Essential Smarts helps organizations and people to do great work by providing coaching, culture and engagement surveys, workshops, and organizational consulting. We focus on innovative startups, and tailor our services to the needs of each client.
Ph.D., Symbolic Systems and Educational Psychology,
M.Ed., Psychology and Human Development,
B.A. Psychology and Computer Science
University of California, San Diego
Ryan Babineaux is a thought leader in the field of career development, whose work has been featured in the New York Times, Oprah.com, The Atlantic, GQ Magazine, and NPR. He is the co-author of the best-selling book Fail Fast, Fail Often: How Losing Can Help You Win, and creator of the popular Stanford University course of the same name.
Ryan’s passion is for understanding how people can lead more vibrant and purposeful lives. He has studied with many leaders in the field of human development, including Howard Gardener, the creator of the theory of multiple intelligences, William Damon, the leading thinker in moral development and career commitment, and John Krumboltz, the creator of the happenstance learning theory. His doctoral thesis at Stanford explored how people embody their spirituality within their careers and daily lives. He has been practicing Buddhism for over 25 years and has studied with a number of teachers including, Tenzin Wangyal, Lama Surya Das, and Steven Tainer. He spends part of each year on retreat, and is an adjunct faculty member in the contemplative psychology program at Naropa University.
Ryan’s is presently leading a Stanford research project that examines the work practices of masters in diverse fields—beer making, journalism, social entrepreneurship, engineering, long-distance running, mathematics, etc. He lives in Boulder, Colorado.
Ihno Lee is a quantitative psychologist and behavioral data scientist with expertise in affective science and data analytics.
With a Ph.D. in quantitative psychology, she works with complex data sets and applies sophisticated modeling, clustering, and classification techniques to better understand the Who, What, When, Where, and Why that drive well-being and engagement in the workplace. Her interests include quantifying work engagement and well-being, using machine learning to build employee segmentations and fine-tune predictive models of engagement (and attrition), and visualizing engagement trends over time.
As a former postdoctoral researcher at Stanford, she worked on numerous projects (e.g., behavioral interventions to increase physical activity, emotional engagement as a driver of prosocial behavior, mindfulness meditation to cultivate compassion, anti-bullying interventions that empower bystanders). She holds a Ph.D. from the University of Kansas, a M.A. from New York University, and a B.A. from Wesleyan University.