Nick Richardson
Email: [first initial] + [jk] + [last name] at cs dot princeton dot edu
Academic and Research
I'm a PhD student at Princeton advised by Ryan Adams. My research is at the intersection of machine learning and scientific computing/computational engineering. I have broad interests across the stack; spanning algorithms (primarily scientific computing and optimization), compilers, architecture, microarchitecture, and hardware design (both conventional CMOS digital systems and experimental paradigms like photonics and analog computing).
Outside of my research, I serve as the system administrator for my group, the Laboratory for Intelligent Probabilistic Systems (LIPS) at Princeton. I'm proficient with various aspects of Unix/Linux system administration including configuration management, distributed authentication, NFS, Nvidia GPU configuration, and containerization, for example. I'm also passionate about related areas like electrical engineering, and have non-professional experience designing PCBs and working with electronics.
I did my BS in Mathematics at Harvey Mudd College with Jon Jacobsen, with a minor in philosophy.
Publications
Nonlinear Computation with Linear Optics via Source-Position Encoding
Nick Richardson, Cyrill Bosch, Ryan P. Adams
Arxiv Preprint [Link]
Crystal Generative Modeling with Explicit Autoregressive Conditional Likelihoods and Nontrivial Space Group Stabilizers
Rees Chang, Alex Guerra, Nick Richardson, Ni Zhan, Sulin Liu, Angela Pak, Ryan Marr, Alex M. Ganose, Ryan P. Adams, Elif Ertiken
ICLR 2025 AI4MAT [Link]
Graph Neural Networks Gone Hogwild
Olga Solodova, Nick Richardson, Deniz Otkay, Ryan P. Adams
ICLR 2025 [Link]
Fiber Monte Carlo
Nick Richardson, Deniz Otkay, Yaniv Ovadia, James C Bowden, Ryan P. Adams
ICLR 2024 [Link]
Vitruvion: A Generative Model of Parametric CAD Sketches
Ari Seff, Wenda Zhou, Nick Richardson, Ryan P. Adams
ICLR 2022 [Link]
Professional
In summer 2022 I interned at Arm with Paul Whatmough and Chu Zhou working on hardware-software codesign for compression on neural processing units (NPUs), resulting in a patent application and a custom internal library written in C++ with CPython bindings.
This past summer I was a research engineering intern at Qualcomm, where I led an R&D effort to explore the use of machine learning models of SoCs with applications for adaptive runtime control. I worked closely with Paul Whatmough in this ML-driven microarchitecture project, within the AI compilers and algorithms group.
In addition to internships, I have experience serving as a technical consultant and contractor. See my Linkedin for my full professional experience.
This fall after finishing the PhD, I'm joining Arm as a senior engineer; working across the computing stack from hardware to algorithms.