Over the next few weeks we’ll be introducing you to our first year doctoral students.
Hello! My name is Becky Danning, and I’m originally from the Boston area. I graduated in 2016 from Amherst College with a BA in Mathematics and in 2019 I received my MSc in Statistics from the London School of Economics. In between (and during) these degrees, I’ve worked in health policy data analysis, statistical genetics and clinical trials research, the healthtech startup world, and most recently in institutional research.
Broadly speaking, I’m interested in developing network analysis community detection algorithms to find latent subtypes within the genetics of complex neuropsychiatric conditions. I wrote my undergraduate mathematics thesis in network analysis, and chose LSE to be able to continue studying network analysis at the graduate level. While at LSE, I particularly loved my course on factor analysis, and began thinking about the ways in which network analysis could be used as a dimensionality reduction tool. Between Amherst and LSE, I had spent a year working for a statistical geneticist in the Department of Preventive Medicine at BWH, and I’m very interested in all the ways in which genotypes and phenotypes can be represented as networks. Finally, my particular interest in complex neuropsychiatric conditions comes from having a brother on the severe end of the autism spectrum, and likewise knowing enough individuals on the same end of this linear classification system to be convinced of the necessity of extending the spectrum to higher dimensions.
Outside of work, I sing in a choir, I read lots of books (particularly Golden Age detective novels), and I go on excessively long walks wearing practical footwear. I’m very excited to meet everyone!
Hi! My name is Raphael Kim. I was born in Seattle, Washington but mainly grew up around the Triangle in North Carolina. I went to UNC Chapel Hill for my undergraduate (Math, Computer Science) and Master’s (Computer Science).
My interests in statistics and medicine began in high school while I was researching 3-state conformational switching in proteins. Being new to the field, I was amazed to see math and statistics impact something as meaningful as drug design. I ended up learning a lot about numerical optimization, which eventually led me to ’Machine Learning’. I became fascinated with the applications, developing a (very rudimentary) chess engine with machine learning, before moving back to medical applications.
At UNC, I primarily worked in neuro-psychiatry. I developed a clinical prediction tool for PTSD to use in the emergency department, while investigating ethnic/sex differences, comorbidities (pain, depression), and the role of miRNA on pain. With collaborators at UCSD, I’ve worked on classification and transfer learning for Autism Spectrum Disorder based on gene expression data. More recently, I’ve attempted generative modeling and prediction on EHR data (Never have I worked with such a potentially rich but unwieldy dataset before!). This past summer, I’ve been getting industry exposure developing software for IoT sensor analysis using dynamic Bayesian networks.
At Harvard I hope to explore causal inference problems in complex observational settings, and high dimensional statistics with applications in genomics/neuroscience. In my free time, I love traveling the world, playing soccer or racquet sports, social dance (big fan of swing), and hiking to reach beautiful outlooks.