We’ll be featuring mini-profiles of our new PhD students over the next few weeks. We look forward to welcoming them into our community!
Hi! My name is Julie-Alexia and I’m originally from Paris, France. I graduated in 2020 from McGill University with a BSc in Mathematics and Statistics and this May I received my MSc in Computational Biology and Quantitative Genetics MSc here at HSPH.My research interests center around the development and application of statistical methods in cancer genetics. I wish to better understand the genetic basis of human cancer in order to improve risk prediction and help develop personalized targeted therapies. During my MSc, I worked in collaboration with Pfizer on developing computational methods to analyze CRISPR-Cas9 knockout screens to identify synthetically lethal genes to develop cancer therapies. We applied our methods to lung cancer cell lines from the Broad Institute aiming to find alternatives to KRAS, previously known to be hard to drug. Recently, I have been developing variant-specific multi-gene, multi-cancer Mandelian risk prediction models within the BayesMendel lab at the Dana Farber Cancer Institute. In particular, I have been integrating BRCA1 and BRCA2 variant-specific risk for breast and ovarian cancer within the existing PanelPRO model. In my free time, I enjoy skiing, hiking and cooking new vegetarian recipes. I’m excited to keep exploring the mountains around Boston and to travel in the nearby states.
Lee DingHey everyone! I’m Lee Ding, and I’m from Newton, MA. Having grown up just a mile away from Longwood in Brookline, MA, I’m excited to start my Ph.D. in the same area where I made many childhood memories, both fond (i.e., trips to the Longwood Galleria) and not-so-fond (i.e., trips to Boston Children’s Hospital). I graduated this year from Brown University with an Sc.B. in Applied Math. I first became interested in biostatistics when I participated in the Boston University Summer Institute for Research Education in Biostatistics (SIBS) in 2020. Coming to BU SIBS as an applied math student struggling to figure out where to apply the math I was studying, I soon became drawn to all the different ways data could be used to inform life-saving interventions. I’ve since grown my interest in biostatistics through projects united by a common thread of leveraging statistics to answer questions in biology, medicine, and public health. At Brown, I began working with Drs. Veronica Ciocanel and Bjorn Sandstede on creating probabilistic approaches for assessing parameter identifiability in a partial differential equation model for protein dynamics. More recently, for my undergrad thesis advised by Dr. Lorin Crawford, I developed an interpretable Bayesian neural network framework for multi-trait association mapping and enrichment analysis in genome-wide association studies. I also interned with the National Cancer Institute’s Division of Cancer Epidemiology and Genetics over the last few summers, where I collaborated with Dr. Philip Rosenberg on a set of non-parametric kernel smoother-based methods for analyzing cancer surveillance data. At Harvard, I plan to continue studying interpretable machine learning while exploring new areas like clinical trials, causal inference, network science, and precision medicine. Through support from theHIV/AIDS training grant, I’m also super excited to learn more about applications in HIV and other infectious diseases. Outside of statistics, I love playing volleyball, kayaking on the Charles, trying out overly complicated board games, and listening to nonfiction and satirical podcasts (like 99% Invisible, Radiolab, and The Bugle), among other things. I’m also a lifelong Boston sports fan eagerly awaiting the inevitable duck boat parades over the next few years. I’m looking forward to meeting everyone!