Stefano Ermon

Stefano Ermon    

Associate Professor, Department of Computer Science
Fellow, Woods Institute for the Environment
Stanford University

Office: Gates Building #330
Phone: (650) 498-9942
Email: ermon AT cs.stanford.edu

Group Website       Research Blog

About Me

I am an Associate Professor in the Department of Computer Science at Stanford University, where I am affiliated with the Artificial Intelligence Laboratory and a fellow of the Woods Institute for the Environment.

The goal of my research is to enable innovative solutions to problems of broad societal relevance through advances in probabilistic modeling, learning and inference. I develop new foundational methods motivated by concrete real-world applications, focusing on a new area that bridges computer science with other disciplines to address core questions in sustainability, including poverty mitigation, food security, and renewable energy.

Teaching

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Honors and Awards

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Publications

2023

2022

2021

2020

2019

2018

More Publications

2017

  • William Gent, Kipil Lim, Yufeng Liang, Qinghao Li, Taylor Barnes, Sung-Jin Ahn, Kevin Stone, Mitchell McIntire, Jihyun Hong, Jay Hyok Song, Yiyang Li, Apurva Mehta, Stefano Ermon, Tolek Tyliszczak, Arthur Kilcoyne, David Vine, Jin-Hwan Park, Seok-Gwang Doo, Michael Toney, Wanli Yang, David Prendergast, and William Chueh
    Coupling Between Oxygen Redox and Cation Migration Explains Unusual Electrochemistry in Lithium-Rich Layered Oxides [PDF]
    Nature Communications. In Nature Communications, DOI: 10.1038/s41467-017-02041-x, December 2017.
  • Volodymyr Kuleshov, Stefano Ermon
    Neural Variational Inference and Learning in Undirected Graphical Models [PDF]
    NIPS-17. In Proc. 31st Annual Conference on Neural Information Processing Systems, December 2017.
  • Jiaming Song, Shengjia Zhao, Stefano Ermon
    A-NICE-MC: Adversarial Training for MCMC [PDF] [Code]
    NIPS-17. In Proc. 31st Annual Conference on Neural Information Processing Systems, December 2017.
  • Yunzhu Li, Jiaming Song, Stefano Ermon
    InfoGAIL: Interpretable Imitation Learning from Visual Demonstrations [PDF] [Code]
    NIPS-17. In Proc. 31st Annual Conference on Neural Information Processing Systems, December 2017.
  • Stephen Mussmann, Daniel Levy, Stefano Ermon
    Fast Amortized Inference and Learning in Log-linear Models with Randomly Perturbed Nearest Neighbor Search [PDF]
    UAI-17. In Proc. 33rd Conference on Uncertainty in Artificial Intelligence, August 2017.
  • Volodymyr Kuleshov, Stefano Ermon
    Deep Hybrid Models: Bridging Discriminative and Generative Approaches [PDF]
    UAI-17. In Proc. 33rd Conference on Uncertainty in Artificial Intelligence, August 2017.
  • Shengjia Zhao, Jiaming Song, Stefano Ermon
    Learning Hierarchical Features from Generative Models [PDF] [Code]
    ICML-17. In Proc. 34th International Conference on Machine Learning, August 2017.
  • Russell Stewart, Stefano Ermon
    Supervising Neural Networks with Physics and other Domain Knowledge [PDF] [Code]
    AAAI-17. In Proc. 31st AAAI Conference on Artificial Intelligence, February 2017.
    AAAI Outstanding Paper Award
  • Volodymyr Kuleshov, Stefano Ermon
    Online Uncertainty Estimation Against an Adversary [PDF] [Code]
    AAAI-17. In Proc. 31st AAAI Conference on Artificial Intelligence, February 2017.
  • Jiaxuan You, Xiaocheng Li, Melvin Low, David Lobell, Stefano Ermon
    Deep Gaussian Process for Crop Yield Prediction Based on Remote Sensing Data [PDF] [Code]
    AAAI-17. In Proc. 31st AAAI Conference on Artificial Intelligence, February 2017.
    Best Student Paper Award (CompSust Track)
  • Colin Wei, Stefano Ermon
    General Bounds on Satisfiability Thresholds for Random CSPs via Fourier Analysis [PDF]
    AAAI-17. In Proc. 31st AAAI Conference on Artificial Intelligence, February 2017.
  • Siamak Yousefi, Hirokazu Narui, Sankalp Dayal, Stefano Ermon, Shahrokh Valaee
    A Survey on Behavior Recognition Using WiFi Channel State Information [PDF] [Code]
    In IEEE Communications Magazine, 55 (10), 98-104, 2017.
  • Biagio Cosenza, Juan Durillo, Stefano Ermon, Ben Juurlink
    Autotuning Stencil Computations with Structural Ordinal Regression Learning [PDF]
    IPDPS-17. In IEEE International Parallel and Distributed Processing Symposium, February 2017.

2016

  • Xiaoyue Duan, Feifei Yang, Erin Antono, Wenge Yang, Piero Pianetta, Stefano Ermon, Apurva Mehta, Yijin Liu
    Unsupervised Data Mining in Nanoscale X-ray Spectro-Microscopic Study of NdFeB Magnet [PDF]
    Scientific Reports. In Scientific Reports, 6, 34406 (2016).
  • Neal Jean, Marshall Burke, Michael Xie, Matthew Davis, David Lobell, Stefano Ermon
    Combining Satellite Imagery and Machine Learning to Predict Poverty [PDF] [Project Website] [Commentary] [Nature Research Highlights] [Code]
    Science. In Science, 353(6301), 790-794, 2016.
  • Aditya Grover, Stefano Ermon
    Variational Bayes on Monte Carlo Steroids [PDF]
    NIPS-16. In Proc. 30th Annual Conference on Neural Information Processing Systems, December 2016.
  • Shengjia Zhao, Enze Zhou, Ashish Sabharwal, Stefano Ermon
    Adaptive Concentration Inequalities for Sequential Decision Problems [PDF] [Code]
    NIPS-16. In Proc. 30th Annual Conference on Neural Information Processing Systems, December 2016.
  • Jonathan Ho, Stefano Ermon
    Generative Adversarial Imitation Learning [PDF] [Code]
    NIPS-16. In Proc. 30th Annual Conference on Neural Information Processing Systems, December 2016.
  • Yexiang Xue, Zhiyuan Li, Stefano Ermon, Carla Gomes, Bart Selman
    Solving Marginal MAP Problems with NP Oracles and Parity Constraints [PDF]
    NIPS-16. In Proc. 30th Annual Conference on Neural Information Processing Systems, December 2016.
  • Mitchell McIntire, Daniel Ratner, Stefano Ermon
    Sparse Gaussian Processes for Bayesian Optimization [PDF] [Code]
    UAI-16. In Proc. 32nd Conference on Uncertainty in Artificial Intelligence, June 2016.
  • Jonathan Ho, Jayesh Gupta, Stefano Ermon
    Model-Free Imitation Learning with Policy Optimization [PDF]
    ICML-16. In Proc. 33rd International Conference on Machine Learning, June 2016.
  • Yexiang Xue, Stefano Ermon, Ronan Le Bras, Carla Gomes, Bart Selman
    Variable Elimination in the Fourier Domain [PDF]
    ICML-16. In Proc. 33rd International Conference on Machine Learning, June 2016.
  • Steve Mussmann, Stefano Ermon
    Learning and Inference via Maximum Inner Product Search [PDF]
    ICML-16. In Proc. 33rd International Conference on Machine Learning, June 2016.
  • Tudor Achim, Ashish Sabharwal, Stefano Ermon
    Beyond Parity Constraints: Fourier Analysis of Hash Functions for Inference [PDF]
    ICML-16. In Proc. 33rd International Conference on Machine Learning, June 2016.
  • Lun-Kai Hsu, Tudor Achim, Stefano Ermon
    Tight Variational Bounds via Random Projections and I-Projections [PDF]
    AISTATS-16. In Proc. 19th International Conference on Artificial Intelligence and Statistics, May 2016.
  • Michael Xie, Neal Jean, Marshall Burke, David Lobell, Stefano Ermon
    Transfer Learning from Deep Features for Remote Sensing and Poverty Mapping [PDF] [Stanford Report] [NYTimes]
    AAAI-16. In Proc. 30th AAAI Conference on Artificial Intelligence, February 2016.
  • Shengjia Zhao, Sorathan Chaturapruek, Ashish Sabharwal, Stefano Ermon
    Closing the Gap Between Short and Long XORs for Model Counting [PDF] [Code]
    AAAI-16. In Proc. 30th AAAI Conference on Artificial Intelligence, February 2016.
  • Carolyn Kim, Ashish Sabharwal, Stefano Ermon
    Exact Sampling with Integer Linear Programs and Random Perturbations [PDF] [Code]
    AAAI-16. In Proc. 30th AAAI Conference on Artificial Intelligence, February 2016.

2015

  • Stefan Hadjis, Stefano Ermon
    Importance sampling over sets: a new probabilistic inference scheme. [PDF] [Code]
    UAI-15. In Proc. 31st Conference on Uncertainty in Artificial Intelligence, July 2015.
  • Michael Zhu, Stefano Ermon
    A Hybrid Approach for Probabilistic Inference using Random Projections. [PDF]
    ICML-15. In Proc. 32nd International Conference on Machine Learning, July 2015.
  • Yexiang Xue, Stefano Ermon, Carla Gomes, Bart Selman
    Uncovering Hidden Structure through Parallel Problem Decomposition for the Set Basis Problem with Application to Materials Discovery. [PDF]
    IJCAI-15. In Proc. International Joint Conference on Artificial Intelligence, July 2015.
  • Stefano Ermon, Yexiang Xue, Russell Toth, Bistra Dilkina, Richard Bernstein, Theodoros Damoulas, Patrick Clark, Steve DeGloria, Andrew Mude, Christopher Barrett, and Carla Gomes
    Learning Large Scale Dynamic Discrete Choice Models of Spatio-Temporal Preferences with Application to Migratory Pastoralism in East Africa. [PDF]
    AAAI-15. In Proc. 29th AAAI Conference on Artificial Intelligence, January 2015.
  • Stefano Ermon, Ronan Le Bras, Santosh Suram, John M. Gregoire, Carla Gomes, Bart Selman, and Robert B. van Dover
    Pattern Decomposition with Complex Combinatorial Constraints: Application to Materials Discovery. [PDF]
    AAAI-15. In Proc. 29th AAAI Conference on Artificial Intelligence, January 2015.

2014

  • Stefano Ermon, Carla Gomes, Ashish Sabharwal, and Bart Selman
    Designing Fast Absorbing Markov Chains [PDF]
    AAAI-14. In Proc. 28th AAAI Conference on Artificial Intelligence, July 2014.
  • Stefano Ermon, Carla Gomes, Ashish Sabharwal, and Bart Selman
    Low-density Parity Constraints for Hashing-Based Discrete Integration [PDF] [Code]
    ICML-14. In Proc. 31st International Conference on Machine Learning, June 2014.

2013

  • Stefano Ermon, Carla Gomes, Ashish Sabharwal, and Bart Selman
    Embed and Project: Discrete Sampling with Universal Hashing [PDF] [Code]
    NIPS-13. In Proc. 27th Annual Conference on Neural Information Processing Systems, December 2013.
  • Stefano Ermon, Carla Gomes, Ashish Sabharwal, and Bart Selman
    Optimization With Parity Constraints: From Binary Codes to Discrete Integration [PDF] [Slides] [Poster] [Code]
    UAI-13. In Proc. 29th Conference on Uncertainty in Artificial Intelligence, July 2013.
    Best Student Paper Award. Best Paper Award Runner-up.
  • Stefano Ermon, Yexiang Xue, Carla Gomes, and Bart Selman.
    Learning Policies For Battery Usage Optimization in Electric Vehicles.
    Machine Learning. In Machine Learning: Volume 92, Issue 1, Page 177-194, 2013.
  • Stefano Ermon, Carla Gomes, Ashish Sabharwal, and Bart Selman
    Taming the Curse of Dimensionality: Discrete Integration by Hashing and Optimization [PDF] [Slides] [Code]
    ICML-13. In Proc. 30th International Conference on Machine Learning, June 2013.

2012

  • Stefano Ermon, Carla Gomes, Ashish Sabharwal, and Bart Selman
    Density Propagation and Improved Bounds on the Partition Function. [PDF] [Poster]
    NIPS-12. In Proc. 26th Annual Conference on Neural Information Processing Systems, December 2012.
  • Stefano Ermon, Carla Gomes, and Bart Selman
    Uniform Solution Sampling Using a Constraint Solver As an Oracle [PDF] [Slides] [Code]
    UAI-12. In Proc. 28th Conference on Uncertainty in Artificial Intelligence, August 2012.
  • Liaoruo Wang, Stefano Ermon, and John Hopcroft
    Feature-Enhanced Probabilistic Models for Diffusion Network Inference. [PDF] [Slides] [Code]
    ECML-PKDD-12. In Proc. of European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, September 2012.
  • Stefano Ermon, Yexiang Xue, Carla Gomes, and Bart Selman
    Learning Policies For Battery Usage Optimization in Electric Vehicles [PDF] [Slides] [Dataset]
    ECML-PKDD-12. In Proc. of European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, September 2012.
  • Stefano Ermon, Ronan Le Bras, Carla Gomes, Bart Selman, and Bruce van Dover
    SMT-Aided Combinatorial Materials Discovery [PDF] [Code]
    SAT-12. In Proc. 15th International Conference on Theory and Applications of Satisfiability Testing, June 2012.
  • Stefano Ermon, Carla Gomes, Bart Selman, and Alexander Vladimirsky
    Probabilistic Planning With Non-linear Utility Functions and Worst Case Guarantees [PDF]
    AAMAS-12. In Proc. 11th International Conference on Autonomous Agents and Multiagent Systems, June 2012.

2011

  • Stefano Ermon, Carla Gomes, Ashish Sabharwal, and Bart Selman
    Accelerated Adaptive Markov Chain for Partition Function Computation [PDF] [Code] [Data]
    NIPS-11. In Proc. 25th Annual Conference on Neural Information Processing Systems, December 2011.
  • Stefano Ermon, Carla Gomes, and Bart Selman
    A Flat Histogram Method for Computing the Density of States of Combinatorial Problems [PDF]
    IJCAI-11. In Proc. 22nd International Joint Conference on Artificial Intelligence, July 2011. .
  • Stefano Ermon, Jon Conrad, Carla Gomes, and Bart Selman
    Risk-Sensitive Policies for Sustainable Renewable Resource Allocation [PDF]
    IJCAI-11. In Proc. 22nd International Joint Conference on Artificial Intelligence, July 2011.
  • Stefano Ermon, Carla Gomes, and Bart Selman
    A Message Passing Approach to Multiagent Gaussian Inference for Dynamic Processes (Short Paper) [PDF]
    AAMAS-11. In Proc. 10th International Conference on Autonomous Agents and Multiagent Systems, May 2011.

2010

  • Stefano Ermon, Carla Gomes, and Bart Selman
    Computing the Density of States of Boolean Formulas [PDF] [Slides] [Code] [Data]
    CP-10. In Proc. 16th International Conference on Principles and Practice of Constraint Programming, September 2010.
    Best Student Paper Award
  • Stefano Ermon, Jon Conrad, Carla Gomes, and Bart Selman
    Playing Games against Nature: Optimal Policies for Renewable Resource Allocation [PDF]
    UAI-10. In Proc. 26th Conference on Uncertainty in Artificial Intelligence, July 2010.
  • Stefano Ermon, Carla Gomes, and Bart Selman
    Collaborative Multiagent Gaussian Inference in a Dynamic Environment Using Belief Propagation (Short Paper) [PDF]
    AAMAS-10. In Proc. 9th International Conference on Autonomous Agents and Multiagent Systems, May 2010.

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Personal

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©2021 Stefano Ermon