I’m a fourth year grad student in Astronomy at UC Berkeley, where I work with Liang Dai on gravitational lensing, specifically on dark matter substructure and lensed stars/star clusters. When I’m not doing astronomy, I enjoy powerlifting and spending time with friends!
I’m a graduate student in the astronomy department at UC Berkeley, and my research focuses on exoplanets, which are planets orbiting stars other than our Sun. I am interested in learning about how similar (or dissimilar) other planetary systems are to our own Solar System. I also enjoy baking and hiking!
Ellianna is building and implementing physics-informed machine learning architectures that accelerate discovery and analysis in astrophysical and earth science settings. A coastal polynya is an area of open water, bounded on one side by land ice, and surrounded on all other sides by sea ice. I am working to create a dataset by developing a deep learning image segmentation pipeline that incorporates the geography necessary for the creation of coastal polynyas. This study will help us to understand the rate at which coastal melting is occurring at the poles, which has long term implications for the continued use of the ocean as a natural carbon sink.
I am a graduate student at UC Berkeley studying cosmic explosions like supernovae and kilonovae that provide key insights into fundamental physics and stellar processes. My research on supernovae has focused specifically on stars that have shown signs of expelling substantial fractions of their own mass in the final decades to millennia before they explode as a supernova in an apparent ‘tantrum’ indicating the star somehow knows of its impending doom. While I look at data across the electromagnetic spectrum, my specialty is in X-ray data reduction and analysis with facilities like the Chandra X-ray Observatory and NuSTAR, both of which are space-based telescopes.
MSc and PhD from the Weizmann Institute in Israel. Led the commissioning of the W-FAST optical observatory, a robotic telescope that takes wide field images (~7 degrees-squared) at a high frame rate (25Hz), looking for fast and rare astronomical phenomena. Developed the software for observatory control, data acquisition and analysis for W-FAST as well as additional image processing algorithms.
Interested in small Solar System bodies and occultation surveys, rapidly changing variable stars such as short-period cataclysmic variables and low mass X-ray binaries, and fast optical counterparts to high energy astrophysical phenomena like GRBs or FRBs. Currently working on SkyPortal, an open source application to store, share, discuss, and process astronomical data, which can be used by small or large collaborations and projects in astronomy.
I am a graduate student at UC Berkeley, studying theoretical astrophysics with a particular emphasis on the formation and evolution of planetary systems (for more details about my research, see here). Before beginning my graduate work, I was an undergraduate at Berkeley, and received my degree in physics in 2019. Outside of research, I like to play tennis, hike, and eat lots of hummus.
Casey Lam is a PhD candidate in the Astronomy Department at UC Berkeley. With her advisor Prof. Jessica Lu, she is hunting for isolated stellar mass black holes. Although there are predicted to be 100 million of them floating throughout our own Milky Way galaxy, no detections have ever been confirmed. Casey’s research is focused on using a technique called gravitational microlensing to make a first detection of one of these elusive isolated stellar mass black holes, and her thesis work tackles this problem through a combination of simulation, modeling, and observation. First to detect isolated black hole – https://youtu.be/NwV7rwAvNlg
I grew up on a farm in Missouri where I learned firsthand the importance of taking care of the earth through sustainable farming. In graduate school, my background inspired me to use my statistical training to study regenerative agriculture and its potential to mitigate climate change. Regenerative agriculture may sequester substantial amounts of atmospheric carbon and restore soil health, making food systems more resilient to a changing climate. My research aims to identify weaknesses in soil carbon surveys, monitoring programs, and experiments, and provide better statistical tools to accomplish these tasks. I hope my work will contribute to more sustainable agriculture and a healthier planet.
I am a fourth year PhD candidate in the Statistics department at UC Berkeley. My research focuses on causal inference with applications in education and political science. I earned my Bachelor’s degree in mathematics and statistics, with a secondary in computer science, from Harvard. At Berkeley, I am co-chair of the Statistics Graduate Student Association’s Diversity Committee, am a Union (UAW 2865) Departmental Steward, and have a monthly column in the Berkeley Science Review blog, “STEMinism in the Spotlight,” where I interview women in STEM fields at UC Berkeley. Outside of Berkeley, I enjoy seeing live music, baseball and crafting (knitting, crocheting and weaving).
Andrew Shi is a PhD student in the department of mathematics. His research is in numerical methods for partial differential equations in the application area of computational fluid dynamics. In particular, his main project is on the subject of high-order shock tracking, which encompasses many fields in applied and computational mathematics such as numerical optimization and mesh generation. Prior to graduate school, he was a financial analyst in New York City. His Bachelor’s degree was also earned at UC Berkeley and he is originally from Dallas, TX.