Doris Jung-Lin Lee

I am a first-year graduate student at University of Illinois, Urbana-Champaign. My current research interest lies in data science and human-computer interaction. I have recently graduated from UC Berkeley in physics and astrophysics where I worked on crowdsourcing, genomics visualizations, fabrication, astrophysical simulations, and data mining for energy sciences and cosmology. I am interested in creating technology that bridges the knowledge and skillsets of different communities.


Comparison of Clustering Techniques for Residential Energy Behavior using Smart Meter Data

AAAI Workshop on Artificial Intelligence for Smart Grids and Smart Buildings 2017


Crowdclass: Designing classification-based citizen science learning modules

HCOMP 2016

[Project Page] [Code] [PDF] [Slides] [Supplementary Material]

Skintillates: Design and Prototype Epidermal Interactions

DIS 2016 Honorable Mention Award

[Project Page] [PDF]

‘I dont want to wear a screen’: Probing perceptions of and possibilities for dynamic displays on clothing

CHI 2016 Best Paper Award

[Project Page] [PDF]

Creating updated, scientifically-calibrated mosaic images for the RC3 Catalogue

Technical Report

[Project Page] [Code] [PDF]

Past Projects

Fabrication tools & Wearables

Shrinky Circuits

Developing a rapid prototyping technique for building circuit boards without involving conventional PCB/chemical etching procedures.

Project Jacquard

Collaboration with Google Advanced Technologies and Projects (ATAP) group on a new type of interactive wearable technology.


Creating low-cost, accessible fabrication technique for on-skin wearable electronics that can integrate with a variety of electronic components.

Cosmological Data Mining
Pattern Discovery and Large-Scale Data Mining on Cosmological Datasets. [Poster]
Workshop on Algorithms for Modern Massive Data Sets (MMDS 2016).

RC3 Mosaics

Designing a pipeline for generating scientifically calibrated images of large nearby RC3 galaxies using an adaptive algorithm for positional update.

Halo Finder

Applying unsupervised machine learning algorithms to cosmological simulations for finding dark matter haloes.

SDSS Imaging Systematics

Investigating how systematics affect the imaging data quality from the Sloan Digital Sky Survey.

Astrophysical Fluid Simulations

Star Formation

Magnetohydrodynamics, adaptive mesh refinement simulations for the evolution of a collapsing dense core.

Accretion Disk

Investigating the effects of Papaloizou-Pringle and magnetorotational instabilities in accretion disk torus.


Jupyter Notebook tutorials on how to build, run, and analyze astrophysical simulations.