John Lambert
Bio I am currently a Staff Research Scientist at Google DeepMind, working on Gemini. Before joining DeepMind, I spent almost 3 years at Waymo (formerly the Google self-driving car project), building generative modeling techniques for data-driven multi-agent and environment simulation.
I received my Ph.D. from Georgia Tech, where I was advised by James Hays and Frank Dellaert, exploring deep learning for 3D perception. During my Ph.D., I also held research roles at Argo AI, Intel Labs, and Zillow Research. Previously, I completed my Bachelor’s and Master’s degrees in computer science at Stanford University in the Stanford Vision Lab and Stanford AI Lab, specializing in artificial intelligence.
news
| Apr 30, 2026 | Two papers are accepted at ICML 2026 – RISE and DPO Unchained. |
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| Mar 25, 2026 | Our work on steering LLMs for culturally localized generation is now on [arXiv]. |
| Dec 31, 2025 | Our work on LLM reasoning behavior intervention is now on [arXiv]. |
| Dec 31, 2025 | Our new work on multi-turn reward modeling is now on [arXiv]. |
| Dec 29, 2025 | Our new work on multi-turn behavior elicitation with RL is now on [arXiv]. |
teaching
Aside from research, another passion of mine is teaching. I enjoy creating teaching materials for topics related to statistical machine learning, computer vision, numerical optimization. A number of teaching modules I've written can be found below:Module 1: Linear Algebra
Foundations: Linear Algebra Without the Agonizing Pain
Necessary background: Projection, Gram-Schmidt, SVD,
Fast Nearest Neighbors
Vectorizing nearest neighbors (with no for-loops!)
Module 2: Numerical Linear Algebra
Direct Methods for Solving Systems of Linear Equations
backsubstitution and the LU, Cholesky, QR factorizations
Computing Eigenvectors and Eigenvalues
Power iteration, QR iteration, QR with shift, Jacobi iteration
Conjugate Gradients
large systems of equations, Krylov subspaces, Cayley-Hamilton Theorem
Least-Squares
QR decomposition for least-squares, modified Gram-Schmidt, GMRES
Module 3: SVMs and Optimization
The Kernel Trick
poorly taught but beautiful piece of insight that makes SVMs work
Gauss-Newton Optimization in 10 Minutes
Derivation, Trust-Region Variant (Levenberg-Marquardt), Numpy Implementation
Convex Optimization Without the Agonizing Pain
Constrained Optimization, Lagrangians, Duality, and Interior Point Methods
Subgradient Methods in 10 Minutes
Convex Optimization Part II
Module 4: State Estimation
The Bayes Filter and Intro to State Estimation
linear dynamical systems, bayes rule, bayesian estimation, and filtering
Lie Groups and Rigid Body Kinematics
SO(2), SO(3), SE(2), SE(3), Lie algebras
Module 5: Geometry and Camera Calibration
Stereo and Disparity
disparity maps, cost volume, MC-CNN
Epipolar Geometry and the Fundamental Matrix
simple ideas that are normally poorly explained
Visual Odometry
The Essential matrix, Nister's 5-Pt Algorithm, and epipolar constraint derivation
Iterative Closest Point
registration, Sim(3) optimization, simple derivations and code examples
Module 6: Deep Learning
Backprop through a Conv Layer
Deriving Backprop through convolution to either the kernel weights or inputs
Generative Adversarial Networks (GANs)
Deriving minimax and non-saturating losses, DCGAN implementation
Normalization Layers
LayerNorm, RMS-Norm, corresponding backprop
PyTorch Tutorial
PyTorch tensor operations, initializing CONV layers, groups, custom modules
JAX Tutorial
Intro to JAX, optax, flax, linen, and training loops for JAX
Module 7: Reinforcement Learning
Policy Gradients
intuition and simple derivations of REINFORCE, TRPO
RLHF
Reinforcement Learning from Human Feedback, Reward Models, PPO, DPO, GRPO
Module 8: Geometric Data Analysis
Module 9: Message Passing Interface (MPI)