Daniel Iong

PhD Candidate in Statistics

University of Michigan, Ann Arbor

Education

PhD Statistics 2017 - 2023

University of Michigan, Ann Arbor
BS Statistics, BA Economics 2013 - 2017

University of California, Davis

Interests

Probabilistic/Bayesian modeling
Time series forecasting
(Statistical) machine learning
Applied Statistics

Experience

Data Scientist Intern

Orbital Insight

May 2022 – Aug 2022 Palo Alto, CA

Developed algorithm based on convolutional neural networks and other computer vision methods to detect rare GNSS interference events in geolocation (AIS, ADS-B) data as a key deliverable for a Department of Defense contract.
Trained convolutional neural networks on generated synthetic data using PyTorch on AWS EC2 instances.

Research Intern

NASA Goddard Space Flight Center

Jun 2021 – Aug 2021 Virtual

Mentors: Charles N. Arge, Michael Kirk, Daniel Da Silva
Identified novel application of dynamic time warping for model evaluation of a physics-based solar wind model
Created web app using Dash and Plotly packages in Python to visualize dynamic time warping (see below).

Projects

Variational inference for robust Gaussian process regression

We developed a scalable approximate inference algorithm for robust Gaussian process regression with contaminated normal noise. Initial experiments with simulated and actual data are promising.

Explainable ML for space weather forecasting

We trained gradient boosted trees to forecast the SYM-H index several hours ahead using solar wind data and used SHAP values to explain predictions.

Modeling heterogeneous causal mechanisms in epidemiology with observational data

We introduced the notion of ‘mechanistic heterogeneity’ in Mendelian Randomization, developed a latent mixture method to model it, and used it to estimate heterogeneous causal effects between HDL cholesterol and coronary heart disease.

Publications

Aniket Jivani, Nishtha Sachdeva, Zhenguang Huang, Yang Chen, Bart van der Holst, Ward Manchester, Daniel Iong, Hongfan Chen, Shasha Zou, Gabor Toth (2022). Global Sensitivity Analysis and Uncertainty Quantification for Background Solar Wind using the Alfvén Wave Solar Atmosphere Model. Space Weather.

DOI PDF

Daniel Iong, Yang Chen, Gabor Toth, Shasha Zou, Tuija Pulkkinen, Jiaen Ren, Enrico Camporeale, Tamas Gombosi (2022). New Findings From Explainable SYM-H Forecasting Using Gradient Boosting Machines. Space Weather.

Project DOI PDF

Daniel Iong, Qingyuan Zhao, Yang Chen (2020). A Latent Mixture Model for Heterogeneous Causal Mechanisms in Mendelian Randomization. Submitted to Annals of Applied Statistics.

Project arXiv PDF