Matt Hintsa

How U of T's computer science dept. scrambled to give students from Ukraine a 'normal summer'

Christopher.Sorensen — Mon, 24 Oct 2022 18:09:14 +0000

How U of T's computer science dept. scrambled to give students from Ukraine a 'normal summer'

Christopher.Sorensen Mon, 10/24/2022 - 14:09

Cutline

Their studies interrupted by war, more than two dozen students from universities in Ukraine conducted research with faculty members in the department of computer science this past summer (photo by Luke Wheeler)

Matt Hintsa

Topic

Global Lens

Computer Science

Faculty of Arts & Science

Global

Ukraine

Vector Institute

Yuliia Kholodetska’s computer science studies at Lviv Polytechnic National University were interrupted on Feb. 24, 2022, but this time it had nothing to do with global pandemic – Russia had invaded Ukraine, throwing daily life into disarray. Similarly, Roman Burakov, a computer science student at National University of Kyiv-Mohyla Academy (KMA), was awoken in Croatia by an early morning call from a friend in Kyiv who reported that missiles were striking across the country.

“It was quite a shock,” Burakov recalls. “It’s really impossible to imagine that you’re here, it’s good. But somewhere in Ukraine, your relatives are under attack.”

Yuliia Kholodetska

Thousands of kilometres away, faculty members, researchers, students and staff across the ��߲ݴ�ý’s three campuses immediately began working on ways to help. In the computer science department in the Faculty of Arts & Science, faculty members, post-doctoral researchers and graduate students stepped into action by strategizing about ways they could play a role in helping students like Kholodetska and Burakov whose studies were interrupted by the invasion.

Professor Michael Brudno envisioned a program built on the structure of the department of computer science’s long-running Undergraduate Summer Research Program – the program’s application and matching systems could be repurposed relatively easily and faculty members were eager to contribute by supervising students and contributing their own research funds.

A collaborative effort was soon underway. The computer science department and Vector Institute for Artificial Intelligence agreed to provide additional financial backing. The Centre for International Experience advised on immigration-related considerations. Innis College co-ordinated housing. And graduate students and post-doctoral fellows in the department of computer science interviewed more than 80 student applicants over the course of two weeks, selected from over 200 applications.

“In three months, we were able to pull this off,” says Brudno. “It was really a huge amount of work for lots of people across the university to actually make it happen.”

Roman Burakov

Students admitted to the program began to arrive in May, with 21 undergraduate and five graduate students participating in the program. The computer science effort was one of several initiatives at the university focused on supporting students from Ukraine. U of T Mississauga also ran a summer program, the Faculty of Arts & Science led an exchange initiative with (KMA) that was supported of a $3.2-million donation by the Temerty Foundation and U of T supported many of the displaced students with Scholars at Risk awards.

As for Burakov, he joined a wearable robotics group headed by Alex Mihailidis, professor in the Temerty Faculty of Medicine’s department of occupational science and occupational therapy and the Institute for Biomedical Engineering, who is cross-appointed to the department of computer science. In that group, Burakov worked closely with post-doctoral researcher Brokoslaw Laschowski, a Ukrainian-Canadian scientist who was also involved in the effort to develop the summer program.

"Imagine you have someone who lost their leg and needs a prosthesis. Our goal is to make prostheses simulate a healthy leg,” Burakov says.

Kholodetska, meantime, joined the lab of Professor Marsha Chechik, former chair of the department of computer science and acting dean of the Faculty of Information, to work on a software engineering project related to bonded satisfiability checking.

In addition to conducting research with a faculty member, students in the program also received instruction in professional English speaking and writing and participated in a range of social events. That included a camping trip to Algonquin Provincial Park, where students got an introduction to canoeing and exploring the Canadian wilderness.

“One of the bigger goals was to give the students a normal summer – or at least as normal as possible, given the situation,” said Brudno.

Kholodetska concurred.

“We can feel how it is to be a student – to go to university, to attend lecture – because COVID and war have destroyed this ability for us,” she says.

Despite having landed in a safer place to continue their studies, Burakov and Kholodetska say the safety of their families and friends still looms large.

Michael Brudno

Kholodetska receives a push alert on her phone every time an air raid siren is activated where her loved ones reside. She and Burakov both message their families daily.

“At one point, I hadn’t heard from my mom for three weeks straight,” Burakov says, citing telecommunications outages in Ukraine.

Looking toward the future, Brudno says there’s interest in using the model of this year’s program to serve the needs of students in other parts of the world who experience similar interruptions to their studies.

“While our program was spurred by the acute crisis in Ukraine, we are re-working the program to be accessible to students displaced by conflict anywhere in the world,” he says. “This is not just a moral imperative, but also benefits our community – we get introduced to talented students and researchers, and we can build strong new collaborations with top universities around the world.”

Most of the students who came through the summer program are staying in Toronto, including both Kholodetska and Burakov. Ten students have been admitted into the department’s graduate program, and many others are continuing as exchange students, taking classes at U of T and continuing their research.

“The program turned out very productive both for my partner and me,” says Burakov. “Despite the short time frame, my colleague Alex (also from the program) and I achieved great results from our research, which resulted in the paper submission to the most prestigious robotics conference: ICRA 2023.”

U of T computer science grads reflect on their studies – and the profs who inspired them

geoff.vendeville — Thu, 24 Jun 2021 16:25:11 +0000

U of T computer science grads reflect on their studies – and the profs who inspired them

geoff.vendeville Thu, 06/24/2021 - 12:25

Cutline

Aniket Kali, Tina Li, Murad Akhundov and Danya Lette shared their thoughts on their studies in computer science at U of T before their graduation this week (photos courtesy of the students)

Matt Hintsa

Topic

Our Community

Computer Science

The ��߲ݴ�ý’s department of computer science, in the Faculty of Arts & Science, recently asked several of its new graduates to reflect on their time at the university as they on a new chapter in their lives.

Some discussed their faculty mentors while others described the courses that changed the way they think about computer science and the world.

Here’s what four of the department’s newest grads had to say:

Aniket Kali

Computer science specialist

University College

Kali thought he wouldn't study anything math-related after high school, but Associate Professor, Teaching Stream, Dan Heap's introductory computer science class helped change his mind.

“I've been quite fortunate to get to know a handful of professors who have been deeply supportive both in and out of academics. I was also lucky to build friendships of great depth, and meet people who seriously influenced the way I think about myself and society. It's hard to point to any one person or event that single-handledly shaped my education, though – much more the sum.”

Tina Li

Computer science specialist and statistics major

Victoria College

Li chose to major in computer science because she wanted to “create helpful tools myself and make a positive, real-world impact.”

“[Assistant] Professor Ishtiaque Ahmed helped me find my research interests, supervised my research and directed me to a clearer academic path. I met Professor Ishtiaque through a research scholarship application at the end of my second year. Since then, I have worked with Professor Ishtiaque and his PhD student at the DGP Lab. Under his mentorship, I realized my passion for using technology to tackle social challenges, gained the complete research experience and decided to pursue graduate studies in the area of human-centered technologies.”

Danya Lette

Computer science specialist

Victoria College

Lette, who has a previous degree in philosophy, initially didn't consider herself a “computer person.” But as she learned more programming, she became more and more interested in computer science. “Curiosity is a powerful motivator,” she says.

“I am very grateful for all the computer science faculty members who were able to convince me that you can learn how to learn, you can learn how to study, you can learn how to take tests, you can learn how to communicate effectively – soft skills are skills, not ingrained personality traits. These lessons carried me through my degree and gave me the confidence to take on a lot of really rewarding challenges.”

Murad Akhundov

Computer science specialist

University College

With Toronto being a major tech centre, Akhundov thought it was logical to study computer science at U of T. After interning at Okta Toronto last summer, he recently got a job with the digital security company as a software engineer.

“I found the first couple of theoretical computer science courses were very important. I already knew how to code before coming to university, but the theoretical side of [computer science] was entirely new to me. The courses really taught me how to think differently, and how to communicate my reasoning in a very logical, precise and clear way. I think it is great that you get to take these courses very early on at U of T – this is not the case at all schools, unfortunately.”

Read a ll of the computer science graduate profiles

Prize-winning U of T student team uses AI to beat banana blight

Christopher.Sorensen — Wed, 17 Feb 2021 17:16:34 +0000

Prize-winning U of T student team uses AI to beat banana blight

Christopher.Sorensen Wed, 02/17/2021 - 12:16

Cutline

A team of U of T undergraduate students earned top prize in a student-organized global AI competition for their plan to use AI to forecast infections of black Sigatoka, a fungal disease that blackens bananas from the inside out (photo by Neil Palmer)

Matt Hintsa

Topic

Global Lens

Temerty Faculty of Medicine

Artificial Intelligence

Climate Change

Computer Science

Faculty of Arts & Science

Global

Research & Innovation

Undergraduate Students

Fuelled by a changing climate, plant pathogens encounter increasingly favourable conditions to spread and wreak havoc on global crop yields. But could artificial intelligence help predict the spread of disease, buying farmers valuable time to take preventative measures?

As part of an effort that took top honours and a $20,000 prize in the recent ProjectX global undergraduate research competition, a team of ��߲ݴ�ý students proposed the use of a new machine learning architecture to forecast infections of black Sigatoka, a fungal disease that blackens bananas from the inside out.

ProjectX, the brainchild of the U of T Artificial Intelligence student group, challenged teams of undergraduate students from universities around the world to use machine learning to address the impacts of climate change. The competition, which concluded in December, was divided into three categories: infectious disease; weather and natural disaster prediction; and emissions and energy efficiency.

ProjectX organizers maintained a strict firewall between themselves and U of T student competitors to ensure fairness in the competition.

The team from U of T that emerged victorious in the infectious disease category included computer science students Yuchen Wang, Matthieu Chan Chee, Ziyad Edher, Minh Duc Hoang and Shion Fujimori; as well as Sornnujah Kathirgamanathan, a molecular genetics and microbiology student in the Temerty Faculty of Medicine.

Operating remotely from Toronto, Vancouver, Japan, and Vietnam, the team members focused on devising a neural network to forecast the infection risk of black Sigatoka.The fungal disease can have devastating consequences for farmers, decreasing yields and driving up costs. The Food and Agriculture Organization of the United Nations reported that, between 2007 and 2009, St. Vincent and the Grenadines faced a 90 per cent decline in banana crop production due to the disease.

Left to righ (top row): Ziyad Edher, Yuchen Wang, Sornnujah Kathirgamanathan, (bottom row) Matthieu Chan Chee, Minh Duc Hoang and Shion Fujimori.

While mathematical models of infectious diseases already exist, it is difficult to incorporate climate effects into those models.

“By extending previous work on Neural Ordinary Differential Equations (Neural ODEs), we devised a new machine learning architecture incorporating weather patterns to predict black Sigatoka infections one month into the future,” says Chan Chee.

Neural ODEs are a family of deep neural network models invented in 2018 by U of T researchers – including computer science PhD student Jesse Bettencourt, the team’s mentor in the competition.

“Our mentor Jesse, being among the inventors of Neural ODEs in 2018, provided amazing support and insight throughout the project,” says Chan Chee.

ProjectX teams also had a network of climate and machine learning experts that they could reach out to throughout the competition.

Reflecting on their experiences, members of the U of T team shared their advice for students who may want to get involved with competitions like ProjectX in the future.

“I had never heard of machine learning before coming to Canada,” says Chan Chee, who is originally from Mauritius. “Regardless of your background, don’t be afraid to try new things and go for it.”

Wang, for her part, encouraged female peers to step up.

“Women in computer science – go take a leadership role and get involved in technology development,” she says. “Don’t be afraid to shine your light. You are more powerful than you’d imagine.”

Kathirgamanathan similarly advises fellow students not to underestimate their abilities.

“In a typical year, there is absolutely no way I would have found myself in a competition like ProjectX simply because I have close to zero experience with AI,” Kathirgamanathan says. “However, I was surprised at the contributions I could make with my limited programming experience.”

Chan Chee says the team was “ecstatic” to see the results of its work contribute to the machine learning community.

“As six undergraduate students, this success encouraged us to continue advancing the technology for humanity in the future.”

To err is not just human: U of T researchers develop AI that plays chess like a person

geoff.vendeville — Wed, 27 Jan 2021 15:49:16 +0000

To err is not just human: U of T researchers develop AI that plays chess like a person

geoff.vendeville Wed, 01/27/2021 - 10:49

Cutline

(Photo by Tuchkovo/Getty Images)

Matt Hintsa

Topic

Breaking Research

Artificial Intelligence

Computer Science

Faculty of Arts & Science

For more than a decade, advances in artificial intelligence have made computers capable of consistently defeating humans in chess. But despite their clever moves, they've made relatively lousy teachers – until now.

By trading raw power for a more human-like playing style, a new neural network chess engine developed by ��߲ݴ�ý researchers and collaborators is poised to make for a more effective learning tool and teaching aid.

The Maia Chess engine can accurately predict the way humans of different skill levels play chess and can even point out the mistakes a player should work on to improve their game.
With this new chess engine, the researchers open the door to better human-AI interaction in chess and other domains

Ashton Anderson, assistant professor in the department of computer science, and PhD student Reid McIlroy-Young collaborated on the project with Jon Kleinberg, a professor of computer science and information science at Cornell University, and Siddhartha Sen, principal researcher at Microsoft Research.

The new chess engine emerged from their paper, “Aligning Superhuman AI With Human Behavior: Chess as a Model System,” presented last year at the Association for Computing Machinery SIGKDD Conference on Knowledge Discovery and Data Mining.

Faced with a problem to solve, self-trained AIs can take a very different route to a solution than a human might. On top of that, a human can also have a hard time learning how the AI completed its task. To bridge the gap in understanding, the researchers attempted to model the individual steps humans take to solve a task, rather than focus on overall human performance.

For Maia, the researchers asked themselves: Instead of designing an AI that focused on the task of playing chess well, what if we designed one that would play chess well in a human-like manner?

“If we algorithmically captured human style, human ability, and crucially, human errors, maybe we would have a chess AI that was much easier to learn from and play with,” Anderson explains, adding that this approach could be expanded to other domains of AI research.

AI first demonstrated its superiority over human chess players in 1997 with IBM's Deep Blue beating then-world champion Garry Kasparov. Now, desktop computers can run chess engines even stronger than Deep Blue.

The U of T researchers trained nine versions of Maia, corresponding to nine different chess skill levels. At each level, the deep learning framework was trained on 12 million online human games.

By training on games played by humans instead of training itself to win every time, Maia can more closely match human play, move by move, the researchers say.

Other attempts to develop chess engines that match human play have been somewhat effective, but Maia’s performance sets the bar higher, they added.

Versions of two popular chess engines, Stockfish and Leela Chess Zero, match human moves less accurately and without always faithfully mimicking human play at specific skill levels. Maia is built on the open-source AlphaZero/Leela Chess framework and is trained on real human games, rather than games played against itself. It achieves higher accuracy than other engines, correctly predicting human moves more than half the time.

In addition to predicting smart moves, Maia is also adept at predicting human mistakes, even egregious ones or “blunders.” This can be especially helpful for players looking to improve.

“Current chess AIs don’t have any conception of what mistakes people typically make at a particular ability level. They will tell you all the mistakes you made – all the situations in which you failed to play with machine-like precision – but they can’t separate out what you should work on,” Anderson says. “Maia can identify the repeated mistakes you make that are typical of your level, and that you could work on to improve. No other chess AI has that ability.”

The researchers are currently developing a personalized version of Maia that can play like a particular person.

“This will make our training tools even more powerful: you could have your own personalized AI that plays like you do, and it could point out the mistakes you make that it predicts you will make – in other words, mistakes you make so often that it correctly guesses you will do it again,” Anderson explains.

Looking ahead, the team plans to conduct a “chess Turing test” to see if human players can tell the difference between a human opponent and Maia.

Ultimately, the researchers hope Maia demonstrates the value in considering the human element when designing AI systems.

“We want to show that AI systems can be easier to work with and learn from if they are built with human interaction, collaboration, and improvement in mind,” Anderson says.

Chess players can face off against three versions of Maia on the free online chess server Lichess: Maia 1100, Maia 1500, and Maia 1900.

Ashton Anderson was supported in part by an NSERC grant, a Microsoft Research Award, and a CFI grant. Jon Kleinberg was supported in part by a Simons Investigator Award, a Vannevar Bush Faculty Fellowship, a MURI grant, and a MacArthur Foundation grant.

Four U of T computer science researchers named CIFAR AI Chairs

Christopher.Sorensen — Wed, 20 Jan 2021 17:46:00 +0000

Four U of T computer science researchers named CIFAR AI Chairs

Christopher.Sorensen Wed, 01/20/2021 - 12:46

Cutline

(photo by Daria Perevezentsev)

Matt Hintsa

Topic

Our Community

Temerty Faculty of Medicine

Artificial Intelligence

CIFAR

Computer Science

Faculty of Applied Science & Engineering

Statistical Sciences

University Health Network

Vector Institute

Four ��߲ݴ�ý computer scientists have been named CIFAR AI Chairs in recognition of their innovative artificial intelligence research in areas that benefit society.

Michael Brudno, David Duvenaud, Rahul G. Krishnan, and Richard Zemel of the department of computer science in the Faculty of Arts & Science conduct research with a wide impact, from streamlining workflows in clinical care settings to automating the design of chemicals.

Each is also a member of the Vector Institute for Artificial Intelligence, launched in 2017 through a partnership between U of T, federal and provincial governments and industry.

CIFAR AI Chairs are provided with five years of dedicated funding to support their research. The program is a cornerstone of the CIFAR Pan-Canadian AI Strategy to recruit the world’s leading AI researchers to Canada and retain existing talent.

“U of T is recognized as a home to pioneering AI research, both within Canada and globally. The announcement of four computer science faculty as new CIFAR AI Chairs reinforces this strength, and will propel societally important research to new heights,” says Marsha Chechik, professor and chair of the department of computer science.

The four computer scientists are the latest additions to a group of U of T researchers who have been named CIFAR AI Chairs since the inaugural cohort was announced in 2018.

“With today’s announcement, we have now attracted more than 100 of the world’s top AI researchers to Canada. That’s an important milestone for the Pan-Canadian AI Strategy as it continues to strengthen Canada’s leadership in responsible AI, reflecting our values of human rights, inclusion and diversity,” says François-Philippe Champagne, Canada’s minister of innovation, science and industry.

In all, CIFAR named 29 new AI Chairs across Canada. The researchers are based at one of three AI centres across the country: Amii in Edmonton, Mila in Montreal and the Vector Institute in Toronto.

U of T’s new CIFAR AI Chairs at a glance:

Michael Brudno is a professor in the department of computer science in the Faculty of Arts & Science. He is also the chief data scientist at University Health Network and the scientific director for HPC4Health, a private computing cloud for Ontario hospitals. His research focuses on improving the ways that computational methods are used to analyze medical data, with the goal of streamlining clinical workflows and enabling faster and better treatments.

David Duvenaud is an assistant professor in the departments of computer science and statistical sciences in the Faculty of Arts & Science. Before joining U of T, he did post-doctoral research at Harvard University and received his PhD from the University of Cambridge. His research focuses on constructing deep probabilistic models to help predict, explain and design things.

Rahul G. Krishnan will be an assistant professor in the department of computer science in the Faculty of Arts & Science and the department of laboratory medicine and pathobiology in the Temerty Faculty of Medicine in fall 2021. His research focuses on building novel machine learning algorithms to automate clinically meaningful problems, and to advance our understanding of human health. He recently completed his PhD at MIT and is currently spending a year as a senior researcher at Microsoft Research in New England.

Richard Zemel is a professor in the department of computer science in the Faculty of Arts & Science. He also serves as research director at the Vector Institute. His research contributions include foundational work on systems that learn useful representations of data without any supervision; methods for learning to rank and recommend items; and machine learning systems for automatic captioning and answering questions about images.

U of T students host global AI competition to address climate change

Christopher.Sorensen — Mon, 02 Nov 2020 14:24:02 +0000

U of T students host global AI competition to address climate change

Christopher.Sorensen Mon, 11/02/2020 - 09:24

Cutline

Organizers of ProjectX say they want to apply machine learning to areas students may not otherwise encounter while pursuing a career in AI – all while getting more students involved in climate change solutions (photo by NASA Goddard)

Topic

Faculty of Arts & Science

Global

Graduate Students

Sustainability

Undergraduate Students

Students from the ��߲ݴ�ý are the driving force behind a global undergraduate research competition that challenges teams to apply machine learning solutions to the impacts of climate change.

Dubbed ProjectX, the remote competition is organized by the U of T Artificial Intelligence student group (UofT AI) and is currently underway during the fall term. Upon final judging, winning teams will share a $70,000 prize pool.

Shardul Bansal and Elias Williams, co-presidents of the student group, lead a dedicated group of approximately 30 student organizers.

Bansal, a fourth-year student studying computer science in the Faculty of Arts & Science, says student organizers had been interested in hosting a competition that improved upon the structure of a typical hackathon.

“We love the opportunity to collaborate with other students, but we felt that the hackathon model didn't give students enough time to come up with meaningful, impact-driven ideas,” says Bansal.

Williams, a fourth-year student studying computer science and biochemistry, adds that he wanted the competition to do more than help a company improve its bottom line.

“As someone who studies artificial intelligence, that’s a depressing thought because we talk about how it has the potential to become the most powerful tool that humans have made, yet we are using it to expedient ends,” he says.

Reflecting on the choice to focus this year’s inaugural ProjectX on climate change, Williams says the intent was to apply machine learning to areas that students may not otherwise encounter while pursuing a career in AI – all while getting more students involved in climate change solutions.

“I think [climate change] is the most urgent problem that we have right now,” Williams says. “I think that pursuing technological solutions is the way that we’re going to get out of the hole that we’re in.”

After observing that students across North America were eager to improve their machine learning knowledge, the ProjectX organizers pitched the idea of the competition to similar student groups at other universities.

“We received positive reactions all around,” recalls Bansal. “We started picking up some steam and had some universities reach out, asking to participate. As organizers, we were happy to accommodate as many bright minds as we could to try to solve this open-ended problem.”

Participating teams hail from top universities across Canada and the United States, but also from countries as far away as Chile, Brazil, Germany and Poland. Each was asked to indicate their preference of three focus areas under the umbrella of climate change: infectious disease; weather and natural disaster prediction; or emissions and energy efficiency.

Once assigned a focus area, teams spoke with experts to find out more information about the problem they intended to address. The teams are now preparing research papers that will ultimately be judged by experts in each of the focus areas. Winners will be announced in mid-December.

The competition was developed with guidance from UofT AI’s faculty adviser Roger Grosse, an assistant professor in the department of computer science and faculty member at the Vector Institute for Artificial Intelligence.

The ProjectX organizers say a broad network of support helped make the competition a reality.

“Something that's been very rewarding is the reaction of the academic community and the industry community to ProjectX,” says Lana El Sanyoura, a first-year master’s student in computer science who graduated from U of T’s undergraduate computer science program earlier this year and serves as a member of the research team on ProjectX’s organizing committee.

She adds that partners have contributed everything from a custom-built collaboration platform to data sets and computing power.

“There’s a large network of people who use machine learning tools to tackle climate change-related issues,” agrees Williams, noting that approximately 50 researchers have contributed their time and expertise to the competition.

ProjectX is supported by a range of partners, including Google, IBM and U of T’s Schwartz Reisman Institute for Technology and Society.

El Sanyoura says she is excited by the prospect of expanding research opportunities for undergraduates while also opening doors for future careers through the networking and mentorship that happen throughout the competition.

Williams says he views ProjectX as a way to demonstrate the strength of U of T’s undergraduate computer science program to peers across North America in hopes of spurring more collaboration in the future.

“Fundamentally, I think ProjectX is about educating people to start thinking in the way we need to think in order to solve the problems that we need to solve,” he says.

UofT AI plans to host a global, virtual AI conference for undergraduates on Jan. 15 and 16 that focuses on applying machine learning to the world’s most pressing issues.

Matt Hintsa

How U of T's computer science dept. scrambled to give students from Ukraine a 'normal summer'

U of T computer science grads reflect on their studies – and the profs who inspired them

Read all of the computer science graduate profiles

Prize-winning U of T student team uses AI to beat banana blight

To err is not just human: U of T researchers develop AI that plays chess like a person

Four U of T computer science researchers named CIFAR AI Chairs

U of T’s new CIFAR AI Chairs at a glance:

U of T students host global AI competition to address climate change

Read a ll of the computer science graduate profiles