I am a PhD candidate at the University of Cambridge, working in ML@CL group lead by Neil Lawrence. My research lies somewhere between machine learning and software systems, leaning towards the latter. I also like to dabble into Bayesian optimization sometimes. Before jumping into the world of academia I have spent more than a decade as a software engineer, developing everything from small webapps to data center network software.
Emukit is the Python package for all kinds of sequential decision making methods under uncertainty: optimization, quadrature, experiment design, sensitivity analysis. It was developed and released by our research group in Amazon Cambridge, but now lives in a neutral territory. I am the lead developer and main maintainer of Emukit.
TTI Explorer is the simulation package we developed in DELVE to study effects of "test-trace-isolate" strategies on spread of COVID-19. Our report on it was released shortly before TTI was deployed in the UK, and received wide press coverage.
GPyOpt is one of the first Python packages for Bayesian optimization. GPyOpt was initially developed mostly by Javier González while he was with Neil Lawrence's group at the University of Sheffield. I took over ownership of GPyOpt from Javier, and lead the package development for few years. GPyOpt is now archived.
Trieste is a Bayesian optimization package built on Tensorflow. I became involved in Trieste during my placement at Secondmind, added several major features and became one of the main overall contributors to the codebase and discussions around it.
I was fortunate enough to contribute to many different parts of Amazon. Some highlights:
Data center network (my code likely powers your internet!)
Supply chain optimization technologies
Selected papers are mentioned here. For a complete list please check out the Google Scholar profile.
Andrei Paleyes, Christian Cabrera-Jojoa, Neil D. Lawrence
International Conference on AI Engineering - Software Engineering for AI (CAIN), 2022
[Paper on arXiv] [Paper at IEEE] [code]
Andrei Paleyes, Mark Pullin, Maren Mahsereci, Cliff McCollum, Neil D. Lawrence, Javier González
Second workshop on machine learning and the physical sciences, NeurIPS, 2019
Brendan Avent, Javier González, Tom Diethe, Andrei Paleyes, Borja Balle
Proceedings on Privacy Enhancing Technologies, 2020 (Andreas Pfitzmann Best Student Paper Award)
Privacy Preserving Machine Learning Workshop, 2019
[video] [code] [paper]
Andrei Paleyes, Raoul-Gabriel Urma, Neil D. Lawrence
The ML-Retrospectives, Surveys & Meta-Analyses Workshop, NeurIPS, 2020
ACM Computing Surveys (CSUR), 2022
[Paper on arXiv] [Paper in journal]
Virginia Aglietti, Xiaoyu Lu, Andrei Paleyes, Javier González
International Conference on Artificial Intelligence and Statistics, 2020
Bobby He, Sheheryar Zaidi, Bryn Elesedy, Michael Hutchinson, Andrei Paleyes, Guy Harling, Anne M. Johnson, Yee Whye Teh, Royal Society's DELVE group
Royal Society open science 8 (3), 2021
Causal Digital Twins workshop, ELLIS Unconference, January 2023
Industry Expert Insights, Cambridge Spark, August 19, 2021
Data Science Africa COVID-19 Webinar, April 8, 2020
ITShare: High load проекты на .Net, December 8, 2012
During the initial phase of the COVID-19 pandemic I became a member of the action team of the DELVE group (thanks to my supervisor Neil Lawrence for inviting!). DELVE (Data Evaluation and Learning for Viral Epidemics) is a multi-disciplinary group, convened by the Royal Society, to support a data-driven approach to learning from the different approaches countries are taking to managing the pandemic. Over the course of 2020 we produced a number of reports, software and datasets, and provided advice to SAGE and ultimately the UK Government.
I was involved in two installments of this workshop (led by Alessandra Tosi): as reviewer in 2020 and as organizer in 2021. We organizing it again for NeurIPS 2022!
[website 2020] [website 2021] [website 2022]