2019 LSSTC Data Science Fellows

 
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Aliya Babul

Columbia University
2019 LSSTC Data Science Fellow

Aliya is a third year graduate student at Columbia University. She studies the relationship between shocks and dust formation in novae.

 
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Devontae Baxter

University of California, Irvine
2019 LSSTC Data Science Fellow

Devontae Baxter is a Ph.D. student at the University of California, Irvine. His research focuses on understanding the physical processes responsible for suppressing star formation in satellite galaxies His current research project involves using supervised machine learning models trained on spectroscopically observed galaxies to classify photometrically observed galaxies as star-forming or quiescent with the ultimate goal of determining how the fraction of quiescent satellite galaxies varies as a function of satellite stellar mass and host halo mass.

 
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Michael Busch

Johns Hopkins University
2019 LSSTC Data Science Fellow

Michael is a 4th year Ph.D student and NSF Graduate Fellow at Johns Hopkins University. He works on understanding the structure and kinematics of molecular gas in the Milky Way using radio observations with the Green Bank Telescope.

 
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Andreia Carrillo

University of Texas at Austin
2019 LSSTC Data Science Fellow

Dreia is graduate student in Astronomy at University of Texas at Austin. She studies resolved stellar populations in the Milky Way and unresolved stellar populations in nearby galaxies to understand how they inform each other about galaxy formation and evolution.

 
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Pia Cortes-Zuleta

Universidad de Chile
2019 LSSTC Data Science Fellow

Pía is a Master student in Astronomy at Universidad de Chile. She is interested in exoplanets, data science applications in Astronomy and science outreach. Currently, she is working on the detection of small unseen companions of transiting exoplanets, using the Timing Transit Variation (TTV) technique and combining data coming from ground-based telescopes and TESS.

 
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Ian Cunnyngham

University of Hawai'i, Mānoa
2019 LSSTC Data Science Fellow

Ian is a graduate student at University of Hawai’i’s Institute for Astronomy focused on solar physics. With experience in data science gained equally in startups and academia, his recent research has bridged those skillsets by training deep learning models to perform spectropolarimetric inversions. Previous work has spanned a large technical range including data pipelines, helioseismology research, and production ML models, while future interests involving instrumentation and expanding his definition of what constitutes a big dataset!

 
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Emily Gilbert

University of Chicago
2019 LSSTC Data Science Fellow

Emily is a PhD candidate at the University of Chicago. She is interested in stellar activity and how it may impact planet habitability. In particular, Emily uses data from TESS to research flaring M dwarfs and the planets they may harbor.

 
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Elaheh Hayati

University of Arizona
2019 LSSTC Data Science Fellow

Elaheh is a PhD student at University of Arizona. She is a research assistant at Steward Observatory and working on simulated LSST weak lensing analysis using the DESK-DC2 (Dark Energy Science Collaboration Data Challenge 2 simulations). She measures correlation functions in redshift bins, model cosmological observable and constrain cosmological parameters. Her goal is to apply modern statistical analysis tools(machine learning and Bayesian statistics) to large datasets which are collected from cosmological surveys like LSST and to explore fundamental physics(modified gravity, dark energy, neutrinos ).

 
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Yuping Huang

California Institute of Technology
2019 LSSTC Data Science Fellow

Yuping is a graduate student in astrophysics at Caltech. He works on radio transient surveys with large radio interferometers. He is also interested in calibration and imaging algorithms for radio interferometry.

 
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Lucia Illari

The George Washington University
2019 LSSTC Data Science Fellow

Lucia Illari is a graduate student at The George Washington University in Washington, DC. They had started a project at the end of their first year where they were searching for X-ray pulsations using the Lomb-Scargle periodogram for several compact objects and over the past summer started working on a Physics Education Research project which is centered on, among other questions, determining if physics undergraduates possess the necessary writing skills for their field when they graduate. They are also working on a hobby project (in the process of publishing) analyzing the data of two large LGBTQ+ online population surveys using networks, k-means clustering, and power-law testing.

 
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Amir Kazemi-Moridani

Rutgers University
2019 LSSTC Data Science Fellow

Amir is a second-year PhD student at Rutgers University, broadly interested neutral gas emission. Amir's work focuses on detection of HI emission in galaxy merger systems. He's a member of the LADUMA project which uses the MeerKAT array to look at HI emission.

 
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Olga Harrington Pinto

University of South Florida
2019 LSSTC Data Science Fellow

Olga is a doctoral candidate at the University of South Florida. Her research focuses on looking at molecular abundance ratios in comets with radio, IR, and UV measurements to gain a better understanding of the chemical composition of our early solar system. She is interested in understanding the mysteries of the universe, and sharing her knowledge.

 
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Joseph Murphy

UC Santa Cruz
2019 LSSTC Data Science Fellow

Joey is a first-year PhD student at the University of California, Santa Cruz. He is broadly interested in exoplanets, young stars, and machine learning applications in astronomy. Recently, Joey used Gaussian processes to model the spectra of actively accreting T Tauri stars.

 
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Justin Myles

Stanford University
2019 LSST Data Science Fellow

Justin Myles is a graduate student in the X-Ray Astronomy and Observational Cosmology group at Stanford University. He is interested in testing cosmological models with galaxy cluster counts and weak gravitational lensing.

 
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Qingling Ni

Pennsylvania State University
2019 LSSTC Data Science Fellow

Qingling Ni is a PhD student at Pennsylvania State University. She is interested in utilizing extragalactic surveys to characterize AGN activity and the co-evolution between black holes and galaxies. She is a member of the LSST AGN Science Collaboration.

 
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Fabio Ragosta

INAF
2019 LSST Data Science Fellow

Fabio is a PhD student at his 2nd year, at University of Naples "Federico II". Fabio's project is focused on supernova rates and their correlation with galaxies parent population properties, in order to constrain the characteristics of the progenitors and of the environment in which the supernovae are detected.

 
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Ekta Shah

Rochester Institute of Technology
2019 LSSTC Data Science Fellow

Ekta is a Ph.D. student at Rochester Institute of Technology. Her research interests are focused on understanding the role of galaxy interactions and mergers in galaxy evolution over the history of the universe. Specifically, she is conducting a statistical analysis of multiwavelength CANDELS and COSMOS data to estimate the effects of galaxy interactions on star formation and AGN activity of galaxies at redshifts between 0 and 3.

 
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Onkabetse Sengate

University of Kwazulu-Natal
2019 LSST Data Science Fellow

Onkabetse is a PhD student at University Of Kwazulu-Natal, South Africa. He is interested in the fast transients survey with the upcoming Hydrogen Intensity and Real-time Analysis eXperiment (HIRAX) . His current PhD work focuses in building a beamforming software for HIRAX.


 
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Katia Slodkowski Clerici

Universidade Federal de Santa Catarina
2019 LSSTC Data Science Fellow

Katia is a masters student in physics at Federal University of Santa Catarina in Brazil (UFSC) interested in galaxy chemical evolution. Her research is focused on investigating the chemical abundance of the SDSS star-forming galaxies using the direct method. In her research, she develops tools for treatment of big data.

 
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Keming Zhang

UC Berkeley
2019 LSSTC Data Science Fellow

Keming is a 2nd year PhD student at UC Berkeley working at the intersection between astronomy and artificial intelligence. He is interested in new ways to extract information from raw astronomy data such as images and time series.