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Modern math

Researchers capitalize on scientific machine learning expertise

Research

By Jeff Green

Memorial can play a big role in the next data-driven scientific discovery, says a Faculty of Science researcher.

Dr. Alex Bihlo, Canada Research Chair in Numerical Analysis and Scientific Computing, Department of Mathematics and Statistics, is spearheading a collaborative research group that’s focused on scientific machine learning.

A relatively new discipline, scientific machine learning uses methods from machine learning — a branch of artificial intelligence (AI) — to tackle problems that have traditionally been investigated using classical scientific computing.

“In other words, machine learning is devoted to learning from data,” Dr. Bihlo explained during a recent interview with the Gazette.

“Given the ever-increasing amount of data that has been generated over the past 50 years or so in virtually all areas of the mathematical sciences, including astronomy, biology, chemistry, geophysics, meteorology and oceanography, the increased interest in applying machine learning to all these areas is quite natural and gave rise to the emerging area of scientific machine learning.”

More than 20 researchers

Dr. Bihlo currently administers the Atlantic Association for Research in the Mathematical Sciences collaborative research group program on scientific machine learning. The association is providing funding for the group.

He’s working with more than 20 members, most of them based in the Atlantic region, many of whom are from a variety of Memorial departments, and others based at universities in Canada, the U.S. and Europe.

Five individuals stand outside a building.
From left are collaborative research group members Drs. Scott MacLachlan, Ron Haynes, Alison Malcolm, Jahrul Alam and Alex Bihlo.
Photo: Rich Blenkinsopp

“This collaborative research group will capitalize on the combined expertise of researchers in the Atlantic region to push the envelope of scientific machine learning both in terms of theoretical developments and a variety of practical applications to the aforementioned fields,” said Dr. Bihlo.

The group’s expertise is helping shed new light on complicated problems such as modelling carbon capture, climate change and cancer research.

Dr. Bihlo says co-ordinating efforts can also be an incubator for other initiatives led by Memorial.

He says the group recently launched a seminar series of scientific machine learning.

They’re also bringing together researchers and practitioners of scientific machine learning to foster both academic and academic-industry collaboration in this area.

Pooling resources

“The goals of this collaborative research group are also in line with Memorial University’s strategic plan and the objectives of the Government of Canada’s Advisory Council on Artificial Intelligence to fulfil the increasing need of homegrown industries, such as Mysa, Nucliq and Verafin, in machine learning and AI to further Canada’s position as a global leader in AI development and research, to better support entrepreneurs and scale-ups and to ensure Canadians have the education and skills they need to succeed in a changing economy,” Dr. Bihlo noted.

Memorial already has a variety of expertise in the area, ranging from statistical analysis to data science. Dr. Bihlo says it makes sense to pool these resources to enhance the university’s research profile in the area of scientific machine learning.

He also says there’s excitement that Memorial will soon have a course-based master’s degree in data science, which has been developed between the Department of Computer Science and the Department of Mathematics and Statistics.

“Having the possibility to train students in this modern field of mathematical research will really be the next step to position Memorial as a leader within the Atlantic region.”


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