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Fred (Farbod) Roosta
School of Mathematics and Physics
University of Queensland, Brisbane, Australia
International Computer Science Institute, Berkeley, USA

Email: fred.roostauq.edu.au


Research Interests

  • Machine Learning
  • Numerical Optimization
  • Randomized Algorithms
  • Computational Statistics
  • Numerical Analysis and Scientific Computing



  • December, 2019: Our new paper, which extends the existing results on Gaussian process iterpretation of multi-layer perceptrons to richer families of priors, is available.
  • November, 2019: Our new paper on the application of RandNLA and leverage score sampling within the context of big-data time series is now available.
  • November, 2019: I will be serving as a senior program committee member for the 29th International Joint Conference on Artificial Intelligence (IJCAI 2020).
  • October, 2019: Our paper on central limit theorems and concentration inequalities for out-of-sample extensions of the adjacency and Laplacian spectral embeddings is now available.
  • September, 2019: Our paper on stability analysis of Newton-MR with respect to Hessian approximations is now available.
  • September, 2019: DINGO Does Vancouver! Our paper on distributed Newton-type methods for optimization of invex objectives has been accepted to NeurIPS 2019!
  • June, 2019: Congratulations to my PhD student, Rixon Crane, for being awarded the Best Poster Award at AMSI Optimise this year.
  • June, 2019: I was selected as a top 5% reviewer for ICML 2019.
  • May, 2019: After 17 months and 21 days, our paper on non-convex Newton-type methods with inexact Hessian information has been accepted to Mathematical Programming.
  • May, 2019: Our paper on the invariance kernels of multilayer perceptrons during training has been accepted to the International Joint Conference on Artificial Intelligence (IJCAI 2019) - 18% acceptance rate.
  • April, 2019: Our paper on the theoretical and practical properties of Monte-Carlo sampling algorithms by implicit discretizations of Langevin SDE is now available on arXiv.

Preprints and Publications




  • Optimization Methods for Inverse Problems
    Nan Ye, Fred Roosta, and Tiangang Cui
    2017 MATRIX Annals, Editors: David R. Wood, Jan de Gier, Cheryl E. Praeger, Terence Tao. MATRIX Book Series, Volume 2, Springer (to appear)


  • Parallel Local Graph Clustering
    Julian Shun, Fred Roosta, Kimon Fountoulakis and Michael W. Mahoney
    Proc. of the VLDB Endowment, 9(12), pp. 1041-1052, 2016.





  • Christopher Van Der Heide (Postdoc, 2019 - present)
  • Robert Salomone (PhD, Completed 2019, Now Postdoc at UNSW)


I am always looking for enthusiastic PhD students...

So, if (a) and (b), then feel free to drop me a line with your current CV and a few words regarding your research/background.

(a) Your research interests lie anywhere in the union of Machine Learning, Numerical Optimization, Randomized Algorithms, Computational Statistics, Scientific Computing, or Numerical Linear Algebra.

You are looking to do a PhD degree in one of the world's leading and highly ranked research universities in beautiful Australia.

Selected Talks



  • Simons Institute (Reunion Workshop for the Foundations of Data Science program)

    Berkeley, California, USA (December, 2019)

  • Institute for Data and Decision Analytics (Chinese University of Hong Kong in Shenzhen)

    Shenzhen, China (February, 2020)

  • SIAM Conference on Mathematics of Data Science (MDS20)

    Cincinnati, Ohio, USA (May, 2020)

  • SIAM Conference on Optimization (OP20)

    Hong Kong (May, 2020)

  • Mathematics of Complex Data (MathDataLab)

    Stockholm, Sweden (June, 2020)

  • Foundations of Computational Mathematics (FoCM 2020)

    Vancouver, BC, Canada (June, 2020)

  • Workshop on Optimization in the Big Data Era (National University of Singapore)

    Singapore (August, 2020)



  • INFORMS Annual Meeting

    Seattle, Washington, USA (October, 2019)

  • European Conference on Numerical Mathematics and Advanced Applications (ENUMATH)

    Egmond aan Zee, The Netherlands (October, 2019)

  • DIMACS Workshop on Randomized Numerical Linear Algebra, Statistics, and Optimization (DIMACS)

    Rutgers University, New Jersey, USA (September, 2019)

  • International Conference on Continuous Optimization (ICCOPT 2019)

    Berlin, Germany (August, 2019)

  • AMSI Optimise

    Perth, Australia (June, 2019)

  • INFORMS Annual Meeting

    Phoenix, Arizona (November, 2018)

  • Randomized Numerical Linear Algebra and Applications - Simons Institute for the Theory of Computing

    Berkeley, California (September, 2018)

  • Foundations of Data Science Boot Camp- Simons Institute for the Theory of Computing

    Berkeley, California (August, 2018)

  • International Symposium on Mathematical Programming (ISMP)

    Bordeaux, France (July, 2018)

  • International Conference on Econometrics and Statistics (EcoSta)

    Hong Kong (June, 2018)

  • International Conference on Artificial Intelligence and Statistics (AISTATS)

    Lanzarote, Canary Islands (April, 2018)

  • International Conference on High Performance Scientific Computing (HPSC)

    Hanoi, Vietnam (March, 2018)

  • SIAM Annual Meeting

    Pittsburgh, Pennsylvania (July, 2017)

  • Matrix Program on Computational Inverse Problems

    Creswick, Australia (June, 2017)

  • SIAM Conference on Optimization

    Vancouver, Canada (May, 2017)

  • SIAM Conference on Computational Science and Engineering

    Atlanta, Georgia (February, 2017)

  • Computational and Methodological Statistics (CMStatistics)

    Seville, Spain (December, 2016)

  • Pacific Institute for the Mathematical Sciences (SCAIM Seminar)

    Vancouver, British Columbia (September, 2016)

  • Institute of Applied Mathematics (IAM)

    Vancouver, British Columbia (September, 2016)

  • SAMSI Program on Optimization (Invited Plenary Talk)

    Research Triangle Park, North Carolina (August, 2016)

  • Recent Advances in Randomized Numerical Linear Algebra

    National Institute of Informatics, Shonan Village Center, Japan (July, 2016)

  • Workshop on Algorithms for Modern Massive Data Sets (MMDS)

    Berkeley, California (June, 2016)

  • SIAM Conference on Uncertainty Quantification

    EPFL, Lausanne, Switzerland (April, 2016)

  • Sparse Representations, Numerical Linear Algebra, and Optimization

    BIRS, Alberta (October, 2014)

  • SIAM Conference on Optimization

    San Diego, California (May, 2014)

  • Pacific Institute for the Mathematical Sciences (SCAIM Seminar)

    Vancouver, British Columbia (November, 2013)