Jessica Chen’s undergraduate experience was defined by her involvement with the , where she took on leadership roles to support student learning and exploration. Through these experiences, she embraced uncertainty and learned to take chances on new opportunities.

Now, she is continuing her studies at the graduate level, with an interest in interdisciplinary applications of artificial intelligence.

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You’ve held multiple leadership roles within UTMIST, including VP Academics. What motivated you to take on such a large role in shaping student learning experiences?

If you had asked me in my first year, I would never have imagined myself in a leadership role. Over the course of my undergrad, I realized I wanted to make a broader impact on the community that gave me so much. Since I've been with UTMIST for so long, it felt natural for me to take on leadership positions and give back.

Through UTMIST, you led initiatives like workshops and competitions. What impact were you hoping these would have on other students?

Many people I know, including myself, didn't really know what they were interested in at the start of university or felt like they were behind. Even in the upper years, people change directions – I did too. Through these workshops and events, we aimed to help students explore topics they’re curious about and build a community where they can learn together.

It's normal not to have everything figured out right away, and I hope we made trying something new feel a little less intimidating. It's absolutely OK if students don't end up enjoying it — what matters is that they tried something they were interested in.

What first drew you to computer science, and was there a moment during your degree when you knew you’d made the right choice?

I was interested in video game development, so computer science felt like a no-brainer. I didn't take any CS courses or do much coding before university, so I was going in pretty much blind. Looking back, I feel like I accomplished more than I could have imagined, especially in my last year.

I don't think there was a moment when I knew I’d made the right choice, but it’s a choice I make the most of every day.

What are your interests outside of computer science?

I keep up with music and dance. I've had opportunities to perform in shows around Toronto and even toured Europe with an orchestra once! I also love gaming. You can probably find me playing late at night, even in the middle of exam season.

What's next for you, and how does computer science fit into that?

This summer, I’m interning at Sledgehammer Games, working on Call of Duty. In September, I'll head to the University of Alberta for a thesis-based master’s program. I'll likely work on an interdisciplinary project at the intersection of AI and biology, although nothing is set in stone.

I don't know what my future career or academic route will look like, but computer science will definitely be part of it in some way.

Blending computer science with creativity and community, Helena Glowacki shaped her undergraduate experience through research, student storytelling and artistic interests. Her path reflects a curiosity about how technology connects with human expression.

Now, she is continuing her studies in computer science, focusing on human-AI interaction and the role of technology in creative work.

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You’ve contributed to the In the Loop podcast — what drew you to that platform, and what did you enjoy most about being part of it?

I discovered the podcast during my internship when I was trying to figure out how to make the most of my final year at U of T, and I ended up listening to every episode in one sitting. Each episode introduces a new topic by bringing together faculty and student perspectives. Even when a topic didn’t directly align with my technical interests, it felt genuine and easy to connect with, so I kept listening.

The Grad School 101 episode especially inspired me to consider graduate studies as a pathway. After working on the podcast, what I enjoyed most was collaborating with Professors Mario Badr and Diane Horton. Their joy and passion are infectious, and I feel grateful I had the chance to work with them.

If you’re a current or prospective CS student, I highly recommend checking out the podcast @uoftintheloop on Instagram! :)

Alongside your work in CS, you’re also involved in the student orchestra — how do your creative and technical interests intersect?

Creative arts have always been a big part of my life, through music and visual arts, and I’ve loved how naturally they connect with computer science through areas like graphics, video game design and UI design. As AI advances quickly, that intersection has become even more important to me.

I’m currently working with undergraduate student Sarah Wang, PhD student Harsh Kumar and Professor Ashton Anderson on research that explores how LLMs may shape emotional expression and the creator-audience relationship. This work made me think about how we can better design AI systems that support, rather than replace, human creativity, and it’s something I hope to keep exploring.

What first drew you to computer science, and was there a moment during your degree when you knew you’d made the right choice?

I took my first computer science course as an elective in high school. It’s hard to explain, but I liked how solving a computer science problem felt different from solving a pure math question or working through other STEM subjects — it felt more like solving a riddle than following a set process.

In university, I never had a single moment where I realized I’d made the right choice. Instead, I'll always remember and cherish the incredible support from the teaching team and CS community, along with the freedom we had to explore. People were excited to hear new ideas and to see each other grow.

If you could give one piece of advice to a first-year computer science student, what would it be and why?

Find your community! U of T is a huge school, and while that can feel overwhelming, it also means there are many opportunities to make friends and build a support system. If there is something that excites you or you want to try something new, whether it's related to computer science or not, there is a way to pursue it.

Of course, make time for your studies, but having a place where you can be and express who you are makes it easier to face the challenges along the way.

What are your interests outside of computer science?

Outside of computer science and the creative arts, I’ve grown to enjoy marketing because of how closely it connects to building communities and helping people feel connected. One of my favourite experiences during my degree was serving as marketing director for the in my third year.

In the months leading up to the conference, I built our platform for outreach and engagement, and it was incredibly rewarding to see that work come to life as more than 400 delegates from across Canada joined us. More than anything, I loved watching people connect, find their community and form new friendships over the weekend.

Although marketing sits outside computer science, I’ve noticed it is increasingly shaping how I approach and share my technical work.

What's next for you, and how does computer science fit into that?

This fall, I’ll return to the U of T ������Ƶ of Computer Science to begin the MSc in Computer Science program! I’m excited to work with Professor in human-AI interaction, building on her work in HCI.

For Jacob Klimczak, studying computer science meant exploring its reach far beyond technology alone. From environmental research to data-driven modelling, he approached his degree with the aim of understanding and shaping complex systems across disciplines.

Now, he is focused on applying those skills to meaningful global challenges, with an interest in work that bridges technology and the natural world.

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What did it mean to you to receive a Computing Research Association (CRA) Outstanding Undergraduate Researcher Honourable Mention at this stage in your academic career?

Receiving a CRA Outstanding Undergraduate Researcher Honourable Mention felt incredibly validating. It was rewarding to see the effort I put into my research throughout my degree recognized by such a prominent organization. It also reinforced the idea that the computer science community values interdisciplinary work that treats computer science as a means rather than an end.

How do you see computer science contributing to broader global challenges?

In my view, increased global digitization has made computer science an integral part of most systems. As a result, digital literacy and software skills are among the most valuable that someone can have, whether they work in tech or not.

As the amount of available data grows, it is also becoming essential for supporting everyday decision-making across many environments. Organizations can use systems to augment human expertise and make more informed, data-driven decisions. This ability to support human judgment across fields is where computer science has the greatest potential to address global challenges.

What first drew you to computer science, and was there a moment during your degree when you knew you’d made the right choice?

I started working as a software engineer at a small company while still in high school, and that experience led me to pursue an education in computer science. At first, I saw a degree as a professional qualification that would broaden my opportunities at work. Over time, I came to see it as much more than that.

U of T exposed me to the fact that computer science was more than a tool for developing software products. I got involved in cutting-edge research that used algorithmic tools to quantify forest structure, understand traffic patterns, model on-chip power consumption and generate high-precision forecasts. In my final year, looking back at the range of fields I explored and the broad applicability of these tools I had learned, I felt confident I had made the right choice. It strengthened my software development skills while showing me how much more computers can do.

If you could give one piece of advice to a first-year computer science student, what would it be and why?

The biggest piece of advice I could give is one I wish I had received going into university: don't be afraid to get involved. It took me time to engage with opportunities on campus, but once I did, I found that the University of Toronto offers more projects, programs and experiences than there are students to fill them.

What are your interests outside of computer science?

Outside of computer science, I am passionate about the natural world and environmental activism. I see major challenges facing the global climate and Earth system over the coming decades, and I’m motivated to get involved in projects that support environmental causes. I try to apply my skills to work that contributes to a better world.

As a DCS ambassador, Raghav Sinha spent much of his time supporting students across different stages of their computer science journeys. Alongside his studies in economics, statistics and computer science, he developed a strong focus on community and real-world applications of technology.

Now, he is ready to bring those skills into the financial sector, applying data-driven approaches to complex challenges.

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As a DCS Ambassador and Lead Ambassador, you’ve supported a large community of students. What did you learn from mentoring others at different stages of their CS journey?

Mentoring students taught me that everyone's tech journey looks completely different, and there’s no single "correct" path to success. Whether someone is writing their first line of code or preparing for complex technical interviews, what they usually need is reassurance and a reminder that imposter syndrome is common. I learned that being a good mentor is less about having all the answers and more about being a patient listener and a reliable sounding board.

You also helped build programs like the Alumni-Student Mentorship initiative — what gap were you trying to address for students?

The Alumni-Student Mentorship Program has been a staple for decades, thanks to the incredible work of Kimberly Huynh-Nguyen. With this latest iteration, our goal was to modernize the way we bridge the gap between academia and industry.

Working closely with the team, I focused on bringing fresh, modern workplace insights and strategies into the program so students could better navigate today's rapidly evolving tech landscape. We wanted to give students a realistic, up-to-date view of industry trends, workplace culture and the transition into a modern career.

What first drew you to computer science, and was there a moment during your degree when you knew you’d made the right choice?

Honestly, I was first drawn to CS in middle school because I wanted to automate tasks I was too lazy to do myself — I loved the classic programmer paradox of spending two hours writing code just to save one minute of manual effort. Later at U of T, I knew I made the right choice when I discovered a passion for scalable machine learning. Seeing how code and statistics could intersect to solve complex data problems in corporate and financial contexts completely hooked me.

If you could give one piece of advice to a first-year computer science student, what would it be and why?

Don't spend all your time hidden behind a screen — actively build a community and talk to the people around you. The friendships, study groups and student clubs you join will not only make challenging weeks more manageable but will also open doors to opportunities you might not find on your own. Tech is highly collaborative, so learning to connect with others early on matters just as much as mastering syntax.

What are your interests outside of computer science?

When I'm not diving into tech, you’ll usually find me on the squash or tennis courts or watching the latest Formula 1 race. I also love applied behavioural science and strategy.

What's next for you, and how does computer science fit into that?

This July, I’ll step into the professional world as a full-time data science associate at TD Bank! Computer science fits perfectly into this next chapter, as the programming, analytical and machine learning skills I've built during my degree are exactly what I’ll use to tackle data-driven financial challenges at scale.

During her time at the University of Toronto, Pratibha Thakur helped shape student life in computer science through her leadership with the and . Drawn to both community-building and academic exploration, she embraced opportunities to support her peers.

Now, she is building on those experiences as she begins her career in software engineering, carrying forward a strong sense of community.

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You’ve been deeply involved in both CSSU and Women in Computer Science (WiCS). What kind of community were you hoping to help build through those roles?

My involvement with both CSSU and WiCS began in my first year. I found their events were not just great places to connect with peers, but also one of the best ways to get real, first-hand advice on navigating university life. Over my time at U of T, I’ve realized that having a community that supports your academic, professional and personal growth is invaluable, and both CSSU and WiCS offered exactly that.

I joined their executive teams to pay it forward and help strengthen our initiatives. Although I'm graduating, I am excited to see how these groups will continue to evolve. The student community here is so dynamic, and I know they'll keep doing amazing things.

Having stepped into leadership during a period of transition, what did you learn about leading teams through change?

When I first stepped into my presidency with CSSU, we hit the ground running. Many previous executives had graduated, so most of the new team was stepped into their roles for the first time. On top of that, we had several major events coming up, with hundreds of students expected to attend.

Leading a new team under that kind of pressure was challenging, but it was also energizing for all of us. I truly believe that a little bit of transition “chaos” is great for a team — it pushes teams to innovate. We used that period to restructure roles across our nine committees and build several new initiatives from scratch that simply didn't exist in previous years.

What first drew you to computer science, and was there a moment during your degree when you knew you’d made the right choice?

Through competitive math, I was exposed to problems in areas like combinatorics and number theory. I found their applications in computer science especially fascinating and decided to try it, knowing I had the flexibility at U of T to change my program if it wasn’t the right fit.

There wasn’t a single moment when I knew computer science was the perfect choice for me, but I remember a conversation in second year when a friend and I realized we couldn’t think of another subject we’d rather spend so many late nights studying. It also helped that my professors and peers were so encouraging.

If you could give one piece of advice to a first-year computer science student, what would it be and why?

If there’s one thing I’ve learned in the past four years, it’s that it’s totally fine if things don’t go as planned. In first and second year, I took everything so seriously. I worried over midterms, course selection, interviews and more, but things were rarely as serious as they felt.

To gain perspective, I found it helpful to share concerns with peers — we’re all in this together, and chances are, their worries are similar to yours.

What are your interests outside of computer science?

I’ve always loved a good book, and recently I’ve been dipping into both creative and technical writing. This summer, I’m also hoping to catch up on the 2025 Oscar-nominated movie list and maybe revisit playing the flute.

What's next for you, and how does computer science fit into that?

In July, I’ll start as a full-time software engineer with Amazon Robotics. I’m looking forward to learning more about the field and applying what I’ve learned over the past four years!

Yuchen Wang’s undergraduate research focused on applying artificial intelligence in healthcare, with work spanning Toronto Rehab and the Vector Institute. Working closely with clinicians and real patient data, he developed a strong interest in building practical, patient-centred solutions.

Now, he is continuing that work in graduate studies, focusing on technologies that drive meaningful impact in clinical settings.

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Your work spans both Toronto Rehab and the Vector Institute. How did those experiences shape your approach to applying AI in healthcare?

My first research experience began at Toronto Rehab, where I was fortunate to receive funding from the Data Sciences Institute. There, I focused on extracting valuable insights from data to support recovery for patients with spinal cord injuries. Through this work, I learned the importance of developing solutions that are practical, interpretable and centred on patient needs.

Thanks to the research opportunities provided through the ������Ƶ of Computer Science at the University of Toronto, I later joined the Vector Institute, where I strengthened my research and analytical skills through exposure to advanced computational methods and interdisciplinary collaboration. Together, these experiences shaped my goal of building technologies that create meaningful impact in clinical settings.

You’ve worked closely with clinicians and real patient data — how did that influence the kinds of problems you chose to focus on?

Working closely with clinicians and real patient data helped me focus on problems with clear clinical relevance and practical impact. I became especially interested in developing methods that could produce interpretable and reliable insights rather than simply optimizing performance metrics. These experiences showed me that meaningful healthcare technologies must address real challenges faced by both patients and care providers.

What first drew you to computer science, and was there a moment during your degree when you knew you’d made the right choice?

I was first drawn to computer science when I realized its power to uncover significant insights from massive amounts of data. I knew I had made the right choice during my work at Toronto Rehab, where I saw how my research could help patients recover faster and more effectively. That experience showed me how computational research can create tangible impact in healthcare and improve real patient outcomes.

If you could give one piece of advice to a first-year computer science student, what would it be and why?

I encourage first-year computer science students to go beyond the classroom and seek opportunities to apply what they learn to real-world problems.

I would also encourage them to find an area they are genuinely passionate about and stay committed to it, because long-term curiosity and persistence drive the most rewarding work.

What are your interests outside of computer science?

Outside of computer science, I enjoy playing badminton. I also love reading books in my spare time, as it allows me to explore new perspectives and continue learning beyond academics.

What’s next for you, and how does computer science fit into that?

This fall, I will begin the Master of Science in Computer Science program at Stanford University, where I hope to further strengthen my technical skills. Computer science will continue to be an important part of my academic and professional journey, especially as I work toward building technologies that can solve real-world problems.

University of Toronto computer scientists have discovered a vulnerability in the memory used in graphics processing units (GPUs) that can allow attackers to take complete control of a computer system.

GPUs are the engines of modern artificial intelligence (AI)—the same chips that render video games also train and run large language models like ChatGPT and other tools reshaping business and science. As organizations race to deploy AI, GPUs have become a critical part of modern computing infrastructure, making their security increasingly important.

The proof-of-concept exploit, called , corrupts the GPU's page tables, its internal records of where data is stored. Once those records have been falsified, the attacker can read and modify anything held in its memory. The attack then extends the intrusion outward into the rest of the machine by exploiting additional weaknesses in the GPU drivers, ultimately giving hackers full control of the host computer.

The technique behind the attack, known as Rowhammer, exploits a quirk of modern memory chips: repeatedly accessing certain memory cells causes electrical interference that can flip bits in neighbouring cells, even cells the attacker has no permission to touch. The team's previous paper, GPUHammer, first showed the technique was possible on NVIDIA GPUs.

The GPUBreach attack works even when a piece of safeguarding hardware is switched on. Built into mainstream computers for more than a decade and recommended by NVIDIA, AMD and Microsoft, the Input-Output Memory Management Unit (IOMMU) is the standard defence against this kind of cross-component break-in. GPUBreach circumvents it by exploiting a previously unknown flaw in the trusted GPU software stack—specifically the GPU drivers running inside the computer’s operating system that manage communication with the GPU.

As a result, the attack can achieve administrator-level access to the entire machine, allowing attackers to steal sensitive data, or tamper with files and compromise other applications on the system, including the AI models running on the GPU itself.

“GPUBreach is a reminder that GPUs are no longer just performance accelerators,” said , assistant professor in the ������Ƶ of Mathematical and Computational Sciences at University of Toronto Mississauga and the tri-campus graduate ������Ƶ of Computer Science. “They now sit at the heart of AI and cloud computing, which means vulnerabilities in GPUs can affect AI security and the security of the entire computing system.”

The team—PhD student , with co-authors Yuqin Yan, Guozhen Ding, Joyce Qu, Joseph Zhu and professors and —recently presented this work at IEEE Symposium on Security & Privacy 2026, the leading academic conference in the field of computer security and privacy.

The paper has been honoured with a Distinguished Paper Award at the conference, one of just 13 chosen from more than 240 accepted papers drawn from over 2,200 submissions.

The paper showcases two further consequences of the attack. First, it demonstrates that an attacker sharing a machine with a victim can steal cryptographic keys, including the kinds designed to resist future quantum computers, while those keys briefly sit in GPU memory.

The team also showed that editing a single instruction inside a widely used NVIDIA library, one that handles the math behind most modern AI systems, can drop the accuracy of major image-recognition models from up to 80 per cent to roughly zero, with negligible effect on response time. The sabotage is all but undetectable.

"GPUBreach affects every GPU user, from a developer running untrusted software on their laptop, to a cloud provider renting GPUs to AI customers," said Saileshwar. "A seemingly harmless application, such as a downloaded game or an AI model pulled from the internet, could become the entry point for an attack."

On the team's test platform—an NVIDIA RTX A6000 workstation graphics card—the entire compromise unfolds in under 20 seconds. The underlying driver flaw was also verified on additional NVIDIA GPUs and driver versions.

The team responsibly disclosed the findings to NVIDIA, Google, Amazon Web Services and Microsoft in November 2025. Google awarded the team a Bug Bounty Award, and NVIDIA has indicated it may update its existing security guidance.

GPUBreach has drawn extensive coverage in outlets including , and , and an extension of the paper will be presented in August at Black Hat USA, the leading industry cybersecurity conference.

This research was supported by the Natural Sciences and Engineering Research Council of Canada, the Communications Security Establishment Canada and the Canada Research Chairs program.

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.

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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.

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.

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.

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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.

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.

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.

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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?

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.

, a professor in the University of Toronto's ������Ƶ of Computer Science, has been elected a Fellow of the Royal Society, the United Kingdom's national academy of sciences and one of the world's oldest scientific institutions.

Considered one of the highest honours in science, the Fellowship is a lifetime appointment whose past holders include Isaac Newton, Charles Darwin, Albert Einstein and Stephen Hawking. Urtasun is among more than 90 researchers from across the globe named to this year's cohort, May 27.

"Their contributions reflect the highest standards of scientific endeavour," Sir Paul Nurse, president of the Royal Society, said in a statement. "Whether advancing our understanding of vaccines or exploring the transformative potential of mathematics and computation, their work exemplifies the enduring value of curiosity, creativity and rigorous inquiry."

A leading expert in artificial intelligence, Urtasun is the founder and CEO of , the Toronto-based autonomous driving company she launched in 2021, and a co-founder of the Vector Institute for Artificial Intelligence. Her research spans machine learning, computer vision and robotics, with a focus on generative AI for the physical world and the next generation of self-driving technology.

“Raquel has fundamentally shaped how machines perceive and reason about the physical world, and she has done so while building one of the most ambitious AI companies in Canada,” said Eyal de Lara, professor and chair of the ������Ƶ of Computer Science.

“The Royal Society's recognition reflects both the depth of that scholarship and the conviction with which she has translated it into real-world impact.”

Urtasun joined U of T in 2014 after earning her PhD at the École Polytechnique Fédérale de Lausanne and completing postdoctoral work at MIT and the University of California, Berkeley. Over her career, she has published more than 200 papers and is named on more than 100 patents in autonomous driving. Her honours include the NSERC E.W.R. Steacie Memorial Fellowship, the Longuet-Higgins Prize, the Everingham Prize and multiple best paper awards at the Conference on Computer Vision and Pattern Recognition. She was named to TIME's list of the 100 Most Influential People in AI in 2023 and a CNBC Changemaker in 2024.

In 2024, Urtasun was also elected a Fellow of the Royal Society of Canada and appointed to the Order of Ontario, the province's highest civilian honour.

Waabi has raised hundreds of millions of dollars in venture financing to develop and deploy fully autonomous trucks, with the most recent round reportedly valuing the company at up to US$1 billion. Urtasun has said her ambition is to make Canada the global leader in AI for the physical world.

Founded in 1660, the Royal Society elects new Fellows each year in recognition of substantial contributions to the advancement of science.

Professor Emeritus spent 30 years at the University of Toronto shaping computer graphics and advancing the applied sciences.

Most recently, his work has earned him a 2025 Lifetime Achievement Award from CS-Can | Info-Can, Canada’s national academic organization for computer science.

The award recognizes current or former faculty members in Canadian computer science departments, schools and faculties who have made “outstanding and sustained contributions” to the field over the course of their careers. The awards can be for achievements in research, teaching or service.

Following his graduate studies at the University of Toronto, Fiume completed postdoctoral research in Europe as a Natural Sciences and Engineering Research Council (NSERC) Postdoctoral Fellow and Maître Assistant at the University of Geneva in Switzerland. He later held visiting appointments in France before returning to the University of Toronto in 1987 to join the faculty.

While at the university, he served as the chair of the ������Ƶ of Computer Science from 1998 to 2004, was the inaugural director of the from 2010 to 2016, and co-directed the , a global leader in computer graphics research.

A leading researcher in computer graphics, Fiume’s work focused on making digital imagery more realistic and grounded in mathematical theory. His research on the simulation of natural phenomena such as fire, water and wind influenced animation, visual effects and design, while his contributions to shape modelling and animation supported the development of more lifelike digital characters and environments.

Over his career, Fiume has published more than 140 research papers and authored two books. He also holds patents in computer graphics and biomedical applications. He maintained long-standing collaborations with industry, including a partnership with Alias (now Autodesk) that earned the NSERC Synergy Award for Innovation. He also co-founded , a facial animation company that began as a university research project and later became a successful commercial venture.

Fiume is a Fellow of the Royal Society of Canada, a Eurographics Fellow and a member of the SIGGRAPH Academy — three of the highest distinctions in Canadian academia and computer graphics.

“Eugene’s career has had a profound impact on computer graphics research and on the development of our department,” said Eyal de Lara, professor and chair of the ������Ƶ of Computer Science. “We are proud to see his contributions recognized by CS-Can | Info-Can.”

Fiume is currently a professor of computing science and dean of the Faculty of Applied Sciences at Simon Fraser University.

Mentoring computer science students has given alumnus Jason Lee a new way to stay connected to campus.

What draws him back each year are conversations he has with undergraduate students, who reflect the same curiosity and ambition he once had as a student.

He said many students come to mentorship conversations searching for direction, and he finds meaning in helping them work through those questions.

Lee recalls mentoring a student who was unsure whether computer science was the right path. Through discussion, the student gained clarity about what they wanted and what they didn’t.

“It was incredibly rewarding to see them make an informed decision about their future,” he said. “Mentorship isn’t just about giving advice. It’s about helping students figure out what they want for themselves.”

For third-year computer science student Yoyo Liu, mentorship also clarified her long-term career goals. She was debating whether to try an industry internship before committing to research.

“After our conversations, I realized that my interests align more with R&D,” she said. “That insight solidified my decision to pursue a PhD and focus on research.”

Lee said mentoring also strengthened his own perspective on the field. Students’ questions about emerging technologies push him to stay current, reflect on his own career decisions and lead his team more effectively.

“I’ve seen generations of students come through the program. Each brings fresh energy, ideas and ambition.”

Liu said she would “100 per cent” recommend the program to other students. “It gave me the chance to ask questions, visit my mentor’s office and hear their perspective on my goals. It was incredibly valuable.”

For Lee, his mentees continuously change his perspective and teach him new things.

“In some cases, their way of doing things could be more efficient and effective than the ‘old ways’ that I have experienced,” he said.

“This mentoring experience reminds me why I’m proud to be part of U of T. I hope to continue mentoring and supporting students all the way to retirement.”

������Ƶ of Computer Science Alumni-Student Mentorship Program

Each winter, undergraduate computer science students pair with alumni for a series of one-on-one conversations about career paths, research opportunities and life after graduation. The mentorship program unfolds over several months, giving students space to ask candid questions and test ideas with someone who has navigated similar decisions.

Launched in 2005, the ������Ƶ of Computer Science’s Alumni-Student Mentorship Program connects undergraduates with alumni working in industry, research and academia, offering perspectives that extend beyond the classroom and evolve through ongoing dialogue.

Learn more about the Alumni-Student Mentorship Program.

For more than a decade, the C.C. “Kelly” Gotlieb Distinguished Lecture Series has brought leading researchers to the University of Toronto to share advances in computer science.

On Thursday, April 16, Yejin Choi, a distinguished scientist at NVIDIA and professor at Stanford University, delivered “The Art of (Artificial) Reasoning” to a full audience at the Schwartz Reisman Innovation Campus. Nearly 400 people attended the talk in-person and online. The ������Ƶ of Computer Science hosted the event in collaboration with the Vector Institute.  

Choi addressed one of artificial intelligence’s central challenges: why today's most advanced systems continue to show uneven, or "jagged" intelligence despite strong benchmark performance. Drawing on research at the intersection of language, reasoning and learning, she examined the limits of large-scale approaches that rely on ever-increasing data and compute.

The lecture series takes its name from C.C. “Kelly” Gotlieb, the ������Ƶ of Computer Science’s inaugural chair and a driving force behind the University of Toronto’s rise as a national leader in computer science. Gotlieb is widely credited with helping usher Canada into the modern age of computing, a legacy reflected in the series that bears his name.

The lecture marked the first time the series welcomed special guests Leo and Jacob Gotlieb. Leo Gotlieb is Kelly Gotlieb’s son and a University of Toronto computer science alumnus.

“I found it fascinating, but also humbling,” Leo Gotlieb said. “The work is incredibly deep and complex. I realized how superficial my own understanding of AI really is once you start seeing how far down the layers go.”

The department will announce details about the next lecture in the series, scheduled for this fall, in the coming months.

Held at the Sheraton Centre Toronto Hotel, the annual showcase brought together more than 3,000 attendees to experience original games created by students across Ontario. This year’s event featured 160 teams from 25 colleges and universities, with 150 industry professionals from 40 companies serving as judges.

Students from , taught by instructor Elias Adum and Professor , represented U of T strongly at the competition. Many of these projects were developed through interdisciplinary collaboration across computer science and music.

Level Up began in 2011 as a small end-of-term exhibition and has since grown into a province-wide event, offering students the opportunity to present their work to industry professionals, alumni and the public. The showcase attracts support from leading game studios and technology companies, including Zynga, AMD, Ubisoft and Sledgehammer Games.

This year's showcase drew special attention, with Toronto Mayor Olivia Chow and Ontario’s Minister of Tourism, Culture and Gaming, Stan Cho, in attendance.

U of T computer science students took home four awards at the 2026 Level Up Showcase:

(U of T + Centennial College)
Best Overall Game, 2nd place

Team:

• Taowen Qian (U of T CS)

• Jerry Lin (U of T CS)

• Min-Gi (Lucas) Kwon (U of T CS)

• Maverick Luke (U of T CS)

• Becky Wong (U of T CS)

• Joshua Manrique (U of T Music)

• Daniel Wang Gomez (U of T Music)

• Heron Li (Centennial College)

• Milica Sotic (Centennial College)

• Xiomara Cano Bibiano (Centennial College)

(U of T + OCADU)
Artistic Achievement, 3rd place
Best Game Trailer, 3rd place

Team:

• Sam Xu (U of T CS)

• Sally Zhang (U of T CS)

• William Liu (U of T CS)

• Emmy Gao (OCADU)

• Komo Hu (OCADU)

• Riley Kernaghan (OCADU)

• Kaiqi Zhang (OCADU)

• Shenglin Xu (OCADU)

• Imane-Cecile Hamiti (OCADU)

• Evan Teti (U of T Music)

• Heidi Cui (U of T Music)

(U of T + Sheridan College)
Achievement in Audio, 1st place

Team:

• Arvin Gingoyon (U of T CS)

• Jiaxin (Evelyn) Li (U of T CS)

• Jisung (Chris) Shin (U of T CS)

• Zein Sulayman (U of T CS)

• Aunonna Kazi (Sheridan College)

• Sydney Smith (Sheridan College)

• Thales Nagato (Sheridan College)

• Mario Ciaralli (Sheridan College)

(U of T + OCADU)
Achievement in Audio, 3rd place

Team:

• Emily Zhou (U of T CS)

• Jo Nguyen (U of T CS)

• Jennifer Fong Li (U of T CS)

• David Gorospe (U of T CS)

• Weijun Wang (OCADU)

• Hayley Shing (OCADU)

• Cheryl Tian (OCADU)

• Noor Amir (OCADU)

• Yidan Zhang (OCADU)

• Eric Liang (U of T Music)

• Josh Yeo (U of T Music)

When a wildfire ravaged Fort McMurray, Alberta, in 2016, 88,000 people were forced to flee their homes. As the embers cooled, residents confronted widespread devastation. 

But the fire could have been even more damaging if the weather had been hotter, windier, or drier, says , professor emeritus in the University of Toronto’s ������Ƶ of Computer Science. And as climate change advances, those scenarios become more plausible. 

The fire is one of several scenarios that Baecker and an interdisciplinary team of researchers across Canada are reinterpreting with the creative use of flood and fire modelling, data visualization, design, planning and environmental psychology. 

Their ultimate goal: to spur individuals, communities and governments to take action against climate change and to give public agencies and citizen groups concrete tools for understanding and evaluating different choices. 

The team behind uses real extreme weather events to illustrate the predicted effects of climate action — or inaction. 

"If we can show people that the floods and fires they're already worried about will get worse with inaction — but that concrete steps can make things better — I think that's a powerful way to get people and society to move," said Baecker. 

Current approaches to climate communications fall into two camps, Baecker says. Some examples “show you masses of scientific data proving how bad it’s going to be, which can be very depressing. Others say, ‘Here are 100 things you could do,’ but they’re not connected to specific places or events people care about.” 

This initiative aims to demonstrate the benefits of concrete action. 

Starting with a documented extreme weather event, the team builds or adapts simulation models that they then validate — a key step that Baecker notes is one of the toughest technical challenges. The data required, like the water depth at specific locations during a flood, can be hard to access or of varying quality. 

“We can systematically vary these models to show it getting worse if you do nothing and to show it getting better if you do some things,” Baecker explained. 

But the model outputs themselves are “just a lot of numbers,” Baecker said, so the team works to convert the raw data into novel images and animations that can help people thoughtfully engage with the hypothetical scenarios. They hope to expand their visualization efforts into immersive experiences and even video games in the future. 

By grounding its simulations in real rivers, creeks and communities, the project aims to turn abstract climate risks into local decisions that could spell the difference between crisis and resilience.

In one example, the researchers focused on the issue of flooding at a specific point along Little Etobicoke Creek in Mississauga, Ontario. Engineers' recommendations have included a new channel and a new bridge at a bend in the creek. The researchers recreated and visualized major flood events from 2013 and 2024 and found that both interventions would be required to mitigate anticipated damage in those scenarios — neither intervention alone would provide sufficient protection. 

The team also applied its modelling to the recently reconstructed mouth of the Don River in Toronto, demonstrating much-improved flood resilience in the Port Lands area of the city. 

The Envisioning Climate Futures includes U of T computer science faculty members , and , as well as , professor emeritus of landscape architecture in U of T’s John H. Daniels Faculty of Architecture, Landscape, and Design. Ten U of T students and recent graduates contribute to the effort. 

Other collaborators across Canada include experts from OCAD University, University of Prince Edward Island, Vancouver Island University and the Canadian Forest Service.

Looking ahead, the team has begun recreating the 2016 Fort McMurray wildfire in simulations and modelling coastal flooding scenarios in partnership with the Climate Smart Lab at UPEI. The researchers also hope to test experimentally whether their visualizations actually change how people think and act on climate. 

“We’re not going to solve the climate problem by ourselves,” Baecker said. “But I’m only 83 years young — time to see what I can do.” 

Grad Visit Days is a three-day, in-person program designed to give newly admitted MSc and PhD students a clear, research-focused view of graduate study in the ������Ƶ of Computer Science (DCS) at the University of Toronto.

What sets this event apart is its emphasis on substantive engagement with research and faculty, featuring faculty research presentations, one-on-one meetings with potential supervisors and small-group interactions within specific research areas.

This year, the department welcomed prospective students travelling from Canada, the United States, Ukraine, Iran, India, Europe, South America, Asia and Africa, reflecting the department’s international reach.

For first-year PhD student Tristan Lueger, who attended Grad Visit Days during his own application process, the department’s research culture stood out early.

“Grad Visit Days painted a very clear picture: you’re coming to U of T to do research,” he said. “That emphasis made a real difference in my decision — and so far, it’s matched my experience.”

He also pointed to the depth of interaction with faculty and students as a defining feature of the event.

“You don’t just hear about the research, you actually get time with the people doing it, and that makes it much easier to understand how the department works day to day.”

Throughout the three days, students meet department leadership, faculty and current graduate students, while learning about funding, housing, entrepreneurship supports and life in downtown Toronto. Group-specific socials, lab tours and student-led events are designed to reflect the department — illustrating what it means to be part of the DCS graduate community.