Graduation Spotlight: Fernando Yánez

Fernando Yánez
PhD direct entry in Computer Science

While completing his PhD in computer science at the University of Toronto, Fernando Yánez built more than a research experience — he built a startup. Driven by a desire for independence and impact, he embraced entrepreneurship midway through his program, learning through failed ventures and major setbacks.

Now, he is focused on reimagining information management, shaped by his work in AI and human-computer interaction.

This Q&A has been edited for clarity and length.

You took an unconventional path by building a company alongside your PhD. What inspired you to pursue entrepreneurship at this stage?

I entered my PhD planning to pursue a career in academic research. While I enjoyed teaching and had many opportunities as a TA and course instructor, I realized I was craving the independence to work on something challenging enough to keep me up at night, which I wasn't getting as a developer at Microsoft.

Midway through the program, I saw my path align with my dad's footsteps and chose to embrace entrepreneurship and learn as much as possible. I built several things at U of T, and most did not work out. Eventually, I landed on something I truly believe in — something I think will revolutionize the world and the way we manage information.

Your journey included major setbacks, including restarting your PhD. How did that experience shape your perspective on success and resilience?

My PhD journey included major setbacks. I switched advisors, reframed my research focus, faced rejection from internships and papers, and went through four failed ventures. What kept me going was confidence in myself and my potential.

Those experiences reminded me that even if I did not yet have what it takes, or my methods didn't fit others' expectations, that was not a reason to quit.

“Resilience is crucial, especially in fast-changing fields like computer science.”

— Fernando Yánez

We will face rejection and closed doors, but we can choose to keep growing and learning. Over time, success becomes a byproduct of that process.

Having built your path across countries and systems, what does “creating your own opportunities” mean to you now?

To me, creating your own opportunities means positioning yourself so you can grasp opportunities that might otherwise not be available. I learned that the hard way.

Growing up in a failed country, I realized I needed to actively place myself in situations where I could show who I am and how I stand out rather than waiting for others to notice me. The more you do it, the more you build that muscle. You learn how to clearly demonstrate your value, and that is what unlocks opportunities that might otherwise feel out of reach.

What first drew you to computer science, and what led you to pursue it at the graduate level?

I have always been academically inclined, with a special interest in math and programming. From a young age, I knew I wanted to pursue a PhD.

When it came time to apply to grad school, a PhD in computer science at the University of Toronto felt like the best opportunity. The program’s connection to advances in machine learning and artificial intelligence made it the right fit for me.

What are you most proud of accomplishing during your time at U of T — whether in your research, collaborations or academic journey?

I am most proud of gaining clarity about where I want to go, what I want to do and what it takes to get there. That clarity helped me focus on what really matters and allowed me to complete a five-year program in four and a half years, even counting one "lost year" after changing advisors and research directions.

That sense of conviction in myself and my future is what I value most. It gave me the confidence to act and accomplish things I would not have otherwise achieved.

What’s next for you, and how do you see your work in computer science shaping that path?

Well, the reality is that computer science is at the centre of everything I am doing. I am building a tech startup that reimagines how information is managed worldwide.

My experience with human-computer interactions and AI from my PhD program shapes this work and informs my vision for the future of document and information management. That is what I am building toward.

Graduation Spotlight: Bingjian Huang

Bingjian Huang
PhD in Computer Science
Interests: human-computer interaction, robotics, haptics

Binjian Huang brings together haptics, robotics and human-computer interaction to build technologies that connect human perception with machines.

During his PhD at the University of Toronto, he developed VibraForge, an open-source toolkit now used by research labs around the world. As he graduates, Huang reflects on the value of interdisciplinary thinking and creating work that extends beyond the lab.

This Q&A has been edited for clarity and length.

Your research spans haptics, robotics and wearable devices. What drew you to working across disciplines rather than within one area?

I genuinely believe the most interesting innovation happens when ideas from different fields collide.

I think of each field as a node, and I try to draw lines between them — connecting haptics, robotics and perception in ways that create something larger than any one area could on its own.

VibraForge has been adopted by researchers globally. What does it mean to see your work extend beyond your own lab?

For research to be truly impactful, it cannot stay a lab prototype. It has to become something people actually use.

Receiving both praise and critique from real users has been one of the most rewarding parts of my PhD. It keeps me honest and pushes me to continue improving the toolkit.

How did navigating a new language, culture and academic system shape your experience as a PhD student?

Toronto is a vibrant city, and its culture and diversity have shaped my experience. What I value most, though, is the people — my supervisors, lab mates and fellow students who made these years wonderful. Some of my favourite memories come from insightful conversations with professors and casual hangouts with friends.

What first drew you to computer science, and what led you to pursue it at the graduate level?

CS gave me both a tool and a mindset — the ability to learn other fields (EE, ME, neuroscience, you name it) and the confidence to build whatever I want.

“CS trains students to be generalists, which matters more than ever in the AI era. It’s less about mastering a specific programming language and more about knowing what you want to create and which tools will help you get there. ”

— Bingjian Huang

What are you most proud of accomplishing during your time at U of T — whether in your research, collaborations or academic journey?

I am most proud of seeing my work grow beyond me. VibraForge started as a lab prototype and is now an open-source toolkit used by more than 20 labs worldwide, including MIT, EPFL, HKUST and Qualcomm XR. I've also mentored five undergraduate and master's students, several of whom went on to PhD programs at U of T, Princeton and NYU.

What's next for you, and how do you see your work in computer science shaping that path?

I'm finishing my PhD this year and currently interning at Meta Reality Labs, where I work on dexterous robot teleoperation with haptic feedback. Longer term, I plan to keep building at the intersection of human perception and embodied AI, making the loop between humans and machines feel more natural, whether in academia or industry.

Graduation Spotlight: Kelly Zhu

Kelly Zhu
MSc in Computer Science

Kelly Zhu combines computer vision, robotics and AI to build systems that make complex phenomena visible. Her path to research began with a hands-on experience that sparked an interest in open-ended problem solving and grew into an MSc at the University of Toronto, where she worked across disciplines and explored applications in imaging and autonomous technologies.

Now, as she prepares to begin her PhD at Carnegie Mellon University, Zhu reflects on the impact of curiosity-driven work and the foundation it creates for what comes next.

This Q&A has been edited for clarity and length.

Your work brings together computer vision, robotics and AI. What do you enjoy most about working across these areas?

What I love most about working in computational imaging, computer vision and AI is the ability to visually see the impact of my work. There's something very exciting about capturing and visualizing phenomena through novel sensors and algorithms in ways that have not been done before.

You've received several awards during your MSc, including the Vector Scholarship in AI. How did those opportunities shape your experience?

During my MSc, I was very fortunate to receive several graduate scholarships, including OGS, QEII-GSST and the Vector Scholarship in AI. I appreciate the support they provided. They allowed me to focus on my research and gave me the confidence to make meaningful contributions to the field early in my graduate journey.

The Vector Scholarship also helped me connect with Ontario's AI community. Through the Vector Institute, I built many meaningful connections that I wouldn't have found otherwise.

Your research connects to real-world systems like imaging and autonomous technologies. What excited you most about seeing your work applied in practice?

What excites me most about working in imaging is knowing the systems we design can support many downstream applications, including autonomous technologies, scientific visualization, medicine and beyond. Exploring these applications during my MSc gave my work an interdisciplinary scope and allowed me to learn and collaborate across different fields.

Knowing that my research has the potential to contribute to real-world systems gives it a deeper sense of purpose and impact.

What first drew you to computer science, and what led you to pursue it at the graduate level?

After my first year of undergrad, I worked in a research lab at U of T on an autonomous bed-making robot. I loved the open-endedness of defining a research problem and navigating my own path to a solution.

That experience made it clear that I wanted to pursue research more deeply, so graduate school felt like a natural next step.

If you could give one piece of advice to a student considering graduate studies, what would it be and why?

“Take advantage of the many research opportunities available over the summer. There are many incredible programs at the University of Toronto and abroad, and they offer a great way to explore academic interests beyond the classroom.”

— Kelly Zhu

Some of my most meaningful undergraduate experiences came from those summers spent doing research, so I encourage students to not be afraid to apply broadly. Shoot your shot!

What are you most proud of accomplishing during your time at U of T — whether in your research, collaborations or academic journey?

I am most proud of the incredible network of friends, peers, collaborators and mentors I built during my time at U of T. My accomplishments throughout both my undergraduate and graduate studies would not have been possible without their support and guidance. It's inspiring to see the remarkable paths everyone goes on to build for themselves after graduation.

I'm also proud of the research skills I've developed along the way, which will serve as a strong foundation for whatever comes next.

What's next for you, and how do you see your work in computer science shaping that path?

This fall, I will start my PhD at Carnegie Mellon University. I look forward to continuing my research in computer vision and pushing the frontiers of what machines can perceive and understand.

Canada can play a leading role in the next wave of AI innovation: Waabi CEO Raquel Urtasun

“There is so much capital that we can attract and there is such incredible talent that we have here," Urtasun told U of T President Melanie Woodin during a BetaKit event at Toronto Tech Week

Raquel Urtasun, a U of T professor of computer science who is an expert in autonomous vehicle technologies, is the founder and CEO of self-driving trucking company Waabi, which recently raised up to US$1 billion (photo by Lilac Media / BetaKit)

From self-driving vehicles to new frontiers in robotics, the next wave of AI is moving beyond the digital world — and Canada has the necessary ingredients to chart a bold path forward.

Attendees at a BetaKit Most Ambitious town hall on May 25 heard how innovators, buoyed by the country’s strong university-based research system, could play a critical role in safeguarding Canadian sovereignty in this new era.

Raquel Urtasun, founder and CEO of self-driving vehicle company Waabi, said transportation is an example of a critical industry that’s undergoing a major shift.

“Transportation is something core where — quoting Evan Solomon, our minister of AI — ‘We need to make sure that we have control over our destiny,’” said Urtasun, who is also a professor of computer science at the University of Toronto, during a fireside chat with U of T President Melanie Woodin.

“We need to make sure we can move goods and people regardless of how geopolitics and the world evolve over the next few years.”

Waabi CEO Raquel Urtasun in conversation with U of T President Melanie Woodin (photo by Johnny Guatto)

Held at the TIFF Bell Lightbox, the event — part of Toronto Tech Week — celebrated the innovators named in BetaKit’s Most Ambitious 2026 issue, nearly a quarter of whom are from the U of T community. It featured remarks from tech, entrepreneurship and political leaders including Solomon, Canada’s minister of artificial intelligence and digital innovation, Toronto Mayor Olivia Chow and Christian Weedbrook, a former U of T postdoctoral researcher who is the founder and CEO of quantum computing company Xanadu, which recently made its debut as a public company.

Urtasun said Canada’s deep roots in AI research and talent offers an opportunity to lead the way in next-generation automotive technology. While the transportation landscape has long been controlled by large car and truck manufacturers, she said that’s changing with self-driving tech.

In addition to Waabi, Urtasun noted that Canada is home to several other key players in autonomous transportation including parts manufacturer Magna International and operating system developer Blackberry QNX. “We have all the important pieces in order to really lead the transportation of the future ... versus ‘Let's just try to follow the U.S. and try to have something that's competitive here,’” Urtasun said.

Evan Solomon, Canada’s minister of artificial intelligence and digital innovation, speaks at the BetaKit event at Toronto Tech Week (photo by Lilac Media / BetaKit)

Waabi has already made major moves to establish itself as a global leader in the category. In January, the company announced it raised US$750 million to accelerate commercialization of its self-driving technology – its investors include Volvo, whose driverless truck is powered by Waabi – in addition to US$250 million in milestone-based funding from Uber to expand into robotaxis.

Urtasun said she hopes to see more Canadian success stories in the sector. “There is so much capital that we can attract and there is such incredible talent that we have here in Toronto, and in Canada in general, that we could become ‘the’ player that dictates what it’s going to be.”

Christian Weedbrook, a former U of T postdoctoral researcher, founded quantum computing company Xanadu (photo by Lilac Media / BetaKit)

Urtasun offered a bold prediction: a majority of vehicles on the road would be “Waabi-powered” within a decade. She also said there were many other potential applications for the company’s physical AI platform, ranging from elder care to mitigation of industrial accidents. “Self-driving is the first big vertical,” she said, adding that “not going all in on physical AI would be such a big miss for the country.”

U of T President Melanie Woodin, then dean of the Faculty of Arts & Science, and Raquel Urtasun on campus with one of Waabi’s self-driving trucks (photo by Nick Iwanyshyn)

The conversation also explored the benefits of academics embarking on entrepreneurial ventures. Recounting Urtasun's proposal to take on a leadership role at Uber’s self-driving lab in Toronto in 2017, Woodin — then the dean of the Faculty of Arts & Science — said the arrangement provided U of T graduate students with a compelling opportunity to conduct research and innovation at the forefront of the field.

She added that Urtasun, Weedbrook and others, including the U of T founders behind AI startup Cohere, have also acted as entrepreneurial role models, inspiring students “to want to follow that path.”

Urtasun, for her part, thanked Woodin and former U of T president Meric Gertler for their support.

“Since then, there are many faculty who have provided similar avenues for their students to not have to compromise between academia and industry — but do something that is better than either one of them alone.”

Read more about U of T innovators at Toronto Tech Week

— Original story by Rahul Kalvapalle at U of T News

CS student leads U of T at Putnam Math Competition and earns U of T Excellence Award

Boyan Litchev (Photo: Sanjana Iyer)

The math problem in front of Boyan Litchev felt familiar — something a professor might pose in class. For the next two hours, the second-year computer science and math specialist worked through it, erasing and starting over more than once. When he set down his pen with 20 minutes to spare, he felt satisfied. And for good reason.

Litchev was the highest University of Toronto scorer at the 2025 William Lowell Putnam Mathematical Competition, a prestigious contest for undergraduate students across Canada, the U.S. and Mexico. The competition awards scholarships and cash prizes of up to $2,500 to top students and up to $25,000 to top schools.

Following the achievement, Litchev also became a U of T Excellence Award (UTEA) recipient — a rare honour for a second-year student.

Finding meaning in the challenge

While the recognition is significant, for Litchev, competitions like Putnam are just as much about something else: a deeper connection to the subject.

“The biggest benefit of Putnam is the opportunity to get excited about math and discuss math with others,” he says. “There’s also something really fun about seeing a question and intuitively knowing why the claim would make sense, but working out the details and making sure your answer is coherent so you can share it with others. It creates a sense of community.”

Professor Ignacio Uriarte-Tuero understands that sense of community well. As the local organizer for Putnam, he helps students prepare through group study sessions. He sees the competition as a strong indicator of ability and potential.

“Success indicates that students have a very good ability to solve problems and high standards of rigour because the marking system is very hard,” he says. “People who have done well in Putnam have often gone on to be very good researchers later. There is a high correlation.”

Unlike more procedural problem-solving, where the path to a solution is often clear, Putnam-style questions require patience and a willingness to explore. Not knowing where a problem will lead and working through the ambiguity is part of the draw. At the same time, Litchev says coursework concepts helped inform his approach, highlighting how competition math and classroom learning reinforce one another.

“There were Putnam problems I solved because of what I had learned in the classroom,” he says. “Analysis and topology especially helped. I’ve also heard people say that competition improves their mathematical maturity and helps them approach problems better, which also helps in class. The process of thinking about abstract math is transitive.”

From competition to research

That trajectory is already taking shape through Litchev’s UTEA fellowship, which will give him direct experience on a faculty-led research project. UTEAs are valued at a minimum of $7,500. Litchev says he’s looking forward to spending 16 weeks in the lab, working with his supervisor and peers on developing a cryptographic protocol.

“I’m excited to be able to work on this project over the summer, and I’m already starting to think about how I’ll approach it,” he says. “I’m also glad the university is valuing this type of research and trusting me to do it. It’s a great opportunity.”

For developing interdisciplinary data sciences courses, faculty receive distinguished Northrop Frye Award

Top (l. to r.): Paul Gries, Adam Hammond, David Liu, Tomomi Parins-Fukuchi; bottom (l. to r.): Michael Widener, Nathan Taback, Mary Pugh.

The prestigious Northrop Frye Award, one of the university’s Awards of Excellence, has been bestowed on the Interdisciplinary Data Science Course Development Team for the creation of three introductory data science courses for students across the faculty — particularly students without a traditional computational or quantitative background.

The team, which includes seven instructors from the humanities, social sciences, life and mathematical sciences, combined their disciplinary and pedagogical expertise to create learning experiences that give students skills applicable to any career, that nurture a critical approach to problems, and that equip them to think outside traditional methods of analysis. The results are innovative courses designed to prepare students to tackle today’s complex challenges.

The courses are: ENG286H1 — Literature and Data; GGR274H — Introductory Computation and Data Science for the Social Sciences; and EEB125H1 — Introductory Computation and Data Science for the Life and Physical Sciences.

The team includes:

• Professor Paul Gries, Teaching Stream, Computer Science

• Associate Professor Adam Hammond, English

• Professor David Liu, Teaching Stream, Computer Science

• Professor Tomomi Parins-Fukuchi, Ecology & Evolutionary Biology

• Professor Mary Pugh, Mathematics

• Professor Nathan Taback, Teaching Stream, Statistical Sciences

• Professor Michael Widener, Geography & Planning

The initiative emerged from the Faculty of Arts & Science Computational and Data Studies Working Group that was established to address growing student demand for computational and data-related learning beyond the departments of Computer Science and Statistical Sciences.

U of T’s Awards of Excellence program has recognized exceptional students, faculty, librarians and administrative staff members since 1921. Though the criteria differ for each of the awards in the suite, recipients all share a commitment to enhancing the university experience of their peers and leave a significant impact on the university through their efforts.

“The award recognizes a deeply interdisciplinary and sustained collaboration that has transformed how students across Arts & Science encounter computation and data analysis,” says Karen Reid, professor, teaching stream in the Department of Computer Science, who nominated the team.

“The sustained impact on student learning, combined with the team’s deep interdisciplinary collaboration and commitment to pedagogical innovation, exemplifies the values recognized by the Northrop Frye Award.”

According to Faculty of Arts & Science vice dean, undergraduate Randy Boyagoda, “These three courses demonstrate that when data science education is designed intentionally — grounded in accessibility, interdisciplinarity and ethical awareness — students from across the faculty eagerly and successfully engage with it.

"Students who take these courses will leave university with greater confidence in knowing how data science works, which will matter to their personal and professional lives and make them all the more willing and able to be good contributors to our shared public life,” says Boyagoda, who is also the university’s provostial advisor on civil discourse and a professor in the Department of English.

The success and impact of the team’s work is reflected in a typical student’s feedback: “With its intersection with computer science and traditional English studies, ENG286 prepared me to think about how developing technologies such as AI and an ever-expanding digital marketplace and database can both enrich traditional legal views while also criticizing and promoting new ways to view precedents.”

Original story by A&S News

Steve Engels receives U of T's Joan E. Foley Quality of Student Experience Award

Photo: Matt Hintsa

Steve Engels, a professor, teaching stream in the University of Toronto's Department of Computer Science, has received the Joan E. Foley Quality of Student Experience Award — one of the university's highest honours recognizing contributions to undergraduate and graduate student life.

Presented annually by the U of T Alumni Association, the $1,000 prize goes to a student, faculty or administrative staff member who has made a distinctive and lasting contribution beyond the expectations of their role. Engels was recognized for his work building video game design education and experiential learning opportunities that have shaped the academic paths of hundreds of students.

In 2007, Engels created CSC404: Introduction to Video Game Design, a course that draws students from Computer Science, OCAD University and U of T's Faculty of Music to collaborate in multidisciplinary teams. In a 2024 interview, Engels recounted creating the course after a student expressed interest in game design. Many first-year students now cite CSC404 as a reason they chose to study computer science at U of T.

The course became the foundation for the Level Up Showcase, which Engels co-founded in 2011 with collaborators from OCAD University and Ontario Tech University. What began as a modest end-of-term exhibition has grown into one of the largest student game showcases in Ontario, drawing more than 4,000 attendees in 2025 and featuring teams from more than 20 colleges and universities. Industry partners including Ubisoft and Zynga participate as judges and audience members, giving students direct exposure to professional networks and career opportunities.

Beyond the classroom and showcase, Engels has developed research and outreach experiences that connect students to real-world applications of game design. Through partnerships with the Royal Ontario Museum, Toronto Rehab Institute, the Institute of Forensic Sciences at U of T Mississauga, U of T’s Ontario Institute for Studies in Education (OISE) and various community organizations, students have contributed to projects ranging from museum interactive exhibits to rehabilitation tools and educational games.

"Steve's work exemplifies what it means to go above and beyond for students," said Eyal de Lara, professor and chair of the Department of Computer Science. "From a single course to a province-wide showcase and a network of community partnerships, he has built something that continues to open doors for students long after they leave the classroom."

The Joan E. Foley Award is named in tribute to the late Joan Foley, who served as the first female Provost of U of T and first female Principal of U of T Scarborough. It is presented as part of the university's annual Awards of Excellence.

Alumnus Liam Kaufman’s entrepreneurial path in digital health innovation

As the University of Toronto celebrates Entrepreneurship Week 2026 from March 2 to 6 — a showcase of innovation, startup success and bold ideas across the tri-campus community — we are highlighting alumni who embody that entrepreneurial spirit. Liam Kaufman is one such graduate, translating cutting-edge research into impactful health technologies and building ventures that bridge science and industry.

Across roles as an entrepreneur, scientist, engineer and strategic leader, Kaufman has built a career focused on translating advanced AI and clinical research into real‑world health care tools.

After completing his BSc in psychology at Western University, Kaufman earned a master’s degree in medical science at the University of Toronto’s Faculty of Medicine (now known as the Temerty Faculty of Medicine) in 2008 and a BSc in computer science in 2011, also from U of T.

Kaufman has always had an entrepreneurial spirit. As a child he went door-to-door shoveling neighbours’ driveways for money and even made crafts to sell at his father’s birthday party. His first adult success came shortly after graduating from U of T, with Understoodit — a tool for collecting anonymous feedback during class. The platform gained international media attention before being acquired by EventMobi.

Currently, Kaufman serves as executive vice president of product and academic at Cambridge Cognition, where he helps guide the company’s global strategy in cognitive assessment technologies and digital biomarkers. Before joining Cambridge Cognition, he was the co‑founder and CEO of Winterlight Labs, which develops speech‑based digital biomarkers for cognitive impairment and mental health (acquired by Cambridge Cognition in 2023).

We talked to Kaufman about his path to working at the intersection of neuroscience, machine learning and digital health innovation.
 

How did you become interested in neuroscience?

I did my undergrad at Western in psychology and kept gravitating to the science side —stats, methods, functional MRI. I’d also been reading pop‑neuroscience books and was captivated by how scientists use tools and methodology to explore how we think and learn. After graduating, I worked at BC Children’s Hospital as an MRI tech/research assistant, which let me apply what I’d learned in a real clinical setting. I liked the rigour and objectivity of science, and neuroscience felt like the intersection of what I loved — plus I wanted to work with patients and see what I was learning in action, day to day.

For your postgrad, how did you land on the Institute of Medical Science at U of T?

I wanted something applied, and IMS put me in a hospital environment (Sunnybrook Health Sciences Centre) working directly with patients, not just in a theoretical or purely academic context.

Candidly, the stipend also mattered. Toronto isn’t cheap for grad students, and IMS had one of the highest stipends, which helped.

The program catered to clinicians and residents, so I didn’t have to TA and could focus on research and data collection. Working with Sandra Black (MD ’78, PGME Neurology) was formative: high rigour, high expectations. I learned to only say what I could back with evidence and got a lot of practice presenting to committees, which was great for building confidence and learning how to talk with experts who know more than you.

What did you study?

My thesis focused on a specific eye‑movement task called the anti‑saccade task. Normally, when something appears in your peripheral vision, you automatically look toward it. We trained people to look in the opposite direction, which requires executive control to inhibit that automatic gaze. Healthy people are generally good at this, but when the frontal lobes are damaged, the task becomes much harder. Alzheimer’s is usually thought of as a memory disorder affecting the temporal lobes, but what we showed was that people with Alzheimer’s and mild cognitive impairment had clear difficulties with this task — they were much more likely to look toward the stimulus. I did a meta‑analysis and published our findings, adding more evidence that Alzheimer’s involves impairments beyond memory.

What prompted you to pivot to computer programming?

I planned to do a PhD and had strong support. But after a late night prepping for a talk, I asked myself if that’s what I wanted for the next three to four years — especially given how competitive hospital scientist jobs are. Meanwhile, I’d taught myself enough programming for side projects and data analyses to realize I liked the challenge and the tangible problem‑solving. Employment prospects also looked stronger, so I decided to bridge the two fields. I hadn’t taken math in years, so I blitzed grade‑10 through grade‑12 material in a few months to be adequately prepared for computer science at U of T. In retrospect, having both skill sets has been really useful.

How did you get your start as a digital health entrepreneur?

Right after graduating, I launched Understood.it. It got good press — CTV, Toronto Star, even the front page of TechCrunch — which gave me a taste of early traction. EventMobi acqui‑hired us; they were more interested in the team than the product, and I led their mobile app group as a developer/manager.

I still wanted to get back to the neuroscience-computer science intersection, so in 2015 I met with Frank Rudzicz who was a U of T faculty member at the University Health Network’s Toronto Rehabilitation Institute at the time. His expertise was in computational linguistics and natural language processing, and his research showed you could probably detect Alzheimer’s with about a minute of speech. I found the work intellectually captivating and I could see the potential for commercialization. I left my job, taught a computer science course to patch together income, and with two of Frank’s grad students we started Winterlight Labs that fall.

How has your medical science education at U of T helped you in your career?

Sandra’s mentorship taught me rigour: if I’m going to say something, I need evidence. As an entrepreneur, that translates directly to how I prepare for investors and customers —choosing words carefully, anticipating questions and backing up claims.

IMS also forced me into regular, polished presentations to advisory committees, which made me a better public speaker and more comfortable engaging experts.

Beyond training, the U of T ecosystem mattered. Winterlight went through Rotman’s Creative Destruction Lab and the Temerty Faculty of Medicine’s Health Innovation Hub (H2i). H2i made pivotal introductions that helped us get pharma traction and funding. U of T’s combination of strong medical research and strong AI created the right environment to build at that intersection.

How are you evolving your product and business now? What’s on the horizon?

The business exploded during COVID, but in 2023–2024 it was tough — biotech funding dropped and studies slowed. In 2025 we’ve seen a real rebound. The tech we’ve built over 10-plus years is now in a lot of trials. We started in Alzheimer’s and, since 2019, have been expanding into schizophrenia and depression. Pharma increasingly wants to measure what matters to patients — communication, memory, orientation — which aligns with our approach.

On the tech side, we’re adding languages (we support nine or 10 now and keep adding), automating more and scaling. Speech is captured in basically every central nervous system clinical trial for quality assurance, so there’s opportunity to analyze speech alongside third‑party assessments — and potentially in health care more broadly, analyzing doctor–patient conversations with consent. We’re still just scratching the surface.

— Original story by Temerty Faculty of Medicine