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About me
I see mathematical statistics as a unifying framework in this time of disruption brought by AI. While causal inference is often described as my research focus, I view it not as confined to a single area but as a thread running through the entire fabric of machine learning and statistics. My passion lies in deriving useful, abstract and elegant results that address data-driven problems arising in clinical medicine and epidemiology, fields undergoing their own quiet revolution.
News
- Feb 24, 2025: Touchdown in Berkeley after a long 13-hour flight. Exciting times ahead at CTML!
- Feb 11, 2025: Causal Survival Embeddings has been published in Statistical Methods in Medical Research! Check it out: https://doi.org/10.1177/09622802241311455
- Dec 18, 2024: Honored to receive the Institute of Mathematical Statistics Award in Nice for Uncertainty quantification for intervals.
class GenerativeInvariance(Estimator):
def __init__(self, intercept=True):
self.intercept = intercept
def fit(self, data, source, target):
# ...
def predict(self, x_new):
x_mean = np.mean(x_new, axis=0)
x_centered = x_new - x_mean
cov_xnew = np.cov(x_new, rowvar=False)
cov_inv = np.linalg.pinv(cov_xnew)
epsy = x_centered @ cov_inv @ self.khat[1:]
y_pred = self.betahat[0] + x_new @ self.betahat[1:] + epsy
return y_pred
def __str__(self):
return self.__class__.__name__