Tuesday, May 12, 2026

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• What to remember when a campus fire drill takes place
• Gautam Kamath receives 2026 Presburger Award
• Interactive exhibit delivers cold hard truth about glaciers
• Tuesday's notes

 What to remember when a campus fire drill takes place

A message from the Safety Office.

Fire drills are an important part of keeping our campus safe and ensuring that everyone knows what to do in an emergency. Thank you to those who participated in the recent spring fire drills across campus. Overall, evacuations were completed efficiently and with minimal disruption. As with any drill, a few common challenges were identified, and these provide a helpful reminder of what to do when a fire alarm sounds.

Please remember:

• When a fire alarm sounds, everyone must evacuate the building immediately. This includes meetings, classes, labs, and offices. There are no exemptions.
• Use the nearest safe exit. Do not wait to finish work, collect belongings, or look for confirmation that it is “just a drill.”
• Once outside, move well away from the building and stay clear of exits and fire routes so others can evacuate and emergency responders have clear access.
• Do not enter a building while a fire alarm is active.
• Follow the direction of Emergency Wardens and Safety staff, who are there to help ensure a safe and orderly evacuation. 

Most challenges observed during the drills involved hesitation to evacuate or people gathering too close to exits after leaving the building. These are exactly the behaviour that drills are designed to identify and improve before a real emergency occurs.

Fire drills are a shared responsibility, and individual actions play a key role in keeping everyone safe.

Interested in helping?

Emergency Wardens support safe evacuations during alarms and emergencies. If you are interested in becoming an Emergency Warden or learning more about the role, please visit the Safety Office website for details and training opportunities.

Thank you for your cooperation and support in helping maintain a safe campus.

Gautam Kamath receives 2026 Presburger Award

This article was originally published on the Cheriton School of Computer Science website.

Professor Gautam Kamath has won the 2026 Presburger Award. This prestigious honour recognizes his exceptional contributions to theoretical computer science, notably his pioneering work on computationally efficient algorithms for fundamental estimation tasks under robustness constraints.

Conferred annually since 2010 by the European Association for Theoretical Computer Science, the Presburger Award recognizes a young researcher for outstanding contributions to theoretical computer science, as documented by a paper or series of papers. The award is named in honour of Mojżesz Presburger, whose groundbreaking research on decidability of the theory of addition laid the foundation for what is now known as Presburger arithmetic.

“Congratulations to Gautam on receiving this year’s Presburger Award,” said Raouf Boutaba, University Professor and Director of the Cheriton School of Computer Science. “This prestigious honour reflects the depth and growing influence of his work. The award also adds to his growing list of professional accolades that includes an Ontario Early Researcher Award in 2025 and the Faculty of Mathematics Golden Jubilee Research Excellence Award in 2023, along with the 2024 Caspar Bowden PET Award and a recent best paper award at ICML.”

Gautam Kamath is an Assistant Professor at the Cheriton School of Computer Science, a Canada CIFAR AI Chair at the Vector Institute, and a member of Waterloo’s Cybersecurity and Privacy Institute. An expert in algorithms, statistics and machine learning, his research focuses on modern data analysis under constraints such as differential privacy and robustness.

As of April 2026, his publications have been cited more than 6,800 times, with an h-index of 41, according to Google Scholar. His most highly cited paper, on efficient robust estimation, whose key ideas are illustrated on the blackboard, helped define a new area of study in high-dimensional statistics.

About Professor Kamath’s research

Professor Kamath has pioneered computationally efficient algorithms for fundamental estimation problems under robustness constraints. His seminal 2016 paper helped launch an entire research area, with a rapidly expanding frontier spanning topics such as list-decodable learning and differential privacy. Over the past decade, he has been a leading contributor to many of these developments.

Professor Kamath’s research begins with mean estimation, the most fundamental statistical question: Given n independent and identically distributed samples from a d-dimensional Gaussian distribution, how can the mean be estimated? The simple, efficient and optimal statistic is the empirical mean.

The question, however, becomes much more interesting when a portion of the data is corrupted (i.e., an η-fraction of the dataset), whether by model misspecification or a malicious adversary. The empirical mean can now be arbitrarily bad. In 2016, Professor Kamath and his collaborators resolved this classical problem by applying an algorithmic lens. They gave the first polynomial-time algorithm that achieves a dimension-independent 𝑂(η) excess error. Their main algorithm is an elegant application of the spectral method. By inspecting the top eigenvalue of the empirical covariance matrix, they can identify and prune outliers by inspecting the corresponding eigenvector direction, or certify that the empirical mean of the remaining points is accurate. He and his collaborators also give algorithms for Gaussians with unknown covariances and a host of other settings.

Their paper, Robust Estimators in High-Dimensions Without the Computational Intractability, was presented at FOCS 2016, the 57^th Annual Symposium on Foundations of Computer Science. Highly acclaimed by the community, the paper was invited to several prestigious venues, among them the SICOMP Special Issue for FOCS 2016, Highlights of Algorithms 2017, and Communications of the ACM Research Highlights. As of April 2026, it has received more than 660 citations and remains the most cited paper from FOCS 2016 and onward.

Professor Kamath and his collaborators have produced several key works in this area subsequently, including algorithms operating under much weaker distributional assumptions at ICML 2017, the 34^th International Conference on Machine Learning; improved algorithms for Gaussians at SODA 2018, the 29^th ACM-SIAM Symposium on Discrete Algorithms; and algorithms for protecting machine learning models from data poisoning attacks at ICML 2019, 36^th International Conference on Machine Learning. Beyond these contributions, his work has helped spark widespread interest across the algorithms community.

Meanwhile, Professor Kamath has made significant contributions to differential privacy. These papers include Privately Learning High-Dimensional Distributions (COLT 2019), The Discrete Gaussian for Differential Privacy (NeurIPS 2020), Locally Private Hypothesis Selection (COLT 2020), and Differentially Private Algorithms for Learning Mixtures of Separated Gaussians (NeurIPS 2019).

Differential privacy can be viewed as a form of robustness, requiring that an algorithm’s output remain stable under small changes to its input. A key open problem was to design efficient optimal algorithms for mean estimation. In Efficient Mean Estimation with Pure Differential Privacy via a Sum-of-squares Exponential Mechanism (STOC 2022), Professor Kamath and his collaborators solved the open problem, again using the algorithmic lens he pioneered in 2016. This connection is not incidental. In subsequent work, Robustness Implies Privacy in Statistical Estimation (STOC 2023), Professor Kamath and his collaborators showed that any robust estimator can be converted to a private one, evidence of deep algorithmic connections that underpin the field.

Beyond his research contributions, Professor Kamath is committed to advancing and sharing these ideas within the broader community. He co-organized workshops on robust and private estimation at the Fields Institute in 2022 and at the Toyota Technological Institute at Chicago in 2024, and authored a survey article on the broader landscape of robustness in algorithmic statistics published in IEEE BITS: The Information Theory Magazine.

Interactive exhibit delivers cold hard truth about glaciers

A special exhibit is coming to East Campus Hall's Artery Gallery this week.

The Life and Legacy of the Athabasca Glacier is an interactive exhibit that tells the story of one of Canada’s most iconic and rapidly disappearing glaciers. A bellwether of the accelerating effects of climate change, the Athabasca Glacier in Jasper National Park is projected to disappear within one generation. This series of art installations invites the audience to experience and reflect on the glacier’s fragile beauty and its role in our shared future.

Incorporating award-winning photography by Guardians of the Ice, the three-chapter immersive experience allows visitors to journey through different phases of the glacier’s life. Contrasting the stark timescales of glacial growth (millennia) and disappearance (decades), the exhibit invites participants to leave a message in honour of the glacier’s legacy.

The Life and Legacy of the Athabasca Glacier is created by Maryia Markhvida, Michelle Rutty, Jayden Hsiao, Kathy Zhang, Dror Margalit (Artful), John Luo, Mave Rubiano, Elaheh Sanoubari, and Hewan Amare, in collaboration with Guardians of the Ice.

The exhibition runs from Thursday, May 14 to Saturday, May 16, from 12 noon to 5:00 p.m. An opening reception and artist’s panel event takes place on Thursday, May 14 at 3:30 p.m. in the Artery Gallery, ECH 1207.

The exhibit is presented with the generous support of the Waterloo Climate Institute, the Canadian Foundation for Innovation, the Cornfield Co-op Fellowship, the Bob Harding & Lois Claxton Humanities and Social Sciences Endowment Fund, Parks Canada, and UWaterloo’s Fine Arts department.

Tuesday's notes

Research Impact Canada's May Dr. RIC session takes place on Thursday, May 14, from 1:15 p.m. to 2:15 p.m. online. "Join us for the May Dr. RIC call, where Cara Evans and Arina Bogdan (Waypoint Centre for Mental Health Care) will share insights on STREAM Lab—a rapid evidence synthesis service that mobilizes knowledge on mental health and substance use to decision-makers across Ontario," says a note from the Office of Research. "We’ll also hear from Kathryn Dong (U Alberta) who will speak about social accountability and how it impacts the communities we serve."

A practical founder working session for students, early-stage founders, and startup operators interested in entrepreneurship, customer discovery, positioning, pricing, sales channels, and go-to-market decisions will take place on Thursday, May 28.

"Why Founders Waste Months on the Wrong Decisions and How to Avoid It" focuses on a common founder problem: losing months by building, selling, or scaling around weak market signals. Participants will use real startup examples to learn how to identify high-risk decisions earlier, separate real market signal from polite feedback, and decide what to test before committing more time and resources.

The session is led by Vivian Wang, founder of Growth Partner AI, which was recently recognized as part of Scale Up Canada’s Waterloo 50.

Register on Luma.