Parivash, known as “Pari” around the office, joined the company when we were just 40 employees. Since then, she’s been along for the exciting hypergrowth journey, which has included many new faces, new clients, and of course, exciting new projects. Here, Pari tells us about her background, what it’s like to be a Research Scientist at Stradigi AI, and how the culture of learning and teamwork inspire her every day.
Tell us a bit about where you’re from and what brought you to Stradigi AI?
I emigrated from Tehran to Manchester in 2011. While living in England, I completed my Masters in Computer Science and Artificial Intelligence, before completing my PhD in Machine Learning while working in Pharmaceutical Data. It was fall when I finally arrived to Montreal, and by December, I was working at Stradigi AI.
What’s your day-to-day like at Stradigi AI?
I’m doing applied research every day, which can include working on a specific client project to solve a very specific solution, or it’s looking at recent papers from my field related to research. Leveraging my research findings, I start to edit and optimize what I’m working on. Our team has a very large emphasis on teamwork, so I’m always learning from the new discoveries of my colleagues.
How would you describe your role to a fellow PhD?
The pace of learning is very intense here, and every day I’m working alongside people with different specialties and learning from them constantly. I feel like I’ve earned another PhD!
I’m on the Image and Video Understanding team, and I have already witnessed rapid developments in this area. Things are constantly changing in Artificial Intelligence, and it requires agile, collaborative teams to stay ahead of the curve. We pride ourselves on this.
When non-technical people ask you about Image and Video Understanding, how do you describe it to them?
We analyze images and videos, to perform specific tasks such as object detection, image segmentation, and object tracking. We find ways to recognize the details within these objects to create an analysis. A common use for image and video understanding is transportation, such as teaching a car to be aware of an object ahead for collision avoidance. Another common use is analyzing video footage at scale, such as hockey or soccer footage, so you can gather insights.
How would you describe working at Stradigi AI to someone else?
Day by day, I feel like I’m constantly growing and learning. When I arrived at the company, we were just 40 people. We’re almost triple that (in less than two years!) and I’ve grown in tandem with the organization. It’s a place for people who are eager to continue to learn and evolve, because AI research is never done!