I am a fifth year PhD candidate in IEOR at UC Berkeley. I received a BS in applied and computational mathematics from Caltech, and a MS in industrial engineering and operations research from Berkeley. Previously, I was a research scientist intern in Amazon – Digital Privacy Team. My research interests include data-driven decision making, with particular emphasis on addressing inefficiencies and inequalities in health systems.
I’m a third year PhD student in Environmental Science, Policy, and Management, where I work with Manuela Girotto and Scott Stephens on post-wildfire snow hydrology in the Sierra Nevada. I also work on global climate modeling of future snow defecits and hydrology applications of machine learning methods. I like running, biking, and trying new foods.
Born and raised in Toulouse, France, I studied materials and mechanical engineering for my undergrad at Ecole des Ponts ParisTech in Paris. In 2020, I became a PhD student at UC Berkeley in the Mechanical Engineering department. Through my different research projects, I’ve specialized in materials computational theory. Part of Chrzan’s research group, I’m currently studying plastic deformation mechanisms in titanium at the atomistic level through computational simulations.
4th year PhD Candidate in Mechanical Engineering working in an Electrical Engineering lab (Pister Group) on multiple applications for MEMS and 3D Printing.
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!
I am the first of my family to attend college and as such I am particularly motivated to help broaden the participation of underrepresented groups in STEM. The Cretaceous-Paleogene boundary (KPB) is associated with one of the five largest mass extinction events in the geologic record and is typified by the rapid loss of taxa such as the non-avian Dinosaurs. The extinction may serve as an analog to the modern ecological crisis. I will work to develop a high-temporal resolution record of the paleoenvironmental and palaeoecological turnover across the KPB using plant and bacterial biomarkers preserved in low grade coals (lignites) in the Hell Creek region of Montana.
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.
Numi Sveinsson was born in Copenhagen, Denmark but grew up in Reykjavik, Iceland and Miami. My research aims at utilizing machine learning to automatize patient specific blood flow simulations for medical purposes. I am currently implementing a machine learning approach to determine automatically the geometry for blood vessels of interest from medical images.
I am an undergraduate student studying computer science and physics who is interested in using these two fields to better understand our immune system. I leverage multi-omics single cell datasets from patients with immune-mediated diseases to increase our understanding of pathogenesis in these diseases. Since these datasets are heavily influenced by biases and patient heterogeneity, I am also developing computational methods for analyzing these datasets more robustly. After college, I hope to pursue a PhD in computational biology and continue using computational tools to help treat immune-mediated diseases.