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Ian Langmore

Ian Langmore

At Google, Ian first worked on Bayesian modeling for local search algorithms before moving on to probabilistic methods in fusion plasma reconstruction. As of 2023, he works on probabilistic weather prediction using differentiable computing. This places him at the intersection of applied probability, software engineering, and physics.
Authored Publications
Google Publications
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    A scalable system to measure contrail formation on a per-flight basis
    Erica Brand
    Sebastian Eastham
    Carl Elkin
    Thomas Dean
    Zebediah Engberg
    Ulrike Hager
    Joe Ng
    Dinesh Sanekommu
    Tharun Sankar
    Marc Shapiro
    Environmental Research Communications (2024)
    Preview abstract In this work we describe a scalable, automated system to determine from satellite data whether a given flight has made a persistent contrail. The system works by comparing flight segments to contrails detected by a computer vision algorithm running on images from the GOES-16 Advanced Baseline Imager. We develop a `flight matching' algorithm and use it to label each flight segment as a `match' or `non-match'. We perform this analysis on 1.6 million flight segments and compare these labels to existing contrail prediction methods based on weather forecast data. The result is an analysis of which flights make persistent contrails several orders of magnitude larger than any previous work. We find that current contrail prediction models fail to correctly predict whether we will match a contrail in many cases. View details
    WeatherBench 2: A benchmark for the next generation of data-driven global weather models
    Alex Merose
    Peter Battaglia
    Tyler Russell
    Alvaro Sanchez
    Vivian Yang
    Rob Carver
    Matthew Chantry
    Zied Ben Bouallegue
    Peter Dueben
    Carla Bromberg
    Jared Sisk
    Luke Barrington
    Aaron Bell
    arXiv (2023) (to appear)
    Preview abstract WeatherBench 2 is an update to the global, medium-range (1-14 day) weather forecasting benchmark proposed by Rasp et al. (2020), designed with the aim to accelerate progress in data-driven weather modeling. WeatherBench 2 consists of an open-source evaluation framework, publicly available training, ground truth and baseline data as well as a continuously updated website with the latest metrics and state-of-the-art models: https://sites.research.google/weatherbench. This paper describes the design principles of the evaluation framework and presents results for current state-of-the-art physical and data-driven weather models. The metrics are based on established practices for evaluating weather forecasts at leading operational weather centers. We define a set of headline scores to provide an overview of model performance. In addition, we also discuss caveats in the current evaluation setup and challenges for the future of data-driven weather forecasting. View details
    Preview abstract Hamiltonian Monte Carlo is discussed in the context of a fusion plasma reconstruction. Ill conditioned covariance and multi-modality are discussed in depth. View details
    Preview abstract TAE Technologies, Inc. (TAE) is pursuing an alternative approach to magnetically confined fusion, which relies on field-reversed configuration (FRC) plasmas composed of mostly energetic and well-confined particles by means of a state-of-the-art tunable energy neutral-beam (NB) injector system. TAE’s current experimental device, C-2W (also called “Norman”), is the world’s largest compact-toroid device and has made significant progress in FRC performance, producing record breaking, high temperature (electron temperature, Te >500 eV; total electron and ion temperature, Ttot >3 keV) advanced beam-driven FRC plasmas, dominated by injected fast particles and sustained in steady-state for up to 30 ms, which is limited by NB pulse duration. C-2W produces significantly better FRC performance than the preceding C-2U experiment, in part due to Google’s machine-learning framework for experimental optimization, which has contributed to the discovery of a new operational regime where novel settings for the formation sections yield consistently reproducible, hot, and stable plasmas. Active plasma control system has been developed and utilized in C-2W to produce consistent FRC performance as well as for reliable machine operations using magnets, electrodes, gas injection, and tunable NBs. The active control system has demonstrated a stabilization of FRC axial instability. Overall FRC performance is well correlated with NBs and edge-biasing system, where higher total plasma energy is obtained with increasing both NB injection power and applied-voltage on biasing electrodes. C-2W divertors have demonstrated a good electron heat confinement on open-field-lines using strong magnetic mirror fields as well as expanding the magnetic field in the divertors (expansion ratio >30); the electron energy lost per ion, ~6–8, is achieved, which is close to the ideal theoretical minimum. View details
    Preview abstract The Hamiltonian Monte Carlo (HMC) method allows sampling from continuous densities. Favorable scaling with dimension has led to wide adoption of HMC by the statistics community. Modern auto-differentiating software should allow more widespread usage in Bayesian inverse problems. This paper analyzes the two major difficulties encountered using HMC for inverse problems: poor conditioning and multi-modality. Novel results on preconditioning and replica exchange Monte Carlo parameter selection are presented in the context of spectroscopy. Recommendations are analyzed rigorously in the Gaussian case, and shown to generalize in a fusion plasma reconstruction. View details
    Preview abstract We determined the time-dependent geometry including high-frequency oscillations of the plasma density in TAE’s C2W experiment. This was done as a joint Bayesian reconstruction from a 14-chord FIR interferometer in the midplane, 32 Mirnov probes at the periphery, and 8 shine-through detectors at the targets of the neutral beams. For each point in time we recovered, with credibility intervals: the radial density profile of the plasma; bulk plasma displacement; amplitudes, frequencies and phases of the azimuthal modes n=1 to n=4. Also reconstructed were the radial profiles of the deformations associated with each of the azimuthal modes. Bayesian posterior sampling was done via Hamiltonian Monte Carlo with custom preconditioning. This gave us a comprehensive uncertainty quantification of the reconstructed values, including correlations and some understanding of multimodal posteriors. This method was applied to thousands of experimental shots on C-2W, producing a rich data set for analysis of plasma performance. View details
    Preview abstract Hamiltonian Monte Carlo is a popular sampling technique for smooth target densities. The scale lengths of the target have long been known to influence integration error and sampling efficiency. However, quantitative measures intrinsic to the target have been lacking. In this paper, we restrict attention to the multivariate Gaussian and the leapfrog integrator, and obtain a condition number corresponding to sampling efficiency. This number, based on the spectral and Schatten norms, quantifies the number of leapfrog steps needed to efficiently sample. We demonstrate its utility by using this condition number to analyze HMC preconditioning techniques. We also find the condition number of large inverse Wishart matrices, from which we derive burn-in heuristics. View details
    Preview abstract Hamiltonian Monte Carlo is a powerful algorithm for sampling from difficult-to-normalize posterior distributions. However, when the geometry of the posterior is unfavorable, it may take many expensive evaluations of the target distribution and its gradient to converge and mix. We propose neural transport (NeuTra) HMC, a technique for learning to correct this sort of unfavorable geometry using inverse autoregressive flows (IAF), a powerful neural variational inference technique. The IAF is trained to minimize the KL divergence from an isotropic Gaussian to the warped posterior, and then HMC sampling is performed in the warped space. We evaluate NeuTra HMC on a variety of synthetic and real problems, and find that it significantly outperforms vanilla HMC both in time to reach the stationary distribution and asymptotic effective-sample-size rates. View details
    Preview abstract Fusion Plasma Reconstruction work done at Google in partnership with TAE is presented. View details
    Preview abstract Bayesian methods are used to infer Field Reversed Configuration (FRC) plasma properties for the C-2W machine at TAE Technologies. The approach starts with a statistical distribution of possible plasma states, where physically-motivated constraints are imposed through the Bayesian prior. Possible states are processed by a forward model for the relevant instruments to assess agreement with corresponding measured experimental data. The resulting probability distribution is known as the posterior, from which the most likely plasma state and the corresponding statistical confidence are extracted. Plasma state reconstruction from multi-instrument Bayesian inference are presented in this study, implemented for the upgraded diagnostics that have come online for C-2W. FIR interferometry, Thomson scattering, Bremsstrahlung radiation measurement, and secondary electron emission detection from the neutral beams are used in reconstruction near the FRC midplane. Magnetic probes and imaging from a high-speed camera provide 3D data throughout the main confinement vessel. This study aims to further the understanding of plasma properties and dynamics, such as electron and ion densities, electron temperature, plasma current, and magnetic field topology. View details
    The Plasma Debugger
    Erik Granstedt
    Erik Trask
    Hiroshi Gota
    Jesus Romero
    Matthew Thompson
    Roberto Mendoza
    Tom Madams
    Yair Carmon
    Preview abstract We built a "Plasma Debugger", a tool to reconstruct the state of the FRC plasma in the TAE Technologies' experimental machine C­2W. This generalized Bayesian inference approach combines data from magnetic sensors, fast cameras, FIR interferometer, Thomson Scattering system, Bremsstrahlung measurements and neutral beams shine­-through SEE detectors. It then reconstructs electron density, temperature and bulk plasma currents, with confidence intervals. Computation takes hundreds of CPUs and is performed in the cloud, with results showing up in the plasma machine control room within several minutes of the experiment. The display shows time evolution of the basic plasma properties, giving machine operators additional insight into the plasma behavior. View details
    Preview abstract Compound distributions allow construction of a rich set of distributions. Typically they involve an intractable integral. Here we use a quadrature approximation to that integral to define the quadrature compound family. Special care is taken that this approximation is suitable for computation of gradients with respect to distribution pa- rameters. This technique is applied to discrete (Poisson LogNormal) and continuous distributions. In the continuous case, quadrature compound family naturally makes use of parameterized transformations of unparameterized distributions (a.k.a “reparame- terization”), allowing for gradients of expectations to be estimated as the gradient of a sample mean. This is demonstrated in a novel distribution, the diffeomixture, which is is a reparameterizable approximation to a mixture distribution. View details
    TensorFlow Distributions
    Josh Dillon
    Dustin Tran
    Dave Moore
    Workshop on Probabilistic Programming Languages, Semantics, and Systems (PPS 2018) (2017)
    Preview abstract The TensorFlow Distributions library implements a vision of probability theory adapted to the modern deep-learning paradigm of end-to-end differentiable computation. Building on two basic abstractions, it offers flexible building blocks for probabilistic computation. Distributions provide fast, numerically stable methods for generating samples and computing statistics, e.g., log density. Bijectors provide composable volume-tracking transformations with automatic caching. Together these enable modular construction of high dimensional distributions and transformations not possible with previous libraries (e.g., pixelCNNs, autoregressive flows, and reversible residual networks). They are the workhorse behind deep probabilistic programming systems like Edward and empower fast black-box inference in probabilistic models built on deep-network components. TensorFlow Distributions has proven an important part of the TensorFlow toolkit within Google and in the broader deep learning community. View details
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