Kevin Zeng Hu

We should all be able to ask questions of our data. Towards this end, I build data visualization and analysis tools that are accessible to the many, not just the few. My approach is to combine human-centered interface design, recommender systems, and machine learning — though the "how?" is secondary to the "why?"

I'm currently a doctoral student with the Collective Learning Group at the MIT Media Lab. Previously, I studied Physics at MIT and interned at several tech companies. My past projects have been published in top academic journals and covered in diverse media outlets. But I'm proudest of having reached the front page of Reddit.




Sherlock (2019)
Deep learning for semantic type detection
VizML (2019)
Machine learning for visualization recommendation
VizNet (2019)
Towards a large-scale visualization learning and benchmaring repository
DIVE (2018)
Recommendation-driven data exploration system
Clinton Circle (2016)
WikiLeaks e-mails visualized as network of connections
Press: Univision
Global Language Network (2014)
Understanding the connections between languages
Info: Video, PNAS Paper
Pantheon (2014)
Quantifying historical cultural production
GIFGIF (2014)
Measuring emotional content of animated GIFs
FOLD (2014)
Platform for reading and writing non-linear and contextual stories


Last Updated on May 2019