“Extensibility” may be the biggest challenge that Bradley needs to solve at Ople. Ultimately, we want Ople’s AI platform to be accessible to anyone with a specific Data Science challenge. For this, the platform must have flawless extensibility.
Tell us a little bit about yourself.
I earned a Ph.D. in Computer Science from Western University. My research was in the area of policy-based management of distributed systems with a focus on managing resources in alignment with business objectives. Upon completion, I went to York University in Toronto for a postdoctoral fellowship where I, in collaboration with the team, focused on adaptive systems, the management of applications on clouds and across multi-clouds and issues relating to the management of Big Data.
After my post-doc, I moved to the Bay Area and joined an ML start-up that had built fast implementations of ML algorithms geared toward working at Big Data scale. While there, I contributed to the development of their enterprise data science platform. That was a fantastic experience, after which, I went to Infosys and continued to work in similar areas.
What made you join Ople?
It was the people. When I first heard about the company, it sounded really, really interesting. But, when I came in and met everyone, I was blown away by the team. I have a bias and see AI as the future for everything. So the vision of the company – making AI easy, cheap, and ubiquitous – in combination with the team was very compelling to me.
Why do you say AI is the future for everything?
Much of my research used a heuristic approach to decision making, but potentially I could have used machine learning. I could see how slotting in ML models to some of my work could improve the results. I see tons of opportunities where we can start using ML models to make things better.
Tell me more about your role at Ople.
I joined Ople as a Principal Architect. My job is to manage and design the systems here at Ople, making the platform more effective and efficient. My goal is to make the systems robust to increase the scalability and extensibility, making it easier for us to add new functions and try more innovative approaches.
Do you have a memorable mistake that you want to go back and fix?
I wanted to become a doctor when I was young. So, my concentration in college was actually Genetics, not Computer Science. The funny thing is that I was advised to look into computer science during my OAC year (senior year in Canada), but I ignored that advice. When I was almost done with college, a friend told me to try computer science. I did, and I loved it. And as I said in the beginning, I studied Computer Science afterward. So in a way, studying Genetics in college was a mistake. However, there is a silver lining. I still learned a lot from the experience and in a general sense, much of my research involved utilizing feedback loops for managing systems. This definitely drew inspiration from how natural systems work.
What do you like to do for fun?
I love to learn and am a lifelong learner. I also try to read lots of books. I play piano, so I definitely love music. I have an electric piano at home, and I enjoy playing that on the weekend. Of course, we have a guitar in the office. Lastly, I love movies. Maybe too much.
What is your favorite number?
21. I like playing blackjack. It makes me think about the probabilities of winning the game, and I enjoy such challenges.