Helen Kontozopoulos is the co-founder and chief tech evangelist of a Toronto-based startup that uses AI and machine learning to collect and analyze a range of pharmaceutical industry data for sales and marketing teams.
Founded in 2018, the company emerged from the University of Toronto incubator, UTEST and Creative Destruction Lab (CDL), and its customers include major players such as Novo Nordisk and AstraZeneca. ODAIA recently secured $34 million CAD in financing, and expects to have 70+ employees in Canada and the U.S. by this summer.
We caught up with Kontozopoulos to learn more about her journey from adjunct professor in U of T‘s Department of Computer Science to startup co-founder.
What inspired you to co-found ODAIA?
We founded ODAIA in 2018, and spun it out of the University of Toronto by professor Periklis Andritsos, University of Lausanne PhD student Gael Bernard, serial entrepreneur Philip Poulidis, and me. We loved analysing customer journeys, and found a great niche in the medical field especially when it comes to understanding patients on their pharmaceutical journey.
The UofT ecosystem also gave us many opportunities to meet others, especially UTEST (University of Toronto Early Stage Technology Program) and then to the Creative Destruction Lab’s Artificial Intelligence stream.
What sparked your interest in AI?
It started about ten years ago when I wanted to change my career. I was working in web and mobile development, but I wanted to be on the back-end tech side of things.
There was an IEEE Conference taking place at the UofT and I volunteered. AI hadn’t crossed my mind at all at this point. At that conference, I met UofT professor Steve Mann and was exposed to real tech royalty, like Marvin Minsky and Ray Kurzweil.
Being in this environment let me meet experts who had led the AI revolution. Hearing Marvin Minsky, the grandfather of AI, speak at this conference sparked something in me. I ended up watching all his videos and buying all of his books. It just clicked.
What excites you the most about the possibilities of AI in healthcare?
I think it’s user interfaces and the ability for non-techies to get transparency and access to explore diseases and disease patterns. More transparency and access can really help caregivers. And this is something that really could’ve helped me when I was caregiving for my 88-year-old Dad [who passed away two years ago].
There’s a lot of fear-mongering about AI right now, especially since it’s been used to accelerate our vices like buying things. But we can also use it to accelerate making patients better and fighting diseases and finding treatments.
What outcomes do you hope your work will eventually lead to?
Our vision is to get treatments to patients faster. There’s something broken or at least very slow about the current system where information isn’t getting to patients or physicians about a new therapy or a new drug or new clinical trials.
Being able to get the right information to the right doctor at the right time about new drugs or clinicial trials, and then to the right patient at the right time, is the most important goal for us. OAIDA has been helping the pharma sales team with this.
What projects are working on these days?
We’re expanding and growing our team in ML and data science and sales. We are also doing a lot of internal initiatives to help everyone understand the industry and tech we’re in. We do a lot of teaching about pharma to our tech people about the industry and vice versa.
What’s the number one piece of advice you’d give to students following in your footsteps?
Meet as many different people as you can! You never know where someone will need you because of your skillset or they’ll be fun to hang out with and help you learn new things. It’s also a lot more fun to learn from different people directly than sitting at home watching a video.
