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Meet Kesha Williams
Kesha Williams —is a software engineer, mentor, professor, speaker, tech blogger, and S.T.E.M. advocate. She is the Founder of Colors of STEM & the Inventor of SAM (a predictive policing machine learning algorithm inspired by Minority Report that predicts the likelihood of crime) & Live Plan Eat (an Amazon Alexa skill that takes the stress out of meal planning); Kesha has spoken about SAM on stages across the world and her Live Plan Eat skill has won awards in competitions hosted by Amazon and Devpost.
Kesha is the chapter director for Technovation Georgia; a mentor with the New York Academy of Sciences; winner of a coveted spot in TED's Spotlight Presentation Academy; a United Nations volunteer; a Google Women Techmaker; and an Anita Borg Institute Syster.
"I just want to thank WWCode for everything they do to empower women in technology. It is because of organizations like this that women get into tech and stay in it. I especially want to thank WWCode for partnering with A Cloud Guru to offer me a subscription to the entire A Cloud Guru’s course library; this opportunity is absolutely life changing."
Q: What do you do in your day to day work life?
By day, I'm a software engineer building next generation cloud applications using Java based technologies and Amazon Web Services (AWS). A portion of my day is also dedicated to “playing” with emerging technologies like Artificial Intelligence (AI),
Computer Vision (CV)/Facial Recognition, Augmented/Virtual Reality, Machine Learning, and the Internet of Things (IoT). By night, I'm an online Java instructor for the University of California, Irvine. I’ve trained and mentored thousands of software developers in the US, Europe, and Asia while teaching at the university.
Q: What is a cool project you worked on recently?
A really cool project that I recently worked on was one involving Facial Recognition. I led an innovation team of six developers to investigate how Computer Vision and Facial Recognition could be used to improve Chick-fil-A’s restaurant operations and customer experiences. My team developed a prototype that recognizes employees as they enter a room and then provides a custom welcome message on a monitor that greets the employee by name. This project was really cool because it is the first step toward using Facial Recognition and Computer Vision on a broader scale.
Q: Has WWCode impacted your career? How?
WWCode has positively improved my life and career in several ways. I gave a talk at WWCode Atlanta’s We Rise technical conference on Artificial Intelligence (AI) and Amazon Alexa. This experience introduced me to several amazing women in technology and helped to expand my network. In addition, to expanding my network, WWCode selected me as a scholarship winner in conjunction with A Cloud Guru. I was given a free subscription to the entire A Cloud Guru course library. I plan to use A Cloud Guru’s courses to study and prepare for the Amazon Web Services (AWS) certification exams.
Q: What advice would you give to your colleagues and peers who are 1-2 years behind you in their career?
My advice to those 1-2 years behind me in their career is to network, network, and network! In addition, work hard to build your brand within and outside of your present employer. You are charged with making sure that people know who you are and that they recognize the value that you bring to the table.
Q: Have you helped others in the same or similar career path?
I am passionate about increasing the diversity in technology; therefore, I volunteer my time with several organizations that share my same vision. I lead the Georgia chapter of Technovation, which teaches middle and high school girls computer programming. I serve as a mentor with the New York Academy of Sciences, which pairs college girls with professional women in technology. I also mentor with WEST (Women Entering & Staying in Tech), which pairs early career women professionals with senior level mentors. I also travel the country speaking at technical conferences about emerging technologies as a way to share and give back to the technical community.
Q: What is your favorite technical protip?
I find that I am a better engineer when I keep my skills sharp and stay current with trends in the industry. I often look at the want ads to see the current skills in demand and then learn those skills in my spare time. After learning a new skill, I often seek ways to use that new skill to move projects forward at my present employer.
Q: What excites you most about your career?
I’m most excited about the opportunities to learn new and exciting technologies. Technology is ever-changing and advances on an almost daily basis. I'm excited to be at the forefront of where emerging technologies like Machine Learning and Computer Vision/Facial Recognition are headed. This may sound cliché, but these technologies (especially when combined) have the ability to change the way we live and can even bring ideas from the wildest science fiction movie to life!
Q: Why is being technical awesome?
Being technical is awesome because technical people have a skill set that can positively impact every industry, society, and even the world! As we know, everyone needs Information Technology (IT). The demand for technology brings about a plentitude of job options for technical people, job security in current roles, job mobility, higher incomes, and even remote work options! There is no other skill set that provides so many great benefits.
Q: What is your most impressive accomplishment / project? Give us a little background on it.
My most impressive project is SAM (Suspicious Activity Monitor) - http://www.iamsam.tech. SAM is a predictive policing machine learning algorithm (inspired by “precrime” from Minority Report) that predicts the likelihood of crime. SAM was developed using Machine Learning (i.e. Amazon Web Services Machine Learning), Computer Vision (i.e. Amazon Web Services Rekognition), and the Twitter API.
The general public can communicate with SAM via Twitter (@iamsam_tech) by tweeting a picture of someone they believe is involved in suspicious activity. SAM retrieves attributes from the photo and combines those with past crime data from the GBI (Georgia Bureau of Investigation) to make a prediction regarding the likelihood of a crime occurring. The prediction is sent back to the user as a tweet. Race was intentionally removed as an attribute used by SAM in making the crime prediction so that SAM would not be accused of racial profiling.
The conscious decision to remove race illustrates that machine learning can actually remove human bias when applied to policing. When human bias is removed, racial profiling becomes a thing of the past. I was able to present about machine learning removing human bias from policing on the TED stage in New York City as a winner of TED + Logitech’s Spotlight Presentation Academy.