Jessica MacInnis

'Queen of the Hurricanes': �山ǿ��'s first alumna of electrical engineering featured in new Heritage Minute

Christopher.Sorensen — Fri, 02 Oct 2020 12:33:12 +0000

'Queen of the Hurricanes': �山ǿ��'s first alumna of electrical engineering featured in new Heritage Minute

Christopher.Sorensen Fri, 10/02/2020 - 08:33

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�山ǿ�� alumna Elsie MacGill, pictured here receiving an honorary degree in 1973, was the first woman in Canada to receive a bachelor’s degree in electrical engineering in 1927 and went on to play a key role in the war effort (photo by Robert Lansdale)

Jessica MacInnis

Topic

Our Community

Alumni

Electrical & Computer Engineering

Faculty of Applied Science & Engineering

Elsie MacGill, an alumna of the University of Toronto, was the first woman in Canada to receive a bachelor’s degree in electrical engineering in 1927 and the world’s first female aeronautical engineer.

In celebration of MacGill’s enduring legacy and contributions to the engineering profession, she has been immortalized in a Heritage Minute, released today by Historica Canada in conjunction with Women’s History Month.

“Elsie MacGill has inspired so many of our alumnae who came after her – including me,” says Professor Deepa Kundur, chair of the Edward S. Rogers Sr. Department of Electrical & Computer Engineering. “We take pride in knowing she is part of our departmental history and that this Heritage Minute will help inspire more young women to follow in her footsteps at ECE.”

Historica Canada is a not-for-profit organization dedicated to enhancing the awareness of Canadian history. It has produced more than 90 Heritage Minutes – bilingual 60-second short films that depict a significant person, event or story in Canadian history. First released in 1991, the videos have become a part of Canadian culture and are shown on television, in cinemas and online.

After graduating from what was then known as the department of electrical engineering, MacGill went on to attend the University of Michigan where she became the first woman to receive a master’s degree in Aeronautical Engineering.

She then enrolled at MIT to continue her post-graduate work in aeronautics and returned to Canada where she became the chief aeronautical engineer at the Fort William plant of Canadian Car and Foundry Company Limited in Thunder Bay, Ont. There, she worked on overall design of the Maple Leaf II, a two-seat, single-engine biplane.

During World War II, the plant was reconfigured to accommodate large-scale production of military aircraft. MacGill led the engineering team that produced the Hawker Hurricane fighter. By 1940, 4,500 staff were producing three fighters each day – in two years, 1,450 Hurricanes were produced – and MacGill had earned the nickname “Queen of the Hurricanes.”

After the war, she founded an aeronautical consulting business and became a leading voice in the women’s rights movement. She was named to the Royal Commission on the Status of Women in Canada and co-authored the report published in 1970. MacGill was bestowed the Order of Canada in 1971 for “services as an aeronautical engineering consultant and as a member of the Royal Commission on the Status of Women.”

�山ǿ�� researchers use mini-computer to remotely monitor COVID-19 patients' blood oxygen

Christopher.Sorensen — Fri, 17 Apr 2020 14:26:45 +0000

�山ǿ�� researchers use mini-computer to remotely monitor COVID-19 patients' blood oxygen

Christopher.Sorensen Fri, 04/17/2020 - 10:26

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Researchers at �山ǿ��'s Faculty of Applied Science & Engineering used a mini-computer to continuously read data from a COVID-19 patient's fingertip probe so health-care workers don't need to physically check the device (photo via Unsplash)

Jessica MacInnis

Topic

Our Community

Coronavirus

Electrical & Computer Engineering

Faculty of Applied Science & Engineering

Graduate Students

Institute of Biomaterials and Biomedical Engineering

Mount Sinai Hospital

Research & Innovation

University Health Network

A team from the University of Toronto’s Faculty of Applied Science & Engineering has created a simple, scalable solution to remotely monitor the vital signs of COVID-19 patients, while preserving vital personal protective equipment (PPE) for health-care workers.

Currently, hospitals use a fingertip probe to monitor the respiratory status of COVID-19 patients. These probes monitor blood oxygen saturation and send the data to bedside monitors that must be read by nursing staff roughly every four hours.

But some patients experience a rapid deterioration in respiratory status that requires even more frequent monitoring, putting additional pressure on staff.

“Because health-care workers need to put on and remove PPE before interacting with patients, this requires considerable time and use of resources,” says Associate Professor Willy Wong in the Edward S. Rogers Sr. department of electrical and computer engineering and the Institute of Biomaterials and Biomedical Engineering, who led the project.

Two weeks ago, clinicians from Mount Sinai Hospital in Toronto reached out to �山ǿ�� Engineering to see if researchers could come up with a solution – quickly – that would help clinicians monitor respiratory probes both continuously and remotely.

In just three days Wong and PhD candidates Bill Shi, Yan Li and Brian Wang put together a proof-of-concept using a Raspberry Pi, a simple and affordable single-board computer. Just a few days later, they were ready to deploy their prototype in a hospital setting.

“The challenge was to find something that was small, affordable and that we could put together very quickly,” says Wong. “I don’t have specific expertise in the area, but as a researcher working in the biomedical engineering group here in ECE, I have experience in the development of medical devices and thought I might be able to help.”

Raspberry Pi is a mini-computer about the size of a credit card. When attached to the fingertip probe, it can continuously read data and communicate via WiFi to a server that Wong’s team deployed.

“The most challenging part has been decoding the data the monitors provide because there are a number of different manufacturers of these probes and each one has its own format,” says Wong. “The students were working day and night to decode the data outputted from these devices.”

One key aspect is the portability of the solution that Wong and his students came up with. “In a hospital setting, we don’t really have the luxury of putting a laptop with wires going everywhere next to each patient,” says Wong. “The Raspberry Pi is something that our undergraduates use a lot in their fourth-year design projects – it just connects by cable to the oxygen saturation probe and then another cable to an outlet in the wall for power. That’s it.”

The new setup enables doctors and nurses to read a patient’s oxygen saturation levels every few minutes or less from a nursing station. The team is working with Mount Sinai and Toronto General Hospital to determine the feasibility and demand for these devices. Their solution is rapidly deployable and scalable to other venues, including emergency hospitals.

“When you put together a solution quickly, there are a few things you need to be mindful of, including concerns like patient privacy and data security, as well as usability of equipment by the front-line medical staff,” says Wong.

Wong adds that it has been a valuable experience for his students.

“This was a quintessentially electrical and computer engineering problem,” says Wong. “I saw the opportunity to help and this group of really smart and hard-working students rose to the challenge.”

�山ǿ�� researcher and global colleagues demonstrate key element of quantum internet

Christopher.Sorensen — Thu, 31 Jan 2019 15:04:32 +0000

�山ǿ�� researcher and global colleagues demonstrate key element of quantum internet

Christopher.Sorensen Thu, 01/31/2019 - 10:04

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�山ǿ�� Professor Hoi-Kwong Lo and his collaborators have performed a proof-of-principle experiment on a key aspect of all-photonic quantum repeaters (photo by Jessica MacInnis)

Jessica MacInnis

Topic

Breaking Research

Department of Physics

Faculty of Applied Science & Engineering

Faculty of Arts & Science

Global

Quantum

Research & Innovation

A University of Toronto researcher is among a global group of experts who have demonstrated, in principle, a device that could serve as the backbone of a quantum internet.

Hoi-Kwong Lo, a professor in the department of electrical and computer engineering in the Faculty of Applied Science & Engineering, and his collaborators have developed a prototype for a key element of all-photonic quantum repeaters, a critical step in long-distance quantum communication.

“An all-optical network is a promising form of infrastructure for fast and energy-efficient communication that is required for a future quantum internet,” says Lo, who is cross-appointed to the department of physics in the Faculty of Arts & Science.

A quantum internet is considered the Holy Grail of quantum information processing, enabling many novel applications including information-theoretic secure communication. By contrast, today’s internet was not specifically designed for security, and it shows: breaches, break-ins and computer espionage are common challenges. Nefarious hackers are constantly poking holes in sophisticated layers of defence erected by individuals, corporations and governments.

In light of this, researchers have proposed other ways of transmitting data that would leverage key features of quantum physics to provide virtually unbreakable encryption. One of the most promising technologies involves a technique known as quantum key distribution, or QKD. QKD exploits the fact that the simple act of sensing or measuring the state of a quantum system disturbs that system. Because of this, eavesdropping by a third party would leave behind a detectable trace, and the communication could be aborted before sensitive information is lost.

Until now, this type of quantum security has been only demonstrated in small-scale systems. Lo and his team are among a group of global researchers who are laying the groundwork for a future quantum internet by addressing some of the challenges of transmitting quantum information over great distances using optical fibre communication.

Because light signals lose potency as they travel long distances through fibre-optic cables, devices called repeaters are inserted at regular intervals along the line. The repeaters boost and amplify the signals to help transmit the information.

But existing repeaters for quantum information are highly problematic. They require storage of the quantum state at the repeater sites, making the repeaters error prone, difficult to build, and very expensive because they often operate at cryogenic temperatures.

Lo and his team have proposed a different approach. They are working on the development of the next generation of repeaters, called all-photonic quantum repeaters, that would eliminate or reduce many of the shortcomings of standard quantum repeaters. With collaborators at Osaka University, Toyama University and NTT Corporation in Japan, Lo and his team have demonstrated proof-of-concept of their work in a paper recently published in Nature Communications.

“We have developed all-photonic repeaters that allow time-reversed adaptive Bell measurement,” says Lo.

“Because these repeaters are all-optical, they offer advantages that traditional – quantum-memory-based matter – repeaters do not. For example, this method could work at room temperature.”

A quantum Internet could offer applications that are impossible to implement in the conventional Internet, such as impenetrable security and quantum teleportation, which takes advantage of the phenomenon of quantum entanglement to transmit information between atoms separated by large distances.

“Our work helps pave the way toward this future,” Lo says.

The research was funded by the Natural Sciences and Engineering Research Council of Canada, among others.

�山ǿ�� researchers train AI algorithms to spot rare diseases with artificial, AI-generated X-rays

Christopher.Sorensen — Mon, 09 Jul 2018 17:33:44 +0000

�山ǿ�� researchers train AI algorithms to spot rare diseases with artificial, AI-generated X-rays

Christopher.Sorensen Mon, 07/09/2018 - 13:33

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Professor Shahrokh Valaee and PhD researcher Hojjat Salehinejad are using machine learning to create simulated chest X-ray images to train AI systems to identify rare pathologies (Photo by Jessica MacInnis)

Jessica MacInnis

Topic

Global Lens

Artificial Intelligence

Faculty of Applied Science & Engineering

machine learning

Research & Innovation

St. Michael's Hospital

Subheadline

"We are using machine learning to do machine learning"

Artificial intelligence, or AI, holds real potential for improving both the speed and accuracy of medical diagnostics. But before clinicians can harness the power of AI to identify conditions in images such as X-rays, they have to “teach” the algorithms what to look for.

Identifying rare pathologies in medical images has presented a persistent challenge for researchers because of the scarcity of images that can be used to train AI systems in a supervised learning setting.

Professor Shahrokh Valaee, in the Edward S. Rogers Sr. Department of Electrical & Computer Engineering, and his team have designed a new approach: using machine learning to create computer generated X-rays to augment AI training sets.

“In a sense, we are using machine learning to do machine learning,” says Valaee. “We are creating simulated X-rays that reflect certain rare conditions so that we can combine them with real X-rays to have a sufficiently large database to train the neural networks to identify these conditions in other X-rays.”

Valaee is a member of the Machine Intelligence in Medicine Lab, a group of physicians, scientists and engineering researchers who are combining their expertise in image processing, artificial intelligence and medicine to solve medical challenges. “AI has the potential to help in a myriad of ways in the field of medicine,” says Valaee. “But to do this we need a lot of data – the thousands of labelled images we need to make these systems work just don’t exist for some rare conditions.”

To create these artificial X-rays, the team uses an AI technique called a “deep convolutional generative adversarial network” to generate and continually improve the simulated images. Generative adversarial networks, or GANs, are a type of algorithm made up of two networks: one that generates the images and another that tries to discriminate synthetic images from real images. The two networks are trained to the point that the discriminator cannot differentiate real images from synthesized ones. Once a sufficient number of artificial X-rays are created, they are combined with real X-rays to train a deep convolutional neural network, which then classifies the images as either normal or identifies a number of conditions.

On the top left of each set of images is a real X-ray image of a patient’s chest – beside it, the synthesized X-ray. Beneath the X-ray images are corresponding heat maps, which is how the machine learning system sees the images (Illustration courtesy of Hojjat Salehinejad/MIMLab)

“We’ve been able to show that artificial data generated by a deep convolutional GANs can be used to augment real datasets,” says Valaee. “This provides a greater quantity of data for training and improves the performance of these systems in identifying rare conditions.”

The lab compared the accuracy of their augmented dataset to the original dataset when fed through their AI system and found that classification accuracy improved by 20 per cent for common conditions. For some rare conditions, accuracy improved up to about 40 per cent – and because the synthesized X-rays are not from real individuals, the dataset can be readily available to researchers outside hospital premises without violating privacy concerns.

“It’s exciting because we’ve been able to overcome a hurdle in applying artificial intelligence to medicine by showing that these augmented datasets help to improve classification accuracy,” says Valaee.

“Deep learning only works if the volume of training data is large enough and this is one way to ensure we have neural networks that can classify images with high precision.”

�山ǿ�� experts are using math to mend hearts

noreen.rasbach — Thu, 08 Feb 2018 21:02:54 +0000

�山ǿ�� experts are using math to mend hearts

noreen.rasbach Thu, 02/08/2018 - 16:02

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(From left) Dr. Laura Jiménez-Juan, Piero Triverio and Dr. Stephen Fremes are working on research that could provide surgeons with better information about coronary artery disease (photo by Jessica MacInnis)

Jessica MacInnis

Topic

Global Lens

Dalla Lana School of Public Health

Faculty of Applied Science & Engineering

Faculty of Medicine

The heart is complex and mysterious, but a team led by a Faculty of Applied Science & Engineering researcher aims to increase our understanding of it using math.

Coronary artery disease – the buildup of plaque in the heart’s arteries – is the second-leading cause of death in Canada. Physicians will often graft an artery or a vein from elsewhere in the patient’s body to bypass the blocked artery. Unfortunately, the complex behaviour of the cardiovascular system makes it difficult to predict the success rate of a graft – up to 30 per cent fail within a year following surgery.

Now, Piero Triverio, an associate professor in the Edward S. Rogers Sr. department of electrical & computer engineering, is collaborating with physicians and mathematicians to explore how computer simulations can help to identify why grafts fail, and improve treatment procedures.

Currently, surgeons rely on information from CT scans to decide when and how to perform graft surgery, but these anatomical scans include little information on a patient’s unique physiology, such as blood velocity and pressure.

“We want to use the information from traditional medical imaging to set up computational simulations that can provide an unprecedented amount of information on blood flow in individual patients, and relate this information to graft failure,” says Triverio. “This study could help to identify under which scenarios graft surgery is more likely to succeed or fail.”

To do this, Triverio is collaborating with Dr. Stephen Fremes, a cardiothoracic surgeon at Sunnybrook Health Sciences Centre and professor at �山ǿ��'s Dalla Lana School of Public Health; Dr. Laura Jiménez-Juan, a cardiothoracic radiologist at Sunnybrook Health Sciences Centre and assistant professor of medical imaging at �山ǿ��; and Gianluigi Rozza, a professor of numerical analysis and scientific computing at the International School for Advanced Studies (SISSA).

In December 2017, the first patients participating in the study received a CT and MRI scan one month after their graft surgery. The team will combine the anatomy of the patient’s vessels obtained from the medical images with Navier-Stokes equations, which describe the behaviour of fluids, to develop comprehensive simulation models for coronary arteries. One year after surgery, researchers will know whether graft surgery has been successful, and will study the correlation between simulation results and graft failure.

“The complexities of the heart are immense; currently we are able to access only small pieces of information in a very large puzzle,” says Fremes. “If we can develop models that predict how blood flows in the coronary arteries of an individual patient, we can tailor bypass surgery for each patient more accurately – the more information we have, the better decisions we can make for a patient.”

�山ǿ�� alumna on Emmy Award-winning team for work on video compression

rasbachn — Wed, 01 Nov 2017 17:52:31 +0000

�山ǿ�� alumna on Emmy Award-winning team for work on video compression rasbachn Wed, 11/01/2017 - 13:52

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Vivienne Sze holds the Emmy her team won at the 69th Engineering Emmy Awards. The �山ǿ�� alumna is now a professor at the Massachusetts Institute of Technology (photo by Minhua Zhou)

Jessica MacInnis

Topic

Our Community

Alumni

Faculty of Applied Science & Engineering

Alumna Vivienne Sze, who graduated in 2004 in electrical engineering at the University of Toronto, was part of a team that was awarded an Emmy at the 69th Engineering Emmy Awards.

Sze is a member of the joint collaborative team on video coding (JCT-VC) that developed High Efficiency Video Coding (HEVC) – the latest video coding standard that has been adopted, or selected for adoption, by all ultra-high definition television distribution channels, including terrestrial, satellite, cable, fibre and wireless.

“HEVC delivers higher compression than previous standards, while still delivering high processing speeds and low power consumption,” says Sze. “It was exciting for the team to be recognized by the television academy with an Emmy – it was such a unique experience.”

This Emmy, announced on Sept. 27, was one of seven conferred to individuals, companies or organizations this year for engineering developments that improve the transmission, recording or reception of television.

“It’s gratifying to translate the things I learned throughout my education – from my undergrad at �山ǿ�� right up to my PhD at MIT – into technology that impacts millions of people when they turn on their televisions and watch videos on their tablets or mobile phones,” says Sze.

“Receiving an Emmy is definitely icing on the cake.”

She also co-edited a book on the topic, entitled High Efficiency Video Coding (HEVC): Algorithms and Architectures.

Sze – now a professor at Massachusetts Institute of Technology (MIT) – is no stranger to receiving recognition for her work: She received the Jin-Au Kong outstanding doctoral thesis prize in 2011 and NSERC’s Julie Payette fellowship in 2004. Her research focuses on energy-aware signal processing algorithms, and low-power hardware design for applications such as computer vision, deep learning, autonomous navigation, and video compression.

“I worked on HEVC as a PhD student at MIT and as a member of technical staff at Texas Instruments,” says Sze. “The JCT-VC is a group of world-renowned video coding experts – it was an honour to be part of this team.”

#UofTGrad17: Computer whiz is �山ǿ��'s top student

ullahnor — Tue, 13 Jun 2017 15:32:13 +0000

#UofTGrad17: Computer whiz is �山ǿ��'s top student

ullahnor Tue, 06/13/2017 - 11:32

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Sandro Young receives a Governor General’s Silver Medal in recognition of his high academic achievement by Farid Najm, chair of electrical & computer engineering at �山ǿ�� (photo by Laura Brown)

Jessica MacInnis

Author legacy

Jessica MacInnis

Topic

Global Lens

Faculty of Applied Science & Engineering

Convocation 2017

Electrical & Computer Engineering

Convocation

Subheadline

Sandro Young will join Google at its Mountain View, Calif., headquarters in September

Sandro Young has always been interested in computers, circuits and electronics.

What started as a middle-school passion – Young was an active participant in science fairs in Grades 7 and 8 – eventually led him to study in The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE) at �山ǿ�� where today, he graduates as the university's top student.

“I never set out to be the top graduating student,” says Young. “But I’ve pretty much always been interested in science, math and technology – especially programming, software and computer architecture so ECE was a great fit for me. I enjoyed all of the courses and projects along the way.”

Young graduates with a number of scholarships, awards and medals in recognition of his academic achievements, including one of three Governor General’s Silver Medals bestowed to �山ǿ��’s most academically outstanding undergraduate students graduating this spring. He is also the recipient of the John Black Aird Scholarship, given to the top student at �山ǿ��.

His GPA? A cool 4.0.

In September, Young will join Google at its Mountain View, Calif., headquarters.

Top marks weren’t Young’s only focus during his undergraduate experience: he was a member of several clubs, including the SPARK Design Club, where he was co-president.

“The Spark club builds interactive electro-mechanical displays three or four times a year and puts them around campus for people to play with and interact with,” says Young. “What was really great was that students across all disciplines would work on these projects so everyone was learning from each other – it’s a very high-energy and collaborative environment.”

Listen to Sandro Young on CBC’s Metro Morning

Young followed his academic interests as he chose his courses in upper years – he took computer architecture, computer vision and classes on machine learning and natural language understanding.

Open-ended design projects completed throughout his courses were challenging but a great undergraduate experience, he says.

“The capstone project in fourth year and the final project in the second-year digital logic course were a lot of fun,” says Young.

“One of the things I really love about engineering is the fact that there is a lot of creativity involved, getting to take some of the concepts we learned and applying them to open-ended problems was one of my favourite things about studying here.”

Young also receives the W.S. Wilson Medal, the Ontario Professional Engineers Foundation for Education Gold Medal, the Henry G. Acres Medal and the Adel S. Sedra Gold Medal.

“The awards and scholarships Sandro has received represent not only his academic ability, but his perseverance, commitment and enthusiasm,” says Professor Farid Najm, chair of ECE. “These qualities will serve him well as he embarks on the next stage of his engineering career – and I look forward to following his continued success.”

At the end of his second year, Young completed his Professional Experience Year at Altera, an Intel-owned American manufacturer of programmable logic devices. He will join Google, after spending two summers working with the company on its Machine Perception and Ads Quality teams.

“I’m really interested in machine learning because I think it has the potential to revolutionize the way we interact with technology and the role technologies plays in our daily lives and in society,” says Young. “I’m still going through the team-matching process at Google, but I’d love to land on a team doing work in that area.”

As for advice to current and future students, Young’s outlook is both philosophical and practical: “I think it’s helpful to keep an open mind about all of the courses you are taking, even if it’s not your favourite subject. If you find ways to make it relevant to you, you can be more engaged,” says Young. “And if you go to class regularly, then you are forced to think about the subject for at least a few hours a week – I mostly took my own advice on that!”