2017 LSSTC Data Science Fellows


Leo AlcorN

Texas A&M University
2017 LSSTC Data Science Fellow

Leo is a PhD student in Astronomy at Texas A&M University. She studies the evolution of galaxies at high redshift in cluster and proto-cluster environments. Her recent work focuses the morphology and kinematics of star-forming galaxies at z~2, the peak of cosmic star-formation history.


Alexandra AmoN

Stanford University
2017 LSSTC Data Science Fellow

Alexandra is a Kavli Fellow at KIPAC, Stanford University, working primarily as part of the Dark Energy Survey (DES) and LSST DESC. She graduated from the University of Edinburgh in 2018 where she worked with the Kilo-Degree Survey (KiDS). Her research uses weak gravitational lensing in conjunction with other cosmological probes to test gravity and study the dark universe.


Babatunde Akinsanmi

Universidade do Porto
2017 LSSTC Data Science Fellow

Tunde is a graduate student at the University of Porto, Portugal. He is interested in the study and detection of exoplanets using radial velocity and transit techniques. Currently his research uses computational tools to explore the possibility of detecting rings around transiting exoplanets.


Ioana Ciuca

University College London
2017 LSSTC Data Science Fellow

Ioana is a PhD student at the Mullard Space Science Laboratory, University College London, studying how the Milky Way formed and evolved by analysing its chemo-dynamical structure using data from ESA's Gaia mission. She is passionate about finding patterns in big data using machine learning. Her other passions include chess, cinema and travelling the world to learn about new cultures and people.


Carl Fields

Michigan State University
2017 LSSTC Data Science Fellow

Carl is an Astronomy & Astrophysics Ph.D. student at Michigan State University. His researches focusses on astrophysical sources of gravitational waves, stellar nucleosynthesis, and multi-dimensional simulations of core collapse supernova explosions and their massive star progenitors.


Daniel George

NCSA and University of Illinois at Urbana-Champaign
2017 LSSTC Data Science Fellow

Daniel is a PhD candidate in Astrophysics and an NVIDIA graduate fellow at the University of Illinois at Urbana-Champaign. He is currently a Research Assistant in the Gravity Group at NCSA and a member of the LIGO, NANOGrav, and DES collaborations working on deep learning and high-performance computing for gravitational wave and multimessenger astrophysics. His long-term interests lie in applying cutting-edge computer science and technology, especially artificial intelligence, to accelerate scientific discoveries.


Daniel Giles

Illinois Institute of Technology
2017 LSSTC Data Science Fellow

Daniel is a Physics Ph.D. student at Illinois Tech working on automating the discovery of anomalous data for large astrophysical surveys. He is using machine clustering techniques to identify lightcurves that don't fit into the norm and might provide new insights into astrophysical phenomena, or simply identify bad data.


Eileen Gonzales

CUNY Graduate Center
2017 LSSTC Data Science Fellow

Eileen is a graduate student at the CUNY Graduate Center in New York City and is part of the Brown Dwarfs in New York City Research Group (BDNYC). She works with distance-calibrated spectral energy distributions and atmospheric retrievals of brown dwarfs and low-mass stars to better understand their atmospheric properties.


Nathanial Hendler

Lunar and Planetary Lab, University of Arizona
2017 LSSTC Data Science Fellow

Nathan is a graduate student at the Lunar and Planetary Laboratory working with Ilaria Pascucci. His research focuses on studying planet formation by way of protoplanetary disk observations with ALMA.


Griffin Hosseinzadeh

Harvard-Smithsonian Center for Astrophysics
2017 LSSTC Data Science Fellow

Griffin is a postdoctoral researcher at the Harvard-Smithsonian Center for Astrophysics, studying supernovae and kilonovae in Edo Berger's time-domain research group. He graduated from the University of California, Santa Barbara, in 2018, where he helped run the Global Supernova Project at Las Cumbres Observatory.


Somayeh Khakpash

Lehigh University
2017 LSSTC Data Science Fellow

Somayeh is a graduate student at Lehigh University. Her research main focus is microlensing exoplanets, and currently, she is developing fast algorithms for classifying microlensing light curves. She is also a member of the LSST Transients/Variables Science Collaboration working on proposing observing strategies for LSST, and is a member of the KELT science team taking part in sorting and vetting of transiting exoplanets.


Adrian B. Lucy

Columbia University
2017 LSSTC Data Science Fellow

Adrian is a PhD candidate at Columbia University, where ey's looking for symbiotic stars—vampiric stellar binaries in which a cool giant detectably transfers mass towards a hotter companion. Sifting through giants in a multi-dimensional parameter space built from several large surveys, Adrian hopes to lay a substrate on which the true extent and impact of astrophysical symbiosis may be revealed.


James Robinson

Queen's University Belfast
2017 LSSTC Data Science Fellow

James is a PhD student at the Astrophysics Research Centre of Queen's University Belfast. He is interested in the formation and evolution of trans-Neptunian binaries and is currently looking at the production of wide, equal mass components with n-body gravitational collapse models


Karina Rojas

Universidad de Valparaíso
2017 LSSTC Data Science Fellow

Karina is a postdoctoral researcher at École Polytechnique Fédérale de Lausanne (EPFL), Switzerland. She works on the search of new strong lensing objects mainly for the Euclid collaboration. She graduated from Universidad de Valparaíso, Chile, where she studied strong lensing objects at different scales in the universe mostly focussing on strong lensing quasars. She enjoys doing outreach in spanish at StarTres.


David Thomas

Stanford University
2017 LSSTC Data Science Fellow

David is a graduate student at Stanford University. He is interested in using statistical inference, machine learning, and high performance computing to advance various LSST projects.