Quick Bio:
I am an assistant professor in the Department of Statistical Science at Duke University. Previously, I was a postdoc fellow at
ETH Foundations of Data Science (ETH-FDS) in ETH Zürich under the supervision of
Prof. Peter Bühlmann. I obtained my PhD in the
Department of Statistics at UC Berkeley in 2019. I was very fortunate to be advised by
Prof. Bin Yu. During my PhD, I was also fortunate to work with
Prof. Martin Wainwright and
Prof. Jack Gallant. Before my PhD study, I obtained my Diplome d'Ingénieur (Eng. Deg. in Applied Mathematics) at
Ecole Polytechnique in France.
My main research interests lie on statistical machine learning, MCMC sampling, optimization, domain adaptation and statistical challenges that arise in computational neuroscience. If you are curious about the main theory directions that I'm going towards in the next 5 years, you may take a look at this
NSF CAREER abstract.
yuansi.chen at duke.edu
"It is necessary and true that all of the things we say in science, all of the conclusions, are uncertain, because they are only conclusions. They are guesses as to what is going to happen, and you cannot know what will happen, because you have not made the most complete experiments."
-- Richard P. Feynman