Christopher Aicher

Bio

Christopher Aicher I am a quantitative researcher at Citadel Securities in Chicago. I received a PhD from the Department of Statistics at the University of Washington where I was advised by Emily B. Fox. Prior to my PhD at the University of Washington, I received a BS and MS degree from the Department of Applied Mathematics at the University of Colorado where I was advised by Aaron Clauset.

Research

My research interests are in scalable approximate inference methods for machine learning, specifically using deterministic Bayesian approximations (e.g. Expectation Propagation and Variational Inference) and stochastic gradient MCMC methods (e.g. SGLD). My research projects include:

Experience

Publications

  1. Aicher, C. (2020). Scalable Learning in Latent State Sequence Models [PhD thesis]. University of Washington. [PDF]
    BibTex 
     @phdthesis{aicher2020scalable,
  title = {Scalable Learning in Latent State Sequence Models},
  author = {Aicher, Christopher},
  school = {University of Washington},
  year = {2020},
  link = {https://digital.lib.washington.edu/researchworks/handle/1773/45550}
}
  2. Aicher, C., Foti, N. J., & Fox, E. B. (2019). Adaptively Truncating Backpropagation Through Time to Control Gradient Bias. Uncertainty in Artificial Intelligence. [PDF] [Code]  
    BibTex 
     @article{aicher2019tbptt,
  title = {Adaptively Truncating Backpropagation Through Time to Control Gradient Bias},
  author = {Aicher, Christopher and Foti, Nicholas J. and Fox, Emily B.},
  journal = {Uncertainty in Artificial Intelligence},
  year = {2019},
  month = may,
  link = {https://arxiv.org/abs/1905.07473},
  code = {https://github.com/aicherc/adaptive_tbptt}
}
  3. Aicher, C., Putcha, S., Nemeth, C., Fearnhead, P., & Fox, E. B. (2019). Stochastic Gradient MCMC for Nonlinear State Space Models. ArXiv Preprint ArXiv:1901.10568. [PDF] [Code]  
    BibTex 
     @article{aicher2019nonlinear,
  title = {Stochastic Gradient MCMC for Nonlinear State Space Models},
  author = {Aicher, Christopher and Putcha, Srshti and Nemeth, Christopher and Fearnhead, Paul and Fox, Emily B.},
  journal = {arXiv preprint arXiv:1901.10568},
  year = {2019},
  month = jan,
  link = {https://arxiv.org/abs/1901.10568},
  code = {https://github.com/aicherc/sgmcmc_ssm_code}
}
  4. Aicher, C., Ma, Y.-A., Foti, N. J., & Fox, E. B. (2019). Stochastic Gradient MCMC for State Space Models. SIAM Journal on Mathematics of Data Science, 1(3), 555–587. [PDF] [Code]  
    BibTex 
     @article{aicher2019stochastic,
  title = {Stochastic Gradient MCMC for State Space Models},
  author = {Aicher, Christopher and Ma, Yi-An and Foti, Nicholas J and Fox, Emily B},
  journal = {SIAM Journal on Mathematics of Data Science},
  volume = {1},
  number = {3},
  pages = {555--587},
  year = {2019},
  publisher = {SIAM},
  link = {https://arxiv.org/abs/1810.09098},
  code = {https://github.com/aicherc/sgmcmc_ssm_code}
}
  5. Aicher, C., & Fox, E. B. (2018). Approximate Collapsed Gibbs Clustering with Expectation Propagation. ArXiv Preprint ArXiv:1807.07621. [PDF] [Code]  
    BibTex 
     @article{aicher2018approximate,
  title = {Approximate Collapsed Gibbs Clustering with Expectation Propagation},
  author = {Aicher, Christopher and Fox, Emily B.},
  journal = {arXiv preprint arXiv:1807.07621},
  year = {2018},
  month = jul,
  link = {https://arxiv.org/abs/1807.07621},
  code = {https://github.com/aicherc/EP_Collapsed_Gibbs}
}
  6. Simonen, K., Huang, M., Aicher, C., & Morris, P. (2018). Embodied Carbon as a Proxy for the Environmental Impact of Earthquake Damage Repair. Energy and Buildings. [PDF]
    BibTex 
     @article{simonen2018embodied,
  title = {Embodied Carbon as a Proxy for the Environmental Impact of Earthquake Damage Repair},
  author = {Simonen, K and Huang, M and Aicher, C and Morris, P},
  journal = {Energy and Buildings},
  year = {2018},
  month = jan,
  link = {https://www.sciencedirect.com/science/article/pii/S0378778817319710}
}
  7. Aicher, C., & Fox, E. B. (2016). Scalable Clustering of Correlated Time Series Using Expectation Propagation. SIGKDD Workshop on MiLeTS. [PDF] [Code]  
    BibTex 
     @article{aicherc2016scalable,
  title = {Scalable Clustering of Correlated Time Series Using Expectation Propagation},
  author = {Aicher, Christopher and Fox, Emily B.},
  journal = {SIGKDD Workshop on MiLeTS},
  year = {2016},
  code = {https://github.com/aicherc/EP_Collapsed_Gibbs}
}
  8. Aicher, C., Jacobs, A. Z., & Clauset, A. (2015). Learning Latent Block Structure in Weighted Networks. Journal of Complex Networks, 3(2), 221–248. [PDF] [Code]  
    BibTex 
     @article{aicher2015learning,
  title = {Learning Latent Block Structure in Weighted Networks},
  author = {Aicher, Christopher and Jacobs, Abigail Z and Clauset, Aaron},
  journal = {Journal of Complex Networks},
  volume = {3},
  number = {2},
  pages = {221--248},
  year = {2015},
  publisher = {Oxford University Press},
  link = {http://arxiv.org/abs/1404.0431},
  code = {http://tuvalu.santafe.edu/%7Eaaronc/wsbm/}
}
  9. Aicher, C., Jacobs, A. Z., & Clauset, A. (2013). Adapting the Stochastic Block Model to Edge-Weighted Networks. ICML Workshop on Structured Learning. [PDF] [Code]  
    BibTex 
     @article{aicher2013adapting,
  title = {Adapting the Stochastic Block Model to Edge-Weighted Networks},
  author = {Aicher, Christopher and Jacobs, Abigail Z and Clauset, Aaron},
  journal = {ICML Workshop on Structured Learning},
  year = {2013},
  link = {http://arxiv.org/abs/1305.5782},
  code = {http://tuvalu.santafe.edu/%7Eaaronc/wsbm/}
}

Teaching