جست‌وجوی استاد و پژوهشگر

Arya Farahiمشاهده پروفایل

استادیار

• Statistical/Machine Learning
• Algorithmic Fairness
• Astroinformatics
• +۳ مورد دیگر

Arya Farahi is an Assistant Professor of Statistics and Data Sciences at the University of Texas at Austin since 2021. His research bridges astroinformatics, urban informatics, and AI ethics, focusing on mitigating algorithmic bias and uncertainty quantification in real-world applications. He holds PhDs in Physics and Scientific Computing from the University of Michigan, where he was a Data Science Fellow at the Michigan Institute for Data Science. Farahi's work includes collaborations with international projects such as the Dark Energy Survey (DES), COsmostatistics Initiative (COIN), and XMM-XXL Consortium. He leads the D3 Lab, which develops AI tools for scientific discovery and societal challenges, emphasizing interdisciplinary collaboration. His open-source contributions include TATTER, KLLR, and PoPE for statistical analysis and visualization. Key awards include the Best Student Paper Award at KDD’18 and a $400k+ grant. He is a volunteer with Statistics Without Borders and actively involved in projects like the Fire and Smoke Digital Twin for urban resilience. His research spans cosmology, healthcare AI, and urban economics, with a focus on trustworthy models and equitable AI systems.

Chad Schaferمشاهده پروفایل

استاد

• Astrostatistics
• High-Dimensional Statistics
• Nonparametric Methods
• +۵ مورد دیگر

Chad Schafer is a Professor in the Department of Statistics & Data Science at Carnegie Mellon University (CMU), affiliated with the Dietrich College of Humanities and Social Sciences. He holds a Ph.D. in Statistics from UC Berkeley (2004), an M.S. from the University of Illinois at Urbana-Champaign, and a B.S. from Western Michigan University. Prior to academia, he contributed to climate modeling at Argonne National Laboratory. His research focuses on astrostatistics, addressing complex data structures in cosmology and astronomical surveys like the Sloan Digital Sky Survey (SDSS) and Large Synoptic Survey Telescope (LSST). Key areas include statistical inference in high-dimensional settings, nonparametric methods, and computational frameworks for large-scale scientific data. He emphasizes collaboration with domain scientists through CMU's McWilliams Center for Cosmology. Publications highlight advancements in galaxy density analysis, machine learning applications, and software frameworks (e.g., LINCC, KBMOD) for astrophysical data. Schafer also advocates for interdisciplinary education, emphasizing technical skills and software sustainability in astronomy. His work bridges statistical theory with real-world challenges in modern astrophysics.

Ricardo Vilaltaمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Pattern Recognition
• +۲ مورد دیگر

Ricardo Vilalta serves as Professor of Computer Science in the Center for Science, Technology, Engineering, and Mathematics at the University of Austin since 2024, following a distinguished career spanning academia and industry. His educational background includes: Ph.D. in Computer Science (1998), University of Illinois at Urbana-Champaign M.S. in Computer Science (1994), University of Illinois at Urbana-Champaign B.S. in Computer Engineering (1989), Instituto Tecnológico y de Estudios Superiores de Monterrey, México Dr. Vilalta's research integrates statistical theory with practical applications in machine learning and artificial intelligence , specializing in pattern recognition and astroinformatics . His work bridges computational methods with astronomical data analysis, advancing algorithmic approaches for complex scientific datasets. His scientific contributions have been honored with: Fulbright scholarship IBM Invention Achievement Award European Conference on Machine Learning Best Paper Award National Science Foundation CAREER Award Complementing his academic role, Dr. Vilalta founded and serves as CEO of AIRD AI Research and Development (AIRD.TECH) since 2018, driving innovation in applied artificial intelligence while maintaining active research leadership.

• Information Systems
• Grid Computing
• Data Storage
• +۵ مورد دیگر

Fokke Dijkstra is a Researcher at the University of Groningen , specializing in Information Systems , Grid Computing , and Petabyte-scale Data Storage . His work focuses on enabling large-scale scientific research through infrastructure development. Key Collaborations: Astro-WISE, LOFAR (Low-Frequency Array) projects Research Themes: Long-term archiving, distributed computing, and grid technology for astrophysics Recent Activity: Contributed to a memorial article for Joop van Lenthe in 2022 Academic Output includes 15+ publications on scaling data systems for astronomy, with notable work on LOFAR and Astro-WISE infrastructure. His research bridges computational methods with astrophysical data challenges. Activities include participation in the BioSB 2020 conference, demonstrating his interdisciplinary engagement.

Matias Carrasco Kindمشاهده پروفایل

استادیار

• Machine Learning
• Deep Learning
• Data Mining
• +۱۸ مورد دیگر

Matias Carrasco Kind serves as the Director of Data Science Research Services (DSRS) within the Gies College of Business and is a Research Assistant Professor in the Departments of Accountancy and Astronomy, as well as at the National Center for Supercomputing Applications (NCSA), all at the University of Illinois Urbana-Champaign (UIUC). He is also a Trusted CI Fellow at the NSF Cybersecurity Center of Excellence and holds a Faculty-in-Residence position at the Discovery Partners Institute (DPI) in Chicago, IL. Education: Ph.D. in Astronomy, University of Illinois, 2014 M.S. in Astronomy, University of Illinois, 2011 His research focuses on data-intensive science , spanning machine learning , deep learning , scientific computing , and photometric redshifts . He applies these techniques to cosmology, galaxy formation, and cross-disciplinary fields like finance, Earth sciences, and bio-imaging. His recent publications emphasize cosmological constraints from multi-survey data (DES, SPT, ACT, HST), weak lensing analysis, and machine learning applications in galaxy clustering and dark matter halo modeling. Key trends include the integration of AI with astrophysical data and interdisciplinary collaborations. Scientific Awards: Trusted CI Fellow at NSF Cybersecurity Center of Excellence He collaborates with teams at the Data Science Research Services (DSRS) , NCSA , and Discovery Partners Institute (DPI) , leveraging cyberinfrastructure for astronomy and finance research.

• Astrophysics
• Stellar Evolution
• Exoplanets
• +۲ مورد دیگر

Iain McDonald is a Research Fellow in the Astronomy and Astrophysics Theory Group at the University of Manchester, specializing in computational astrophysics and stellar evolution studies. Research focuses on theoretical modeling of nonequilibrium astrophysical systems, including evolved stars and extrasolar planets. Current projects investigate molecular inventories of oxygen-rich stars using ALMA data, machine learning approaches for stellar classification, and automated spectral energy distribution analysis. Publications demonstrate advanced computational techniques applied to stellar spectroscopy and photometry, with recent work developing PySSED for automated stellar spectral fitting. Collaborations span international observatories and data science initiatives.

Abel YANG Jiahuiمشاهده پروفایل

مدرس ارشد

• Galaxy Clustering
• Large Scale Structure of the Universe
• Astroinformatics
• +۳ مورد دیگر

Abel YANG Jiahui is a Senior Lecturer at the National University of Singapore, specializing in galaxy clustering and the large-scale structure of the universe. His research spans astroinformatics, cosmology, and statistical mechanics, with a focus on modeling galaxy distributions. Current Research: Galaxy Clustering, Large Scale Structure, Astroinformatics, and Astronomy Education Email: phyyja@nus.edu.sg | Office: S12-02-03 | Phone: +65 6516 1246 His publications primarily address galaxy distribution patterns and cosmological modeling, including studies on statistical mechanics in cosmic systems and hands-on astronomy education. Notable works include analyses of SDSS data and the effects of galaxy merging on distribution functions.

• Astronomy
• Cosmology
• Globular Clusters
• +۱۰ مورد دیگر

S. George Djorgovski is a Professor of Astronomy and Data Science at the California Institute of Technology (Caltech), holding positions since 1987. He serves as Director of the Center for Data-Driven Discovery (CDDD) since 2014 and was Executive Officer for Astronomy (2016–2019). His academic journey includes a B.A. from the University of Belgrade (1979), M.A. and Ph.D. from the University of California (1981 and 1985). Research Interests: Djorgovski’s work spans astronomy and cosmology, including galactic structure, dark energy, gravitational lenses, and astroinformatics. He pioneers the use of machine learning and extended reality in data-driven discovery. Notable areas include large-scale sky surveys, Virtual Observatory infrastructure, and interdisciplinary applications of data science in medicine. Awards: He has been honored with the AAAS Fellowship, Boeing-Griffith Prize, and multiple fellowships including Sloan and Harvard Junior Fellowships. His contributions to data science methodologies and astrophysical research are internationally recognized. Leadership: As CDDD Director, he drives advancements in computational tools and collaborative human-AI systems. He teaches advanced courses on astroinformatics and cosmology, actively mentoring graduate students and shaping interdisciplinary research initiatives.

Norman Grayمشاهده پروفایل

مدرس

• Astronomical Data Management
• Astroinformatics
• Semantic Technologies
• +۳ مورد دیگر

Dr Norman Gray is a Lecturer in the School of Physics and Astronomy and Principal Engineer (Educational Technology) at the University of Glasgow's College of Science and Engineering. His primary affiliation is with the School of Physics and Astronomy, located in the Kelvin Building. His research focuses on astronomical data management, astroinformatics, and semantic technologies, including projects like MaRDI-Gross and Explicator. He has contributed to initiatives such as the Virtual Observatory, serving as Vice-chair of the IVOA Semantics Working Group. Teaching responsibilities include Special Relativity, General Relativity, and modules in the Astronomy Honours program. He authored books on Special and General Relativity (Cambridge University Press, 2022 and 2019). He leads educational technology efforts, coordinating Software Carpentry courses for the SUPA graduate school. His roles also include advising on data infrastructure projects and interdisciplinary collaborations with computing science and archives. Gray is actively involved in outreach and adult education, contributing to courses like 'Fundamental Physics and the Nature of Reality' and 'Galileo' through the Centre for Open Studies. His work bridges academic research, pedagogy, and technological innovation in astrophysics and data science.

• Astronomy
• Astrophysics
• Cosmology
• +۷ مورد دیگر

George Djorgovski is Professor of Astronomy and Data Science at the California Institute of Technology (Caltech), where he has served since 1987, progressing from Assistant Professor to full Professor in 1997. He currently serves as Director of the Center for Data-Driven Discovery (CD3) since 2014 and previously served as Executive Officer for Astronomy from 2016-2019. His research spans a broad range of topics in astronomy and cosmology including the structure and dynamics of globular clusters, fundamental properties of galaxies and their evolution, gamma-ray bursts, early phases of galaxy and structure formation, distant quasars, gravitational lenses, dark energy, and exploration of the time domain. He is a pioneer in large digital sky surveys, Virtual Observatory, and astroinformatics, with a particular focus on applying Machine Learning and AI to analyze complex astronomical datasets. Djorgovski is deeply interested in how computational and information technology transforms scientific methodology across disciplines. His work on data science methodology transfer between fields, particularly from astronomy to medicine, represents a significant contribution to interdisciplinary science. He is developing tools for collaborative human-AI discovery and exploring Extended Reality technologies for science and education. Fellow, American Association for Advancement of Science, 2014 First Prize, Boeing-Griffith Science Writing Contest, 2008 Presidential Young Investigator, 1991–1997 ISI 1000 most cited physicists, 1981–1997 NASA Group Achievement Award, 1996 Dudley Observatory Award, 1991 Alfred P. Sloan Foundation Fellow, 1988–1991 Harvard Junior Fellow, 1985–1987 As Director of the Center for Data-Driven Discovery, Djorgovski leads initiatives that address the challenges of exponential data growth across scientific disciplines. His work bridges astronomy with data science, creating general tools for 21st century scientific methodology. He is active in multiple professional societies including the International Astrostatistics Association and International Astroinformatics Association, which he helped establish.

• Statistical Inference
• Machine Learning
• Computational Biology
• +۳ مورد دیگر

Jon McAuliffe is an Assistant Professor at the Department of Statistics, University of California, Berkeley. He earned his Ph.D. in Statistics at Berkeley in 2005 under Michael Jordan, with a dissertation titled "Statistical Methods for Comparing Genomes." His research spans statistical inference, machine learning, and their applications in astronomy, computational biology, and theoretical statistics. Department: Statistics University: University of California, Berkeley Academic Rank: Assistant Professor Ph.D. Year: 2005 Advisor: Michael Jordan McAuliffe's research interests include Bayesian inference , variational methods , extreme event modeling , and scalable statistical algorithms . His work addresses challenges in astronomical image analysis, cancer genomics, and probabilistic modeling of complex datasets. Recent publications focus on time-uniform confidence sequences , deblending starfields , and applications in climate science . While no specific awards are listed, his contributions to methodological statistics and interdisciplinary applications are notable. He has not been explicitly linked to advising students in the provided texts but has collaborated on projects like the Celeste astronomical catalog , emphasizing computational efficiency and probabilistic modeling at petascale levels.

• Artificial Intelligence
• Atmospheric Science
• Air Quality
• +۱ مورد دیگر

Dr. Ekta Sharma is Lecturer in Mathematics and AI researcher at the University of Southern Queensland, specializing in atmospheric science applications. Her ONI-funded work develops AI solutions for air quality monitoring and satellite sustainability. Recipient of the Women in AI Asia Pacific Award and National Intelligence Grant. Leads projects for Defence Science and Technology Group and Bureau of Meteorology. Developed early warning systems for air pollution commended by Australian High Commission Author of SHE Series books promoting women in STEMM

• Mineral Physics
• Planetary Science
• Impact Cratering
• +۱ مورد دیگر

Tianqi Xie is an Assistant Professor in the Department of Geological Sciences at the University of Saskatchewan, College of Arts and Science. His research focuses on high-pressure mineral physics, impact cratering events, and planetary surface evolution, with specialization in shock metamorphism and phase transitions in minerals under extreme conditions. Research Interests: Dr. Xie's work integrates experimental mineral physics with planetary science, using techniques including Raman spectroscopy, X-ray diffraction, and high-pressure experiments to study shock metamorphism in lunar and terrestrial samples. His research areas include: Phase transitions in minerals under high-pressure conditions Impact cratering processes and shock metamorphism Evolution of rocky planetary surfaces in the Solar System Crystallographic analysis of shocked minerals Experimental simulation of deep Earth conditions Recent publications focus on experimental mineral physics with thematic concentrations in high-pressure phase transitions, shock metamorphism characterization, lunar sample analysis, and planetary analog missions. Research trends show increasing application of machine learning techniques for spectral analysis and simulation methodologies for space exploration scenarios. Laboratory and Field Work: Dr. Xie conducts experimental research in mineral physics laboratories and participates in analogue planetary missions, including the CanMoon Lunar Sample Return Analogue Mission, where he contributes to field geology operations and instrumentation deployment protocols.

Dana Casettiمشاهده پروفایل

دانشیار

• Astrophysics
• Astronomy
• Galaxy Formation
• +۲ مورد دیگر

Dana Casetti is an Associate Professor in the Department of Physics at Southern Connecticut State University. Born in Bucharest, Romania, she holds a Master's degree in Physics from the University of Bucharest and a PhD in Astronomy from Yale University. Her research focuses on astrophysics, astronomy, large astronomical surveys, and Milky Way structure formation. Dr. Casetti's work emphasizes astronomical image processing and astrometric calibration techniques. Her publications demonstrate consistent focus on galactic dynamics, stellar populations, and advanced instrumentation. Recent articles highlight her contributions to space telescope calibration and galaxy kinematics research. She maintains active research collaborations, including NASA-funded projects on astrometric calibration of space telescopes. Dr. Casetti advises undergraduate and graduate students in research projects and independent studies, guiding them in astronomical data analysis techniques.

• Bayesian Analysis
• Astrophysics
• Solar Physics
• +۵ مورد دیگر

David van Dyk is a Professor of Statistics and Chair in Statistics at Imperial College London's Department of Mathematics within the Faculty of Natural Sciences. He holds a PhD from the University of Chicago and has held academic positions at Harvard University and the University of California, Irvine before joining Imperial College in 2011. He has served as Head of the Department of Mathematics (2017–2023) and Department Chair at UC Irvine (2010–2011). His research focuses on Bayesian methodology, computational statistics, and their applications in astrophysics, solar physics, and high-energy physics, with a particular emphasis on interdisciplinary collaboration. He coordinates the CHASC International Astrostatistics Center and develops methods for efficient computation in complex statistical models, including EM algorithms and MCMC methods. Van Dyk has received prestigious awards such as the Wolfson Merit Award (2011) and the ASA Founders Award (2019), and is an elected Fellow of multiple statistical and astrostatistics organizations. Van Dyk's educational background includes a BS from Michigan State University (1991) and a PhD from the University of Chicago (1995). His academic trajectory includes roles at Harvard (1996–2003) and UC Irvine (2003–2011), where he contributed to department leadership and advanced statistical research. His work emphasizes bridging statistical theory with practical challenges in astronomy, such as calibration concordance for X-ray telescopes and analyzing gravitational lens time delays. He has pioneered software tools like LIRA for low-count image reconstruction and BAHAMAS for supernova analysis. His research interests span computational methods for Bayesian analysis, statistical modeling of astronomical phenomena, and addressing systematic uncertainties in observational data. He actively participates in astroinformatics initiatives, advocating for rigorous statistical practices in data-driven astronomy. His recent work addresses covariate shift adaptation in machine learning (StratLearn), time series analysis of stellar flares, and improving white dwarf age estimation using Gaia data. Awards: Wolfson Merit Award, ASA Founders Award, Fellowships in ASA, IMS, and IAA Labs/Teams: CHASC International Center for Astrostatistics, Imperial's Bayesian Methods and AI/ML networks Grants/Funding: Not explicitly listed but implied through leadership roles and software development