Maia Fraser

Associate Professor

Department of Mathematics & Statistics
School of E. Eng. & Comp. Sci. (cross-appointed)
Brain and Mind Research Institute (member)
University of Ottawa

Research

    I use geometric and topological tools both within pure Math and Computer Science. My main area of research is Machine Learning (statistical machine learning, topological data analysis) but I also work on certain problems in Contact/Symplectic Geometry as well as Computational Geometry. My focus in Machine Learning is its interaction with Neuroscience - understanding theoretical principles governing both artificial and biological learning, developing data-analytic tools for neuroscience data, and taking inspiration from biological learning to develop machine learning algorithms. Google Scholar entry.

Recent publications and preprints

  1. C Mirmiran, M Fraser, L Maler, Finding food in the dark: how trajectories of a gymnotiform fish change with spatial learning. Journal of Experimental Biology 225 (23), jeb244590, 2022.

  2. V Létourneau, M Fraser, Inexperienced RL Agents Can’t Get It Right: Lower Bounds on Regret at Finite Sample Complexity. Conference on Lifelong Learning Agents (CoLLAs), 327-334, 2022.

  3. G Northoff*, M Fraser*, J Griffiths, D Pinotsis, P Panangaden, RJ Moran, K Friston, Augmenting human selves through artificial agents–lessons from the brain. Front. Comput. Neurosci. 63, 2022. (*=equal contribution)

  4. A Bezerra, G Andrade, L Sanchez, M Fraser, Automated Assessment of Akali-Aggregate Reaction in Concrete. 16th International Conference on Alkali-Aggregate Reaction in Concrete (ICAAR), 2022.

  5. C Beeler, X Li, M Crowley, M Fraser, I Tamblyn, Dynamic programming with partial information to overcome navigational uncertainty in a nautical environment. (arXiv preprint), 2021.

  6. M. Golesorkhi, J. Gomez-Pilar, S. Tumati, M. Fraser, G. Northoff, Temporal hierarchy converges with spatial hierarchy: Intrinsic neural time scales follow core-periphery organization. Nature Communications in Biology, 4(1), 1-14, 2021.

  7. M. Fraser, G. Northoff, Temporospatial hierarchy and reinforcement learning - towards more general AI. NAISys, 2020.

  8. A. Bezera, L. Sanchez, M. Fraser, Automated Assessment of Damage in Concrete. ICAAR 2020.

  9. N. Denis, M. Fraser, Options in multi-task reinforcement learning - transfer via reflection. CanAI2019, May 2019.

  10. N. Denis, M. Fraser, Options and partial observability: regret bounds by analogy with semi-supervised learning. NeurIPS2018 Reinforcement Learning Under Partial Observability Workshop, December 2018.

  11. M. Fraser, L. Polterovich, D. Rosen, On Sandon-type metrics for contactomorphism groups. Ann. Math. Québec, 2018. online-doi. (arxiv version)

  12. N. Denis, M. Fraser, Landmark Options Via Reflection (LOVR) in Multi-Task Lifelong Reinforcement Learning. NIPS2017 Hierarchical Reinforcement Learning Workshop (oral presentation and poster; non-proceedings pdf), December 2017.

  13. M. Fraser, Contact non-squeezing at large scale in ℝ2n x S1. International Journal of Mathematics, (27)13, pp. 60-85, 2017. (arxiv version)

  14. M. Fraser, Multi-step learning and underlying structure in statistical models. NIPS2016. (proceedings pdf)

  15. M. Fraser, Contact non-squeezing via generating functions: A low-tech proof in the language of persistence modules. Poster in Summer School 2016 on Symplectic Topology, Sheaves and Mirror Symmetry, Paris IJM-PRG, 2016. (poster)

  16. M. Fraser, Contact spectral invariants and persistence, preprint 2015. (arxiv version)

  17. M. Fraser, Group Actions in Topological Data Analysis and Hierarchical Learning. PhD Thesis, Dept. of Computer Science, University of Chicago, August 2013.

  18. M. Fraser, Tight Linear Lower Memory Bound for Local Routing in Planar Digraphs. In Proceedings of Canadian Conference on Computational Geometry (CCCG12), August 2012. (proceedings pdf)

  19. M. Fraser, Persistent Homology of filtered covers. 2012. (arxiv version)

  20. M. Fraser, Local Routing in Graphs Embedded on Surfaces of Arbitrary Genus. 2012. (arxiv version)

  21. M. Fraser, Structural Observations on Neural Networks for Hierarchically Derived Reproducing Kernels. University of Chicago Master's thesis, November 2011. (revised version) (slides)

  22. M. Fraser, Two Extensions to Manifold Learning Algorithms Using α-Complexes. Dept. of Computer Science, University of Chicago, Technical Report TR-2010-07, 2010.

  23. A. Fraser, D. Fraser, M. Fraser, Curvature Revisited and the Bayes-Frequentist Divergence. In Journal of Statistical Research, Vol 44 number 2, 2010. (pdf)

  24. Y. Eliashberg and M. Fraser, Topologically Trivial Legendrian Knots. In Journal of Symplectic Geometry, Vol. 7, pp.77-127, 2009. (arxiv version)

  25. E. Chávez, M. Fraser and H. Tejeda, Proximal Labeling for Oblivious Routing in Wireless Ad Hoc Networks. In Proceedings of ADHOC-NOW 2009, Springer Verlag LNCS 5793, pp. 360-365.

  26. M. Fraser, E. Kranakis, J. Urrutia, Memory Requirements for Local Geometric Routing or Traversal in Digraphs. In Proceedings of Canadian Conference on Computational Geometry (CCCG08), August 2008. (proceedings pdf)

  27. M. Fraser, Local Routing on Tori (extended, invited version of next paper). In Ad Hoc and Sensor Wireless Networks, journal issue dedicated to ADHOC-NOW 2007, Vol. 6, pp. 179-196, 2008.

  28. M. Fraser, Local Routing on Tori. In proceedings of ADHOC-NOW 2007, Springer Verlag LNCS 4686, pp. 153-166, E. Kranakis and J. Opatrny (Eds.), Morelia, September 2007.

Teaching

  • MAT4155: Elementary Manifold Theory
  • MAT4373/5314: Statistical Machine Learning
  • MAT3555/MAT3155: Géométrie Différentielle/Differential Geometry
  • MAT2355: Introduction to Geometry
  • MAT2143: Algebraic Structures: Intro. to Group Theory
  • MAT1741: Algèbre linéaire
  • MAT3153: Introduction to Topology

Previously at University of Toronto:
TA'ing at University of Chicago: