Welcome to the “Mathematics of Data Science and Machine Learning” research group, housed in the Applied Mathematics department of the University of Waterloo.
We conduct research on mathematical and computational aspects of Data Science and Machine Learning. Mathematics is one of the main pillars of Data Science, providing the mathematical foundations that underly the tremendous technological innovations that are central to the data revolution. We regularly organize reading groups and seminars on topics in Data Science and Machine Learning. Prospective graduate students, join us for leading-edge research on mathematical and computational aspects of Data Science and Machine Learning!
- Giang Tran (sparse modeling and sparse optimization methods, Data Science, compressed sensing)
- Hans De Sterck (tensor decomposition, optimization methods for Data Science, differential equations and deep learning)
- Jun Liu (learning for control and dynamical systems, optimization methods for Data Science)
- Achim Kempf (physics of information, Quantum Machine Learning, machine learning for natural language processing)
Students interested in graduate research within our Data Science or Applied Mathematics graduate programs are invited to contact the above faculty members for opportunities.
News and Links:
- new SIAM Conference on Mathematics of Data Science (MDS20, May 2020, Cincinnati)
- new SIAM Journal on Mathematics of Data Science (SIMODS)
- reading group on the Mathematics of Deep Learning (Fall 2019)
- AMATH900: Introduction to Deep Learning (Fall 2019)
- AMATH900: Introduction to Quantum Computer Programming (with Cirq) (Winter 2020)
- Masters Programs in Data Science at Waterloo (you may also conduct research on Data Science and Machine Learning topics as part of your graduate studies in Applied Mathematics, see https://uwaterloo.ca/applied-mathematics/graduate-students/applying)
- Waterloo Artificial Intelligence Institute