Sangwon (Justin) Hyun

Table of Contents


About me

Post-doctoral Researcher
Department of Data Sciences and Operations
University of Southern California
Email:

I'm currently a post-doc working with Jacob Bien. My current focus is statistical analysis and modeling of ocean data, as part of the CBIOMES initiative of the Simons Foundation.

I also work on epidemiological forecasting, and have been part of the COVID19
response effort of the DELPHI group.

Favorite quote

From The Universe and the Teacup by K.C. Cole:

"So the improved Newtonion Universe must cease and grow cold," says Thomasina's tutor Septimus in Stoppard's Arcadia, suddenly realizing the import of his student's mathematical discovery that disorder is the inevitable direction of things. "Dear me."

"Yes," Thomasina replies, "we must hurry if we are going to dance."

Education

B.S. in Statistics and Mathematics, University of Michigan - Ann Arbor (2013)
M.A. in Statistics, Carnegie Mellon University (2014)
Ph.D. in Statistics, Carnegie Mellon University (2018)

My PhD advisors are Max G'sell and Ryan Tibshirani.

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

Research

My research interests include oceanographic data analysis, epidemiological
modeling/forecasting, changepoint detection, and selective inference.

Ocean data analysis

Density-based Approaches to Evaluate Biogeochemical Models by Satellite Derived Chlorophyll

Bror F. Jönsson, Christopher L. Follett, Jacob Bien, Stephanie Dutkiewicz, Sangwon Hyun, Gemma Kulk, Gael Forget, Christian L. Müller, Marie-Fanny Racault, Christopher N. Hill, Thomas Jackson, Shubha Sathyendranath
In submission to AGU Biogeochemical Cycles (2021)

A Flexible Bayesian Approach to Estimating Size-structured Matrix Population Models
Kristof Glauninger, Jann Paul Mattern, Greg Britten, John Casey, Sangwon Hyun, Zhen Wu, E. Virginia Armbrust, Zaid Harchaoui, François Ribalet
Under revision with PLOS Computational Biology (2021)

Modeling Cell Populations Measured By Flow Cytometry With Covariates Using Sparse Mixture of Regressions
Sangwon Hyun, Mattias Rolf Cape, François Ribalet, Jacob Bien
Under revision with Annals of Applied Statistics (2020)

Changepoint detection and selective inference

Post-selection Inference for Changepoint Detection Algorithms with Application to Copy Number Variation Data
Sangwon Hyun, Kevin Lin, Max G'Sell, Ryan Tibshirani
Biometrics (2020)

Exact Post-Selection Inference for the Generalized Lasso Path
Sangwon Hyun, Max G'sell, Ryan Tibshirani
Electronic Journal of Statistics (2018)

Disease modeling and forecasting
An Open Repository of Real-Time COVID-19 Indicators
Various authors
In submission PNAS (2021)

Can Auxiliary Indicators Improve COVID-19 Forecasting and Hotspot Prediction?
Daniel J. McDonald, Jacob Bien, Alden Green, Addison J. Hu, Nat DeFries, Sangwon Hyun, Natalia L. Oliveira, James Sharpnack, Jingjing Tang, Robert Tibshirani, Valérie Ventura, Larry Wasserman, Ryan Tibshirani
In submission to PNAS (2021)

A Human Judgment Approach to Epidemiological Forecasting
David Farrow, Logan Brooks, Sangwon Hyun, Ryan Tibshirani, Donald S. Burke
PLOS Computational Biology (2017)

Flexible Modeling of Epidemics with an Empirical Bayes Framework
Logan Brooks, David Farrow, Sangwon Hyun, Ryan Tibshirani, Roni Rosenfeld
PLOS Computational Biology (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
PLOS Neglected Tropical Diseases (2014)

Nonmechanistic Forecasts of Seasonal Influenza with Iterative One-week-ahead Distributions
Logan Brooks, David Farrow, Sangwon Hyun, Ryan Tibshirani, Roni Rosenfeld
PLOS Computational Biology (2018)

Results from the Second Year of a Collaborative Effort to Forecast Influenza Seasons in the United States
Various authors
Epidemics (2018)

An Open Challenge to Advance Probabilistic Forecasting for Dengue Epidemics
Various authors
Proceedings of the National Academy of Sciences (2019)

Activities/Talks

JSM 2021 talk
'Optimal Transport for Analyzing Ocean Data'
Seattle, WA

Center for Computational Mathematics (CCM) talk (2020)
'Sparse Multivariate Mixture of Regressions Modeling for Flow Cytometry Data'
Flatiron institute, New York, NY

JSM 2020 talk
'Joint Modeling of Continuous Flow Cytometry Data With Environmental Covariates'
(Session on Ocean Statistical Methodology and Application)
Philadelphia, PA

SLDS 2020 invited talk
'Joint Modeling of Continuous Flow Cytometry Data With Environmental Covariates'
Newport Beach, California (Canceled)

CMStatistics 2019 invited talk
'Joint Modeling of Continuous Flow Cytometry Data With Environmental Covariates'
London, UK

SDSS 2018 poster session
'Knockoff variable selection for changepoint detection'
Reston, VA

JSM 2017 talk
'On changepoint inference using Binary Segmentation Inference'
Session on New Developments in Time Series Analysis and Change Point Detection
Baltimore, MD

Invited talk
'Forecasting of dengue risk in 2016 for Southeast Asia'
2016 Southeast Asia regional meeting on climate and dengue forecasting
Kuala Lumpur, Malaysia

2016 AAAS annual meeting poster
'Epidemiological Forecasting with Statistical Models'
Best student poster (Technology, Engineering and Math)
Washington DC

JSM 2016 talk
`On changepoint inference after selection'
(Session on modern inference for selected models)
Chicago, IL

INFORMS 2016 talk
`On changepoint inference after selection'
Session on Detection of Structure and Anomalous Patterns in Data
Nashville, TN

Teaching

Experience
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 was also teaching assistant 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.

Teaching statistics and data science
I'm interested in how to improve teaching in statistics and data science. I was involved in the CMU statistics department's effort to revamp and improve the undergraduate curriculum, starting with introductory courses. I 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

Identifying misconceptions of introductory data science using a think-aloud protocol
Poster presentation, eCOTS 2018, Pittsburgh, PA

Other

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, bicycling, weightlifting, reading books, and cooking.
I'm an avid novice user of Emacs and Org mode.
Fun fact: I served 2 years in the military at the Joint Security Area, at the border of North/South Korea.

Author: Sangwon Hyun

Created: 2021-09-23 Thu 14:02

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