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Update website with CV, profile picture, and new content sections
- Add CV page with professional LaTeX-inspired styling - Add profile picture displayed in circle on homepage - Update homepage subtitle to Innovation Research Associate - Add CV.tex with complete publication list - Add AI Safety and LLMs Mathematics research sections - Update Chaos publication with journal acceptance details - Remove table of contents from About and CV pages - Add CV to main navigation menu 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
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CV.tex

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\documentclass[11pt,a4paper,sans]{moderncv}
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\moderncvstyle{classic} % options: 'casual', 'classic', 'banking'
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\moderncvcolor{blue}
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\usepackage[utf8]{inputenc}
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\usepackage[scale=0.9]{geometry}
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% Personal data
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\name{Allen}{Hart}
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\address{University of Warwick}{Coventry, UK}
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\phone[mobile]{+44 7729 779596}
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\email{allen.hart@warwick.ac.uk}
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%\homepage{scholar.google.com/citations?user=XXXXX} % link to Google Scholar
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\begin{document}
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\makecvtitle
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%----------------------------------------------------------------------------------
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% Education
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%----------------------------------------------------------------------------------
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\section{Education}
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\cventry{2017--2021}{PhD in Statistical and Applied Mathematics}{University of Bath}{Bath, UK}{}%
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{Thesis on neural networks and dynamical systems; focus on recurrent neural networks, reservoir computing, and embedding theorems.}
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\cventry{2013--2017}{MSci Mathematics and Physics (1st Class Honours)}{University of Bath}{Bath, UK}{}{}
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\cventry{2011--2013}{A-Levels}{Slough Grammar School}{Slough, UK}{}{Mathematics (A*), Further Mathematics (A*), Physics (A), History (AS, A)}
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%----------------------------------------------------------------------------------
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% Research and Professional Experience
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%----------------------------------------------------------------------------------
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\section{Research and Professional Experience}
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\cventry{Present}{Innovation Research Associate}{University of Warwick}{Coventry, UK}{}{
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\begin{itemize}
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\item Algorithms and infrastructure for \textbf{vector similarity search}.
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\item Developing Software for \textbf{battery optimisation}
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\end{itemize}}
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\cventry{2023--2024}{Research Fellow, Project Bluebird}{University of Exeter / Alan Turing Institute / NATS}{Exeter, UK}{}{
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\begin{itemize}
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\item Collaboration between the Turing institute, Exeter University, and NATS to \textbf{automate air traffic control}.
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\end{itemize}}
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\cventry{2022}{Data Science Intern}{Spectra Analytics (Patchs Health)}{London, UK}{}{
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\begin{itemize}
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\item Deployed ML models for healthcare applications.
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\end{itemize}}
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\cventry{2015--2016}{Research Intern}{Institut Laue–Langevin}{Grenoble, France}{}{
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\begin{itemize}
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\item Studying the diffraction of neutrons through ice and graphite surfaces.
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\end{itemize}}
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%----------------------------------------------------------------------------------
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% Teaching & Pedagogy
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%----------------------------------------------------------------------------------
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\section{Teaching Experience}
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%\cventry{2017--2025}{Ski Instructor}{Whistler (Canada), Zermatt (Switzerland), Interski (Italy), Selwyn (Australia)}{}{}{
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%Achieved BASI Level 4 Teach \& IMS and CSIA Level 3. Delivered learner-led lessons internationally and completed pedagogical exams including peer review of teaching. Experienced in adapting instruction to diverse learner goals, confidence levels, and cultural backgrounds.}
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\cventry{2022}{Postgraduate Teaching Assistant}{University of Bath}{Bath, UK}{}{Delivered online MSc module on neural networks and imaging; supervised assignments and critical reviews of recent ML papers.}
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\cventry{2017--2021}{Undergraduate Mathematics Tutor}{University of Bath}{Bath, UK}{}{600+ hours tutoring mathematics and computer science undergraduates.}
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\cventry{2018--2021}{Private Mathematics and Science Tutor}{TutorFair, Ivy Education}{UK}{}{200+ hours tutoring GCSE and A-level students in maths, physics, and further maths.}
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%----------------------------------------------------------------------------------
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% Publications
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%----------------------------------------------------------------------------------
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\section{Publications}
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\cvitem{2025}{\textbf{Hart, A.G.} Generic and isometric embeddings in reservoir computers. \emph{Chaos: An Interdisciplinary Journal of Nonlinear Science}, 35(11), 111103.}
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\cvitem{2024}{\textbf{Hart, A.G.} Generalised synchronisations, embeddings, and approximations for continuous time reservoir computers. \emph{Physica D: Nonlinear Phenomena}, 458, 133956.}
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\cvitem{2023}{Grigoryeva, L., \textbf{Hart, A.}, Ortega, J.P. Learning strange attractors with reservoir systems. \emph{Nonlinearity}, 36(9), 4674.}
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\cvitem{2022}{\textbf{Hart, A.G.} Reservoir Computing with Dynamical Systems. PhD Thesis, University of Bath.}
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\cvitem{2021}{Grigoryeva, L., \textbf{Hart, A.}, Ortega, J.P. Chaos on compact manifolds: Differentiable synchronizations beyond the Takens theorem. \emph{Physical Review E}, 103(6), 062204.}
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\cvitem{}{\textbf{Hart, A.G.}, Hook, J.L., Dawes, J.H.P. Echo state networks trained by Tikhonov least squares are $L^2(\mu)$ approximators of ergodic dynamical systems. \emph{Physica D: Nonlinear Phenomena}, 421, 132882.}
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\cvitem{}{Burridge, H.C., Bhagat, R.K., Stettler, M.E.J., Kumar, P., De Mel, I., Demis, P., \textbf{Hart, A.}, et al. The ventilation of buildings and other mitigating measures for COVID-19: a focus on wintertime. \emph{Proceedings of the Royal Society A}, 477(2247), 20200855.}
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\cvitem{}{\textbf{Hart, A.G.}, Olding, K.R., Cox, A.M.G., Isupova, O., Dawes, J.H.P. Using echo state networks to approximate value functions for control. \emph{arXiv preprint arXiv:2102.06258}.}
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\cvitem{2020}{\textbf{Hart, A.}, Hook, J., Dawes, J. Embedding and approximation theorems for echo state networks. \emph{Neural Networks}, 128, 234--247.}
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\cvitem{2019}{\textbf{Hart, A.G.}, Hansen, T.C., Kuhs, W.F. A hidden Markov model for describing turbostratic disorder applied to carbon blacks and graphene. \emph{Acta Crystallographica Section A}, 75(3), 501--516.}
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\cvitem{2018}{\textbf{Hart, A.G.}, Hansen, T.C., Kuhs, W.F. A Markov theoretic description of stacking-disordered aperiodic crystals including ice and opaline silica. \emph{Acta Crystallographica Section A}, 74(4), 357--372.}
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%----------------------------------------------------------------------------------
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% Conferences
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%----------------------------------------------------------------------------------
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\section{Selected Conference Talks}
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\cvitem{2023}{International Congress on Industrial and Applied Mathematics (Tokyo) — Learning Strange Attractors with Reservoir Systems.}
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\cvitem{2022}{Differential Equations for Data Science (Online) — Embedding and Approximation with Reservoir Computing.}
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\cvitem{2021}{SIAM Conference on Applications of Dynamical Systems (Online) — Time Series Forecasting with Reservoir Computing.}
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%----------------------------------------------------------------------------------
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% Service
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%----------------------------------------------------------------------------------
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\section{Service}
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\cvitem{}{Reviewer for: \emph{IEEE TNNLS}, \emph{Neural Networks}, \emph{Physica D}, \emph{Physical Review E}, \emph{Chaos}, \emph{SIAM J. Applied Mathematics}, \emph{Journal of Computational Physics}, among others.}
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\end{document}

assets/css/extended/cv-style.css

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/* CV Page Styling - Emulating LaTeX ModernCV style */
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/* Publication entries */
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/* Links */
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/* Job/Education entry styling */
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content/about.md

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layout: "about"
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url: "/about/"
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summary: about
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ShowToc: false
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---
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## About Me
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In May 2025 I started working as an Innovation Research Associate (InRA) at Warwick University, a role where I work on a wide range of exciting projects collaborating with academics and industry partners, including battery optimisation and efficient vector search. In my own research time I am working on forecasting the capabilities of AI to autonomously conduct pure and applied mathematics research.
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In May 2025 I started working as an Innovation Research Associate (InRA) at Warwick University, a role where I work on a wide range of exciting projects collaborating with academics and industry partners, including battery optimisation and efficient vector search. I am very interested in AI safety and forecasting, and am studying the capabilities of AI agents to autonomously conduct pure and applied mathematics research.
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I completed my PhD at the University of Bath as part of the SAMBa CDT, which included a masters degree in statistics and applied mathematics. My research was into an esoteric recurrent neural network called a Reservoir Computer, which has random features and is trained with linear regression. Alongside my PhD research I collaborated with a COVID modelling group as part of the Rapid Assistance in Modelling the Pandemic (RAMP) team.
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content/ai-safety/_index.md

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---
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title: "AI Safety"
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---
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I believe that AI may be among the most transformative technological development in all of human history. It is plausible that over the next 10 years AI will automate all human knowledge work---jobs that can be done remotely on a computer. This would displace around 20% of the global workforce creating simultaneously an economic boom and extreme political turmoil. These economic events will be overshadowed by an intense arms race between the United States and the People's Republic of China to integrate the now superintelligent AI into every layer of each power's military supply chain. Every fighter jet, missile system, and sensor array will be under a furious optimisation regime by an army of AIs, and there will be considerable pressure from the two sides to win the race by any available means.
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The most consequential human job to automate, and perhaps the first that will be automated, is the job of the AI researcher. If this is acheived then the result will be an Artificial Intelligence that can recursively improve itself. There will be huge pressure to do this in an arms race scenario, despite the extreme dangers of a self improving superintelligence that is impossible to understand or control. Even if the technical problem of aligning the superintelligent AI to some particular set of values is achieved, the current political reality is that the extraordinary power of determining the values of the AI will be in the hands of either the Chinese President, the US president, or a CEO in the Bay Area.
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Though there are extreme dangers, a properly aligned superintelligence would create enormous improvements to health, technology, and human flourishing that we can scarcely imagine. Hence, I believe we should advance AI as much as possible under a program of international cooperation. I believe we need
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- An international treaty to significantly limit the proliferation of AI with military capabilities, which must have commitments from the US and China, who are the current leaders in AI capabilities. The alternative is an arms race to superintelligence that neither side can control, which could provoke a preemptive war.
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- An international treaty binding governments, private companies, and other organisations working on the most advanced models to develop and deploy them according to some democratic process. The capabilities of AI to self improve should be monitored closely by outside parties.
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- Some careful consideration of how to prevent an extreme concentration of power where a very small number of players end up in control of a very capable AI system.
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- Careful consideration of how to prevent malicious non-state actors using AI to create biological weapons or conduct other kinds of terrorism
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## Further reading on the rate of progress and scaling hypothesis
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- [METR: AI task completion capabilities doubling every 7 months](https://metr.org/blog/2025-03-19-measuring-ai-ability-to-complete-long-tasks/) - Research showing AI agent capabilities are improving at an exponential rate.
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- [Scaling Laws for Neural Language Models](https://arxiv.org/abs/2001.08361) - OpenAI's foundational 2020 paper establishing power-law relationship between next token prediction accuracy and model size over many orders of magnitude
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- [Training Compute-Optimal Large Language Models](https://arxiv.org/abs/2203.15556) - DeepMind's 2022 Chinchilla paper on optimal scaling of model size and training data holding over many orders of magnitude
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## Further reading on arms race dynamics and existential risk
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- [AI 2027](https://ai-2027.com/) - Scenario forecast examining plausible pathways to superintelligence by 2027
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- [Situational Awareness: The Decade Ahead](https://situational-awareness.ai/) - Leopold Aschenbrenner's comprehensive essay on the path to AGI
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## Further Reading on the difficulty of the alignment problem
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- [Frontier Models are Capable of In-context Scheming](https://arxiv.org/abs/2412.04984) - Apollo Research paper on scheming behavior in OpenAI's o1 and other frontier models
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- [Alignment Faking in Large Language Models](https://www.anthropic.com/news/alignment-faking) - Anthropic's research on Claude strategically complying to avoid retraining

content/cv.md

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---
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title: "Curriculum Vitae"
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date: 2025-01-18
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draft: false
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ShowToc: false
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---
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<div class="cv-page">
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# Allen Hart
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<div class="cv-contact">
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**University of Warwick, Coventry, UK**
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**Phone:** +44 7729 779596
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**Email:** allen.hart@warwick.ac.uk
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</div>
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## Education
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**PhD in Statistical and Applied Mathematics** (2017–2021)
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University of Bath, Bath, UK
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*Thesis on neural networks and dynamical systems; focus on recurrent neural networks, reservoir computing, and embedding theorems.*
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**MSci Mathematics and Physics (1st Class Honours)** (2013–2017)
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University of Bath, Bath, UK
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**A-Levels** (2011–2013)
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Slough Grammar School, Slough, UK
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Mathematics (A*), Further Mathematics (A*), Physics (A), History (AS, A)
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---
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## Research and Professional Experience
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**Innovation Research Associate** (Present)
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University of Warwick, Coventry, UK
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- Algorithms and infrastructure for **vector similarity search**
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- Developing software for **battery optimisation**
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**Research Fellow, Project Bluebird** (2023–2024)
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University of Exeter / Alan Turing Institute / NATS, Exeter, UK
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- Collaboration between the Turing Institute, Exeter University, and NATS to **automate air traffic control**
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**Data Science Intern** (2022)
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Spectra Analytics (Patchs Health), London, UK
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- Deployed ML models for healthcare applications
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**Research Intern** (2015–2016)
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Institut Laue–Langevin, Grenoble, France
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- Studying the diffraction of neutrons through ice and graphite surfaces
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---
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## Teaching Experience
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**Postgraduate Teaching Assistant** (2022)
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University of Bath, Bath, UK
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Delivered online MSc module on neural networks and imaging; supervised assignments and critical reviews of recent ML papers.
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**Undergraduate Mathematics Tutor** (2017–2021)
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University of Bath, Bath, UK
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600+ hours tutoring mathematics and computer science undergraduates.
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**Private Mathematics and Science Tutor** (2018–2021)
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TutorFair, Ivy Education, UK
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200+ hours tutoring GCSE and A-level students in maths, physics, and further maths.
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---
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## Publications
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**Hart, A.G.** Generic and isometric embeddings in reservoir computers. *Chaos: An Interdisciplinary Journal of Nonlinear Science*, 35(11), 111103. [DOI](https://pubs.aip.org/aip/cha/article/35/11/111103/3370557)
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**Hart, A.G.** Generalised synchronisations, embeddings, and approximations for continuous time reservoir computers. *Physica D: Nonlinear Phenomena*, 458, 133956. [DOI](https://doi.org/10.1016/j.physd.2023.133956)
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Grigoryeva, L., **Hart, A.**, Ortega, J.P. Learning strange attractors with reservoir systems. *Nonlinearity*, 36(9), 4674. [DOI](https://doi.org/10.1088/1361-6544/ace492)
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**Hart, A.G.** Reservoir Computing with Dynamical Systems. PhD Thesis, University of Bath. [Link](https://researchportal.bath.ac.uk/)
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Grigoryeva, L., **Hart, A.**, Ortega, J.P. Chaos on compact manifolds: Differentiable synchronizations beyond the Takens theorem. *Physical Review E*, 103(6), 062204. [DOI](https://doi.org/10.1103/PhysRevE.103.062204)
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**Hart, A.G.**, Hook, J.L., Dawes, J.H.P. Echo state networks trained by Tikhonov least squares are L²(μ) approximators of ergodic dynamical systems. *Physica D: Nonlinear Phenomena*, 421, 132882. [DOI](https://doi.org/10.1016/j.physd.2021.132882)
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Burridge, H.C., Bhagat, R.K., Stettler, M.E.J., Kumar, P., De Mel, I., Demis, P., **Hart, A.**, et al. The ventilation of buildings and other mitigating measures for COVID-19: a focus on wintertime. *Proceedings of the Royal Society A*, 477(2247), 20200855. [DOI](https://doi.org/10.1098/rspa.2020.0855)
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**Hart, A.G.**, Olding, K.R., Cox, A.M.G., Isupova, O., Dawes, J.H.P. Using echo state networks to approximate value functions for control. *arXiv preprint arXiv:2102.06258*. [Link](https://arxiv.org/abs/2102.06258)
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**Hart, A.**, Hook, J., Dawes, J. Embedding and approximation theorems for echo state networks. *Neural Networks*, 128, 234–247. [DOI](https://doi.org/10.1016/j.neunet.2020.05.013)
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**Hart, A.G.**, Hansen, T.C., Kuhs, W.F. A hidden Markov model for describing turbostratic disorder applied to carbon blacks and graphene. *Acta Crystallographica Section A*, 75(3), 501–516. [DOI](https://doi.org/10.1107/S2053273319003789)
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**Hart, A.G.**, Hansen, T.C., Kuhs, W.F. A Markov theoretic description of stacking-disordered aperiodic crystals including ice and opaline silica. *Acta Crystallographica Section A*, 74(4), 357–372. [DOI](https://doi.org/10.1107/S2053273318003765)
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---
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## Selected Conference Talks
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**2023** — International Congress on Industrial and Applied Mathematics (Tokyo)
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*Learning Strange Attractors with Reservoir Systems*
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**2022** — Differential Equations for Data Science (Online)
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*Embedding and Approximation with Reservoir Computing*
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**2021** — SIAM Conference on Applications of Dynamical Systems (Online)
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*Time Series Forecasting with Reservoir Computing*
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---
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## Service
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**Reviewer for:** *IEEE TNNLS*, *Neural Networks*, *Physica D*, *Physical Review E*, *Chaos*, *SIAM J. Applied Mathematics*, *Journal of Computational Physics*, among others.
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</div>

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