Professional experience

09/2022 – present     AI/ML Technical Lead at SS&C Blue Prism (London, UK)

  • Technical AI leadership across the SS&C Blue Prism product portfolio.
  • Management of machine learning projects from ideation through to delivery.
  • Focus R&D activity on delivering iterative value and product differentiation.
  • Drive collaboration with product management, architecture and engineering across product delivery lifecycle.
  • Performance management and career development for the machine learning team.

06/2021 – 08/2022     ML Project Lead at Blue Prism AI Labs (London, UK)

  • Established technical vision and product strategy for an R&D project to leverage computer vision to understand desktop screens and enable users to automate GUI application workflows.
  • Project manager leading a team of 6 researchers: coordinating R&D roadmap and delivery, establishing working practice, providing technical guidance (certification: AgilePM Foundation & Practitioner.
  • Acted as R&D proxy-product-manager/owner, managing stakeholders across product management, architecture, engineering, legal, security, as well as presenting to senior management.
  • Progressed the project from inception to the point of deploying a functional prototype to alpha customers for user testing and handing over development to engineering to productionise the prototype.

07/2019 – 05/2021     Research Engineer at Blue Prism AI Labs (London, UK)

  • Worked on 3 research projects: program synthesis from demonstrations, optical character recognition dedicated to rendered font on screens, and computer vision applied to application GUIs.
  • Independently designed, developed, and validated an improved OCR capability which has been integrated into the core product. As part of that I led stakeholder interactions, advised product engineering on implementation considerations, and provided technical support.
  • Drove academic engagement: based on a series of internal talks I gave, we organised a conference tutorial on Reinforcement Learning for Information Retrieval, presented 3x at SIGIR 2021, ECIR 2021, Search Solutions 2020.

04/2018 – 06/2019     Machine Learning Lead, Core Team at Synthesized (London, UK)

  • Led machine learning development at a start-up focusing on data simulation to address data inefficiencies.
  • Designed the machine learning backend architecture of the MVP and developed the core generative models for highly semantic tabular data.
  • Engaged with early customers, partners, and investors: delivering technical pitches, explaining technology and market differentiators, and presenting product vision.

Open-source experience

04/2017 – 01/2022     Lead developer of Tensorforce

  • Tensorforce is a TensorFlow library for applied reinforcement learning.
  • One of the three creators of the framework and the sole maintainer of the framework since 2018.
  • Designed the modular and flexible architecture of the framework as it is today.
  • Managed the open-source community by actively supporting users with the application of the framework to their reinforcement learning use cases, which has led to longer-term research collaborations and publications.

Research experience

10/2015 – 01/2020     PhD in Computer Science (University of Cambridge, UK)

  • Research project: Evaluating visually grounded language capabilities using microworlds.
  • Highly commended as one of two runners-up in the UK-wide CPHC/BCS Distinguished Dissertation Award 2020.
  • Supervised 3 MPhil projects/theses:
    • 2017/18: “Evaluating image description systems with truth-conditional semantics” (Palantir Prize awarded to Highly Commended MPhil Projects).
    • 2017/18: “How clever are the models exhibiting ‘super-human’ performance on the CLEVR VQA dataset?”.
    • 2016/17: “Emergence of communication in visually-grounded signalling games” (Google Prize for Best MPhil Project Report 2017).
  • Supervised various Computer Science modules: Advanced Algorithms, Artificial Intelligence I + II, Complexity Theory, Databases, Information Retrieval, Machine Learning and Bayesian Inference, Machine Learning and Real-World Data, Natural Language Processing, Prolog, Types.

07/2015 – 09/2015     Internship at Illumina (UK)

  • Research project: new models for representing genome reference data.
  • Implemented framework for a DNA graph sequence data structure in Python and C++.
  • Investigated and evaluated the use of this framework for various DNA alignment applications.

06/2014 – 09/2014     Research Assistantship at Karlsruhe Institute of Technology (Germany)

  • Research project: dynamic state estimation for rigid motions.
  • Implemented current algorithms for dynamic rigid motion estimation based on projected Gaussian distributions using Matlab.
  • Developed simulations to evaluate existing and newly implemented estimation techniques.


10/2015 – 01/2020     PhD Computer Science (University of Cambridge, UK)

  • PhD thesis: Evaluating visually grounded language capabilities using microworlds.
  • Supervisor: Ann Copestake, Professor of Computational Linguistics.
  • Highly commended as one of two runners-up in the UK-wide CPHC/BCS Distinguished Dissertation Award 2020.

10/2014 – 07/2015     MPhil Advanced Computer Science (University of Cambridge, UK)

  • Thesis: Active learning to rank for assessing the linguistic quality of sentences.

04/2012 – 04/2014     BSc Mathematics (Karlsruhe Institute of Technology, Germany)

  • Thesis: Branching processes.

10/2010 – 04/2014     BSc Informatics (Karlsruhe Institute of Technology, Germany)

  • Thesis: Modeling uncertain data using monads and an application to the sequence alignment problem (in cooperation with German Cancer Research Center Heidelberg).