Hi there.

Welcome to the AARMS Collaborative Research Group Mathematical foundations and applications of Scientific Machine Learning .

What we do.

Scientific Machine Learning is concerned with using methods from machine learning to tackle problems that have traditionally been investigated using classical scientific computing. Until very recently, science, and in particular scientific computing, has followed the classical formula From rules to data, meaning one first defines a mathematical theory or a computational algorithm which generates predictions (data), that is then compared to some benchmarks, such as real-world observations. Machine learning reverses the direction to From data to rules. Data is being analysed and rules are being derived from it. In other words, machine learning is devoted to learning from data.

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. 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.


Who we are.

Atlantic Canadian Researchers

  1. Dr. Jahrul Alam (Mathematics and Statistics, Memorial University)
  2. Dr. Alex Bihlo (Mathematics and Statistics, Memorial University) Academic coordinator
  3. Dr. Stijn De Baerdemacker (Chemistry, University of New Brunswick)
  4. Dr. Ronald Haynes (Mathematics and Statistics, Memorial University)
  5. Dr. Viqar Husain (Mathematics and Statistics, University of New Brunswick)
  6. Dr. Theodore Kolokolnikov (Mathematics and Statistics, Dalhousie University)
  7. Dr. Peter Lelievre (Mathematics and Computer Science, Mount Allison University)
  8. Dr. JC Loredo-Osti (Mathematics and Statistics, Memorial University)
  9. Dr. Scott MacLachlan (Mathematics and Statistics, Memorial University)
  10. Dr. Alison Malcolm (Earth Sciences, Memorial University) Equity, Diversity & Inclusion coordinator
  11. Dr. Paul Muir (Mathematics and Computing Science, St.\ Mary's University)
  12. Dr. Jeffrey Picka (Mathematics and Statistics, University of New Brunswick)
  13. Dr. Jiju Poovvancheri (Mathematics and Computing Science, St.\ Mary's University)
  14. Dr. Nicholas Touikan (Mathematics and Statistics, University of New Brunswick)
  15. Dr. Hamid Usefi (Mathematics and Statistics, Memorial University)
  16. Dr. Asokan Variyath (Mathematics and Statistics, Memorial University)
  17. Dr. Nanwei Wang (Mathematics and Statistics, University of New Brunswick)

Affiliated Researchers

  1. Dr. Leopold Haimberger (Meteorology and Geophysics, University of Vienna)
  2. Dr. Luke Olson (Computer Science, University of Illinois)
  3. Dr. Roman O. Popovych (Mathematics, University of Vienna)
  4. Dr. Francis Valiquette (Mathematics, Monmouth University)
  5. Dr. Andy Wan (Mathematics and Statistics, University of Northern British Columbia)
  6. Dr. Justin Wan (Computer Science, University of Waterloo)
  7. Dr. Matt West (Mechanical Science and Engineering, University of Illinois)



We are currently organizing a biweekly online seminar on Scientific Machine Learning. The latest schedule along with the connection information can be found here: AARMS Scientific Machine Learning seminar

We also plan to organize a hybrid 2-3 day workshop around the 2022 CMS Summer Meeting, which will take place June 3-6 in St. John's, Newfoundland and Labrador. Stay tuned for further updates!


Say Hello.

If you are interested in this Collaborative Research Group and want to stay in the loop then please just send a message to Alex Bihlo, abihlo@mun.ca.