I am a postdoctoral scholar in the Department of Statistics at Stanford University, advised by Prof. Julia A. Palacios. I earned my Ph.D in Statistics in 2018 from the Department of Decision Sciences at the Bocconi University, under the supervision of Prof. Stephen G. Walker and Prof. Sonia Petrone. During my Ph.D, I was a visiting scholar at the Department of Statistics and Data Science at UT Austin.
I seek to provide computationally tractable methods and scalable algorithms that are tailored to big data problems. My research is motivated by concrete questions arising in applications - in particular, population genetics and applied mathematics - and I address these questions via statistical methods. My research spans theory and applications of statistical inference
Scalable coalescent-based inference, working on alternative to Kingman coalescent to make inference feasible for large data sets
Statistical approaches to inverse problems, by framing some questions in combinatorics and applied mathematics as a statistical problem and solving those with tools from the statistical literature
Recursive algorithms in Bayesian statistics, studying and developing recursive algorithms to approximate predictive distributions in Bayesian nonparametrics model
Phylodynamics, by developing more accurate estimators of evolutionary parameters
Applications in infectious diseases and ancient DNA, using coalescent methods to infer evolutionary parameters such as effective population size in order to monitor the ongoing SARS-CoV-2 pandemic and to study question in anthropology