I am a Ph.D. candidate at MIT CSAIL, advised by Professor David Sontag in the Clinical Machine Learning group. My research focuses on machine learning and its applications to solving important real-world problems including healthcare and fairness.
Prior to MIT, I worked at Dropbox as Data Scientist, Chief of Staff, and Machine Learning Engineer. I graduated from Harvard with a joint AB/SM in Applied Math and Computational Engineering where I researched with Michael Luca and Ben Edelman.
You can email me at iychen [at] mit [dot] edu or reach me on Twitter.
Current research projects include
- Congestive Heart Failure: How can we combine electronic health records with mechanistic information to better treat heart failure? What signal do echocardiograms contain? In collaboration with Beth Israel Deaconess Medical Center.
- Health Knowledge Graph: How can we build a structure to capture causal information on symptoms and diseases? Can we capture and quantify error in the model?
- Fairness in machine learning: How can we make models that represent people of all genders and races? In a world of limited resources, how can we create more inclusive models?
- Why Is My Classifier Discriminatory? Irene Chen, Fredrik D. Johansson, David Sontag. Preprint. [arXiv]
- Sources of Unfairness in Intensive Care Unit Mortality Scores. Irene Chen, Fredrik D. Johansson, David Sontag. Women in Machine Learning Workshop at NIPS 2017.
At Harvard, I was awarded the Derek Bok Center Certificate of Distinction in Teaching for outstanding teaching evaluations.
I have served on the teaching staff for the following Harvard classes.
- Linear Algebra and Real Analysis I, Paul Bamberg
- Microeconomic Theory, Ed Glaeser
- Linear Algebra and Real Analysis II, Paul Bamberg
- Multivariable Calculus, Evelyn Hu and Avi Shapiro
- Differential Equations, Margo Levine and Avi Shapiro
- Algorithms and Data Structures, Jelani Nelson
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