At the Aspen Ideas Festival in the summer of 2019, the Bezos Scholars Program connected with Rana el Kaliouby, CEO and co-founder of Affectiva. Rana has an infectious warmth, and her joy in speaking about her life’s work in humanizing technology and interacting with Scholars was palpable. She is an Egyptian-American scientist, entrepreneur, author, and AI thought leader on a mission to bring emotional intelligence to our digital world.

Rana’s company Affectiva is a MIT Media Lab spin-off and leading provider of Human Perception Artificial Intelligence (AI) software that analyzes facial and vocal expressions to identify complex human emotional and cognitive states. Their vision is for technology to sense, adapt, and respond to an individual’s non-verbal signals, mental states, emotions, and reactions.

Rana introduced our team to Taniya Mishra, Affectiva’s former Director of AI Research and Lead Speech Scientist, who joined the Bezos Scholars Selection Committee. In 2016, while at Affectiva, Taniya founded EMPath (Emotion Machine Pathway), a virtual AI training program to fuel her passion for STEM education and mentoring. She actively shares her knowledge and enthusiasm for AI and machine learning as a mentor to new technologists. Through mentoring, her mission is to create early and diverse talent pipelines that produce a new generation of diverse tech leadership, whose diversity of experience, thought, and perspectives will help build better, more ethical AI.

In the summer of 2020, out of 350 applicants, 52 were selected to participate in the five-week long EMPath training program. Out of those 52, four were Bezos Scholar Alumni: Jacob Urbina from the 2016 cohort; Hoang Le and Jelissa Kayo Kamguem from 2018; and Sarah Tran from 2019. During the program, trainees built personal relationships with each other and their mentors while building real-world solutions for tangible challenges using technology with a focus on emotions, ethics, and community.

The first two weeks of the program were dedicated to the learning the fundamentals of machine learning, artificial intelligence, and coding libraries to be able to implement machine learning models. This was provided in daily, in manageable portions, so trainees could learn and practice at their own pace. Sarah described the experience as “an expedited and condensed bootcamp to further programming skills, before later applying them in the second phase of the program”. Trainees participated in regular check-ins with their mentor to ask questions, review content, and practice programming problems.

The remaining three weeks of the program were dedicated to preparing for their “Makeathon” Competition. Using a five-step entrepreneurial process, 13 teams selected a problem based on themes presented to them, conducted market-based research, and eventually pitched a prototype they collaboratively developed to a panel of expert judges.

Sarah’s team created a Zoom integration platform for virtual teachers to better assess how well students are comprehending materials and to detect if they are engaged, bored, or confused. Hoang’s team focused their prototype on children with learning disorders who had difficulty engaging during online therapeutic sessions. Jacob worked on a road rage detector, while other teams created systems that estimated markers of attentive driving based on facial expressions and built an in-vehicle music recommendation solution that responds to passenger emotions.

Scholars built incredible networks, gained relevant experience, and built tangible, real world skills. For Jacob, “Learning the foundations of neural networks and machine learning helped provide support for my senior capstone project that I wouldn't have gained elsewhere”. Jelissa enjoyed connecting with graduate students and CEOs, some of whom are helping her identify more internship opportunities to further her learning and build her experience while in college. Hoang worked with talented people from all over the globe and gained a greater appreciation for the versatility of Python (a programming language) and applying his new skills in several class projects. As Sarah reflected, “Working closely with Taniya as a mentor, I was absolutely blown away by how much depth she has in the field of AI and her passion for AI and empowering underrepresented communities in the field literally transcends the computer screen”.

Taniya Mishra

Recently, Taniya branched off from Affectiva to launch her own organization, SureStart, to focus on her mission to mentor and build up the next generation of AI leaders. SureStart serves students ages 16-23 and is hoping to address research that shows this is the prime age group tech is losing women and other underrepresented students in STEM. “Being with others who are pursing the same purpose and goals and working with proximate mentors, people with talent who are also impacted by the issue they are working on, creates such a positive environment for learning and growth,” Taniya says.

SureStart will launch their first cohort of interns in the spring of 2021. We can’t wait to see what this organization and our Bezos Scholar alumni will do and create to influence the future of humanized, emotionally and culturally relevant AI.

For more interest in becoming a partner with SureStart or applying to their internship program, please contact them.