CV

Overview

I’m a researcher in computational neuroscience, with a background in machine learning and physics. I’m interested in spiking neural networks and plasticity rules, with a focus on spatial cognitive maps. I’m interested in interdisciplinary approaches that ground theory and simulations in activity recordings and connectomics datasets.

Experience

MRC Laboratory of Molecular Biology

Cambridge, UK
Career Development Fellowship (Data Scientist/Software Developer) - Supervised by Marco Tripodi
May 2023 – May 2025

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

  • 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

Teaching

  • Teaching assistant: King’s College Cambridge (Sept 2024 – May 2025) I led undergraduate supervisions (small group classes) for 8 students for the Cambridge Natural Sciences Part IA Mathematical Biology course.
  • Master’s Supervision: Cyril van Leer (June 2023 - Sept 2023) I co-supervised a master’s student working on a project constructing a brain registration and cell detection pipeline for use on our lab’s section image datasets.

Misc.

  • Reading Group: I founded and led the LMB’s computational neuroscience reading group, which covered Dayan and Abbott. See my notes at Dayan and Abbott Notes.
  • Simons Computational Neuroscience Imbizo: (Jan 2025 – Feb 2025) I attended the Imbizo summer school in Cape Town, where I worked on a project doing network-based modelling of learning in the Drosophila mushroom body.

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