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Research Associate in Machine Learning, interested in the intersection of multi-modal learning and meta-learning.
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
University of Edinburgh
Data Scientist
June 19, 2026 – Present
minimal-ml-template
January 25, 2023 – April 8, 2023
A very minimal ml project template that uses HF transformers and wandb to train a simple NN and evaluate it, in a stateless manner compatible with Spot instances kubernetes workflows
View ProjectFewShotContinualLearningDataProvider
February 18, 2020 – April 15, 2020
The original code for the data providers and the datasets of the paper "Defining Benchmarks for Continual Few-Shot Learning".
View ProjectFewShotContinualLearning
November 24, 2019 – August 18, 2020
The original code for the paper "Benchmarks for Continual Few-Shot Learning".
View ProjectLearning_to_Learn_via_Self-Critique
May 30, 2019 – August 10, 2019
The original code for the paper "Learning to Learn via Self-Critique".
View ProjectHowToTrainYourMAMLPytorch
October 20, 2018 – December 5, 2023
The original code for the paper "How to train your MAML" along with a replication of the original "Model Agnostic Meta Learning" (MAML) paper in Pytorch.
View ProjectDAGAN
November 24, 2017 – March 24, 2023
DAGAN: Data Augmentation Generative Adversarial Networks
View ProjectMatchingNetworks
May 25, 2017 – July 1, 2018
An attempt at replicating the Matching Networks for One Shot Learning in Tensorflow - Paper URL: https://arxiv.org/pdf/1606.04080.pdf
View ProjectDeepClassificationBot
April 10, 2016 – July 18, 2016
A deep learning powered bot capable of classifying images into user-specified categories
View Projectmlpractical
September 22, 2015 – February 15, 2020
Machine Learning Practical course repository
View ProjectCultural Fit Analysis
The candidate's profile shows a strong inclination towards research and academic-style projects, often replicating or implementing papers. This suggests a fit for environments that value deep technical exploration and innovation. However, the lack of diverse project types (e.g., production deployments, business-focused applications) and limited information on team collaboration makes a comprehensive cultural fit assessment challenging. The single listed experience as 'Data Scientist' at a university aligns with their research-heavy project portfolio.
Soft Skills & Operational Fit
Insufficient data to assess soft skills and operational fit. The candidate's profile primarily highlights technical project work without details on collaboration, problem-solving approaches, or communication in a team setting.