects his leadership in advancing theoretical studies through computational and data-driven approaches. Polsterer’s contributions include over 50 peer-reviewed articles, spanning astroinformatics, machine learning applications in astronomy, and computational methodologies. He advocates for interdisciplinary research, bridging astrophysics, computer science, and medical data analysis. His ERC Synergy Grant project exemplifies his commitment to large-scale, collaborative science addressing fundamental questions in cosmology and data science.

Eric Fordمشاهده پروفایل

استاد

• Exoplanet Surveys & Populations
• Extremely Precise Radial Velocity Surveys
• Orbital Dynamics & Planet Formation
• +۴ مورد دیگر

Eric Ford is a Distinguished Professor and ICDS Co-Hire in the Department of Astronomy and Astrophysics at Pennsylvania State University. His research focuses on exoplanet surveys, orbital dynamics, and astrostatistics. He is affiliated with the Institute for Computational & Data Sciences, Center for Exoplanets & Habitable Worlds, and Center for Astrostatistics. Ford earned his Ph.D. in Astrophysical Sciences from Princeton University and holds dual B.S. degrees in Physics and Mathematics from MIT. His NASA Hubble Fellowship (2006–2007) and Miller Institute Fellowship (2003–2006) underscore his contributions to theoretical astrophysics. Education highlights include a NASA Hubble Fellowship at Harvard-Smithsonian Center for Astrophysics and a Miller Research Fellowship at UC Berkeley. His work bridges theory and observation, emphasizing radial velocity surveys, exoplanet demographics, and high-precision data analysis. Notable collaborations include the NEID and HPF radial velocity projects, and NASA's Kepler mission. Research interests include exoplanet population studies, extreme precision radial velocity techniques, and the application of astrostatistics to exoplanet detection. His lab develops computational tools like GPLinearOdeMaker.jl and StellarSpectraObservationFitting.jl to model stellar activity and improve exoplanet characterization. Ford has led teams in analyzing Kepler data, uncovering planetary architectures and occurrence rates. His awards include the Simons Fellowship (2020–2021), Helen B. Warner Prize (2012), and Urey Prize (2011). Advising spans PhD and master’s students involved in NEID solar observations, granulation noise mitigation, and exoplanet dynamics. Grants support projects like the NEID Earth Twin Survey and EPRV research coordination networks. Lab affiliations include the Center for Exoplanets & Habitable Worlds and the NEID Science Team. Current work explores strategies to detect Earth-analog planets and mitigate stellar variability using 3.5 years of Sun-as-a-Star observations. Future research aims to advance data science applications in astronomy and prepare for direct imaging missions.

Kaisey Mandelمشاهده پروفایل

استاد

• Astrostatistics
• Data Science
• Supernova Cosmology
• +۵ مورد دیگر

Kaisey Mandel serves as Professor of Astrostatistics and Data Science at the University of Cambridge with a joint appointment between the Statistical Laboratory of the Department of Pure Mathematics and Mathematical Statistics (Faculty of Mathematics) and the Institute of Astronomy. Her astronomy office is located at the Kavli Institute for Cosmology. As of 2024, she is Past Chair of the Astrostatistics Interest Group of the American Statistical Association, a member of the Council of the International Astrostatistics Association, and a Turing Fellow Alumnus of The Alan Turing Institute. She founded and co-organizes the Cambridge Astro Data Science Discussion Group. University: University of Cambridge School: Faculty of Mathematics Departments: Department of Pure Mathematics and Mathematical Statistics, Institute of Astronomy Office Location: Kavli Institute for Cosmology (Kavli K03) Mandel's research focuses at the intersection of astrophysics, cosmology, statistics, and machine learning. Her primary research areas include supernova cosmology, astrostatistics, astronomical machine learning, astroinformatics, time-domain and transient astronomy, Bayesian modeling and inference, and statistical computation. She leads a research group that partners with the Young Supernova Experiment time-domain survey using Pan-STARRS telescopes and participates in LSST:UK, Time-Domain Extragalactic Survey (TiDES), International CHASC Astro-Statistics Collaboration, and MSCA-RISE ASTROSTAT-II Collaboration Network. Her recent publications (2023-2025) demonstrate a strong focus on Bayesian hierarchical modeling applied to supernova cosmology, particularly addressing standardization of Type Ia supernovae, dust extinction properties, and cosmological parameter estimation. Her work increasingly incorporates machine learning techniques while maintaining rigorous statistical foundations. The publications span top astronomy journals including Monthly Notices of the Royal Astronomical Society and Astrophysical Journal. European Research Council Consolidator Grant (2020) ISBA Savage Award for Outstanding Doctoral Dissertation in Applied Statistical Methodology (2011) Mandel has received significant research funding including an ERC Consolidator Grant and has mentored numerous students who have won awards in astrostatistics competitions. She teaches Astrostatistics courses for Part III Maths/Astrophysics students during Lent terms. Her research group actively collaborates with major astronomical surveys and has developed specialized statistical methodologies for time-domain astronomy. She maintains active research laboratories within both the Statistical Laboratory and the Institute of Astronomy, facilitating interdisciplinary work that bridges astronomy and statistics. Her group develops computational tools for astronomical data analysis and contributes to major international collaborations focused on cosmological parameter estimation.