Hi 👋 I’m Dimitris Spathis

Doctoral Researcher
University of Cambridge

I am a doctoral researcher in Computer Science at the University of Cambridge, supervised by Prof. Cecilia Mascolo. I enjoy problems involving deep representation learning, high-dimensional data (time-series, sparse matrices), large-scale population studies, and their interplay.

In particular, I work on data-efficient models that learn generalizable and personalized representations through self-supervision and transfer learning, and their applications in human behavior modeling & mobile health. My research is supported by Jesus College Cambridge, the EPSRC, and the ERC.

Previously, during my studies I have been fortunate to work at diverse industries including multinational telcos (Telefonica Research), internet startups (Qustodio), retail tech companies (Ocado), and research labs. Further, our recent research in audio AI diagnostics (covid-19-sounds.org) has drawn international attention (covered by BBC, The Guardian, Forbes, The Times, El País).




Exploring Automatic Diagnosis of COVID-19 from Crowdsourced Respiratory Sound Data
Chloë Brown*, Jagmohan Chauhan*, Andreas Grammenos*, Jing Han*, Apinan Hasthanasombat*, Dimitris Spathis*, Tong Xia*, Pietro Cicuta, and Cecilia Mascolo (*equal contribution, alphabetical order)
International Conference on Knowledge Discovery and Data Mining (KDD), Virtual Event, USA

Publisher PDF Blog post Oral presentation


Sequence Multi-task Learning to Forecast Mental Wellbeing from Sparse Self-reported Data
Dimitris Spathis, Sandra Servia, Katayoun Farrahi, Cecilia Mascolo, Jason Rentfrow
International Conference on Knowledge Discovery and Data Mining (KDD), Anchorage, USA

Passive mobile sensing and psychological traits for large scale mood prediction
Dimitris Spathis, Sandra Servia, Katayoun Farrahi, Cecilia Mascolo, Jason Rentfrow
International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth), Trento, Italy

Interactive dimensionality reduction using similarity projections
Dimitris Spathis, Nikolaos Passalis, Anastasios Tefas
Knowledge-Based Systems, 165: 77-91


Fast, Visual and Interactive Semi-supervised Dimensionality Reduction
Dimitris Spathis, Nikolaos Passalis, Anastasios Tefas
European Conference on Computer Vision (CEFLR workshop), Munich, Germany


Diagnosing Asthma and Chronic Obstructive Pulmonary Disease with Machine Learning
Dimitris Spathis, Panayiotis Vlamos
Health Informatics Journal, 25(3): 811–827 (issue published in 2019)

Class-based Prediction Errors to Detect Hate Speech with Out-of-vocabulary Words
Joan Serra, Ilias Leontiadis, Dimitris Spathis, Gianluca Stringhini, Jeremy Blackburn, Athena Vakali
Annual Meeting of the Association for Computational Linguistics (ALW Workshop), Vancouver, Canada


A comparison between semi-supervised and supervised text mining techniques on detecting irony in greek political tweets
Basilis Charalampakis, Dimitris Spathis, Elias Kouslis, Katia Kermanidis
Engineering Applications of Artificial Intelligence, 51: 50-57


Detecting Irony on Greek Political Tweets: A Text Mining Approach
Basilis Charalampakis, Dimitris Spathis, Elias Kouslis, Katia Kermanidis
International Conference on Engineering Applications of Neural Networks, Rhodes, Greece


Glocal News: An Attempt to Visualize the Discovery of Localized Top Local News, Globally
Dimitris Spathis, Theofilos Mouratidis, Spyros Sioutas, Athanasios Tsakalidis
International Conference on Conceptual Modeling, Hong Kong, China


Photo-Quality Evaluation based on Computational Aesthetics: Review of Feature Extraction Techniques
Dimitris Spathis
arXiv preprint, 1612.06259, 2016


I find it incredibly stimulating working with ambitious students in research projects. Some recent examples:

  • Chi Ian Tang — Semi-supervised learning in human activity recognition (paper submitted)
  • Benjamin Searle — Predicting compulsive behaviours with smartwatches (paper submitted)
  • Kevalee Shah — Contrastive learning in unlabeled mobile health data (ongoing)
  • Chuen Low — Pure attention models for timeseries (ongoing)

I have also taught small groups of undergrad students and evaluated their code assignments in lab sessions for the following courses:


Community pop references

Communitypoprefs.com is a data visualization project, where we present every pop-culture reference during the 5 seasons of the TV sitcom Community #sixseasonsandamovie.

Map out your music taste on Spotify

Visualizing my favourite songs on Spotify with statistics, non-linear dimensionality reduction and one-class learning. Published in Cuepoint Magazine .

How do popular book authors use language differently?

Text mining on Game of Thrones, Harry Potter, Hunger Games and Lord of the Rings books. Featured in Medium's Editor Picks .

Share your kid’s pictures online without worrying

Mobile app with face recognition, age estimation, & emotion recognition to blur underage people in pictures or replace their face with emotion-based emoji. Developed during HackZurich 2018.

Discover top local news globally

Glocalne.ws was a mashup of Google News and Google Maps. Unfortunately now defunct due to API discontinuance.

Composing music and text with Recurrent Neural Networks

Training deep neural networks on massive amounts of musical notation (Irish folk) and literature (Shakespeare's oeuvre) and letting them create their own art. Essay in Greek but you can still see/listen to the results.


Non-academic things about me: I love music, both playing and listening. Lately, I have been into art rock—indie folk. I am accordionist by training, but over the last years I've been playing mostly piano and ukulele. I have performed in classical orchestras, cover bands and critically acclaimed indie bands like The Children of the Oldness (aka Kore Ydro). You can listen to the album I participated, "Consortium in Amato", here. Sometimes I associate still depictions of the real world with words, lyrics or people; a subset of them is on Flickr. Lastly, and perhaps most importantly, I'm always on the lookout for ways to move items from the "non-academic list" to the "academic list"—let me know if you'd like to help!