Michael Poli

Founding Scientist at Liquid AI.
Computer Science Ph.D. Student at Stanford.
machine learning ∩ systems ∩ signal processing

Advised by Stefano Ermon. Working closely with friends at Hazy Research. Affiliated with Stanford AI Lab (SAIL) and the Center for Research on Foundation Models (CRFM).

Research:
I lead a research group at Liquid AI. We are building hardware-aware, custom and efficient AI systems at every scale, with a design theory for models grounded in classical first principles of dynamical systems, signal processing and numerical linear algebra. We are hiring exceptional researchers with diverse backgrounds (AI, systems, hardware, control theory etc), reach out if our mission resonates with you.

My interests are at the intersection of machine learning, systems and signal processing:

In a previous life, I have also spent time working on neural differential equations, time series and dynamical systems. These days I am mostly interested in "full-stack" design of large deep learning models, from numerics, systems, training, all the way to finetuning and deployment.

Short bio:
I am a Founding Scientist at Liquid AI and a Ph.D. student in Computer Science at Stanford University. I am grateful to a long list of brilliant researchers and friends that have advised me through the years and (inexplicably) believe in my work: Stefano Ermon, Chris Ré, Eric Horvitz, Bryan Wilder, Seong Joon Oh, Animesh Garg, Ilija Ilievski, Jinkyoo Park, among others.
I am originally from Bologna, Italy, and I have had the wonderful opportunity to spend 5 fun years in Asia (China and South Korea). My Chinese name is 宁致远.