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About

I am a CRM Applied Math Lab - and IVADO-funded postdoctoral researcher at McGill University in compressed sensing and machine learning with an interest in high-dimensional probability and optimization. I'm currently supervised by Drs. Tim Hoheisel (McGill) and Simone Brugiapaglia (Concordia). I completed my Ph.D. Applied Mathematics in 2021 at the University of British Columbia, advised by Drs. Yaniv Plan and Özgür Yılmaz. I completed my M.Sc. Applied Mathematics in 2014 at the University of Toronto, supervised by Dr. Adrian Nachman. Before that, I completed a B.Sc. Math & Stats at McMaster University.

Publications & pre-prints

  1. AB, Brugiapaglia, S., Joshi, B., Plan, Y., Scott, M., & Yilmaz, Ö. (2022). A coherence parameter characterizing generative compressed sensing with Fourier measurements. submitted to IEEE JSAIT. arXiv:2207.09340 (url)
  2. AB, Ozturan, G., Delavari, P., Maberley, D., Yilmaz, Ö., & Oruc, I. (2022). Learning from few examples: Classifying sex from retinal images via deep learning. submitted to PLOS One. arXiv:2207.? (url)
  3. Hoheisel, T., Brugiapaglia, S., & AB. (2022). LASSO reloaded: a variational analysis perspective with applications to compressed sensing. arXiv:2205.06872 (url)
  4. AB. (2021). On LASSO parameter sensitivity (Doctoral dissertation). University of British Columbia. (url)
  5. AB. (2021). Deep generative demixing: error bounds for demixing subgaussian mixtures of lipschitz signals. In ICASSP 2021 — 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 4010–4014). doi:10.1109/ICASSP39728.2021.9413573
  6. AB., Plan, Y., & Yilmaz, Ö. (2021). On the best choice of LASSO program given data parameters. IEEE Transactions on Information Theory. doi:10.1109/TIT.2021.3138772
  7. AB. (2020). Deep generative demixing: recovering Lipschitz signals from noisy subgaussian mixtures. arXiv:2010.06652
  8. AB, Plan, Y., & Yilmaz, Ö. (2020). Sensitivity of ℓ1 minimization to parameter choice. Information and Inference: A Journal of the IMA. doi:10.1093/imaiai/iaaa014
  9. AB, Plan, Y., & Yilmaz, Ö. (2019). Parameter instability regimes in sparse proximal denoising programs. In 2019 13th International conference on Sampling Theory and Applications (SampTA) (pp. 1–5). doi:10.1109/SampTA45681.2019.9030982
  10. Karagiannis, G. S., AB, Dimitromanolakis, A., & Diamandis, E. P. (2013). Enrichment map profiling of the cancer invasion front suggests regulation of colorectal cancer progression by the bone morphogenetic protein antagonist, gremlin-1. Molecular oncology, 7(4), 826–839. doi:10.1016/j.molonc.2013.04.002

Teaching

  1. MATH 315: Ordinary Differential Equations (McGill University; Fall 2022)
  2. MATH 387: Honours Numerical Analysis (McGill University; Winter 2022)
  3. DSCI 551: Descriptive Statistics & Probability (University of British Columbia; Fall 2020)

Other information

For a list of talks that I have given, and other information, please see my CV.