Sam Scivier

Academic Profile

I graduated with a M.Sci. in Physics from the University of Birmingham in 2022. My fourth-year research project was in deep learning for efficient detection and parameter estimation of massive black hole binary mergers in the future Laser Interferometer Space Antenna mission, led by the European Space Agency. I was awarded the SWJ Smith Prize as the Physics M.Sci. graduating student with the highest overall mark.
In the summer 2019 I worked as a Quantum Research Intern at D-Wave Systems, a quantum computing company based in Burnaby, Canada. In the summer 2021 I worked as a Quantum Science Intern at Riverlane, a quantum software company based in Cambridge, UK, that is building an operating system for quantum computers. I worked on improving resource requirement estimation for performing quantum computations.

Current Research

I am working with Prof Tarje Nissen-Meyer (Exeter), Dr Paula Koelemeijer, and Dr Atılım Güneş Baydin (Oxford) on probabilistic deep learning for physics-based seismic hazard assessment.

 

My research is focused on developing and applying probabilistic machine learning methods to improve uncertainty quantification – in particular relating to seismic velocity models – in physics-based seismic hazard assessment. I am currently working on a deep learning method for the probabilistic merging of seismic velocity models. I aim for the methods I am developing to be more generally applicable to spatial datasets in other areas of environmental science.

 

Please feel free to reach out if you have any questions or want to find out more!

Publications

From previous work: N. S. Blunt, J. Camps, O. Crawford, R. Izsák, S. Leontica, A. Mirani, A. E. Moylett, S. A. Scivier, C. Sünderhauf, and P. Schopf et al., Perspective on the current state-of-the-art of quantum computing for drug discovery applications, J. Chem. Theory Comput. 18, 7001 (2022). E. M. Lykiardopoulou, A. Zucca, S. A. Scivier, and M. H. Amin, Improving nonstoquastic quantum annealing with spin-reversal transformations, Phys. Rev. A 104, 012619 (2021).