CV

(Out of date) PDF copy of my CV available here.

Education

• University of Oxford (2021-2026): DPhil Engineering Science
• University of Oxford (2017-2021): MEng Engineering Science (First class)
• A-Levels (2017): A*A*A*A | GCSEs (2015): 10 A*s, 2 As

Industry

Deep Learning Intern, QuantCo (Jun-Aug 2024)

• Worked on internal research project investigating LLM reasoning over long-context documents; prompt engineering and fine-tuning with noisy, multimodal long document (~30+ pages) data

Visiting Data Scientist, BCG X (Mar-May 2024)

• Data science/consulting internship; worked on the ‘Pathfinder’ flight schedule optimiser for British Airways
• Software engineering and data science in Python, working with front-end developers to implement user-controlled constraints and new features in the backend

Spring into Quant Finance, G-Research (Apr 2023)

• Selected for competitive Spring Insights programme with training in ML, data science and finance; opportunities to network with senior researchers, and a summer internship fast-track opportunity.

Intern, QinetiQ (Jul-Sep 2018, Jun-Sep 2019)

• Awarded student scholarship; undertook two summer internships in RF, Secure Networks and Comms
• Worked with team developing Counter-UAV Radar system, completed projects in MATLAB including data analysis on Doppler signatures and calibration files of a phased array radar, and used Monte Carlo simulation to find optimal calibrations.

Research

Graph Neural Networks, Networks & Society Group, MLRG, Oxford University (Aug 2022 - Present)

• Ongoing research in collaboration with Francesco Di Giovanni, Michael Bronstein and Xiaowen Dong.
• Developed a GNN message-passing framework that allows for adaptive information flow using dynamic rewiring and delayed propagation. First-author paper, “DRew: Dynamically Rewired Message Passing with Delay”, accepted to ICML 2023.

Zero Shot Coordination Project, Foerster Lab for AI Research, Oxford University (Apr 2022 – Aug 2022)

• AIMS mini-project investigating zero-shot coordination for continuous state-action spaces.
• Experimentation with (multi-agent) reinforcement learning algorithms in accelerated physics engine, using Jax/Flax.

HumBug Project, Machine Learning Research Group, Oxford University (Jun 2020 – Sep 2021)

• Summer research placements supervised by Prof. Stephen Roberts.
• Project concerns the use of low-cost smartphones and audio data analysis for the identification and tracking of mosquito species, for malaria prevention.
• Working on codebase and obtaining experimental results for Bayesian NN species classification for HumBugDB paper, accepted at NeurIPS 2021.
• Worked on testing neural network mosquito classifier, and building/testing voice activity detector for removal of human speech from recordings (2020).

Final Year Undergraduate Project, Bayesian Exploration Lab, Oxford University (Sep 2020 – June 2021)

• MEng Masters project supervised by Prof. Michael Osborne (project report pdf available here).
• Investigating the use of single-sample gradient estimators of evidence lower bound for variational inference, working with Edward Wagstaff.

Extracurriculars

ML Project Leader, Engineers Without Borders Oxford (Sep 2020 – Jun 2021)

• Led a team of ten in conducting a biomedical data analysis project, using ML techniques for identification of seizures and extracting biological information from noisy data.
• Also served as Director of Partnerships (Aug 2019 – Jul 2020).

STEP UP Ambassador, New College Oxford, Access & Outreach Dept. (Nov 2017 – Jun 2020)

• State school ambassador; led tours of college, produced revision materials for Oxford entrance exam.

Skills

• Python: experienced across a range of personal and academic projects, with data science (NumPy, SciPy, Pandas, etc.) and deep learning libraries (PyTorch, Jax/Flax).
• MATLAB: experience working with during QinetiQ internships and for and undergraduate projects.
• Grade 8 Jazz Clarinet with Distinction, Trinity College London (2016).

Publications

DRew: Dynamically Rewired Message Passing with Delay (2023)

Accepted at the 40th International Conference on Machine Learning (ICML 2023), Honolulu, Hawaii, USA. PMLR 202, 2023

Gutteridge, B., Dong, X., Bronstein, M. and Di Giovanni, F., 2023

HumBugDB: A Large-scale Acoustic Mosquito Dataset (2021)

Accepted at the 35th Conference on Neural Information Processing Systems (NeurIPS 2021) Track on Datasets and Benchmarks.

Kiskin, I., Sinka, M., Cobb, A.D., Rafique, W., Wang, L., Zilli, D., Gutteridge, B., Dam, R., Marinos, T., Li, Y. and Msaky, D., 2021. HumBugDB: A Large-scale Acoustic Mosquito Dataset. arXiv preprint arXiv:2110.07607.

HumBug–An Acoustic Mosquito Monitoring Tool for use on budget smartphones (2020)

Published in Methods in Ecology and Evolution, 12(10), pp.1848-1859.

Sinka, M.E., Zilli, D., Li, Y., Kiskin, I., Kirkham, D., Rafique, W., Wang, L., Chan, H., Gutteridge, B., Herreros‐Moya, E. and Portwood, H., 2021. HumBug–An Acoustic Mosquito Monitoring Tool for use on budget smartphones. Methods in Ecology and Evolution, 12(10), pp.1848-1859.