CV

Education

• Ph.D in Statistical Machine Learning, University of Oxford, 2024 (expected)
• M.Sc. in Statistics (Research), London School of Economics and Political Science, 2019
• B.Sc. in Economics, Leibniz University Hannover, 2018
• B.Sc. in Mathematics, Leibniz University Hannover, 2018
• A levels with best mark with only 16 years of age (find press releases here, here and here)

Work experience

• Summer 2022: Research Intern at DeepMind, London
  • Working under Borja Balle in the Robust and Verified AI group on differentially private diffusion models
• Summer 2021: Research Intern at Microsoft Research Cambridge, Cambridge
  • Working under the supervision of Javier González in the Health Intelligence Team on invariant risk minimisation for detecting relationships between TCR cells and diseases in corporation with Adaptive Biotechnologies
• Summer 2019: Associate Consulting Intern in the Digital Finance Group at KPMG, Dusseldorf
  • Extended Huff Model for location-based turnover prediction to reduce the mean absolute error to under 10%
  • Implemented turnover prediction and feature selection methods in Python using Azure DevOps for collaboration
• Spring 2019: Data Science Intern at MealIQ, London
  • Cleaned recipe database and helped debugging algorithm for accurate computation of calories in Python
  • Matched products of Tesco and Ocado to recipe ingredients using common NLP methods (i.e. Levenstein’s distance and skipgram)
• Summer 2018: Undergraduate Research Intern with the Federal Bank of Germany, Frankfurt
  • Independently researched on asymptotic behaviour of seasonal tests applied on daily data
  • Extensively programmed in R aiming to construct an efficient Monte Carlo study
  • Presented findings in order to instruct the remaining working unit
• Summer 2017: Associate Intern with the Boston Consulting Group, Stuttgart
  • Staffed on a project on the IT-Transformation of a large bank
  • Supported the program management office smoothing the project flow by gathering data and presenting the progress
  • Analyzed and detailed deep-dive topics in the communications work stream ensuring a smooth project flow on the client side
  • Evaluated several analytical ad hoc topics such that all deadlines could be met
• Summer 2016: Analyst Intern at Deloitte, Dusseldorf
  • Interned in the Corporate Finance - Transaction Services Division
  • Examined reports, evaluated cost centres, analysed P&Ls and balance sheets, supported the project team in many other matters
• 2015 - 2018: Undergraduate Research Assistant at the Institute of Statistics of the Leibniz University, Hanover
  • Assisted in scientific research in topics of financial time series analysis by writing reports/literature reviews or analysing data
  • Assisted in academic teaching: in particular managing the shift of the lectures from the German into the English language

Selected Prizes

• Microsoft Research PhD Fellowship (USD 15,000)
• Science Prize of Lower-Saxony (Euro 3,500): awarded to the 5 most exceptional students from Lower-Saxony (press coverage available here, here, here, and here)
• Winton Prize (GBP 500): Best Master Thesis of LSE Stats programmes
• RSS prize (GBP 500): awarded to the 2nd best performing student in one of the LSE Stats programmes
• Best BSc Economics student (non-monetary prize): awarded to the best performing student in the Economics programme of the University of Hannover

Skills

• Python and hopefully all important packages
• R
• Microsoft Office
• Linux (Ubuntu)
• Git

Publications

Deep Generative Pattern-Set Mixture Models for Nonignorable Missingness Imputation

S. Ghalebikesabi, R. Cornish, L. Kelly, C. Holmes (2021). " Deep Generative Pattern-Set Mixture Models." Proceedings of the 24th International Conference on Artificial Intelligence and Statistics (AISTATS).

Quasi-Bayesian nonparametric density estimation via autoregressive predictive updates

Ghalebikesabi, S., Holmes, C., Fong, E., & Lehmann, B. (2022). Quasi-Bayesian nonparametric density estimation via autoregressive predictive updates. Uncertainty in Artificial Intelligence (pp. 658-668). PMLR.

Identification of Underlying Disease Domains by Longitudinal Latent Factor Analysis for Secukinumab Treated Patients in Psoriatic Arthritis and Rheumatoid Arthritis Trials

Zhu X, Falck F, Ghalebikesabi S, Kormaksson M, Vandemeulebroecke M, Zhang C, Santos L, Hei Kwok C, West D, Mallon A, Martin R, Readie A, Gandhi K, Ligozio G, Nicholson G. (2021). "Identification of Underlying Disease Domains by Longitudinal Latent Factor Analysis for Secukinumab Treated Patients in Psoriatic Arthritis and Rheumatoid Arthritis Trials." ACR Convergence 2021. 1(2).

On Locality of Local Explanation Models

S. Ghalebikesabi, L. Ter-Minassian, K. Díaz-Ordaz, C. Holmes (2021). "On Locality of Local Explanation Models." 35th Conference on Neural Information Processing Systems (NeurIPS 2021). 1(2).

Bias Mitigated Learning from Differentially Private Synthetic Data: A Cautionary Tale

S. Ghalebikesabi, H. Wilde, J. Jewson, S. Vollmer, A. Doucet, C. Holmes (2021). " Bias Mitigated Learning from Differentially Private Synthetic Data: A Cautionary Tale." arXiv preprint arXiv:2108.10934..

Differentially Private Diffusion Models Generate Useful Synthetic Images (Best Student Paper Award at FL’IJCAI 2023)

Ghalebikesabi, S., Berrada, L., Gowal, S., Ktena, I., Stanforth, R., Hayes, J., ... & Balle, B. (2023). " Differentially private diffusion models generate useful synthetic images. " arXiv preprint arXiv:2302.13861..

A Rigorous Link between Deep Ensembles and (Variational) Bayesian Methods

Wild, V. D., Ghalebikesabi, S., Sejdinovic, D., & Knoblauch, J. (2023). " A Rigorous Link between Deep Ensembles and (Variational) Bayesian Methods." arXiv preprint arXiv:2305.15027.

Differentially Private Statistical Inference through $eta$-Divergence One Posterior Sampling

Jewson, J., Ghalebikesabi, S., & Holmes, C. (2023). Differentially Private Statistical Inference through $\beta $-Divergence One Posterior Sampling. arXiv preprint arXiv:2307.05194.

Service to the Community

• Communications Chair at NeurIPS 2022/NeurIPS 2023
• Publications Chair at AISTATS 2022
• Workshop organizer at ICBINB 2022, Advisor at at ICBINB 2023
• Reviewer at NeurIPS and AISTATS (Best Reviewer Award at AISTATS2023)
• Discussant at UAI
• Co-Founder of EquiStats - an equity and diversity group for middle and senior members of the Department of Statistics, Oxford