Ekaterina Abramova

Adjunct Assistant Professor of Management Science and Operations

PhD (Imperial College London) MSc (Birkbeck, University of London) BSc (Loughborough University)

Ekaterina Abramova holds a PhD in Artificial Intelligence and Machine Learning from Imperial College London, an MSc in Financial Engineering from Birkbeck University of London, and a BSc in Chemistry with Forensic Analysis from Loughborough University. Her research specialises in the reinforcement learning branch of machine learning, with a particular focus on solving nonlinear optimal control problems. Additionally she is interested in python programming, algorithmic trading, statistics and the application of machine learning and econometric techniques to financial data analysis and forecasting. Her work has been included in various journals including Multidisciplinary Digital Publishing Institute (MDPI) Energies, European Workshop on Reinforcement Learning (EWRL) and Imperial College Computing Student Workshop (ICCSW).

PhD Awards

  • Cognitive Robotics Conference: Best Presentation Award 2014
  • Google: PhD Poster Competition Award 2014
  • Imperial College Graduate Research Symposium, Highly Commended Poster 2014
  • Cosyne Conference: Qualcomm Travel Award ($500) 2013

BSc Awards

  • Departmental Prize for Best Academic Performance 2003


Optimal Daily Trading of Battery Operations using Arbitrage Spreads

Abramova E; Bunn D W

Energies 2021 Vol 14:16 p e4931


Forecasting the intra-day spread densities of electricity prices

Abramova E; Bunn D

Energies 2020 Vol 13:3 p 687


Hierarchical, Heterogeneous Control of Non-Linear Dynamical Systems using Reinforcement Learning

Abramova E; Dickens L; Kuhn D; Faisal A

Journal of Machine Learning Research 2012 Vol 1-10


Combining Markov Decision Processes with Linear Optimal Controllers

Abramova E; Kuhn D; Faisal A

Proceedings of ICCSW ‘11 September 29th–30th 2011, London UK

Teaching portfolio

Our teaching offering is updated annually. Faculty and programme material are subject to change.