CV

Overview

I’m currently working as a data scientist in a systems neuroscience lab, and have a strong background in machine learning and physics. As an aspiring computational neuroscientist I’m interested in spiking neural networks and plasticity rules, and how these lead to complex computations and representations in the brain. I’m interested in interdisciplinary approaches that ground theory in known biological mechanisms and recordings.

Experience

MRC Laboratory of Molecular Biology

Cambridge, UK
Career Development Fellowship (Data Scientist) - Supervised by Marco Tripodi
May 2023 – Present

My work in the lab focuses on our data engineering and processing requirements, spanning the full life cycle from acquisition to publication. Work I have done includes:

  • Working on our behaviour rig codebases to collect and stream data from Arduinos, camera feeds, and electrophysiology and calcium imaging acquisition systems.
  • Writing custom DataJoint-based processing pipelines on our HPC including DeepLabCut tracking, spikesorting, and ROI extraction and calcium signal deconvolutions.
  • Implementing automatic dimensionality reduction algorithms on this data.
  • Creating interactive visualisation dashboards of our data.
  • Collaborations on publication-ready analysis, statistics and figures with lab members.

April19 Discovery Inc

London, UK
Internship/MSc Project Collaboration - Supervised by Brooks Paige and Andrea Karlova
May 2022 – Oct. 2022

My MSc project looked at machine learning approaches to virtual screening. In particular, I designed a novel graph neural network that can predict protein-ligand binding affinities from undocked ligands, using graph-based representations from the modern docking model EquiBind. With April19 I worked as part of a team using generative AI models applied to fragment-based lead discovery, searching for a drug that can inhibit the enzyme MurD ligase.

Technical Skills

  • Programming Languages: Proficient: Python, Bash, SQL; Working knowledge: C++
  • ML/Data Science Tools: PyTorch, Jax, NumPy, Pandas, Scikit-learn, Matplotlib, Plotly, Streamlit
  • High-Performance Computing: Docker/Singularity, Slurm, Nextflow, Numba (CUDA), HDFS, AWS + S3
  • Neuroscience Tools: DataJoint, DeepLabCut, CaImAn, KiloSort, NWB, CEBRA
  • Misc.: Linux, Git, LaTeX

Education

University College London

London, UK
MSc Machine Learning; Distinction (81%)
Sept. 2021 – Sept. 2022

Oxford University

Oxford, UK
BA Physics; First Class (80%)
Sept. 2017 – Jan. 2021

Winchester College

Winchester, UK
Pre-U: Maths, D1; Further Maths, D1; Physics, D1; Philosophy and Theology, D2
Sept. 2012 – June 2017

Miscellaneous

  • Supervision: I co-supervised a master’s student working on an image processing summer project. This project involved constructing a brain registration and cell detection pipeline for use on our lab’s section image datasets.
  • Reading Group: I currently lead the LMB’s computational neuroscience reading group, which covers material such as Dayan and Abbott, and for which I provide chapter notes and lead the discussion. See my notes at Dayan and Abbott Notes.
  • Simons Computational Neuroscience Imbizo: I’ve been selected to attend the 2025 summer school in Cape Town.

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