Samuel Holt

Samuel Holt

PhD Researcher

University of Cambridge


Hi, I am a third-year Ph.D. student in Machine Learning at the University of Cambridge advised by Mihaela van der Schaar in the Machine Learning and Artificial Intelligence group. To date, I have published six papers in top-tier ML conferences (NeurIPS, ICML [long oral], ICLR [spotlight] and AISTATS).

  • Robotics
  • Large Language Models (LLMs)
  • Transformer Architectures
  • Reinforcement Learning (model-free and model-based)
  • Generative Models
  • Control
  • Time Series Forecasting
  • Symbolic Regression (discovery)
  • Continuous-time models (ODEs and Laplace transforms)
  • Applications to healthcare and scientific discovery
  • PhD in Machine Learning, 2021 - 2025

    University of Cambridge

  • MEng in Engineering Science, 2013 - 2017

    University of Oxford

Academic Service & Volunteering

October 2021 – Present
  • Conference Reviewer: ICML 2022, AISTATS 2023, NeurIPS 2023, ICLR 2024, ICML 2024, Nature Machine Intelligence 2024, NeurIPS 2024.
  • Workshop Reviewer: ICLR 2023 AI4ABM, NeurIPS 2022 & 2023 SyntheticData4ML.
Online Course | Packt Publishing
Machine Learning Course Teacher
October 2019 – June 2020

Sole Author and Teacher for Machine Learning and Deep Learning Course.

  • Authored ML video course, nine chapters, 10.5 video hours covering theory and code examples of ML and Deep Learning in Supervised Learning, Unsupervised Learning and Reinforcement Learning.
  • Covered Deep Learning for Computer Vision (GANs, VAEs, Style Transfer, Semantic Segmentation, CNNs), NLP, RNNs, Sequence to Sequence, Transformers, Time Series forecasting and Deep Reinforcement Learning (MDPs, Q-Learning, Value and Policy based methods, Multi-armed bandits, Inverse RL, Model-based RL, i.e. Alpha Zero).
  • All teaching Jupyter notebooks are online. Also contributed to open source ML frameworks, TensorFlow Core, OpenAI libraries and ML Wikipedia pages.
Founders Academy
Data Science Teacher
Founders Academy
February 2020 – February 2020
Week long course, classroom of 20 students introducing data science and machine learning.



Python, Javascript, Typescript, RUST, MATLAB, Bash, SQL, C, C++


Jax, TensorFlow, PyTorch, Keras, NumPy, SciPy, Pandas, Asyncio, Nltk, Jupyter, PyTest


git, Linux, LaTeX, Google Cloud Platform, Amazon Web Services, Docker, GitLab CI

Invited Talks

Deep Learning in the Laplace Domain
AZ: Scientific Discovery through Scaling Symbolic Regression
Inspiration Exchange: Neural Differential Equations

Recent Publications

(2024). Discovering Preference Optimization Algorithms with and for Large Language Models. Arxiv.

PDF Cite Code Project

(2024). L2MAC: Large Language Model Automatic Computer for Extensive Code Generation. (ICLR 2024).

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(2024). ODE Discovery for Longitudinal Heterogeneous Treatment Effects Inference. (ICLR 2024) [Spotlight, top 5% of papers].

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(2023). Active Observing in Continuous-time Control. (NeurIPS 2023).

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(2023). Deep Generative Symbolic Regression. (ICLR 2023).

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(2023). Neural Laplace Control for Continuous-time Delayed Systems. (AISTATS 2023).

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(2022). Neural Laplace: Learning diverse classes of differential equations in the Laplace domain. (ICML 2022) [Long Oral, top 2% of papers].

PDF Cite Code Video