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| Original file line number | Diff line number | Diff line change |
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| --- | ||
| title: "About" | ||
| permalink: /about | ||
| layout: single | ||
| author_profile: true | ||
| --- | ||
| <script | ||
| src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML" | ||
| type="text/javascript"> | ||
| </script> | ||
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| # Intro | ||
| Hello, I am a Machine Learning Engineer with a background in Physics and Chemistry. | ||
| I am very interested in applications of ML to science, particularly to the problem of climate | ||
| change. | ||
| I am currently working as a Machine Learning Engineer at [Carbon Re]("https://carbonre.com/"), | ||
| a startup that uses machine learning to help heavy industry companies reduce their carbon footprint. <br> | ||
| <br> | ||
| Most of my interests revolve around the intersection of ML and the physical sciences. | ||
| Right now I am particularly interested in: | ||
| applications of deep learning to PDEs (PINNs, NeuralODEs...), | ||
| Reinforcement Learning for dynamical systems and Bayesian Deep Learning | ||
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| # My favourite derivations | ||
| ## The MAP estimate of a linear model | ||
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| $$ | ||
| \begin{align*} | ||
| \theta_{\text{MAP}} &= \arg\max_{\theta} p(\theta | \mathcal{D})\\ | ||
| &= \arg\max_{\theta} \frac{p(\mathcal{D} | \theta) p(\theta)}{p(\mathcal{D})} \\ | ||
| &= \arg\max_{\theta} p(\mathcal{D} | \theta) p(\theta) \\ | ||
| &= \arg\max_{\theta} \log p(\mathcal{D} | \theta) + \log p(\theta) \\ | ||
| &= \arg\max_{\theta} \log \prod_{i=1}^N p(y_i | x_i, \theta) + \log p(\theta) \\ | ||
| &= \arg\max_{\theta} \sum_{i=1}^N \log p(y_i | x_i, \theta) + \log p(\theta) \\ | ||
| &= \arg\max_{\theta} \sum_{i=1}^N \log \mathcal{N}(y_i | \theta^T x_i, \sigma^2) + \log \mathcal{N}(\theta | 0, \alpha^2) \\ | ||
| &= \arg\max_{\theta} \sum_{i=1}^N -\frac{1}{2\sigma^2} (y_i - \theta^T x_i)^2 - \frac{1}{2\alpha^2} \theta^T \theta \\ | ||
| &= \arg\min_{\theta} \sum_{i=1}^N (y_i - \theta^T x_i)^2 + \frac{\sigma^2}{\alpha^2} \theta^T \theta \\ | ||
| &= \arg\min_{\theta} \sum_{i=1}^N (y_i - \theta^T x_i)^2 + \lambda \theta^T \theta \\ | ||
| \end{align*} | ||
| $$ | ||
| Which is the well-known Ridge regression problem. | ||
| ## The Particle in a box | ||
| The time-independent Schrödinger equation for a particle in a box is given by: | ||
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| $$ | ||
| \begin{align*} | ||
| -\frac{\hbar^2}{2m} \frac{d^2 \psi(x)}{dx^2} + V(x) \psi(x) &= E \psi(x) \\ | ||
| -\frac{\hbar^2}{2m} \frac{d^2 \psi(x)}{dx^2} &= (E - V(x)) \psi(x) \\ | ||
| \end{align*} | ||
| $$ | ||
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| Where $$V(x) = 0$$ for $$0 < x < L$$ and $$V(x) = \infty$$ otherwise. | ||
| Solving the equation by inspection we can see that : | ||
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| $$ | ||
| \begin{align*} | ||
| \psi(x) &= A \sin(kx) + B \cos(kx) \\ | ||
| k &= \frac{\sqrt{2mE}}{\hbar} | ||
| \end{align*} | ||
| $$ |
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| --- | ||
| title: "Blogs" | ||
| permalink: /blogs | ||
| layout: single | ||
| author_profile: true | ||
| --- | ||
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| hello world |
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| --- | ||
| title: "Invited Talks" | ||
| permalink: /invited-talks | ||
| layout: single | ||
| author_profile: true | ||
| --- | ||
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| <details> | ||
| <summary><i>London 2050</i>, UCL Engineering 2024</summary> | ||
| <br> | ||
| Invited for a panel with BNP Paribas and ABB to talk about the future of London. | ||
| </details> | ||
| <br> | ||
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| <details> | ||
| <summary><i>Machine Learning Engineer at a Climate Tech Startup</i>, UCL Statistics Society, 2023</summary> | ||
| <br> | ||
| I spoke about my experience as a Machine Learning Engineer at Carbon Re, a climate tech startup. I talked about the challenges of working in a startup as well as the experience of shifting from academia to industry. | ||
| </details> | ||
| <br> | ||
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| <details> | ||
| <summary><i>Tips on running a succesful Masters Project</i>, UCL AI for Sustainable Development cohort 2022</summary> | ||
| <br> | ||
| My Masters project was a huge success and I was invited to give a talk to the incoming students on how to run a successful project by my supervisor Maria Perez Ortiz who is the head of the course. I talked about the importance of a preparing correctly. Especially, given that I had created my own topic. | ||
| </details> | ||
| <br> | ||
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| <details> | ||
| <summary><i>What's the big deal with the James Webb Space Telescope?</i>, UCL AI Centre, 2022</summary> | ||
| <br> | ||
| I was invited to give a talk at the UCL AI Centre on the James Webb Space Telescope. I talked about the scientific goals of the mission and its potential impact for the big topics in astrophysics and cosmology such as the Hubble tension and galaxy seeding. | ||
| </details> |
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| --- | ||
| title: "Publications" | ||
| permalink: /publications | ||
| layout: single | ||
| author_profile: true | ||
| --- | ||
| <details> | ||
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| <summary> <a href="https://arxiv.org/abs/2312.06527" target=_blank >[1]</a> <i>Can Reinforcement Learning support policy makers? A preliminary study with Integrated Assessment Models</i>, <br> NeurIPS 2023 Workshop on Tackling Climate Change with AI </summary> | ||
| <br> | ||
| As part of my Masters at UCL, I worked on using Reinforcement Learning in an Integrated | ||
| Assessment model. This was very novel work and there was very little prior work to base my work on. | ||
| <br><br> | ||
| It was a hugely challenging project, but I learned a lot about RL and the problems in applying it to dynamical systems. | ||
| Notably, I wrote my own implementations of A2C, PPO, DQN, Double DQN (with duelling nets + PER buffer) in Pytorch. | ||
| I subsequently published my work at the workshop "Tackling Climate Change with AI" at NeurIPS 2023. | ||
| <br><br> | ||
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| You can find my NeurIPS publication <a href="https://arxiv.org/abs/2312.06527" target=_blank >here</a> and my poster <a href="/assets/images/neuripsposterfinal.png" target=_blank >here</a>. | ||
| </details> | ||
| <br> | ||
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| Looking a bit empty here... Help me fill this up :) |
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