Professor Fred Roosta
Professor

Fred Roosta

Email: 
Phone: 
+61 7 336 53259

Availability

Professor Fred Roosta is:
Available for supervision

Fields of research

Applied mathematics Information and Computing Sciences Mathematical Sciences Numerical analysis Numerical and computational mathematics Operations research Optimisation Statistical theory Statistics Theory of computation

Qualifications

  • Doctor of Philosophy, The University of British Columbia

Research interests

  • Artificial Intelligence

  • Machine Learning

  • Numerical Optimization

  • Numerical Analysis

  • Computational Statistics

  • Scientific Computing

Search Professor Fred Roosta’s works on UQ eSpace

51 works between 2014 and 2025
All (51) Journal Article (23) Conference Publication (26) Book Chapter (2)

41 - 51 of 51 works

2018

Conference Publication

Invariance of weight distributions in rectified MLPs

Tsuchida, Russell, Roosta-Khorasani, Farbod and Gallagher, Marcus (2018). Invariance of weight distributions in rectified MLPs. 35th International Conference on Machine Learning, Stockholm, Sweden, 10-15 July 2018. Cambridge, MA, United States: M I T Press.

Invariance of weight distributions in rectified MLPs

2017

Journal Article

Variational perspective on local graph clustering

Fountoulakis, Kimon, Roosta-Khorasani, Farbod, Shun, Julian, Cheng, Xiang and Mahoney, Michael W. (2017). Variational perspective on local graph clustering. Mathematical Programming, 174 (1-2), 553-573. doi: 10.1007/s10107-017-1214-8

Variational perspective on local graph clustering

2017

Conference Publication

The Union of Intersections (UoI) method for interpretable data driven discovery and prediction

Bouchard, Kristofer E, Bujan, Alejandro F, Roosta-Khorasani, Farbod, Prabhat, Snijders, Jian-Hua Mao, Chang, Edward F, Mahoney, Michael W and Bhattacharyya, Sharmodeep (2017). The Union of Intersections (UoI) method for interpretable data driven discovery and prediction. 31st Annual Conference on Neural Information Processing Systems (NIPS), Long Beach, CA United States, 4-9 December 2017. Maryland Heights, MO, United States: Morgan Kaufmann Publishers.

The Union of Intersections (UoI) method for interpretable data driven discovery and prediction

2016

Journal Article

Algorithms that satisfy a stopping criterion, probably

Ascher, Uri and Roosta-Khorasani, Farbod (2016). Algorithms that satisfy a stopping criterion, probably. Vietnam Journal of Mathematics, 44 (1), 49-69. doi: 10.1007/s10013-015-0167-6

Algorithms that satisfy a stopping criterion, probably

2016

Conference Publication

Sub-sampled Newton methods with non-uniform sampling

Xu, Peng, Yang, Jiyan, Roosta-Khorasani, Farbod, Re, Christopher and Mahoney, Michael (2016). Sub-sampled Newton methods with non-uniform sampling. Neural Information Processing Systems 2016, Barcelona Spain, 5 - 10 December 2016 . La Jolla, CA United States: Neural Information Processing Systems Foundation.

Sub-sampled Newton methods with non-uniform sampling

2016

Conference Publication

Parallel local graph clustering

Shun, Julian, Roosta-Khorasani, Farbod, Fountoulakis, Kimon and Mahoney, Michael W. (2016). Parallel local graph clustering. International Conferenceon Very Large Data Bases, New Delhi, India, 5-9 September 2016. New York, United States: Association for Computing Machinery. doi: 10.14778/2994509.2994522

Parallel local graph clustering

2015

Journal Article

Schur properties of convolutions of gamma random variables

Roosta-Khorasani, Farbod and Szekely, Gábor J. (2015). Schur properties of convolutions of gamma random variables. Metrika, 78 (8), 997-1014. doi: 10.1007/s00184-015-0537-9

Schur properties of convolutions of gamma random variables

2015

Journal Article

Improved bounds on sample size for implicit matrix trace estimators

Roosta-Khorasani, Farbod and Ascher, Uri (2015). Improved bounds on sample size for implicit matrix trace estimators. Foundations of Computational Mathematics, 15 (5), 1187-1212. doi: 10.1007/s10208-014-9220-1

Improved bounds on sample size for implicit matrix trace estimators

2015

Journal Article

Assessing stochastic algorithms for large scale nonlinear least squares problems using extremal probabilities of linear combinations of gamma random variables

Roosta-Khorasani, Farbod, Székely, Gábor J. and Ascher, Uri M. (2015). Assessing stochastic algorithms for large scale nonlinear least squares problems using extremal probabilities of linear combinations of gamma random variables. SIAM/ASA Journal on Uncertainty Quantification, 3 (1), 61-90. doi: 10.1137/14096311X

Assessing stochastic algorithms for large scale nonlinear least squares problems using extremal probabilities of linear combinations of gamma random variables

2014

Journal Article

Data completion and stochastic algorithms for PDE inversion problems with many measurements

Roosta-Khorasani, Farbod, van den Doel, Kees and Ascher, Uri (2014). Data completion and stochastic algorithms for PDE inversion problems with many measurements. Electronic Transactions on Numerical Analysis, 42, 177-196.

Data completion and stochastic algorithms for PDE inversion problems with many measurements

2014

Journal Article

Stochastic algorithms for inverse problems involving pdes and many measurements

Roosta-Khorasani, Farbod, Van Den Doel, Kees and Ascher, Uri (2014). Stochastic algorithms for inverse problems involving pdes and many measurements. SIAM Journal on Scientific Computing, 36 (5), S3-S22. doi: 10.1137/130922756

Stochastic algorithms for inverse problems involving pdes and many measurements

Current funding

  • 2025 - 2028
    Next Generation Newton-type Methods with Minimum Residual Solver
    ARC Discovery Projects
    Open grant
  • 2021 - 2026
    ARC Training Centre for Information Resilience
    ARC Industrial Transformation Training Centres
    Open grant
  • 2021 - 2025
    CropVision: A next-generation system for predicting crop production
    ARC Linkage Projects
    Open grant

Past funding

  • 2021
    Big time series data and randomised numerical linear algebra
    University of Melbourne
    Open grant
  • 2019
    Approximate solutions to large Markov decision processes
    University of Melbourne
    Open grant
  • 2018 - 2024
    Efficient Second-Order Optimisation Algorithms for Learning from Big Data
    ARC Discovery Early Career Researcher Award
    Open grant

Availability

Professor Fred Roosta is:
Available for supervision

Looking for a supervisor? Read our advice on how to choose a supervisor.

Available projects

  • Non-convex Optimization for Machine Learning

    Design, analysis, and implementation of novel optimization algorithms for optimization of modern non-convex machine learning problems.

  • Interpretable AI - Theory and Practice

    This project will extend and innovate, both theoretically and practically, interpretable methods in AI that are transparent and explainable to improve trust and usability. It will also explore novel approaches for uncertainty quantification and understanding causality.

  • Exploring Predictivity--Parsimony Trade-off In Scientific Machine Learning

    This project will investigate, both theoretically and empirically, novel statistical techniques to explore the trade-offs between high-generalization performance and low-model complexity for scientific machine learning.

  • Novel Machine Learning Models for Scientific Discovery

    To extend the application range of machine learning to scientific domains, this project will design, analyze and implement novel machine learning techniques that learn from data, while conform with known properties of the underlying scientific models.

  • Automated Discovery of Optimization and Linear Algebra Algorithms

    Using reinforcement learning to automate algorithmic discovery, this project aims to develop novel variants of first- and second-order optimization methods, randomized numerical linear algebra techniques, and mixed-integer programming approaches.

  • Second-order Optimization Algorithms for Machine Learning

    This project aims to develop the next generation of second-order optimization methods for training complex machine learning models, with particular focus on constrained problems arising in scientific machine learning applications.

  • Distributed Optimization Algorithms for Large-scale Machine Learning

    This project aims to design, analyze and implement efficient optimization algorithms suitable for distributed computing environments, with focus on large-scale machine learning.

Supervision history

Current supervision

  • Doctor Philosophy

    Stochastic Simulation and Optimization Methods for Machine Learning

    Principal Advisor

  • Doctor Philosophy

    Newton type methods for constrained optimization

    Principal Advisor

  • Doctor Philosophy

    Novel Machine Learning Models for Scientific Discovery

    Principal Advisor

  • Doctor Philosophy

    AI/ML Framework for Mixed-integer Nonlinear Optimisation

    Principal Advisor

  • Doctor Philosophy

    Interpretable AI-Theory and Practice

    Principal Advisor

    Other advisors: Dr Quan Nguyen, Dr Maciej Trzaskowski

  • Doctor Philosophy

    Faithful-Newton Framework: Bridging between Inner and Outer Solvers

    Principal Advisor

    Other advisors: Associate Professor Marcus Gallagher

  • Doctor Philosophy

    Offline Reinforcement Learning Theory and Algorithms

    Associate Advisor

    Other advisors: Dr Nan Ye

  • Doctor Philosophy

    Forecasting the Market Capitalisation of ASX Listed Junior Resource Companies through an Artificial Neural Network

    Associate Advisor

    Other advisors: Associate Professor Mehmet Kizil, Dr Micah Nehring

Completed supervision

Enquiries

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