As many U of T students were wrapping up classes in March, first-year engineering student Hannah Le and her team won the third Pioneer Tournament — a worldwide competition that rewards participants for developing innovative ideas — for their project that used machine learning to identify and understand human biomarkers that predispose individuals to certain diseases.

Competition participants submit their project online and post weekly progress updates. The project then earns points awarded by contestants, who vote on the updates. After three weeks, the project becomes eligible to win a weekly prize, which is awarded to the team that wins the highest number of points at the end of that week. A project that places as a finalist for three weeks wins the team a larger award.

Le and her team members — Samarth Athreya, 16, and Ayaan Esmail, 14 — earned a top spot on the leaderboard in March and were awarded $7,000 from Pioneer to put toward their project. 

How the team got together

“Samarth, Ayaan and I met each other at an organization called The Knowledge Society in 2017,” wrote Le to The Varsity. The Knowledge Society is a startup incubator that exposes high school students to emerging technologies, such as artificial intelligence (AI), virtual reality, and brain-computer interfaces.

When the three innovators met, Esmail was working on a project that could accurately pinpoint and target cancer cells, while Athreya was working with machine learning models. With Le’s interest in genetics, the three decided to team up and investigate whether there was a way to use metabolic data to predict the onset of a disease.   

“I became incredibly curious on how we can decode the 3 billion letters [of DNA] in every cell of our body to increase human lifespan and healthspan,” wrote Le.

“Inspired by my grandmother who passed away due to cancer, I started asking myself the question: [could] there possibly be a way for us to predict the onset of cancer before it happens, instead of curing it?”

How Le’s team developed a model for predicting the risk of cancer development

At its core, the team’s AI platform uses a patient’s biological information to predict their risk of developing certain forms of cancer.

Metabolites are molecules that play a key role in maintaining cellular function, and some studies have shown that high levels of certain metabolites can signal the progression of lung cancer. But to develop and test their model, the team needed a large amount of metabolic data.

“To overcome such [a] limitation, we had the fortune to reach out to mentors such as the Head of Innovation at JLABS, [a Johnson & Johnson incubator], for further guidance and advice,” wrote Le. “As our team cultivates a stronger database, we would be able to produce more reliable results.”

“As teenagers we were far from experts [in] the field — but we were really hungry to learn,” added Le.

As participants of the Pioneer Tournament, Le and her team received the opportunity to select a board of virtual advisors, who would provide guidance for their project.

“I recalled contacting Josh Tobin at OpenAI to ask him about the use of synthetic data in genomics research,” wrote Le. “[That enabled] us to understand both the strengths and weaknesses of such [an] approach, allowing us to pivot on what models to implement.”

The competition as a learning experience

Le remembers the Pioneer Tournament as an exciting chance to learn about different machine learning models and what made them effective as well as other projects that fellow participants were working on, all while attending courses at U of T.

“First year was an interesting journey of challenging course content, intertwined with unexpected personal growth,” wrote Le. “I learned how to strike a balance between working on personal projects, meeting interesting people, while completing my school work.”

And while Le is intrigued by the intersection of machine learning and genomics, she wrote, “I hope to keep an open mind and continue to be curious about the world around me.”

A team of U of T students won first place at the Mac Design League Designathon 2019 hosted at McMaster University in January, with a design for shielding cameras on surveillance aircraft.

The team, dubbed “The Avengineers,” was composed of undergraduate students Nick Bajaikine, Kyle Damrell, Christopher Tong, and Mubtaseem Zaman, each enrolled in engineering programs.

Over a period of 36 hours from January 19–20, they competed against 243 other students from across Ontario to solve real-world problems presented by industry sponsors of the event.

McMaster’s designathon was originally created in response to the popularity of hackathons.

Whereas hackathons focus on programming skills, the Mac Design League sought to create a multidisciplinary outlet for students to showcase their talents in mechanics and design. Featured challenges included designing EpiPens and lunar rovers.

The victorious U of T team was tasked with designing a way to shield cameras attached to the bottom of military planes from being damaged in the event of landing-gear failure or ‘soft-crash’ landing. The challenge was posed by aerospace imaging firm L3 Wescam,  an aerospace imaging firm which works with defence and military agencies around the world, posed the challenge to the team.

“Intuitively, the idea that comes in [first] is to make a mechanism that pulls the camera inside the aircraft and keeps it safe, like an elevator mechanism,” said Zaman in an interview with The Varsity. Zaman explained that many of their opponents attempted solutions along these lines. However, the team eventually noticed that such a solution might not be easy to adapt to other planes.

Zaman said that their next idea, which involved releasing the camera when it senses an impending crash landing was also scrapped, due to concerns that the camera might be lost or could injure someone if dropped.

“Then I came up with an idea: how about I roll the camera around the body of the aircraft, to the top, before landing? So you can attach something like a roller-coaster rail around the body of the aircraft, and the camera will go up from the bottom on top to save itself.”

Zaman added that this would not only save the camera in worst-case scenarios, but increase its functionality by allowing for surveillance photography from multiple angles.

This flexible design ultimately netted the team the top prize, as well as additional opportunities from competition sponsors.

The team was invited to present their unique solution to company executives at L3 Wescam’s secure facility, and were each awarded a $300 gift card from sponsor 3D Printing Canada. 

Moving forward, the Avengineers want to bring similar opportunities to U of T for students to showcase their design and engineering skills. “I was so inspired that I decided to make a consulting club at U of T,” said Zaman. “There are a bunch of consulting clubs, [but] they are mostly business consulting clubs. What I am trying to do is to make an engineering design consulting club. Our plan is to ask for problems from different industries and voluntarily, as a student team, solve those problems.”

Plans are also in early development for U of T’s own designathon, to be held next year.

In the meantime, Zaman gave advice for budding designers on the fence about attending competitions: “Even if you don’t have the skills, don’t worry. Just go there. Just participate. You will learn a lot.”

Netra Rajesh is an undergraduate Engineering Science student specializing in Biomedical Systems Engineering. She is currently on her Professional Experience Year (PEY) at the Massachusetts Institute of Technology where her research lies at the crossroads of nanomedicine, medical engineering, and oncology.

At 14, Rajesh designed an experiment and attempted to conduct the research in a lab, though she was unable to due to her age. Instead, she built a laboratory environment in her basement.

Rajesh is particularly interested in cancer research. In an email to The Varsity she wrote, “We are in desperate need for promising treatments and targets.”

Rajesh has pursued various internships focused on cancer research. At age 16, she got her first lab placement at the Sunnybrook Research Institute where she researched genetic therapies.

Rajesh had the chance to network and learn about different opportunities in other institutions during the internship.

Rajesh has participated in Engineering Science Summer Research Programs, first doing research on cancer radiotherapy at the National University of Singapore in the summer following her first year. There, she collaborated with oncologists and radiotherapists in order to “test a patient’s response to radiation using microplates and microfluidic platforms.”

Last summer, she participated in an exchange at the California Institute of Technology where she “was able to assist in designing, building and testing a novel bioreactor device for cancer vaccine production.”

Rajesh is currently working on designing nanoparticle vehicles with the goal of cancer therapeutics delivery.

Other areas of research she is interested in include engineering the immune system using nanomedicine to harness the body’s defence system, and clinical research.

One of the biggest challenges Rajesh identified for girls and women interested or pursuing an education in Science, Technology, Engineering, Arts, and Mathematics was “finding real-world opportunities for hands-on learning.”

Earlier this year, Rajesh was one of the speakers at TEDxMississauga, where she spoke about the importance of acquiring experience working with companies, and the importance of mentors and role models in academic or industrial labs as they encouraged her to “pursue challenges.”

While academic courses teach the foundational knowledge, placements in labs provide the opportunity to apply acquired knowledge. Furthermore, students can explore their areas of interest  and network with professionals.

“I believe that pursuing real-world, hands-on learning in STEAM enables multidisciplinary thinking — ultimately preparing us to solve problems that we don’t even know exist!”

The Faculty of Applied Science & Engineering’s new Artificial Intelligence (AI) minor and certificate programs will be available for enrolment by students in the Core-8 and Engineering Science programs in January.

Students are required to fulfil three full course equivalents (FCE) to complete the minor, while students enrolled in the certificate program must complete 1.5 FCEs. Since a few of the courses required for the program fall out of the scope of students’ main discipline, some students may need to take extra courses to complete the requirements.

Students who complete the minor or certificate will receive a notation on their transcript.

Professor Jason Anderson from the Department of Electrical & Computer Engineering, a key figure behind the program, explains that all students will be required to take one foundational course, as well as courses in data structures and algorithms relevant to AI and machine learning.

Students enrolled in the certificate program can choose between traditional AI or machine learning for specialization. Students in the minor will learn about both and choose an additional area of interest to specialize in, such as computer vision or natural language processing.

Anderson explains that machine learning is one aspect of AI. In traditional AI, computers can make decisions on their own. In machine learning, computers use and learn from data to make decisions.

“The computer is actually trained to recognize images in different categories. In traditional AI, that’s more encoded in rules.”

“Students who take the certificate or minor will have hands on experience applying AI and machine learning techniques to real engineering problems,” says Anderson. In addition, students will be exposed to the ethical questions surrounding AI technology.

While there is currently no specific Professional Experience Year Co-op (PEY) opportunity for the AI minor and certificate, Anderson says that many students are already working with AI to some extent during their PEY.

Anderson also notes that AI ties in with other engineering disciplines in several ways. For instance, AI technologies can be used by civil engineers to understand traffic patterns or by chemical engineers in drug discovery.

In his own field, Anderson notes that AI technology is being used in computer-aided design tools that “create complicated digital circuits” in order “to produce higher quality designs, for example, that use less silicon area, that use less power, operate faster, to make predictions.”

“We want students who can research in this area but also have applied AI techniques,” says Anderson. Through this program, he hopes to foster engineering talent that will lead students to create startups, develop new AI technology, or further their education through graduate studies.