Frank Wood

Dr. Frank Wood
Associate Professor
fwood@cs.ubc.ca
Google Scholar: Recent, Cited

2366 Main Mall #201
Vancouver, BC
V6T 1Z4
Canada

+16048223061
frank_wood (Skype)

Selected recent papers

  1. T.A. Le, Igl, M., Jin, T., Rainforth, T., & Wood, F. (2018). Auto-Encoding Sequential Monte Carlo. ICLR. BIB PDF
    @article{tale2017aesmc,
      title = {Auto-Encoding {S}equential {M}onte {C}arlo},
      author = {T.A.~Le and Igl, M. and Jin, T. and Rainforth, T and Wood, F.},
      journal = {ICLR},
      year = {2018}
    }
    
  2. Baydin, G., Cornish, R., Martinez-Rubio, D., Schmidt, M., & Wood, F. (2018). Online Learning Rate Adaptation with Hypergradient Descent. ICLR. BIB PDF
    @article{baydin2017online,
      title = {Online Learning Rate Adaptation with Hypergradient Descent},
      author = {Baydin, G. and Cornish, R. and Martinez-Rubio, D. and Schmidt, M. and Wood, F.},
      journal = {ICLR},
      year = {2018}
    }
    
  3. Siddarth, N., Paige, B., Desmaison, A., van de Meent, J. W., Goodman, N., Kohli, P., … Torr, P. H. S. (2017). Learning Disentangled Representations with Semi-Supervised Deep Generative Models. In NIPS. BIB PDF
    @inproceedings{iffsidnips2017,
      title = {Learning Disentangled Representations with Semi-Supervised Deep Generative Models},
      author = {Siddarth, N. and Paige, B. and Desmaison, A. and van~de~Meent, J.W. and Goodman, N. and Kohli, P. and Wood, F. and Torr, P.H.S},
      booktitle = {NIPS},
      year = {2017}
    }
    
  4. Le, T. A., Baydin, A. G., & Wood, F. (2017). Inference Compilation and Universal Probabilistic Programming. In AISTATS. BIB PDF
    @inproceedings{le2016inference,
      author = {Le, Tuan Anh and Baydin, Atılım Güneş and Wood, Frank},
      booktitle = {AISTATS},
      title = {Inference {C}ompilation and {U}niversal {P}robabilistic {P}rogramming},
      year = {2017},
      file = {../assets/pdf/le2016inference.pdf},
      link = {https://arxiv.org/abs/1610.09900}
    }
    
  5. Paige, B., & Wood, F. (2016). Inference Networks for Sequential Monte Carlo in Graphical Models. In ICML (Vol. 48). BIB PDF
    @inproceedings{paige2016inference,
      title = {Inference Networks for Sequential {M}onte {C}arlo in Graphical Models},
      author = {Paige, B. and Wood, F.},
      booktitle = {ICML},
      series = {JMLR},
      volume = {48},
      year = {2016}
    }
    

Prospective students

I am always looking for excellent, academically-motivated, AI-inspired PhD students. Please visit the departmental web page for prospective students to apply. An excellent strategy for getting an offer to work with me is to propose research in your proposal that extends and explicitly cites recent work of my own. Better still, working with me beforehand is a major leg up.

Postdocs

I am constantly looking for postdocs with strong programming languages, statistics, and applied machine learning skills. Please contact me directly.

Highlights

I was one of the initial developers of the Anglican probabilistic programming language and am active in ensuring its continued maintenance.