Doris Jung-Lin Lee

I am a PhD student at UC Berkeley working with Professor Aditya Parameswaran. My current research interest lies in interactive data analytics, visualization, and human-computer interaction. I am currently working on tools that assist data scientists in the process of exploratory data analysis and machine learning. 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.

News Updates

  • 10/20/2019: VIS 2019 in Vancouver
  • 08/12/2019: I've moved back to Berkeley!
  • 06/06/2019: Our paper based on our design studies on visual query systems is accepted to IEEE VAST 2019!
  • 06/01/2019: Our vision paper for a Mixed-Initiative Machine Learning Environment (MILE) is now on IEEE Data Bulletin.
  • 05/13/2019: Starting my summer internship at IBM Research Almaden!
  • 04/13/2019: CRA-W Grad Cohort
  • 03/16/2019: IUI in LA
  • [More News]

Publications

A Human-in-the-loop Perspective on AutoML: Milestones and the Road Ahead

IEEE Data Bulletin 2019

You can't always sketch what you want: Understanding Sensemaking in Visual Query Systems

IEEE Visual Analytics Science & Technology (TVCG Track at VAST'19 at VIS)

Avoiding Drill-down Fallacies with VisPilot: Assisted Exploration of Data Subsets

ACM Intelligent User Interface (IUI) 2019

The Case for a Visual Discovery Assistant: A Holistic Solution for Accelerating Visual Data Exploration

IEEE Data Bulletin 2018

Crowdclass: Designing classification-based citizen science learning modules

AAAI Human Computation and Crowdsourcing (HCOMP) 2016

Skintillates: Design and Prototype Epidermal Interactions

Honorable Mention Award

ACM Designing Interactive Systems (DIS) 2016

‘I don't want to wear a screen’: Probing perceptions of and possibilities for dynamic displays on clothing

Best Paper Award

ACM Computer-Human Interaction (CHI) 2016




Past Projects

Social Behavioural Data Mining

Crowdsourced Image Segmentation

HCOMP 2018, Demo Paper

Developed techniques for evaluating the quality of crowdsourced image segmentation by aggregating across multiple workers.

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.