Hi! I’m Mahima.


I design novel software interfaces that visualize data, to help people understand AI.





I’m a UX Designer on the Big Picture Data Visualization Group at Google AI, which specializes in information visualization to make complex data accessible, useful, and even fun.

I tend to wear many hats – UI/UX, visual design, strategy and design research - to create better human-ai partnerships, such as helping doctors diagnose cancer using AI. The products and processes that I have designed, such as the What-If Tool and Facets, have been widely used to advance better practices in machine learning by companies like Google, Disney, Yahoo! and GoPro, as well as in academia by MIT, Harvard University and Belmont University. As a part of Google's People + AI Research Initiative, I study the impact of AI on design.

Prior to Google, I worked as a product designer at Innovation by Design, a global think-tank, MIT's Design Lab,  and designed self-evolving visualization tools at Ion Interactive, a SaSS platform for content marketers.

I hold an Masters of Fine Arts in Information Design & Data Visualization from Northeastern University, where I occasionally teach. I studied design as an undergraduate at Srishti School of Art, Design & Technology (Bangalore, India) and at University of Michigan (Ann Arbor, MI).
Outside of work, I chair the Diversity Committee at UXPA’s Boston chapter, and have been a UX mentor to student groups at MIT.

Sometimes I write on my Personal Blog and Google Design
Occasionally I’ll speak or give a workshop.

[Upcoming Presentation, IEEE VIS 2019] Wexler, J., Pushkarna, M., Bolukbasi, T., Wattenberg, M., Viegas, F., & Wilson, J. (2019). The What-If Tool: Interactive Probing of Machine Learning Models. arXiv preprint arXiv:1907.04135.

    How UX changes the world, One AI at a Time, UXPA Boston Annual Conference, May 2019. Event page here.


  Through the Looking Glass. World IA Day Boston 2019, Massachusetts College of Art & Design, Feb 2019. Recording

   The What-If Tool: Code-free probing of machine learning models for fairness and interpretability, Workshop, ComputeFest 2019, Harvard University, January 2019

   Learning Machine Learning: Implications for Design, UXPA Boston 17th Annual User Experience Conference, May 2018

    Machine Learning, Implications for Design, Invited Talk, Northeastern University, College of Arts, Media and Design, February 2018


Mark
Mahima Pushkarna