Tyler Tuan worked 3 years as a full-stack data scientist in Silicon Valley. He implements many machine learning products in Big Data environments. Coming to Northeastern, he brought his industrial experience and decided to study causality, which is a missing piece of the current Data Science industry. He hopes to build a basic framework for causality that can be widely used, applied and understood.
- MS, Statistics, Stanford University
- BS, Applied Math, University of California San Diego
- Hometown: Vietnam
- Field of Study: Statistics
- PhD Advisor: Olga Vitek
What are the specifics of your graduate education (thus far)?
Statistical Machine Learning.
What are your research interests?
Causal inference and its application in biological systems and economic systems. This field is relevant when the decision makers aim to make decisions influencing the outcomes of interests such as cell manipulation, changes in the educational system or marketing campaigns. The technical challenges arise when a randomized assignment is impractical in the design of the experiment. Such cases would require relying on observational approaches. My interests are in integrating machine learning techniques into these causal frameworks.
What’s one problem you’d like to solve with your research/work?
Mining the proteins in a cell that acts as a causation to a phenotype of interests such as the death of the cell.
What aspect of what you do is most interesting?
Causality is a huge field with great potential applications. It answers questions meaningfully in a way that machine learning would not be able to do. It is very relevant to a decision maker.
What are your research or career goals, going forward?
Causality for now. The future is unknown and uncertain.