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Department of Computer Science and Technology

  • Research Associate


I am a Post-Doctoral Research Associate at the Affective Intelligence and Robotics (AFAR) Lab. My research focusses on Continual Learning for Social and Affective Robots, aimed towards enabling lifelong adaptability in robots operating in real and open-world settings. In particular, my work focuses on embedding robots with a personalised and continually adaptive understanding of human behaviour, allowing them to learn socially and contextually appropriate behaviours in human-centred environments. 

My current post-doctoral work focuses on Federated Continual Learning of Socially Appropriate Robot Behaviours where I am exploring how social robots can adapt to differences in social norms, learning from each others' experience, in a federated manner. 

I completed my PhD at Trinity Hall. My doctoral work titled, Continual Learning for Affective Robotics was funded by the EPSRC International Doctoral Scholarship and the Premium Research Studentship of the Computer Lab, University of Cambridge. My other research interests include Deep Learning, Computer Vision, Neuro-inspired AI, and Human-Robot Interaction. Please visit my personal website for recent updates.


My current research focusses on Lifelong or Continual Learning of Affect in Social robots, aimed towards enabling lifelong human-robot relationships. My other research interests include Deep Learning, Computer Vision, Federated Learning, Neuro-inspired AI, and Human-Robot Interaction (HRI).
Research Interests:
Affective Computing, Continual Learning, Human-robot Interaction, Deep Learning, Computer Vision, Federated Learning

Professional Activities

I served as the Computing Officer for Trinity Hall MCR from 2018-2020. I also served as the PhD student representative on the Faculty Board of the Computer Laboratory from 2019-2020 as well as the representative for the Rainbow group at the Graduate Students Forum from 2018-2020.


Please see my Google Scholar page for details.


Contact Details

Office phone: 
(01223) 7-67024