Researchers are using government money to fuel the next generation of scientists.
On Aug. 30, $11.8 million in funding was announced for Queen’s University researchers from the Natural Sciences and Engineering Research Council (NSERC), through their Discovery Grant and Research Tools and Instruments Grant programs.
Assistant Professor of Social and Personality Psychology David Ha received $202,500 to his research into vaccine resistance, which explores the mental processes and reasoning styles vaccine resistors use to their beliefs and meta-beliefs.
“When you put people in a task that’s designed to see how much evidence and information they require before they reach a conclusion, we found this initial tendency, where people who resist vaccines tend to draw conclusions after looking at minimal amounts of evidence,” Ha said in an interview with The Journal.
The long-term goal of Ha’s research program is to explore the driving factors behind the belief vaccine resistors have done their research, when in fact they haven’t done their research.
While extremely topical given the COVID-19 pandemic, this research topic was initially developed in 2019 by Andrew Hall, a current PhD candidate in the psychology department, who was exploring general vaccine resistance.
“A significant part of this research involves paying those who participate in studies. [The grant] provides funds so we can provide incentives for participants, and for dissemination of the research, so we get a chance to present it at conferences,” Ha said.
Ha shared the vast majority of funding from the NSERC Discovery Grant goes towards graduate students and helps pay their salaries, for research supplies, and conference travel.
Like Ha, Ting Hu, associate professor of computing, plans to put her Discovery Grant toward training the next generation of scientists.
Hu renewed a previous NSERC grant and received $205,000 to her research into evolutionary computing, focusing on transparent machine learning and AI algorithms.
“There are some criticisms about applying machine learning and AI techniques in medicine due to the black box nature of a lot of machine learning algorithms,” Hu said. “The long-term goal of my research program is to develop next generation learning algorithms that are more transparent, that are more understandable for applications in medicine.”
Through her interdisciplinary background in AI and computational genetics, Hu realized there’s misalignment in the priorities of computer scientists and medical experts. This inspired her current research which helps create machine learning interfaces that can learn from and apply data, but also communicate with the end .
“When we don’t really understand how the algorithm works, we don’t understand how the machine has come to its decision, then you won’t trust it,” Hu said.
Another more novel approach Hu is working toward is integrating computer-human dialogue into the interface, like the ability to concepts to one another. This method involves integrating specific domains of knowledge into the machine learning models.
“If we really want to push a wider application of these powerful machine learning tools, explanations are a must. Otherwise, no one is willing to adopt these powerful machines. Even though they perform well, if you don’t understand it, if you don’t trust it, you can’t really convince people to use it.”
Hu received from Queen’s Research Services throughout the NSERC application process, under the Vice-Principal (Research) portfolio.
“They hosted seminars, where they invited people who were on the reviewing committee last year or who were very successful in their recent grant applications to share their experiences with those currently going through the process,” Hu said.
Felicia Magpantay, associate professor in the department of mathematics and statistics, echoed this sentiment, sharing how she received more from Queen’s during the NSERC application process than from different institutions in the past.
Magpantay received $195,000 in funding through her NSERC Discovery Grant. She works with mathematical modelling, using differential equations to describe the laws governing how systems evolve over time.
A primary goal of this NSERC-funded research program is to develop theory behind transient systems, so others can understand how to analyze and explore more transient dynamics in the future.
“In mathematical modelling, a lot of the focus is on equilibria, which is what happens in equilibrium, the steady state after a long time has ed. These models are important in many fields, such as
physics,” Magpantay said in an interview with The Journal.
Transient dynamics describe the periods before reaching equilibrium. Magpantay is interested in long transient dynamics, which last for such a long time you might think you’re in equilibrium, but you haven’t actually reached a steady state.
The specific mathematical models that Magpantay is developing can be applied to epidemiological concepts, such as disease transmission.
A common application of transient dynamics is the honeymoon period of disease transmission.
If a population has a childhood disease, and then that population undergoes mass vaccination, the disease incidence decreases. Sometimes it may stay that way for decades.
While this may be perceived as equilibrium and the disease may be considered “eradicated,” it’s not. This is a transient dynamic that will end at some point, when the disease could become more volatile, or outbreaks could occur.
Magpantay hopes to apply for more grants which will allow her to collaborate with other fields in the future, such as public health, to broadly develop and apply these theories.
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