Hi! I am Lorenzo and I am currently a postdoc at University of California, Santa Cruz hosted by Vaggos Chatziafratis. In September, I am going to join IBM as a Goldstine fellow hosted by Kenneth Clarkson. I recently graduated from BARC, University of Copenhagen, where I was fortunate to be advised by Mikkel Thorup and Mikkel Abrahamsen.
I am a theoretical computer scientist and I study algorithms for massive datasets. My work focuses on sublinear algorithms and high-dimensional geometry.
Email: [firstname]2[lastname]@gmail.com
Numerous algorithmic innovations in data science stem from the observation that certain tasks can be approximately solved by algorithms that observe or store only a small fraction of the input, so-called sublinear algorithms. My coauthors and I have developed sublinear algorithms for estimating normalization constants [BT22], counting the number of edges in a graph [BCCS25], and testing whether a function depends only on a few relevant features [BHK25].
High-dimensional GeometrySeveral data science tasks involve solving geometric problems over datapoints embedded in high-dimensional spaces. However, the fastest known algorithms for many such problems often run in time exponential in the dimension, reflecting the so-called curse of dimensionality. For instance, computing Optimal Transport or Earth Mover’s Distance (EMD) typically requires time quadratic in the input size or exponential in the dimension. To address this, my coauthors and I developed a sub-quadratic time approximation scheme for EMD [BR24] [BCJW25].