Sangwon (Justin) Hyun

"Wheresoever you go, go with all your heart." - Confucius

PhD Student
Department of Statistics and Data Science
Carnegie Mellon University
5000 Forbes Avenue (Mailing Address)
Pittsburgh, PA 15213-3890 USA
DELPHI Group
Email: shyun [AT] cmu.edu

Education
B.S. in Statistics and Mathematics, University of Michigan - Ann Arbor (go blue!)
M.A. in Statistics, Carnegie Mellon University (go Tartans!)

Research
My research interests include epidemiological modeling/forecasting, changepoint detection, and post-selection inference. My PhD advisors are Max G'sell and Ryan Tibshirani.

My ORCID ID is 0000-0003-0377-897X

Exact Post-Selection Inference for the Generalized Lasso Path.
Sangwon Hyun, Max G'sell, Ryan Tibshirani (2016) Electronic Journal of Statistics

A human judgment approach to epidemiological forecasting
David Farrow, Logan Brooks, Sangwon Hyun, Ryan Tibshirani, Donald S. Burke, (2017)

Flexible Modeling of Epidemics with an Empirical Bayes Framework
Logan Brooks, David Farrow, Sangwon Hyun, Ryan Tibshirani, Roni Rosenfeld (2015)

Risk of Dengue for Tourists and Teams during the World Cup 2014 in Brazil
Wilbert Van Panhuis, Sangwon Hyun, Kayleigh Blaney, Ernesto T. A. Marques Jr, Giovanini E. Coelho, João Bosco Siqueira Jr, Ryan Tibshirani, Jarbas B. da Silva Jr, Roni Rosenfeld (2014)

Activities/Talks
JSM 2017 talk, `On changepoint inference using Binary Segmentation Inference'. Session on New Developments in Time Series Analysis and Change Point Detection, Baltimore, MD (slides)
Invited speaker, 2016 Southeast Asia regional meeting on climate and dengue forecasting, Kuala Lumpur, Malaysia
Best student poster (Technology, Engineering and Math), 2016 AAAS annual meeting , Washington DC
JSM 2016 talk, `On changepoint inference after selection'. Session on modern inference for selected models, Chicago, IL (slides)
INFORMS 2016 talk, `On changepoint inference after selection'. Detection of Structure and Anomalous Patterns in Data, Nashville, TN (slides)

Teaching
I have TA'd for several statistics courses at the undergraduate and graduate level:
36-217 Probability theory and random processes,
36-225 & 36-226 Mathematical statistics sequence for undergraduates,
36-350 Undergraduate statistical computing,
36-402 Undergraduate advanced data analysis,
36-617 Applied linear models,
36-618 Topics in statistics,
36-725 Convex Optimization,
36-750 Statistical Computing.

I have taught the following two courses:
36-220 Engineering Statistics and Quality Control (Summer 2015)
36-217 Probability Theory and Random Processes (Summer 2016)

I'm also interested in how to improve teaching statistics and data science. I have been actively involved in the department's effort to revamp and improve the undergraduate curriculum, starting with introductory courses. I have co-organized two seminar courses, both named 36-764, in which we discussed literature on learning, created a repository of assessment test questions (focused on testing conceptual understanding), and interviewed students in order to probe their misunderstanding and improve test questions. See more details in the group's website.

Assessment of Student Learning and Misconception Identification in Intro Statistics Poster presentation, Eberly Teaching and Learning Summit 2017, Pittsburgh, PA

Here is my teaching statement.

Work Experience
In the summer of 2015, I went to NASA Research to do some top secret stuff.
In the past, I've have worked in statistical consulting at the University of Michigan (CSCAR), and have interned at a finance firm.

And more
I enjoy playing games that involve bouncy spheres, weightlifting, reading books, cooking.
I have something like a blog, where I write about things that interest me while procrastinating from work.
Fun fact: I served 2 years in the military at the Joint Security Area, at the border of North/South Korea.