### About me

I am a Staff Research Scientist at DeepMind. I currently work on training LLMs.

Before joining DeepMind as a Research Engineer in 2016, I was a Software Engineer at Google Zurich working on NLP using neural networks.

I am a Staff Research Scientist at DeepMind. I currently work on training LLMs.

Before joining DeepMind as a Research Engineer in 2016, I was a Software Engineer at Google Zurich working on NLP using neural networks.

I obtained my PhD at UCL, supervised by Prof. Marc Deisenroth. I passed my viva without corrections in May 2023. My examination committee was formed by Dr. Ferenc HuszĂˇr and Dr. Patrick Rebeschini. You can find my PhD thesis here.

I finished my 4 year Computing MEng degree from Imperial College London in 2014, with first honours and a prize of excellence for an outstanding overall performance.

Generative models, reinforcement learning, natural language processing, scalable and safe machine learning.

I am currently focused on understanding optimisation in deep learning.

- On a continuous time model of gradient descent dynamics and instability in deep learning
- Why neural networks find simple solutions: the many regularizers of geometric complexity
- Discretization Drift in Two-Player Games
- Book chapter on GANs in Kevin Murphy's Probabilistic Machine Learning book.
- Spectral Normalisation for Deep Reinforcement Learning: an Optimisation Perspective
- A case for new neural network smoothness constraints
- Monte Carlo Gradient Estimation in Machine Learning
- Measure-Valued Derivatives for Approximate Bayesian Inference
- Training language GANs from scratch
- Deep Compressed Sensing
- Variational Approaches for Auto-Encoding GANs
- Many Paths to Equilibrium: GANs Do Not Need to Decrease a Divergence At Every Step
- Distribution matching in variational inference
- Learning Implicit Generative Models with the Method of Learned Moments
- Sequence-to-sequence neural network models for transliteration
- Networks with emotions, master thesis

- Lecture on Optimisation at the Mediterranean Machine Learning Summer School. Slides.
- Lecture at UCL on the intricacies of deep learning for generative models. Slides.
- Talk at ETH on the importance of discretisation drift in deep learning, August 2023. Slides.
- Invited talk at the Math Machine Learning seminar at the Max Planck Institute for Mathematics in the Sciences and UCLA, February 2023.
- Mediterranean Machine Learning Summer School: On GANs: the divergence and optimisation views (2022). Slides.
- Talk on smoothness constraints at the UCL AI journal club (2022).Slides
- Invited talk at the Leipzig Symposium on Intelligent Systems on discretization drift in deep learning (2022).
- Invited talk at the Neurips Workshop ICBINB 2021. Talk video.
- Invited lecture on generative models at the University of Liepzig (2021).
- Invited talk at Imperial College London on discretization drift in Deep Learning (2021). Video
- ProbAI summer school 2021 lecture on constructing GANs from divergences and distances. Slides.
- Invited lecture on GANs for Computer Vision at Politehnica University of Bucharest. Slides
- DeepMind UCL lecture series 2020: Generative adversarial networks. Video, Slides
- DeepMind UCL lecture series 2020: Unsupervised representation learning. Video, Slides
- Eastern European summer school 2020 unsupervised learning talk
- Training language GANs from Scratch at Ganocracy 2019
- VAE-GAN Hybrids at the GAN tutorial at CVPR 2018
- The power and the promise of generative models (University of Bath talk)
- Unsupervised learning for building intelligent systems
- Autoencoder GAN talk at GAN workshop at ICCV 2017
- TensorFlow talk at the Deep Learning, Tools and Methods workshop
- TensorFlow talk at DevFest 2016
- TensorFlow meetups in London and Madrid (2016)

- Optax: JAX optimization library. Github
- Monte Carlo Gradient estimation in machine learning source code. Github
- Deep Compressed Sensing source code. Github
- High performance GPU implementation of deep belief networks to assess their performance on facial emotion recognition from images. Github
- Hopfield networks and RBM implementation in Haskell. Github

- Organiser for the ICML 2022 workshop on "Continous time perspectives for ML". Website.
- Top ICML 2021 reviewer and expert reviewer. Top 200 reviewer NeurIPS 2018, Top reviewer NeurIPS 2019. Conference reviewer since 2017. JMLR and TMLR reviewer.
- Lab instructor for all labs and creator of the Bayesian learning lab at EEML.
- Lab instructor at TMLSS, teaching vision, autoregressive text models and latent variable image models.
- Website for the GAN tutorial at ICCV 2017 that I co-organized with Ian Goodfellow.