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.

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My recent work is centered around understanding the challenges that users face during data analysis and developing intelligent systems that guides users in the data science process. You can learn more about this line of research in this blogpost or this podcast.

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.


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.