Principal Investigator
Postdoctoral Fellows
Cyrill Bösch
My research focuses on thermodynamic computing, leveraging the natural dynamics of small-scale nonlinear physical systems coupled to thermal baths for generative modeling. While platform-agnostic, I have a primary interest in networks of nano- and opto-mechanical resonators, exploring their potential for energy-efficient computation. More broadly, I investigate how physical systems can be harnessed for computation, from simulating classical wave equations on quantum hardware to embedding control and sensing in mechanical metamaterials for robotics.
Jenny (Ni) Zhan
Personal WebsiteI am a postdoctoral researcher in the lab, currently working on machine learning and simulation for atomic scale materials. I completed my BS from UT Austin, and PhD in chemical engineering and MS in machine learning (ML) at Carnegie Mellon University, where I worked on physical phenomena of liquid alloys using molecular dynamics simulation, neural network potentials, and uncertainty quantification methods for ML.
Graduate Students
Joshua Aduol
Alex Guerra
Arin Mukherjee
I am a master’s student in the lab, currently researching diffusion models - specifically, designing a generative process that allows for varying output dimensionality without relying on fixed-dimensional constraints like padding or masking. Previously, I completed my BSE also at Princeton, where my senior thesis was focused on optimizing the structure of mechanical metamaterials to improve performance in specific deformation tasks.
Nick Richardson
Personal WebsiteI have broad interests across the computing stack, spanning hardware design to algorithms. My research is broadly concerned with machine learning applied to computational engineering, with previous work on generative modeling for CAD and molecule design, asynchronous graph neural networks for swarm robotics, a differentiable Monte Carlo variant for computational geometry and topology optimization, and inverse design for optical neural networks. Before coming to Princeton, I did my BS in mathematics at Harvey Mudd College.
Olga Solodova
Sowmya Thanvantri
I am a PhD student interested in exploring the intersection of machine learning and physics. Previously, I earned my bachelor’s degree in computer science from Berkeley, where I worked on applying machine learning based anomaly detection techniques to astrophysics and developed algorithms for analyzing many-body quantum systems.
Kathryn Wantlin
Personal WebsiteMy research interests center around designing flexible and robust algorithms for embodied AI. Currently, I’m investigating intrinsic motivation objectives for causal, model-based RL. I’m also working to improve multi-task inverse RL with temporal contrastive learning, aiming to replace expert demonstrations with self-supervised exploration. Previously, I completed my Master’s thesis on designing spatial birth-death point processes to model decentralized morphogenesis. I received my M.S.E. from Princeton University and my A.B. from Harvard University, both in computer science.
Daniel Williams
Personal WebsiteI am broadly interested in optimization, inverse design, and electromagnetics, with a focus on developing algorithms for clean energy systems such as nuclear fusion reactors. Currently, my work explores accelerating engineering design processes using machine learning and optimization methods. Prior to starting my PhD, I completed my bachelor’s degree in Computer Science at the University of Maryland, Baltimore County.
Cindy Zhang
Personal WebsiteI am broadly interested in machine learning methods that are theoretically motivated and have applications in materials science. Currently, my work focuses on symmetry groups and Fourier representations in machine learning. Prior to starting my PhD, I completed my bachelor’s degree at MIT, where I worked on ML fairness research with Professor Devavrat Shah.
Undergraduate Students
Aditya Palaparthi
Personal WebsiteHi, I’m Aditya! I’m an undergraduate student studying computer science at Princeton. Here at LIPS, I work on finding better solutions for common mechanical design problems using deep generative modeling. My research interests are generative modeling, ML-accelerated design/simulation, and deep reinforcement learning, but I am always exploring new research in the AI space. Outside of the lab, I love teaching CS and enjoy designing/building full-stack applications.
Yash Sharma
I am undergraduate student at Princeton University in the Math department with interests in Probability Theory and Machine Learning. At LIPS, I am working on solving PDE constrained optimization problems using stochastic methods.
Alumni and Friends
- Jordan Ash
- Elaine Angelino
- Samuel Barnett
- Alex Beatson
- Diana Cai
- SueYeon Chung
- Eyal Dechter
- Finale Doshi-Velez
- Bianca Dumitrascu
- David Duvenaud
- Michael Gelbart
- Jonathan Huggins
- Jose Miguel Hernandez Lobato
- Matthew Johnson
- Scott Linderman
- Sulin Liu
- Dougal Maclaurin
- Eder Medina
- Andrew Miller
- Mehran Mirramezani
- Shamim Nemati
- Yaniv Ovadia
- Deniz Oktay
- Jad Rahme
- Yakir Reshef
- Oren Rippel
- Ardavan Saeedi
- Ari Seff
- Jasper Snoek
- Xingyuan Sun
- Daniel Suo
- Jose Garrido Torres
- Alex Wiltschko
- Tianju Xue
- Wenda Zhou