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Department of Computer Science and Technology

  • PhD Candidate

Dimitris is a doctoral researcher in Computer Science at the University of Cambridge, supervised by Prof. Cecilia Mascolo. His work as a whole enables machine learning models to learn richer semantics of high-dimensional and complex data (mobile sensors, time-series, audio, images, text or other modalities). He is thankful to be supported by Jesus College Cambridge, the EPSRC, and the ERC.

His research is driven by doing more with less information. The most prominent bottleneck of deep learning today is access to labelled datasets, carefully curated for niche tasks. To this end, he works on data-efficient models that learn generalizable and personalized representations by leveraging the fundamental paradigms of self-supervision, transfer learning, and multi-tasking.

Data-driven models of human behaviour encode many complexities of the real world and hence he spends an inordinate amount of time thinking about sparsity, irregular sampling, long-term dependencies, noise, multi-modality, and long-tails. Beyond theory, he collaborates closely with world-class experts from other high-impact areas (health, natural and social sciences) to apply robust concepts from data science and accelerate scientific discovery.

Previously, during his studies, he has been fortunate to work in diverse industries including multinational telcos (Telefonica Research), internet startups (Qustodio), retail tech companies (Ocado), and research labs. Further, he is in the core team of the audio AI study which has drawn international attention (covered by BBC, The Guardian, Forbes, The Times, Slate).


Professional Activities

Program Committee (PC): AAAI 2021, IJCAI 2020, KDD 2020 & 2021

Reviewer: Nature Scientific Reports, Nature Digital Medicine, ICLR, ICML, AAAI, IJCAI, KDD, CHI, Ubicomp/IMWUT, ICASSP, Expert Systems with Applications, Neurocomputing, WWW/The Web Conference, Engineering Applications of Artificial Intelligence, ICWSM, ICPR, and more.


Full list of publications is available on Google Scholar

Contact Details

Office phone: 
(01223) 7-63636