Crowdsourced Image Segmentation
HCOMP 2018, Demo Paper
Developed techniques for evaluating the quality of crowdsourced image segmentation by aggregating across multiple workers.
Doris Jung-Lin Lee
I am a PhD student at UC Berkeley working with Professor Aditya Parameswaran.
My research interest lies in interactive data analytics, visualization, and human-computer interaction. I am currently working on 
, a Python library for accelerating and simplifying the process of data exploration.
Before this, I was at the computer science department at the University of Illinois, Urbana-Champaign. I received my Bachelor's degree in physics and astrophysics from UC Berkeley in 2016.
I am awarded the 2020 Facebook PhD Fellowship in Systems for Machine Learning.
News Updates
- 10/2020: Lux-related news: v0.2 release! Presented a poster at JupyterCon, and hands-on tutorial at RISE Camp
- 07/2020: Spoke about Lux at the Women in Tech event at Zillow, slides here.
- 06/2020: Our new paper on understanding ML development via empirical user workflow traces will be virtually presented at the HILDA workshop in SIGMOD.
- 05/2020: Spoke about automation in data science for the Alter Everything Podcast.
Publications
Past Projects
Social Behavioural Data Mining
Fashion Account Discovery
KDD 2017, ML4Fashion Workshop
Classification and crawling Twitter data to identify fashion accounts in social networks.
Energy Usage Patterns
AAAI 2017, AI for Smart Grids & Buildings Workshop
Comparison of Clustering Techniques for Residential Energy Behavior using Smart Meter Data.
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.
Skintillates
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.
astroSim-tutorial
Jupyter Notebook tutorials on how to build, run, and analyze astrophysical simulations.