Dr Tiffany Vlaar

  • Lecturer in Applied Mathematics (Mathematics)

Biography

I am an ELLIS (European Laboratory for Learning and Intelligent Systems) Member. Prior to becoming a lecturer at the University of Glasgow, I was a postdoctoral researcher at Mila - Quebec AI Institute and McGill University in climate change AI and mathematics of deep learning. I obtained my Mathematics PhD from the University of Edinburgh, during which I was a Turing Enrichment student, and also have a background in physics (MSc, Perimeter Institute).

Pronouns: she/her.

Research interests

See website for more info on research projects: tiffanyvlaar.github.io

Research groups

Publications

List by: Type | Date

Jump to: 2024 | 2022 | 2021 | 2019 | 2016
Number of items: 6.

2024

Müller, M., Vlaar, T. , Rolnick, D. and Hein, M. (2024) Normalization Layers Are All That Sharpness-Aware Minimization Needs. In: 37th Conference on Neural Information Processing Systems (NeurIPS 2023), New Orleans, Louisiana, USA, 10-16 December 2023,

2022

Vlaar, T. and Leimkuhler, B. (2022) Multirate Training of Neural Networks. In: 39th International Conference on Machine Learning (ICML2022), Baltimore, Maryland, USA, 17-23 July 2022, pp. 22342-22360.

Vlaar, T. J. and Frankle, J. (2022) What Can Linear Interpolation of Neural Network Loss Landscapes Tell Us? In: 39th International Conference on Machine Learning (ICML2022), Baltimore, Maryland, USA, 17-23 July 2022, pp. 22325-22341.

2021

Leimkuhler, B., Vlaar, T. , Pouchon, T. and Storkey, A. (2021) Better Training using Weight-Constrained Stochastic Dynamics. In: 38th International Conference on Machine Learning (ICML2021), 18-24 July 2022, pp. 6200-6211.

2019

Leimkuhler, B., Matthews, C. and Vlaar, T. (2019) Partitioned integrators for thermodynamic parameterization of neural networks. Foundations of Data Science, 1(4), pp. 457-489. (doi: 10.3934/fods.2019019)

2016

Chojnacki, L., Cook, C. Q., Dalidovich, D., Hayward Sierens, L. E., Lantagne-Hurtubise, É., Melko, R. G. and Vlaar, T. J. (2016) Shape dependence of two-cylinder Rényi entropies for free bosons on a lattice. Physical Review B, 94(16), 165136. (doi: 10.1103/PhysRevB.94.165136)

This list was generated on Tue May 20 16:10:37 2025 BST.
Jump to: Articles | Conference Proceedings
Number of items: 6.

Articles

Leimkuhler, B., Matthews, C. and Vlaar, T. (2019) Partitioned integrators for thermodynamic parameterization of neural networks. Foundations of Data Science, 1(4), pp. 457-489. (doi: 10.3934/fods.2019019)

Chojnacki, L., Cook, C. Q., Dalidovich, D., Hayward Sierens, L. E., Lantagne-Hurtubise, É., Melko, R. G. and Vlaar, T. J. (2016) Shape dependence of two-cylinder Rényi entropies for free bosons on a lattice. Physical Review B, 94(16), 165136. (doi: 10.1103/PhysRevB.94.165136)

Conference Proceedings

Müller, M., Vlaar, T. , Rolnick, D. and Hein, M. (2024) Normalization Layers Are All That Sharpness-Aware Minimization Needs. In: 37th Conference on Neural Information Processing Systems (NeurIPS 2023), New Orleans, Louisiana, USA, 10-16 December 2023,

Vlaar, T. and Leimkuhler, B. (2022) Multirate Training of Neural Networks. In: 39th International Conference on Machine Learning (ICML2022), Baltimore, Maryland, USA, 17-23 July 2022, pp. 22342-22360.

Vlaar, T. J. and Frankle, J. (2022) What Can Linear Interpolation of Neural Network Loss Landscapes Tell Us? In: 39th International Conference on Machine Learning (ICML2022), Baltimore, Maryland, USA, 17-23 July 2022, pp. 22325-22341.

Leimkuhler, B., Vlaar, T. , Pouchon, T. and Storkey, A. (2021) Better Training using Weight-Constrained Stochastic Dynamics. In: 38th International Conference on Machine Learning (ICML2021), 18-24 July 2022, pp. 6200-6211.

This list was generated on Tue May 20 16:10:37 2025 BST.

Supervision

If you are interested in my research, please feel free to reach out to me to discuss PhD options at the University of Glasgow.
In your email please include your CV, university transcripts, and any dissertations/papers you may have written (but do not worry if you do not have any publications at this stage).

  • I have a fully funded PhD position available in mathematics of deep learning for an October 2025 start. This is for home applicants, but international applicants are still encouraged to apply as an international fee waiver may be available. Contact me via email if you're interested in learning more. More info on the type of project can be found on the FindAPhD page 

 

Other opportunities

  • The University of Glasgow offers James McCune Smith scholarships for black UK domiciled students. More information: https://www.gla.ac.uk/scholarships/mccune-smith/

  • I am a PhD supervisor within the DiveIn CDT. This CDT prioritises diversity and aims to produce transformative interdisciplinary research in various areas, including Net Zero and AI.
    It offers fully funded four-year interdisciplinary PhDs starting in September 2025.
    More information: DiveIn CDT and my supervisor profile
    Please don't hesitate to reach out to me if you have been accepted onto this CDT and are interested in my work. 

Past/upcoming supervision

  • I will be supervising a project on Deep Learning Optimization and Design Choices for Marine Biodiversity Monitoring with Dr. De Clippele (University of Glasgow) within the Leverhulme Programme for Doctoral Training in Ecological Data Science. 

  • I am a co-supervisor on the marine rewilding effect project with Dr. De Clippele (main, University of Glasgow), Dr. Hogdson (CreditNature), and Dr. Wartmann (University of Aberdeen) within the NETGAIN CDT. 

  • I am a co-supervisor on the Developing GPU-accelerated digital twins of ecological systems for population monitoring and scenario analyses project with Prof. Torney (main, University of Glasgow), Prof. Morales (University of Glasgow), Prof. McCrea (Lancaster University), and Prof. Husmeier (University of Glasgow) within the ExaGEO CDT. 
  • Deploying deep learning for marine biodiversity monitoring with Dr. De Clippele (main, University of Glasgow) and Dr. Smith (University of Copenhagen) within the Leverhulme Programme for Doctoral Training in Ecological Data Science.
  • UG Summer Research project on Monitoring vegetation patterns from drone imagery using machine learning, with Dr. Eizenhöfer (University of Glasgow).
  • Machine learning for camera trap image classification, EPSRC Vacation Project with Dr. Peter Stewart (University of Glasgow).
  • AIMS Ghana MSc in Mathematical Sciences projects.
  • EPSRC Vacation Internship on physics-informed neural networks.
  • Level 4 Projects on neural network optimisation.
  • MSci project with industry co-supervisor Dr. Hodgson (CreditNature) on Simulating Scottish Ecosystems for Landscape Ecology Metrics.

Teaching

Mathematics 1G: Introduction to Algebra, Geometry & Networks (MATHS1016).

Large-Scale Computing for Data Analytics (STATS5083), University of Glasgow, 2025.

Additional information

I'm a board member of the One World Seminar on Mathematics of Machine Learning (https://www.oneworldml.org/), come along to the talks if you’re interested!

I am also an organizer of the Network on Mathematical Data Science for Materials Science. 

I am passionate about increasing diversity in postgraduate research programmes in STEM. I am  a Women in Machine Learning (WiML) board member and was General Chair of the WiML workshop at NeurIPS 2024. I co-founded the Piscopia Initiative (piscopia.co.uk) to provide information about how to apply to PhDs and what doing a PhD is like. The events are particularly aimed at women and non-binary people in Mathematics, but anyone is welcome to attend. Check out the https://piscopia.co.uk/contacts/ section for info on related initiatives. 


Tiffany Vlaar