Tin Nguyen is a PhD Student at MIT. He is advised by Tamara Broderick. His research develops computational methods for inference and sensitivity detection in Bayesian inference.
Publications
(2023).
On regularization and inference with label constraints.
In ICML 2023.
(2023).
Independent finite approximations for Bayesian nonparametric inference.
Bayesian Analysis Advance Publication.
(2022).
Are you using test log-likelihood correctly?.
In ICBINB Workshop at NeurIPS 2022.
(2022).
Many processors, little time: MCMC for partitions via optimal transport couplings.
In AISTATS 2022.
(2022).
Measuring the robustness of Gaussian processes to kernel choice.
In AISTATS 2022.
Skills
R
3 years experience
Python
4 years experience
SQL
1 year experience
Experience
Quant Research Intern
Quantbot Technologies LP
Research Intern
IBM Research
Reduced runtime of approximate cross-validation for structured models including hidden Markov models and conditional random fields.
Research Intern
IBM Research
Developed specification tests to reject the null hypothesis that neural networks trained on clean image data are well-specified for corrupted image data.
Awards
Student Poster Award
Outstanding Reviewer Award
Student Travel Award
Professional Services
NeurIPS Reviewer
2021, 2022
AISTATS Reviewer
2022
TMLR Reviewer
2022