About me



Staff Machine Learning Engineer


March 2023 – Present London, UK

Senior Machine Learning Engineer


September 2019 – February 2023 London, UK
As a Senior Machine Learning Engineer, I lead projects in Content Platform Research, a squad undertaking research across the Content Platform tribe. Our tribe is responsible for maintaining, enriching and serving the vast Spotify catalog to our users (including content and metadata). I have worked on Knowledge Graph recommendations, Entity Resolution, Graph ML.

Senion Research Scientist

Kheiron Medical Technologies

October 2017 – September 2019 London, UK
Kheiron is a medical imaging startup devoted to helping radiologists improve their accuracy and productivity in breast cancer screening. As a senior member of the Machine Learning team my main focus is identifying and pursuing innovative directions inspired from recent academic advances in the field. I also actively contribute to the improvement of the company’s core products and shared codebase.

Senior Engineer

Toyota Motor Europe

November 2016 – October 2017 Brussels, Belgium
As part of the Recognition Technology team at Toyota, I was responsible for developing Deep Learning methods for vision-based automated driving. My efforts were focused on both adding new functionality as well as making the underlying algorithms more computationally affordable. More specifically I have had the opportunity to work on state-of-the-art network architectures for Semantic Segmentation, Depth Estimation and Visual Odometry, using Caffe and Tensorfow.

Computer Vision Scientist


May 2016 – November 2016 London, UK
As part of the Science Department and the Special Projects Lab at AIG, I have had the opportunity to work on a few innovative projects within the business. More specifically, I proposed a complete solution to build a virtual assistant at AIG (similar to Alexa, Siri or Cortana), including appropriate methods and data collection process. I investigated models based predominantly on Recurrent Neural Networks for intent tracking and slot-filling in interactive dialogue systems. As part of my work on this project, I also created a demo application in Python for the purpose of showcasing the potential benefits to the business. I also worked on the following projects: a) Real-time Object Detection using Tensorflow. b) Risk modelling for insurance claims data, using hierarchical Bayesian methods.

Research Scientist

Conrtexica Vision Systems

October 2014 – May 2016 London, UK
Cortexica is a computer vision start-up, spin-off from Imperial College, providing content-based image retrieval to several major retailers (JohnLewis, Macy’s) For over a year, I was responsible for devising machine learning algorithms focused towards improving the retrieval pipeline. Finally for almost a year I served as Scrum Master of the Research Team, responsible for applying Agile principles and practices.

Ph.D. Researcher

Imperial College London

October 2010 – October 2014 London, UK
During my PhD I had the opportunity to work with a very wide variety of predominantly Bayesian methods for the purpose of modelling human motion. I worked extensively on Bayesian non-parametrics and Dirichlet processes, Gaussian processes, Gaussian process latent variable models (GPLVM) and many more. I was performing approximate inference by means of Variational Bayesian and MCMC sampling (Gibbs, HMC and ESS). More specifically, during the second half of my PhD I worked on Bayesian matrix and tensor factorisation for the purpose of rectifying severely damaged time-series in an unsupervised manner. Similar approaches are frequently utilised in recommender systems..

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