I am an PhD student at Princeton in Computer Science. I am currently doing research with Professor Jia Deng. I did my B.Sc. and M. Eng. at MIT, working with Professors Joshua Tenenbaum and Jiajun Wu. Here is my CV.
Research Interest
I am working 3D synthetic datasets, and how it can help build better vision and robotics models.
Publications ( show selected / show all by date / show all by topic )
Topics: Vision & Graphics. Scene Understanding. Concept Learning (* indicates equal contribution)
Evaluating Robustness of Monocular Depth Estimation with Procedural Scene Perturbations
Jack Nugent*, Siyang Wu*, Zeyu Ma*, Beining Han*, Meenal Parakh, Abhishek Joshi, Lingjie Mei, Alexander Raistrick, Xinyuan Li, Jia Deng
Violations of physical and psychological expectations in the human adult brain
Shari Liu, Kirsten Lydic, Lingjie Mei, Rebecca Saxe
FALCON: Fast Visual Concept Learning by Integrating Images, Linguistic descriptions, and Conceptual Relations
Lingjie Mei, Jiayuan Mao, Ziqi Wang, Chuang Gan, Joshua B. Tenenbaum,
The fine structure of surprise in intuitive physics: when, why, and how much?
Kevin Smith*, Lingjie Mei*, Shunyu Yao*, Jiajun Wu, Elizabeth S. Spelke, Joshua B. Tenenbaum, Tomer Ullman
Modeling Expectation Violation in Intuitive Physics with Coarse Probabilistic Object Representations
Kevin Smith*, Lingjie Mei*, Shunyu Yao*, Jiajun Wu, Elizabeth S. Spelke, Joshua B. Tenenbaum, Tomer Ullman
GitHub Projects
Paper projects
princeton-vl/infinigen: core contributor to Princeton VL's flagship procedural scene generator (6.9k ★).
ADEPT-Model-Release: model code for the ADEPT intuitive-physics project (NeurIPS 2019).
ADEPT-Dataset-Release: dataset generation code for ADEPT.
FALCON-Release: PyTorch reference implementation for the FALCON visual concept learner (ICLR 2022).
princeton-vl/cvdpack: contributor to tooling for reorganizing and compressing RGB/Depth/Flow/Normal frame datasets.
Non-paper projects
blocher/dailyoffice2019: contributor via pull requests to the Daily Office 2019 website codebase.
WinogradConvolution: Julia + CUDA implementation of Winograd convolution kernels.
NonlinearPDE: multigrid-based iterative solver for nonlinear PDEs.
BayesianLanguageLearningPython: compositional language evolution from a Bayesian perspective.
Contour3D: 3D contour reconstruction and visualization experiments.
FastMultipleMethod: implementation of 1D fast multipole method.
StarRailCopilot: maintained fork for Honkai: Star Rail automation tooling.
ece475-proj: RISC-V 32I processor with vectorization support.
Competitive_SIR: simulation of competing rumor dynamics with compartmental models.
AtariRepresentation: experiments on representation generalization in Atari games.