Where AI Meets Pharma: The Value of a Second Master’s
If you ask Summer Deng what drives her, the answer is simple: building things that actually matter.
Before joining the MScAC program, Summer had already built a strong academic and professional foundation. She completed both her bachelor’s and first master’s degree in software engineering at Sun Yat-sen University, then spent three years at Tencent as an AI Product Manager working on computer vision systems like facial recognition.
So why go back for a second master’s degree?
“I realized computer vision was becoming mature… and when large language models started emerging, I just felt there was so much more to explore.”
That instinct led her to the MScAC program, and more specifically, to what makes the program stand out: its ability to bridge cutting-edge research with real-world impact.
Why MScAC
For Summer, the structure of the program was a big draw. It also didn’t hurt she landed in Toronto.
“After I came here, I realized how much cutting-edge AI actually starts here, especially at U of T. You’re surrounded by people who are building the future.”
With institutions like the Vector Institute and programs like Mitacs, Summer quickly found herself immersed in a highly collaborative, fast-moving AI ecosystem.
Breaking into the Pharmaceutical Industry
For her internship, Summer took a bold step into a completely new space: the pharmaceutical industry, joining Roche.
“It was my first time in pharma. I didn’t know much about it at all.”
What drew her in? The opportunity to build AI agents from scratch, a space she was deeply interested in. And the experience delivered.
“I learned how to design systems, not just code. In the AI era, that’s the key: how you structure and guide the AI to get the best outcome.”
Beyond the technical growth, she gained something equally valuable: a clearer understanding of how AI moves from research into real business impact. Through her internship experience, she was exposed to how machine learning systems are applied in industry settings: not just to build models, but to solve concrete problems such as designing AI agents and adapting cutting-edge techniques to real operational needs.
Working in a commercial environment also gave her perspective on how products are shaped by user needs, constraints and deployment realities. It reinforced the importance of bridging research and application, translating technical capability into systems that are actually usable in practice.
In her case, that shift came from working on applied AI projects where the focus wasn’t only on model performance, but on how the technology integrates into broader workflows and delivers value in real-world settings.
Second Master’s, Totally Different Perspective
Summer is very clear: her two master’s experiences couldn’t be more different. Her first was heavily research focused. Her MScAC experience?
“Now I see the bigger picture. What the market needs, how technology solves real problems. It feels like I’m actually making an impact.”
After graduating, Summer stepped into an AI Engineer role at Next Pathway, where she continues building AI agents, this time helping businesses modernize their data systems. But she’s not stopping there.
She’s also working on a side project with her MScAC peers, building on the applied experience and shared technical interests developed through the program. It’s a reflection of one of its biggest strengths: a network of classmates who are equally focused on translating machine learning work into real, practical applications.
“We all share similar goals. And the program really supports you — even if you want to build something outside of class.”
Her Advice to Incoming Students
“First: be proud. Everyone here is excellent.” And then, the part many students tend to overlook:
“Use the resources. Talk to alumni. Talk to your classmates. Ask questions when you’re unsure.”
She also highlights something that often goes unnoticed:
“The program staff are incredibly supportive. If you want to build something, they’ll help you make it happen.”
