My name is Nic Fishman, I'm currently a PhD student in Statistics at Harvard University.

I work on machine learning for science, particularly social science and biology. My projects tend to fit into one of two buckets: either they are 1) big data projects trying to pull useful information out of enourmous observational datasets or they are 2) experimental projects where I develop methods for estimating causal effects in complex experiments or desigining adaptive procedures that allow getting the most out of experimental designs.

On the big data side I have studied socio-economic segregation from location data, how science is produced using massive grant and abstract databases, and regulatory sequence and protein design using generative models (VAEs, GANs, diffusion models, flow matching models).

In the more experimental setting I work on non-parametric inference for causal inference under interference and in panel settings and adaptive/active learning designs for online combinatorial optimization both in the conjoint setting for political science and in protein/regulatory sequence design for biology.

These seemingly disparate problems are united in requiring careful attention to underlying optimization problems, which are often non-convex, and requiring novel methods for uncertainty quantification.

I also think a lot about the sociology, history, and philosophy of science, and particularly the role of quantification, statistics, and computerization in the history of the social and biological sciences.

You can find a more complete resume here, and you can reach me at njwfish [at]