Machine learning engineer / data scientist / dabbler in full-stack and iOS development.
I’m a consultant data scientist who has developed software for particle accelerators, built models to assess COVID spread, used XAI to identify novel, life-saving drugs, among other things. I’m a privacy and open-source enthusiast, working on developing the future on machine learning in my spare time.
Publications
All publications have been worked on in my spare time.
- Koker, T., Mireshghallah, F., Titcombe, T. and Kaissis, G., 2021. U-Noise:Learnable Noise Masks for Interpretable Image Segmentation. arXiv preprint arXiv:2101.05791. [arxiv]
- Angelou, N., Benaissa, A., Cebere, B., Clark, W., Hall, A.J., Hoeh, M.A., Liu, D., Papadopoulos, P., Roehm, R., Sandmann, R., Schoppmann, P. and Titcombe, T., 2020. Asymmetric Private Set Intersection with Applications to Contact Tracing and Private Vertical Federated Machine Learning. arXiv preprint arXiv:2011.09350. [arxiv]
Latest Posts
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Bias Persists
In March 2021, a certain corner of the internet was shocked by a tweet which purports to highlight rampant sex-based bias in Google Translate. The tweet shows a translation...
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The Ten-Year Language
I recently read the Paul Graham essay “The Hundred-Year Language”, in which he envisions what the programming languages of the future will look like. While it makes for an...