Dr. Vivienne Ming
Dr. Vivienne Ming is a theoretical neuroscientist, serial entrepreneur and author of "How to Robot-Proof Your Kids" and "The Tax on Being Different".
Travels From Berkeley, California
Frequently featured for her research and inventions in The Financial Times, The Atlantic, Quartz and the New York Times, Dr. Vivienne Ming is a theoretical neuroscientist, entrepreneur, and author. She co-founded Socos Labs, her fifth company, a mad science incubator. Previously, Vivienne was a visiting scholar at UC Berkeley's Redwood Center for Theoretical Neuroscience, pursuing her research in cognitive neuroprosthetics. In her free time, Vivienne has invented AI systems to help treat her diabetic son, predict manic episodes in bipolar sufferers weeks in advance, and reunited orphan refugees with extended family members. She sits on the boards of numerous companies and nonprofits.
For relaxation, she is a wife and mother of two.
Dr. Ming speaks regularly for conference keynotes, company events, and day-long advisory consulting. If Dr. Ming is traveling out of the Bay Area for an engagement, we often encourage our clients to set up multiple events to make sure that they are getting the most out of the visit. This can include having Vivienne connect with teams for a forum-style discussion around an important company topic or presenting more than one keynote to address different groups within the company.
Sample Speaking titles include, but are not limited to:
- A Post-Gender Business World: Is it Possible? (Diversity/Inclusion, Neuroscience)
- Engineering Environments for Success (Neuroscience, Cultural Genome, Diversity/Inclusion)
- The Tax on Being Different (Diversity/Inclusion)
- How to Robot Proof Your Kids (AI, Education, Neuroscience)
- The Economist, Pride & Prejudice Keynote Event (Diversity/Inclusion, Economics)
- From cognitive modeling to labor markets: estimating the economic cost of unrealized human potential - EdLab, Columbia University (Diversity/Inclusion, Neuroscience)
- Unleashing Human Potential - Chair, ReWork Deep Learning Conference (AI, Education, Neuroscience)
- Maximizing Human Potential Using Machine Learning-Driven Applications - Berkeley Data Science Lecture Series (AI, Education, Neuroscience)
- Tech Industry and the LGBT Community - Bloomberg News Panel (Diversity/Inclusion, Economics)
- Keeping the Promise of Educational Technology, Keynote at SXSWedu (AI, Education, Neuroscience)
- Future of Job Creation and HR Tech Summit - Future of X (Diversity/Inclusion, Economics)
- Groundbreakers - EdLab, Columbia University (AI, Education, Neuroscience)
- The Future of Work: Transforming the Ways We Collaborate - The Atlantic Silicon Valley Summit (Diversity/Inclusion, Economics)
The Most Influential Women in the Bay Area - San Francisco Business Times
"Can you see the real me?" - O Magazine
Invented Jitterbug, which predicts optimal insulin dosing, when her son was diagnosed with Type 1 Diabetes
Published in scientific journals like Nature, Neural Information Processing Systems, Neural Computation, and the Public Library of Science.
If Kids Were Bonds They’d be the Backbone of the World Economy, Techonomy November 2015
There is a tax on being different, Financial Times July 2016
The Workforce of the Future TEDMED June 2016
I Built a Superpower to Treat My Son's Diabetes Aspen Ideas Blog April 2016
- Modeling student conceptual knowledge from unstructured data using a hierarchical generative model Ming & Ming (2012) NIPS2012 Workshop: Personalizing Education With Machine Learning. South Lake Tahoe, CA.
- Sparse codes for speech predict spectrotemporal receptive fields in the inferior colliculus Carlson, Ming & DeWeese (2012). PLoS CompBio.
- Efficient auditory coding Smith & Lewicki (2006). Nature 439, Num. 7079.
- Heirarchical coding of natural signals in a dynamical system model Bumbacher & Ming (2012) Cosyne 2012.
- An approach to automatic recognition of spontaneous facial actions Bartlet, Braathen, Littlewort, Smith & Movellan (2003) Advances in Neural Information Processing Systems 15. MIT Press, Cambridge, Massachusetts.