Upon graduating from ASU in 2012, Bardia Nikpourian became the CTO of a silicon valley startup “UnitesUs”, a company featured in The New York Times, Popular Mechanics, and eventually incorporated into the IBM Watson Ecosystem. Now, as a senior specialist cloud technical manager in artificial intelligence and machine learning at Amazon, Bardia democratizes artificial intelligence and delights customers. With his career experience and academic background, Bardia has helped to integrate practitioner knowledge into the online Master of Computer Science Degree Program.
We spoke with him about machine learning, artificial intelligence and the future of the field of computer science.
ASU: As someone who’s been in the field, can you tell us from your perspective, what is computer science?
Computer science blends the worlds of mathematics, language and art to model a problem and then solve it using programming methods. The general computer science field can be broken down into many different areas of depth, from embedded hardware programing to artificial intelligence models being trained and running in the cloud.
ASU: What kind of entry-level jobs can someone achieve after earning a graduate engineering or computer science degree? What were your entry-level job opportunities after earning your engineering degree? And how did you build it into a profitable career?
In the field of development the world is your oyster. Choosing an entry level job is more about deciding what you initially want to specialize in. Front-end development, back-end development, systems architecture or even machine learning. That being said not everyone stays with the type of development they initially get hired for. Furthermore, some choose not to go the route of finding an entry level job and instead build one of their own passion projects from scratch and then turn those into companies that they become founders of. Computer science is an industry where innovation and creativity pay dividends. For example, I’ve worked for an insurance company as a back-end developer / system architect, a startup as a CTO/ Co-founder, and now I’m leveraging all that experience in a customer-facing role as manager who is specialized in machine learning.
ASU: You recently worked with the ASU Online team to develop additions to CSE 575 Statistical Machine Learning. First, what is machine learning? Second, tell us what work you were able to do on the machine learning course.
Machine learning is a subfield of Artificial Intelligence that allows machines to learn through the use of data, observations, and interaction with the real world instead of being explicitly programmed to do so. The goal of which is to use that gained knowledge to do better at a given task in the future given the past.
I was able to give the course a unique spin that differentiates it from other machine learning courses available. The spin largely comes from my own experiences at Arizona State University. Primarily that “theory for theory’s sake” often isn't an effective way of retaining real world knowledge. You need to add context to the material, and you need to demonstrate the opportunity for innovation in order to have a meaningful course. I accomplished this by providing examples to students of how machine learning and deep learning are being utilized to change the world around us and by tying that into mathematical theory and programming paradigms.
ASU: What drove you to get involved in the MCS degree program?
A couple of things. I attended a keynote at a technical conference early in my career where the speaker ended his talk with a quote by former General Electric CEO and Chairman, Jack Welch: “Before you are a leader, success is all about growing yourself. When you become a leader, success is all about growing others.”
That quote has stuck with me since I heard it. I told myself that day that if I was ever in a position to help others succeed in their careers, I would do my best to do so. Getting involved in the Master of Computer Science degree program is one of those ways.
Furthermore, I believe that my education at Arizona State University has prepared me not only to enter the workforce from a hard skills standpoint, but also non-tangible skill sets. My overall experience at Arizona State, especially the focus on innovation, helped me to think differently about my approach to solving problems, and rewarded the creative side of computer science that’s often lost. Giving back to a program that prioritizes innovation was a no-brainer.
Finally, it’s hard to find qualified candidates in the machine learning field with the skill set to solve some of the world’s most difficult machine learning problems. I wanted to contribute to teaching these skills at a graduate level so that we can produce quality machine learning practitioners.
ASU: What are you most excited about when it comes to the field of computer science?
I may be biased, but I think the artificial intelligence field is currently one of the most interesting specialties in the field of computer science. Both in democratizing AI to make it easy for developers to use and on the research side of the house. I’m currently binge-reading academic papers on multimodal and multi-scale deep neural networks that are being used in practical applications, from early diagnosis of diseases in images to predicting pressure and flow dynamics.
ASU: What tips or advice do you have for individuals looking to advance their Computer Science skill set?
Education is a lifelong endeavor: learn and be curious. Curriculum such as the Master of Computer Science program at ASU can help you get a great start. Don’t be afraid to try new things and chase big ideas, not all of them will result in success but learn from the failures and fail quickly. When you find the area of computer science that you are passionate about, dive deep into it and become an expert. When you become a leader in your field, don't forget to reach out and help others achieve success as well.