UW MODE Lab
About

About

The University of Washington is a leader in both Computer Science and Statistics, creating a unique institution for performing cutting-edge Machine Learning research. This research occurs around many areas of campus, lead by a diverse set of faculty. The MODE Lab brings together faculty, students, and postdocs in CSE and Statistics. The focus of the lab is on Machine learning, Optimization, Distributed systems, and (E) statistics (or, for Spanish speakers, Estadística). Some example topics include:

  • streaming inference algorithms
  • distributed implementations
  • models and algorithms for large-scale time series analysis
  • statistical network modeling
  • Bayesian nonparametrics
  • computational neuroscience

Our Amazing Team

Faculty

Emily Fox

Associate Professor

Carlos Guestrin

Associate Professor

Postdocs

Michael Fire

Postdoctoral Fellow

Nicholas Foti

Postdoctoral Fellow

Chris Glynn

Postdoctoral Associate

Graduate Students

Christopher Aicher

Graduate Student

Tianqi Chen

Graduate Student

Hyunsu Cho

Graduate Student

Alec Greaves-Tunnell

Graduate Student

Tyler Johnson

Graduate Student

Yian (Yi-An) Ma

Graduate Student

Rahul Nadkarni

Graduate Student

Marco Tulio Ribeiro

Graduate Student

Alex Tank

Graduate Student

Chris Xie

Graduate Student

Our Amazing Papers

2017

    2016

    1. Net2Net: Accelerating Learning via Knowledge Transfer. Chen, T. and Goodfellow, I. and Shlens, J. International Conference on Learning Representation. 2016.
      BibTeX
       @inproceedings{Chen:ICLR16,
        title = {Net2Net: Accelerating Learning via Knowledge Transfer},
        author = {Chen, T. and Goodfellow, I. and Shlens, J.},
        booktitle = {International Conference on Learning Representation},
        link = {http://arxiv.org/abs/1511.05641},
        year = {2016}
      }
       
    2. Identifiability of Non-Gaussian Structural VAR Models for Subsampled and Mixed Frequency Time Series. Tank, A. and Fox, E. B. and Shojaie, A. Causal Discovery KDD Workshop. 2016.
      BibTeX
       @inproceedings{Tank:KDD_cd_16,
        title = {Identifiability of Non-Gaussian Structural VAR Models for
        Subsampled and Mixed Frequency Time Series},
        author = {Tank, A. and Fox, E. B. and Shojaie, A.},
        booktitle = {Causal Discovery KDD Workshop},
        link = {http://nugget.unisa.edu.au/CD2016/fulltext/KDD_subsamp.pdf},
        year = {2016}
      }
       
    3. Scalable clustering of correlated time series using expectation propagation. Aicher, C. and Fox, E. B. 2nd SIGKDD Workshop on Mining and Learning from Time Series. 2016.
      BibTeX
       @inproceedings{Aicher:KDDMiLeTS16:Scalable,
        title = {Scalable clustering of correlated time series using expectation propagation},
        author = {Aicher, C. and Fox, E. B.},
        booktitle = {2nd SIGKDD Workshop on Mining and Learning from Time Series},
        year = {2016},
        link = {http://www-bcf.usc.edu/%7Eliu32/milets16/paper/MiLeTS_2016_paper_23.pdf}
      }
       
    4. Sparse plus low-rank graphical models of time series for functional connectivity in MEG. Foti, N. and Nadkarni, R. and Lee, A. KC and Fox, E. B. 2nd SIGKDD Workshop on Mining and Learning from Time Series. 2016.
      BibTeX
       @inproceedings{Foti:Nadkarni:Lee:Fox:KDDMiLeTS16,
        title = {Sparse plus low-rank graphical models of time series for functional connectivity in MEG},
        author = {Foti, N. and Nadkarni, R. and Lee, A. KC and Fox, E. B.},
        booktitle = {2nd SIGKDD Workshop on Mining and Learning from Time Series},
        link = {http://www-bcf.usc.edu/%7Eliu32/milets16/paper/MiLeTS_2016_paper_22.pdf},
        year = {2016}
      }
       
    5. Granger Causality Networks for Categorical Time Series. Tank, A. and Fox, E. B. and Shojaie, A. 2nd SIGKDD Workshop on Mining and Learning from Time Series. 2016.
      BibTeX
       @inproceedings{Tank:KDD_ts_16,
        title = {Granger Causality Networks for Categorical Time Series},
        author = {Tank, A. and Fox, E. B. and Shojaie, A.},
        booktitle = {2nd SIGKDD Workshop on Mining and Learning from Time Series},
        link = {http://www-bcf.usc.edu/%7Eliu32/milets16/paper/MiLeTS_2016_paper_24.pdf},
        year = {2016}
      }
       
    6. Spatio-Temporal Low Count Processes with Application to Violent Crime Events. Aldor-Noiman, S. and Brown, L.D. and Fox, E.B. and Stine, R.A. 2016.
      BibTeX
       @article{Aldor-Noiman:StatisticaSinica2016,
        title = {Spatio-Temporal Low Count Processes with Application to Violent Crime Events},
        author = {Aldor-Noiman, S. and Brown, L.D. and Fox, E.B. and Stine, R.A.},
        journal = {Statistica Sinica},
        volume = {26},
        pages = {1587--1610},
        year = {2016}
      }
       
    7. A Novel Seizure Detection Algorithm Informed by Hidden Markov Model Event States. Baldassano, S. and Wulsin, D. and Ung, H. and Blevins, T. and Brown, M.-G. and Fox, E.B. and Litt, B. 2016.
      BibTeX
       @article{Baldassano:JNE2016,
        title = {A Novel Seizure Detection Algorithm Informed by Hidden {M}arkov Model Event States},
        author = {Baldassano, S. and Wulsin, D. and Ung, H. and Blevins, T. and Brown, M.-G. and Fox, E.B. and Litt, B.},
        journal = {Journal of Neural Engineering},
        volume = {13},
        number = {3},
        pages = {036011},
        year = {2016}
      }
       
    8. Mining Continuous Intracranial EEG in Focal Canine Epilepsy: Relating Interictal Bursts to Seizure Onsets. Davis, K. and Ung, H. and Wulsin, D. and Wagenaar, J. and Fox, E.B. and Patterson, E. and Vite, C. and Worrell, G. and Litt, B. 2016.
      BibTeX
       @article{Davis:Epilepsia2016,
        title = {Mining Continuous Intracranial {EEG} in Focal Canine Epilepsy: {R}elating Interictal Bursts to Seizure Onsets},
        author = {Davis, K. and Ung, H. and Wulsin, D. and Wagenaar, J. and Fox, E.B. and Patterson, E. and Vite, C. and Worrell, G. and Litt, B.},
        journal = {Epilepsia},
        volume = {57},
        number = {1},
        pages = {89--98},
        year = {2016}
      }
       
    9. A Unifying Framework for Devising Efficient and Irreversible MCMC Samplers. Ma, Y.-A. and Fox, E. and Chen, T. and Wu, L. 2016.
      BibTeX
       @unpublished{Ma:Fox:Chen:Wu:2016,
        title = {A Unifying Framework for Devising Efficient and Irreversible MCMC Samplers},
        author = {Ma, Y.-A. and Fox, E. and Chen, T. and Wu, L.},
        link = {https://arxiv.org/abs/1608.05973},
        year = {2016}
      }
       

    2015

    1. Bayesian structure learning for stationary time series. Tank, A and Foti, N. J. and Fox, E. B. Uncertainty in Artificial Intelligence. 2015.
      BibTeX
       @inproceedings{Tank:Foti:Fox:2015,
        title = {Bayesian structure learning for stationary time series},
        author = {Tank, A and Foti, N. J. and Fox, E. B.},
        booktitle = {Uncertainty in Artificial Intelligence},
        link = {http://arxiv.org/pdf/1505.03131v2.pdf},
        year = {2015}
      }
       
    2. Expectation-Maximization for Learning Determinantal Point Processes. Gillenwater, J. and Kulesza, A. and Fox, E.B. and Taskar, B. Neural Information Processing Systems 27. 2015.
      BibTeX
       @inproceedings{Gillenwater:NIPS2014,
        author = {Gillenwater, J. and Kulesza, A. and Fox, E.B. and Taskar, B.},
        title = {Expectation-Maximization for Learning Determinantal Point Processes},
        booktitle = {Neural Information Processing Systems 27},
        year = {2015},
        publisher = {MIT Press}
      }
       
    3. Blitz: A Principled Meta-Algorithm for Scaling Sparse Optimization. Johnson, T. B. and Guestrin, C. International Conference on Machine Learning. 2015.
      BibTeX
       @inproceedings{Johnson:ICML2015,
        author = {Johnson, T. B. and Guestrin, C.},
        title = {Blitz: A Principled Meta-Algorithm for Scaling Sparse Optimization},
        booktitle = {International Conference on Machine Learning},
        link = {http://jmlr.org/proceedings/papers/v37/johnson15.pdf},
        year = {2015}
      }
       
    4. Efficient Second-Order Gradient Boosting for Conditional Random Fields. Chen, T. and Singh, S. and Taskar, B. and Guestrin, C. International Conference on Artificial Intelligence and Statistics. 2015.
      BibTeX
       @inproceedings{Chen:AISTATS15,
        author = {Chen, T. and Singh, S. and Taskar, B. and Guestrin, C.},
        title = {Efficient Second-Order Gradient Boosting for Conditional Random Fields},
        link = {http://homes.cs.washington.edu/%7Etqchen/data/pdf/GBCRF-AISTATS15.pdf},
        booktitle = {International Conference on Artificial Intelligence and Statistics},
        year = {2015}
      }
       
    5. Streaming variational inference for Bayesian nonparametric mixture models. Tank, A. and Foti, N. J. and Fox, E. B. International Conference on Artificial Intelligence and Statistics. 2015.
      BibTeX
       @inproceedings{Tank:2015a,
        author = {Tank, A. and Foti, N. J. and Fox, E. B.},
        title = {Streaming variational inference for {B}ayesian nonparametric mixture models},
        booktitle = {International Conference on Artificial Intelligence and Statistics},
        link = {http://arxiv.org/abs/1412.0694},
        year = {2015}
      }
       
    6. A Complete Recipe for Stochastic Gradient MCMC. Y.-A, Ma and Chen, T. and Fox, E. B. Advances in Neural Information Processing Systems. 2015.
      BibTeX
       @inproceedings{Ma:Chen:Fox:2015,
        title = {A Complete Recipe for Stochastic Gradient {MCMC}},
        author = {Y.-A, Ma and Chen, T. and Fox, E. B.},
        booktitle = {Advances in Neural Information Processing Systems},
        link = {http://papers.nips.cc/paper/5891-a-complete-recipe-for-stochastic-gradient-mcmc.pdf},
        year = {2015}
      }
       
    7. Guest Editors’ Introduction to the Special Issue on Bayesian Nonparametrics. Adams, R.P. and Fox, E.B. and Sudderth, E.B. and Teh, Y.W. 2015.
      BibTeX
       @article{AdamsFoxSudderthTeh:2015,
        title = {Guest Editors’ Introduction to the Special Issue on Bayesian Nonparametrics},
        author = {Adams, R.P. and Fox, E.B. and Sudderth, E.B. and Teh, Y.W.},
        journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
        volume = {37},
        number = {2},
        pages = {209--211},
        year = {2015},
        publisher = {IEEE}
      }
       
    8. Bayesian Nonparametric Covariance Regression. Fox, E.B. and Dunson, D.B. 2015.
      BibTeX
       @article{Fox:JMLR2015,
        title = {Bayesian Nonparametric Covariance Regression},
        author = {Fox, E.B. and Dunson, D.B.},
        journal = {Journal of Machine Learning Research},
        volume = {16},
        pages = {2501--2542},
        year = {2015}
      }
       

    2014

    1. Learning the Parameters of Determinantal Point Process Kernels. Affandi, R.H. and Fox, E.B. and Adams, R.P. and Taskar, B. Proc. International Conference on Machine Learning. 2014.
      BibTeX
       @inproceedings{Affandi:ICML2014,
        author = {Affandi, R.H. and Fox, E.B. and Adams, R.P. and Taskar, B.},
        title = {Learning the Parameters of Determinantal Point Process Kernels},
        booktitle = {Proc. International Conference on Machine Learning},
        year = {2014},
        month = jun
      }
       
    2. Approximate Inference in Continuous Determinantal Processes. Affandi, R.H. and Fox, E.B. and Taskar, B. Neural Information Processing Systems 26. 2014.
      BibTeX
       @inproceedings{Affandi:NIPS2013,
        author = {Affandi, R.H. and Fox, E.B. and Taskar, B.},
        title = {Approximate Inference in Continuous Determinantal Processes},
        booktitle = {Neural Information Processing Systems 26},
        year = {2014},
        publisher = {MIT Press}
      }
       
    3. Stochastic Gradient Hamiltonian Monte Carlo. Chen, T. and Fox, E. B. and Guestrin, C. International Conference on Machine Learning. 2014. [ Code ]
      BibTeX
       @inproceedings{Chen:ICML14,
        author = {Chen, T. and Fox, E. B. and Guestrin, C.},
        title = {Stochastic Gradient {H}amiltonian Monte Carlo},
        booktitle = {International Conference on Machine Learning},
        link = {http://arxiv.org/abs/1402.4102},
        code = {http://www.github.com/tqchen/ML-SGHMC},
        year = {2014}
      }
       
    4. Stochastic variational inference for hidden Markov models. Foti, N. J. and Xu, J. and Laird, D. and Fox, E. B. Advances in Neural Information Processing Systems. 2014.
      BibTeX
       @inproceedings{Foti:Xu:Laird:Fox:2014,
        title = {Stochastic variational inference for hidden {M}arkov models},
        author = {Foti, N. J. and Xu, J. and Laird, D. and Fox, E. B.},
        booktitle = {Advances in Neural Information Processing Systems},
        link = {http://papers.nips.cc/paper/5560-stochastic-variational-inference-for-hidden-markov-models.pdf},
        year = {2014}
      }
       
    5. Joint Modeling of Multiple Related Time Series via the Beta Process with Application to Motion Capture Segmentation. Fox, E.B. and Hughes, M.C. and Sudderth, E.B. and Jordan, M.I. 2014.
      BibTeX
       @article{Fox:AOAS2014,
        title = {Joint Modeling of Multiple Related Time Series via the Beta Process with Application to Motion Capture Segmentation},
        author = {Fox, E.B. and Hughes, M.C. and Sudderth, E.B. and Jordan, M.I.},
        journal = {Annals of Applied Statistics},
        volume = {8},
        number = {3},
        pages = {1281--1313},
        year = {2014}
      }
       
    6. Handbook on Mixed Membership Models. Fox, E.B. and Jordan, M.I. 2014.
      BibTeX
       @inbook{FoxJordan:14,
        author = {Fox, E.B. and Jordan, M.I.},
        chapter = {Mixed Membership Models for Time Series},
        editor = {Airoldi, E. and Blei, D. and Erosheva, E. and Fienberg, S.E. and Bokalders, K.},
        publisher = {Chapman \& Hall},
        title = {Handbook on Mixed Membership Models},
        year = {2014}
      }
       
    7. Modeling the Complex Dynamics and Changing Correlations of Epileptic Events. Wulsin, D. and Fox, E.B. and Litt, B. 2014.
      BibTeX
       @article{Drausin:AIJ2014,
        title = {Modeling the Complex Dynamics and Changing Correlations of Epileptic Events},
        author = {Wulsin, D. and Fox, E.B. and Litt, B.},
        journal = {Artificial Intelligence},
        volume = {216},
        pages = {55--75},
        year = {2014}
      }
       
    8. A Bayesian Approach for Predicting the Popularity of Tweets. Zaman, T. and Fox, E.B. and Bradlow, E.T. 2014.
      BibTeX
       @article{Zaman:AOAS2014,
        title = {A {B}ayesian Approach for Predicting the Popularity of Tweets},
        author = {Zaman, T. and Fox, E.B. and Bradlow, E.T.},
        journal = {Annals of Applied Statistics},
        volume = {8},
        number = {3},
        pages = {1583--1611},
        year = {2014}
      }
       

    2013

    1. Parsing Epileptic Events Using a Markov Switching Process Model for Correlated Time Series. Drausin, W. and Fox, E.B. and Litt, B. Proc. International Conference on Machine Learning. 2013.
      BibTeX
       @inproceedings{Wulsin:ICML2013,
        author = {Drausin, W. and Fox, E.B. and Litt, B.},
        title = {Parsing Epileptic Events Using a {M}arkov Switching Process Model for Correlated Time Series},
        booktitle = {Proc. International Conference on Machine Learning},
        year = {2013},
        month = jun
      }
       
    2. Nystrom Approximation for Large-Scale Determinantal Processes. Affandi, R.H. and Kulesza, A. and Fox, E.B. and Taskar, B. Proc. International Conference on Artificial Intelligence and Statistics. 2013.
      BibTeX
       @inproceedings{Affandi:AISTATS2013,
        author = {Affandi, R.H. and Kulesza, A. and Fox, E.B. and Taskar, B.},
        title = {Nystrom Approximation for Large-Scale Determinantal Processes},
        booktitle = {Proc. International Conference on Artificial Intelligence and Statistics},
        year = {2013},
        month = apr
      }
       
    3. Representing Documents Through Their Readers. El-Arini, K. and Xu, M. and Fox, E.B. and Guestrin, C. Proc. Conference on Knowledge, Discovery, and Data Mining. 2013.
      BibTeX
       @inproceedings{ElArini:KDD2013,
        author = {El-Arini, K. and Xu, M. and Fox, E.B. and Guestrin, C.},
        title = {Representing Documents Through Their Readers},
        booktitle = {Proc. Conference on Knowledge, Discovery, and Data Mining},
        year = {2013},
        month = aug
      }
       
    4. Multiresolution Gaussian Processes. Fox, E.B. and Dunson, D.B. Neural Information Processing Systems 25. 2013.
      BibTeX
       @inproceedings{FoxDunson:NIPS2012,
        author = {Fox, E.B. and Dunson, D.B.},
        title = {Multiresolution {G}aussian Processes},
        booktitle = {Neural Information Processing Systems 25},
        year = {2013},
        publisher = {MIT Press}
      }
       
    5. Effective Split-Merge Monte Carlo Methods for Nonparametric Models of Sequential Data. Hughes, M.C. and Fox, E.B. and Sudderth, E.B. Neural Information Processing Systems 25. 2013.
      BibTeX
       @inproceedings{Hughes:NIPS2012,
        author = {Hughes, M.C. and Fox, E.B. and Sudderth, E.B.},
        title = {Effective Split-Merge {M}onte {C}arlo Methods for Nonparametric Models of Sequential Data},
        booktitle = {Neural Information Processing Systems 25},
        year = {2013},
        publisher = {MIT Press}
      }
       

    2012

    1. Hierarchical Latent Dictionaries for Models of Brain Activation. Fyshe, A. and Fox, E.B. and Dunson, D.B. and Mitchell, T.M. Proc. International Conference on Artificial Intelligence and Statistics. 2012.
      BibTeX
       @inproceedings{Fyshe:AISTATS2012,
        author = {Fyshe, A. and Fox, E.B. and Dunson, D.B. and Mitchell, T.M.},
        title = {Hierarchical Latent Dictionaries for Models of Brain Activation},
        booktitle = {Proc. International Conference on Artificial Intelligence and Statistics},
        year = {2012},
        month = apr
      }
       
    2. Markov Determinantal Point Processes. Affandi, R.H. and Kulesza, A. and Fox, E.B. Proc. Conference on Uncertainty in Artificial Intelligence. 2012.
      BibTeX
       @inproceedings{Affandi:UAI2012,
        author = {Affandi, R.H. and Kulesza, A. and Fox, E.B.},
        title = {Markov Determinantal Point Processes},
        booktitle = {Proc. Conference on Uncertainty in Artificial Intelligence},
        year = {2012},
        month = aug
      }