جستجوی استاد راهنمای دانشگاهی

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.

Johannes Ulf Langeمشاهده پروفایل

استادیار

• Cosmology
• Galaxy Formation
• Machine Learning
• +۴ مورد دیگر

Johannes Ulf Lange is an Assistant Professor in the Department of Physics at American University's College of Arts and Sciences since 2024. His research bridges cosmology, galaxy formation, and machine learning through analysis of large astronomical datasets from major surveys including DESI, Dark Energy Survey, Hyper Suprime-Cam, and Kilo-Degree Survey. His educational background includes a PhD in Astronomy from Yale University under Frank van den Bosch, an MSc in Physics from Heidelberg University, and a BSc in Physics from Free University of Berlin, with exchange programs at UC Santa Barbara and Chinese University of Hong Kong. Prior to AU, he held postdoctoral fellowships as a Cosmology Fellow jointly appointed by UC Santa Cruz and Stanford University, followed by a Leinweber Fellowship at the University of Michigan's Leinweber Institute for Theoretical Physics. Lange's research focuses on fundamental cosmological questions: properties of dark energy and dark matter, validity of general relativity on cosmological scales, and the galaxy-halo connection. He employs advanced statistical methods including galaxy clustering, redshift-space distortions, and gravitational lensing on nonlinear scales. His recent publications demonstrate strong integration of machine learning techniques with cosmological analysis, particularly through his development of the NAUTILUS Bayesian inference sampler. His software contributions include major open-source projects: nautilus - Neural Network-Boosted Importance Nested Sampling dsigma - Galaxy-Galaxy Lensing Pipeline halotools - Large-Scale Structure Analysis Package TabCorr - Tabulated Correlation Functions These tools have become widely adopted in cosmological research. Lange actively mentors undergraduate researchers at American University, with current students investigating gravitational lensing, FLAMINGO simulations, and DESI data analysis. His former students have pursued diverse projects in galaxy-quenching mechanisms, control variates for simulations, and neural network applications in cosmology. His teaching includes PHYS-230 (Changing Views of Universe) and PHYS-396 (Data Mining/Machine Learning for Natural Sciences), with exceptional student evaluations highlighting his expertise in statistical methods and coding instruction.

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.

• Gravitational-Wave Astronomy
• Cosmology
• Black Holes
• +۳ مورد دیگر

Maya Fishbach is an Assistant Professor at the Canadian Institute for Theoretical Astrophysics (CITA), University of Toronto. Her research focuses on gravitational-wave astronomy, cosmology, and the astrophysics of compact objects like black holes and neutron stars. She utilizes data from gravitational-wave observatories such as LIGO, Virgo, and KAGRA to study binary systems, stellar evolution, and cosmic phenomena. Her work integrates astrostatistics and computational methods to analyze gravitational-wave signals, constrain cosmological parameters, and explore the origins of compact binaries. Fishbach collaborates on projects involving multi-messenger astronomy, linking gravitational-wave observations with electromagnetic counterparts and theoretical models. Recent research highlights include studies of black hole spin dynamics, hierarchical mergers, and the connection between binary black holes and globular clusters. She contributes to open-source tools like tBilby for gravitational-wave data analysis and actively participates in observational campaigns to detect transient events. Her academic affiliations include the CITA community and collaborations with international gravitational-wave detector networks. Fishbach’s research spans theoretical astrophysics, observational cosmology, and computational methods, making significant contributions to understanding the dynamic universe through gravitational waves.

• observational cosmology
• theoretical cosmology
• cosmic microwave background
• +۳ مورد دیگر

Renée Hložek is an Associate Professor at the University of Toronto's Dunlap Institute for Astronomy & Astrophysics (Dunlap) and the Department of Astronomy & Astrophysics (DADDAA). Her research focuses on theoretical cosmology and statistical methods, particularly using cosmic microwave background (CMB) data from collaborations like the Atacama Cosmology Telescope and the Simons Observatory. She also contributes to the Dark Energy Science Collaboration of LSST, analyzing supernova data to study dark energy. Hložek holds a Ph.D. from Oxford (2011) and is active in science communication and the intersection of art and science. Affiliations: Dunlap Institute for Astronomy & Astrophysics Department of Astronomy & Astrophysics (DADDAA) Atacama Cosmology Telescope collaboration Simons Observatory Dark Energy Science Collaboration (LSST) Research Interests: Combines cosmological modeling with advanced statistical techniques to explore early universe physics, star formation, and dark energy using large-scale observational datasets. Specializes in CMB angular resolution analysis and photometric supernova studies.

Robert Izzardمشاهده پروفایل

مدرس ارشد

• Astrophysics
• Stellar Evolution
• Binary Star Systems
• +۷ مورد دیگر

Robert Izzard is a Senior Lecturer in the Astrophysics Group at the University of Surrey, with affiliations to the experimental nuclear physics group. He holds a PhD from the University of Cambridge (2004) and a Graduate Certificate in Learning and Teaching (2018). His research focuses on binary star evolution, nucleosynthesis, computational astrophysics, and education outreach. Izzard leads the binary_c software framework, modeling stellar populations and their impact on galactic chemical evolution. His work spans topics like common-envelope evolution, gamma-ray burst progenitors, and the formation of planetary systems around binary stars. He has supervised numerous PhD students and contributes to projects like Stars for Schools , integrating coding and visualization for STEM education. Research highlights include studies on the role of binary systems in aluminum-26 production, the impact of binaries on galaxy evolution via dust and metals, and the statistical analysis of core-collapse supernova progenitors. Izzard has published over 50 peer-reviewed articles and secured grants from STFC and the Royal Society. His awards include the Lynne Millward Academic of the Year Award (2021). Teaching roles include Advanced Nuclear Astrophysics and supervision of final-year projects. Current projects explore pulsational pair-instability supernovae and X-ray binaries in the early universe. His research network includes collaborations with institutions like the University of Bonn, Monash University, and the ERC-funded Asterochronometry project. Key tools developed include the binary_c-python interface and the MINT interpolation library for stellar evolution models.

Collin Politschمشاهده پروفایل

پژوهشگر

• Astrophysics
• Statistics
• Machine Learning
• +۳ مورد دیگر

Collin Politsch is a former academic researcher transitioning to industry. He held postdoctoral roles at the University of Cambridge (Kavli Institute for Cosmology and Institute of Astronomy) and Carnegie Mellon University (Machine Learning Department), where he led the Delphi Group's forecasting initiative for the CDC's pandemic response. He was a guest researcher at the Flatiron Institute's Center for Computational Astrophysics. Education: Joint Ph.D. in Statistics and Machine Learning from Carnegie Mellon University (2020), supervised by Larry Wasserman, Jessi Cisewski-Kehe, and Rupert Croft. M.Sc. in Machine Learning (Carnegie Mellon). Research focuses on astrostatistics, cosmology, and statistical methods for astronomical data. Notable contributions include trend filtering algorithms for signal denoising and epidemiological forecasting models. Awards include the 2020–21 Gavaskar Award for Best Dissertation and finalist placements in astrostatistics competitions.

Larry Wassermanمشاهده پروفایل

استاد

• nonparametric inference
• causal inference
• machine learning
• +۶ مورد دیگر

Larry Wasserman is a UPMC University Professor at Carnegie Mellon University, jointly appointed in the Department of Statistics and Data Science and the Machine Learning Department. He received his Ph.D. from the University of Toronto in 1988 and is recognized as one of the leading statisticians of his generation. His research spans theoretical and applied statistics, with core interests in: Foundational inference : Nonparametric methods, asymptotic theory, causal frameworks Modern applications : Machine learning, high-dimensional statistics, astrostatistics Interdisciplinary domains : Bioinformatics, genomics, physical sciences via the STAMPS group His recent publications demonstrate strong emphasis on causal methodology, optimal transport, and robust inference, with applications ranging from particle physics to genomic analysis. Articles frequently develop novel nonparametric techniques with minimax optimality guarantees. Award highlights include: COPSS Presidents' Award (1999) - Top honor for statisticians under 40 CRM-SSC Prize (2002) - Landmark contributions to statistics Fellowships: American Statistical Association, Institute of Mathematical Statistics, AAAS He leads the Statistical Machine Learning Theory Group and founded STAMPS (Statistical Methods for Physical Sciences). His textbooks All of Statistics and All of Nonparametric Statistics are widely used in graduate programs globally.

Peter Freemanمشاهده پروفایل

دانشگاهی

• 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.

Senthil Balaji Girimuruganمشاهده پروفایل

استاد

• High Dimensional Statistics
• Bioinformatics
• Genomics
• +۵ مورد دیگر

Senthil Balaji Girimurugan is an Associate Dean of the Honors College at Florida Gulf Coast University (FGCU), holding a Ph.D. in Statistics from Florida State University (2014). His academic focus lies in high-dimensional statistics, wavelet transforms, and their applications in bioinformatics, genomics, and neuroimaging. He teaches advanced statistics and mathematics courses, including Applied Mathematical Statistics and Time Series Analysis. Education: Ph.D., Statistics, Florida State University, 2014 Research Interests: Dr. Girimurugan develops statistical methods for analyzing genomic and neuroimaging data, leveraging wavelet transforms and machine learning. His work includes projects such as the NUPRIME epigenomics initiative and Human Connectome Project neuroimaging analysis. He emphasizes innovative teaching methods like Team-Based Learning and integrates computational tools (Python/R, MySQL) into research. Conference Contributions: Presented on topics including wavelet-based bioinformatics, statistical education innovations, and genomic data segmentation at institutions like Dartmouth College and the Maize Genomics Conference. Labs/Teams: Collaborates with interdisciplinary teams in genomics and computational biology, mentoring over 15 students in research projects across disciplines.

Paul T. Bakerمشاهده پروفایل

استادیار

• Gravitational Wave Astronomy
• Astrophysics
• Astrostatistics
• +۳ مورد دیگر

Paul T. Baker is an Assistant Professor of Physics at Widener University, located in Chester, PA. His primary affiliations include the Physics Department within the College of Arts & Sciences. He holds a PhD in Physics from Montana State University (2013), an MS in Physics from Montana State University (2008), and a BA in Physics from Reed College (2006). His teaching responsibilities include introductory physics for non-majors, Modern Physics, and Computational Physics. Dr. Baker’s research focuses on gravitational wave detection, particularly through pulsar timing array collaborations like NANOGrav and IPTA. His work combines physics, astronomy, computing, and statistics to analyze data from supermassive black hole mergers. He has contributed to breakthrough studies such as the International Pulsar Timing Array’s search for gravitational wave backgrounds and the NANOGrav 12.5-year dataset analysis. He has received the Breakthrough Prize in Fundamental Physics (2016) as part of the LIGO Scientific Collaboration. His professional memberships include the American Physical Society, American Association of Physics Teachers, and American Astronomical Society. Baker has secured National Science Foundation funding through NANOGrav’s Physics Frontiers Center grant. His research highlights interdisciplinary methods, including Bayesian statistical techniques and Python-based pulsar signal simulation tools. Beyond academia, he enjoys户外 activities in mountainous environments.

EIICHIRO KOMATSUمشاهده پروفایل

استاد

• Cosmology
• Cosmic Microwave Background (CMB)
• Inflationary Theory
• +۵ مورد دیگر

Eiichiro Komatsu is Director of the Department of Physical Cosmology at the Max Planck Institute for Astrophysics (MPA) in Germany and Senior Fellow at the Kavli Institute for the Physics and Mathematics of the Universe (Kavli IPMU) at the University of Tokyo. He holds an honorary professorship at Ludwig Maximilians University (LMU) in Munich. His work focuses on understanding the universe's origins and evolution through cosmic microwave background (CMB) radiation studies, inflationary cosmology, and large-scale structure analysis. Education: PhD in Science (2001) from Tohoku University, Japan, with a thesis on non-Gaussian fluctuations in the CMB. Master's (1999) and Bachelor's (1997) degrees in Science from Tohoku University. Research Interests: Cosmic Microwave Background (CMB) anisotropies and polarization Inflationary theory and early universe physics Dark matter and dark energy Large-scale structure formation Cosmological parameter estimation Awards: Recipient of prestigious honors including the Breakthrough Prize in Fundamental Physics (2018), Gruber Cosmology Prize (2012), and multiple national and international awards recognizing contributions to cosmology and astrophysics. Grants and Funding: Lead or co-investigator on numerous grants, including the European Union's CMB-INFLATE project (€73,500) and the German Excellence Cluster ORIGINS (€7.5M annual contribution). Managed over $1.3M in US grants during his tenure at the University of Texas. Professional Activities: Editor for Astronomy and Astrophysics Review and Journal of Cosmology and Astroparticle Physics . Serves on review panels for NASA, ESA, and the European Research Council. Active in public outreach, including a monthly column in the Munich Japanese Society Newsletter and authoring popular science books. Labs/Teams: Founded the Texas Cosmology Center (2010–2012) and led the WMAP Science Team analysis. Currently oversees MPA's cosmology research and collaborates with global initiatives like LiteBIRD (CMB polarization satellite) and HETDEX (galaxy survey).

• Astronomy
• Astrophysics
• Machine Learning
• +۳ مورد دیگر

Arvind C. N. Hughes is a Part-Time Lecturer at Macquarie University and a Postdoctoral Researcher at Imperial College London. At Imperial, he develops Bayesian pipelines to identify high-redshift quasars in the Euclid Survey. At Macquarie, he teaches business analytics and statistics courses. Previously, he completed his PhD at the Max Planck Institute for Astronomy and Macquarie University, focusing on statistical and machine learning methods for Milky Way studies. His educational background includes: Ph.D. Candidate (2019-2023) at Max Planck Institute for Astronomy and Macquarie University Masters of Research (2016-2017) at Macquarie University Bachelor of Science in Astronomy & Astrophysics (2013-2015) at Macquarie University Bachelor of Arts in Mathematics and Bachelor of Economics in Econometrics (2007-2012) at Macquarie University Dr. Hughes' research interests lie at the intersection of astronomy and data science. He applies machine learning and statistical techniques to large astronomical surveys, with a focus on galactic archaeology and the search for high-redshift quasars and extremely metal-poor stars. His work leverages datasets from missions like Gaia and Euclid. He has presented his research at conferences including the European Astronomical Society Meeting and the Astronomical Society of Australia meetings. Additionally, he has industry experience as a Chief Data Scientist in the property market sector, where he developed valuation tools. Dr. Hughes has been affiliated with the Gaia Galactic Survey Mission group at the Max Planck Institute for Astronomy and currently works in the Astrostatistics Group at Imperial College London.

Anne Medlingمشاهده پروفایل

دانشیار

• Galaxy Evolution
• Black Hole Feedback
• Integral Field Spectroscopy
• +۱ مورد دیگر

Anne Medling is an Associate Professor specializing in galaxy evolution and black hole feedback. Her research combines multiwavelength observations with simulations to study AGN feedback mechanisms and star formation quenching. She previously worked at Australian National University and Caltech. Recent JWST observations focus on molecular gas excitation in luminous infrared galaxies and developing new classification systems for galactic power sources. She maintains strong involvement in the SAMI Galaxy Survey collaboration.

Eric Feigelsonمشاهده پروفایل

استاد

• X-ray Astronomy
• Astrostatistics
• Star Formation
• +۳ مورد دیگر

Eric Feigelson is a Distinguished Senior Scholar and Professor at the Department of Astronomy and Astrophysics and Department of Statistics at Pennsylvania State University. He holds dual appointments and has been a faculty member since 2002. His research focuses on X-ray astronomy, particularly star and planet formation, and the interdisciplinary field of astrostatistics. He co-founded the Center for Astrostatistics and has organized major conferences like the Statistical Challenges in Modern Astronomy series and the Summer Schools in Statistics for Astronomers. Education: B.A. in Astronomy from Haverford College (1975), M.A. and Ph.D. in Astronomy from Harvard University (1978, 1980). Research Contributions: Pioneered X-ray studies of young stellar populations using observatories like Chandra, leading projects such as the Chandra Orion Ultradeep Project (COUP) and MYStIX. Co-authored the seminal textbook Modern Statistical Methods for Astronomy with R Applications (2012). Currently explores exoplanet detection via statistical methods and prepares for the Rubin Observatory's sky surveys. Awards & Roles: Served as Statistical Scientific Editor of the American Astronomical Society Journals since 2016. Recognized with the PROSE Award, Bruno Rossi Prize, and multiple honorary titles. Holds visiting professorships at Imperial College London (2016) and the University of Padua (2019). Teaching & Mentorship: Taught all levels of astronomy courses at Penn State. Advised numerous students through research projects and the Summer School programs, fostering interdisciplinary education in statistics and astronomy.