I am a Machine Learning Engineer (Research Software Engineer) at Uber’s Applied AI team. I develop probabilistic and statistical machine learning frameworks. I am inspired by real-world problems from, for example, genomics, neuroscience, and business.

In my free time, I do research on machine learning and its application to computational biology.

Research Interests

     Machine Learning: Probabilistic and Statistical Modeling
     Machine Learning: Graphs
     Machine Learning: Applications

Education

     Ph.D. in Computational Biology, School of Computer Science, Carnegie Mellon University, 2023
     B.S. in Computer Science, Columbia University, 2017

Publications

• Learning Gene Networks Under SNP Perturbation Using SNP and Allele-Specific Expression Data
  Jun Ho Yoon, Seyoung Kim.
  bioRxiv doi: 10.1101/2023.10.23.563661, 2023.
  [preprint, software]

• Doubly Mixed-Effects Gaussian Process Regression
  Jun Ho Yoon, Daniel P. Jeong, Seyoung Kim.
  Proceedings of the 25th International Conference on Artificial Intelligence and Statistics (AISTATS), PMLR 151:6893-6908, 2022.
  Accepted for oral presentation (2.6% of submitted papers).
  [paper, software]

• EiGLasso for Scalable Sparse Kronecker-Sum Inverse Covariance Estimation
  Jun Ho Yoon, Seyoung Kim.
  Journal of Machine Learning Research, 23(110):1−39, 2022.
  [paper, software]

• EiGLasso: Scalable Estimation of Cartesian Product of Sparse Inverse Covariance Matrices
  Jun Ho Yoon, Seyoung Kim.
  Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI), PMLR 124:1248-1257, 2020.
  [paper, talk, software]