Tag: Canada

Simon Fraser University’s Invent the Future is Canada’s first and only summer enrichment program focused on bringing Artificial Intelligence (AI) expertise, community, and mentorship to young women in grade 9 to 12. During this AI scholars program, participants from across Canada work on team projects and connect with mentors and industry experts in a supportive yet challenging environment.

The program is hosted in association with AI4ALL, a non-profit organization working to increase inclusion and diversity in the field of AI. SFU was one of six universities participating in AI4ALL’s official rollout in 2018 and is the only participating university in Canada.

The program normally takes place in person at SFU’s Burnaby campus, but given the challenges of COVID-19, has taken place virtually since 2020, which has in fact allowed the program to attract participants from locations outside of British Columbia. “We’re thrilled that the virtual format allowed us to reach more students than ever before,” says program co-director and computing science professor Angelica Lim.

Despite the format change, the goals of the program remained. “We wanted to maintain four main goals of our program: building community, meeting role models, gaining expertise in AI and developing a portfolio project,” says Lim. To ensure the program was accessible for all participants, the organizers provided laptops to those who needed it.

While some students join the program with previous experience with AI, others are just getting started in the field. “It’s a really inclusive community where no one judges you if you don’t know certain things,” says Geri Vaflor, a participant who moved to Vancouver from the Philippines just a few months ago. “We’re here to learn, not to compete.”

The community aspect is what encourages alumni mentors like Helen Geng to give back to the program by sharing their knowledge with the participants. “I just want to be in an environment where I’m surrounded by like-minded people,” says Geng, who did not have prior experience with AI before joining the program in 2019. She is now preparing to study biomedical engineering at the University of Minnesota and says that she was influenced by the Invent the Future program when choosing her research path.

This influence can be attributed to the AI research and industry experts that the participants hear from as part of the program. Many of these talks are shared publicly as part of the program’s AI Week lecture series and also serve to show the participants what the future could have in store for them.

“If you go to people that are younger and show them that this can be in their career path, you can change who gets to go to grad school and who gets to be an amazing researcher,” says Sanja Fidler, a professor at the University of Toronto and presenter during the event. “This is the way that the Invent the Future program is changing the world.”

New, human-centred, visual computing and computer graphics technologies offer innovative ways for machines to generate functional and engaging 3D content for computer-aided designs, smart homes, and in simulated learning environments for training doctors, military personnel—even autonomous vehicles.

At the forefront of these technologies is Simon Fraser University computing science professor Richard Zhang. A visual computing researcher, he specializes in developing computer graphics and computational methods for understanding, processing and generating visual data.

In particular, he’s exploring how we might use machine learning and shape analysis to automatically generate 3D objects and spaces optimized for human interaction. To do this, however, the machines must first “understand” how humans interact with their environment and how objects perform their functions.

“Learning to generate is at the heart of artificial intelligence (AI), and the human element must be there,” says Zhang. He focuses on 3D data—such as furniture shapes in smart homes, industrial models for design and production, and scene environments—for virtual and augmented reality.

His first step is to develop methods and algorithms that examine how humans interact with objects to reveal their functions. For example, how a person interacts with a chair and how the chair supports the person. He and his collaborators then capture these human-object interactions to learn how to generate functional and engaging 3D content.

These 3D objects and scenes can then be applied to design and manufacturing, or to create simulated environments in which doctors, military personnel, machine operators and others can learn new skills.

Zhang is currently working with Autodesk, a leader in 3D design software, to transform 3D machine-generated models so they can be more easily manufactured using traditional fabrication methods such as computer numerical control (CNC) and 3D printing.

He is also working with Google to train deep neural networks to understand, predict and generate 3D shapes in an interpretable way. This collaboration led to a research publication that won the Best Student Paper Award at the annual Computer Vision and Pattern Recognition (CVPR) conference, which is renowned for having the highest impact among all computing science conferences.

While Zhang’s expertise is regularly sought for industry projects, his academic contributions are also significant. He has published more than 180 papers, and has been cited more than 10,000 times. And according to csrankings.org, Zhang ranks first among Canadian university researchers for publishing in the top computer graphics publications.

Zhang also helped establish SFU’s Professional Master of Science Program in the School of Computing Science, serving as program director and developing the visual computing concentration.

Now, he has turned his keen mind to the topic of computational creativity, an emerging AI area focused on a machine’s ability to create. It’s a relatively new and unexplored area, but Zhang and his team are determined to find answers.

“Personally, this is my dream problem,” he says. “In one of our earlier works, we mimicked the evolution process where random mutation and crossover operations over 3D shapes led to potentially creative outcomes.”

“With advancements in machine learning, we are exploring means to break the barrier of traditional learning paradigms, which would allow a trained network to venture beyond the training data, gradually but surely, and hopefully lead to creativity.”

SFU’s School of Computing Science boasts one of the strongest computer vision and graphics research groups in the world, according to CS Rankings.

Tuberculosis (TB) is a deadly infectious killer but Simon Fraser University computing science professor Maxwell Libbrecht is determined to fight it — using artificial intelligence.

Antibiotic resistance occurs when the TB bacterium evolves its ability to fight against the antibiotics meant to cure the patient. This poses an increasing challenge for healthcare practitioners. The WHO estimates that by 2050, TB will be responsible for more than 25 per cent of the 10 million annual deaths from drug-resistant infections. And while recent efforts to improve access and adherence to TB treatment have shown some promise, drug resistance could thwart these efforts by limiting the pool of effective treatment options.

The project will combine the researchers’ expertise in infectious disease, machine learning, and genomics to create a new, artificial intelligence platform for understanding and predicting drug resistance in TB. To do this, the team will develop a new computer algorithm that, when given information about the genomics of drug-resistant and drug-sensitive TB bacteria, will learn from the data and make predictions about new samples of the bacteria based on their genetic traits alone.

Libbrecht and his research collaborators are combining their expertise in infectious disease, machine learning and genomics to create an artificial intelligence platform for understanding and predicting drug resistance in TB. To do this, the team is developing a computer algorithm that, when given information about the genomics of drug-resistant and drug-sensitive TB bacteria, will learn from the data and make predictions about new samples of the bacteria based on their genetic traits alone.

Investigating the genomic mechanisms of TB

“We have a lot of information about the human genome, however we do not have the same depth of information about bacteria. We do not necessarily know what gene or gene combination is getting mutated that is granting the TB its resistance. This study may help us better understand these mechanisms,” says Libbrecht.

And, just as the bacteria will continue to evolve over time, so too will the predictions made by the computer algorithm as it learns from new data given to it. New data sets with the genomes of drug-resistant and drug-sensitive TB and other bacteria provided by researchers from the Harvard Medical School and University College London, among others, will advance the algorithm, improve its reliability, and inform the predictions it makes.

It is an important example of applying the latest in artificial intelligence to a pressing public health challenge.

The study could also have important implications for other types of drug-resistant infections such as malaria, gonorrhea, and diarrheal diseases such as Clostridium difficile (also known as C. diff).