I am a research scientist at Nokia Bell Labs and a visiting researcher at the University of Cambridge. My work enables AI to make the most out of real-world data (e.g. timeseries, audio, mobile sensors, or other modalities), through data-efficient and robust models. I am particularly interested in the following paradigms:
Previously, I completed a PhD in Computer Science at the University of Cambridge working with Prof. Cecilia Mascolo. During my studies, I was fortunate to work in diverse industries and companies including Microsoft Research, Telefonica Research, Ocado, Qustodio (acquired for $50M), and research labs.
My research has been published in top venues in artificial intelligence, ML for health, human-computer interaction, and signal processing. Further, I am part of COVID-19 Sounds, an audio-AI study which builds predictive models for COVID-19 using smartphone respiratory recordings. Recent projects have been featured in international media such as the BBC, CNN, Forbes, Financial Times, and Guardian (see more below).
december 2022 •
A paper on fitness prediction with wearables appeared in Nature Digital Medicine and was covered by the University of Cambridge and the Communications of the ACM. I was also invited to present this line of work at the Precision Health Informatics Data Lab of King's College London.
november 2022 •
Presented a paper on out-of-distribution detection and a paper on domain adaptation in ML4H 2022 (co-located with NeurIPS 2022 in New Orleans), where I also served as a senior panel/roundtable chair.
september 2022 • Organized and co-chaired WellComp 2022, held in conjunction with Ubicomp 2022 in Cambridge. Our paper "Investigating Domain-agnostic Performance in Activity Recognition using Accelerometer Data" was published at the HASCA workshop of the same conference. I will also serve on the organizing committee of CHIL 2023 as track chair -consider submitting your best works!
august 2022 • Gave an interview to IndiaAI.gov, the AI initiative of the government of India. Also, the CS department at Cambridge covered our recent JMIR paper.
july 2022 • Attended MobiUK 2022 at UCL with one contributed talk/abstract.
june 2022 • Gave a keynote talk at the AI Summit of the London Tech Week. We also released the recordings of the Federated Sensing tutorial at Mobicom'21.
may 2022 • Joined Bell Labs in Cambridge as a research scientist!
apr 2022 • Our Covid-19 Sounds work has received the 2021 Hall of Fame Better Future Award from the Department of Computer Science and Technology at the University of Cambridge! A recent paper "COVID-19 Disease Progression Prediction via Audio Signals: A Longitudinal Study" has been accepted in JMIR. Also, our paper "Detecting sleep outside the clinic using wearable heart rate devices" has been accepted in Scientific Reports.
mar 2022 • Gave a talk on "Self-supervised learning for health signals" at the Feinstein Institutes of Northwell Health in New York (remotely), hosted by Theo Zanos.
feb 2022 • Our paper "Universals and variations in musical preferences: A study of preferential reactions to Western music in 53 countries" was published in the Journal of Personality and Social Psychology. The University of Cambridge covered it in a featured story.
dec 2021 • Received an ACM SIGMOBILE travel award to attend MobiCom this coming March in New Orleans, USA.
nov 2021 • Our paper "Sounds of COVID-19: exploring realistic performance of audio-based digital testing" has been accepted to Nature Digital Medicine! A short paper "Evaluating Contrastive Learning on Wearable Timeseries for Downstream Clinical Outcomes" is accepted to ML4H 2021, while our perspective "Breaking away from labels: the promise of self-supervised machine learning in intelligent health" will appear in Cell Patterns.
Dimitris Spathis*, Ignacio Perez-Pozuelo*, Tomas I. Gonzales, Yu Wu, Soren Brage, Nicholas Wareham, Cecilia Mascolo (*equal contribution)
Nature Digital Medicine, 5(176)
Jing Han*, Tong Xia*, Dimitris Spathis, Erika Bondareva, Chloë Brown, Jagmohan Chauhan, Ting Dang, Andreas Grammenos, Apinan Hasthanasombat, Andres Floto, Pietro Cicuta, Cecilia Mascolo
Nature Digital Medicine, 5(16)
Dimitris Spathis, Ignacio Perez-Pozuelo, Laia Marques-Fernandez, Cecilia Mascolo
Cell Patterns, 3(2)
Ignacio Perez-Pozuelo, Marius Posa, Dimitris Spathis, Kate Westgate, Nicholas Wareham, Cecilia Mascolo, Soren Brage, Joao Palotti
Scientific Reports, 12 (7956)
Ting Dang, Jing Han, Tong Xia, Dimitris Spathis, Erika Bondareva, Chloë Brown, Jagmohan Chauhan, Andreas Grammenos, Apinan Hasthanasombat, Andres Floto, Pietro Cicuta, Cecilia Mascolo
Journal of Medical Internet Research (JMIR), 24(6)
David Greenberg, Sebastian Wride, Daniel Snowden, Dimitris Spathis, Jeff Potter, Jason Rentfrow
Journal of Personality and Social Psychology, 122(2)
Altmetric Top 5% of all research outputs
Dimitris Spathis, Stephanie Hyland
Machine Learning for Health, New Orleans, USA
Yu Wu, Dimitris Spathis, Hong Jia, Ignacio Perez-Pozuelo, Tomas I Gonzales, Soren Brage, Nicholas Wareham, Cecilia Mascolo
Machine Learning for Health, New Orleans, USA
Apinan Hasthanasombat, Abhirup Ghosh, Dimitris Spathis, Cecilia Mascolo
UbiComp workshop on Human Activity Sensing Corpus & Applications (HASCA @ UbiComp), Cambridge, UK
Tong Xia*, Dimitris Spathis*, Chloe Brown, Jagmohan Chauhan, Andreas Grammenos, Jing Han, Apinan Hasthanasombat, Erika Bondareva, Ting Dang, Andres Floto, Pietro Cicuta, Cecilia Mascolo
Neural Information Processing Systems (NeurIPS), Datasets and Benchmarks Track
Dimitris Spathis, Ignacio Perez-Pozuelo, Soren Brage, Nicholas Wareham, Cecilia Mascolo
Conference on Health, Inference, and Learning (CHIL), Virtual event, USA
Jing Han, Chloë Brown*, Jagmohan Chauhan*, Andreas Grammenos*, Apinan Hasthanasombat*, Dimitris Spathis*, Tong Xia*, Pietro Cicuta, Cecilia Mascolo
International Conference on Acoustics, Speech, & Signal Processing
(ICASSP), Toronto, Canada
Chi Ian Tang, Ignacio Perez-Pozuelo*, Dimitris Spathis*, Soren Brage, Nicholas Wareham, Cecilia Mascolo
Proc. on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT/Ubicomp), 5(1)
Björn W. Schuller, ... Dimitris Spathis, Tong Xia, Pietro Cicuta, Leon J. M. Rothkrantz, Joeri Zwerts, Jelle Treep, Casper Kaandorp
Conference of the International Speech Communication Association (Interspeech), Brno, Czechia
Ignacio Perez-Pozuelo, Dimitris Spathis, Jordan Gifford-Moore, Jessica Morley, Josh Cowls
Journal of the American Medical Informatics Association, 28(9)
Benjamin Searle, Dimitris Spathis, Marios Constantinides, Daniele Quercia, Cecilia Mascolo
ACM International Conference on Mobile Human-Computer Interaction (MobileHCI), Toulouse, France
Kevalee Shah, Dimitris Spathis, Chi Ian Tang, Cecilia Mascolo
Machine Learning for Health (ML4H), Virtual event
Stefanos Laskaridis, Dimitris Spathis, Mario Almeida
ACM International Conference on Mobile Computing and Networking (MobiCom), New Orleans, USA (tutorial)
Ignacio Perez-Pozuelo, Dimitris Spathis, Emma Clifton, Cecilia Mascolo
Digital Health, Chapter 3
Chloë Brown*, Jagmohan Chauhan*, Andreas Grammenos*, Jing Han*, Apinan Hasthanasombat*, Dimitris Spathis*, Tong Xia*, Pietro Cicuta, Cecilia Mascolo
International Conference on Knowledge Discovery and Data Mining
(KDD), San Diego, USA
Oral presentation Cambridge University Hall of Fame Better Future Award
Dimitris Spathis, Ignacio Perez-Pozuelo, Soren Brage, Nicholas Wareham, Cecilia Mascolo
NeurIPS Machine Learning for Mobile Health workshop (ML4MH @ NeurIPS), Vancouver, Canada
Chi Ian Tang, Ignacio Perez-Pozuelo, Dimitris Spathis, Cecilia Mascolo
NeurIPS Machine Learning for Mobile Health workshop (ML4MH @ NeurIPS), Vancouver, Canada
Dimitris Spathis, Sandra Servia, Katayoun Farrahi, Cecilia Mascolo, Jason Rentfrow
International Conference on Knowledge Discovery and Data Mining
(KDD), Anchorage, USA
Oral presentation (Top 6%)
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
Fast, Visual and Interactive Semi-supervised Dimensionality Reduction
Dimitris Spathis, Nikolaos Passalis, Anastasios Tefas
ECCV Efficient Feature Representation Learning workshop (CEFRL @ ECCV 2018), Munich, Germany
Diagnosing Asthma and Chronic Obstructive Pulmonary Disease with Machine Learning
Dimitris Spathis, Panayiotis Vlamos
Health Informatics Journal, 25(3)
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
ACL Abusive Language Online workshop (ALW @ ACL 2017), 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
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
Machine learning to model health with multimodal mobile sensor data
PhD thesis
University of Cambridge, 2021
Learning to interact with high-dimensional data
MSc thesis
Aristotle University, 2017
Organizer:
Program Committee Member: AAAI 2021-2023, IJCAI 2020, KDD 2020-2023 (PC & Session Chair), SIAM SDM 2022, Sensiblend @ Ubicomp 2021, Mobiquitous 2022.
Reviewer: NeurIPS, ICLR, ICML, AAAI, IJCAI, KDD, CHI, Ubicomp/IMWUT, CHIL, Nature Digital Medicine, Nature Scientific Reports, ICASSP, Expert Systems with Applications, Neurocomputing, WWW/The Web Conference, Engineering Applications of Artificial Intelligence, Sensors, ICWSM, ICPR, Pervasive and Mobile Computing, Computer Networks, The Visual Computer, and more.
Audio AI for COVID-19: Cambridge University (1), (2), (3), (4), BBC, The Guardian, Financial Times, The Times, Forbes, Slate, Huffington Post, DailyMail, ITV, IEEE Spectrum, TheNextWeb, STAT, EPFL, TheScientist, The Register, KDnuggets, NPR/WBUR, Royal Society of Biology, Lancet Digital Health, Psychology Today, El Pais, RAI, Corriere della Sera, Focus, DerStandard.
AI for wearables: Cambridge University (1), (2), VentureBeat, Business Insider, Communications of the ACM, National Committee for Quality Assurance.
Big data for psychology: Cambridge University, The Times, CNN, The Telegraph, Sky News, ITV, DailyMail, Inc., CTV, ZDF, ABC.ES, ABC.AU, ELLE, Cosmopolitan, RTBF, TEDx.
Interviews: IndiaAI.gov
I find it incredibly stimulating working with university students. Some recent research projects I supervised:
I have also been a teaching assistant for the following undergraduate courses:
“The next big thing in technology often starts off looking like a toy”
Communitypoprefs.com is a data visualization website, where we present every pop-culture reference over the course of 5 seasons of the TV series Community.
Visualizing my favourite songs on Spotify with dimensionality reduction and anomaly detection. Data essay published in Cuepoint Magazine, Medium's premier music publication.
Text mining Game of Thrones, Harry Potter, Hunger Games and Lord of the Rings books. Data essay featured in Medium's Editor Picks.
Mobile app with face recognition, age estimation, & emotion recognition to blur kids or replace their face with emotion-based emoji. Developed during HackZurich 2018.
Glocalne.ws was a mashup of Google News and Google Maps. Unfortunately it is now defunct due to API discontinuance.
Training neural networks on massive amounts of musical notation and literature 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. I am mostly into art rock and indie folk, with the occasional exception of some well-crafted pop. Although I am an accordionist by training, over the last few years I've been playing mostly piano and ukulele. In a previous life, I performed with the critically acclaimed band The Children of the Oldness (aka Kore Ydro) and recorded the album "Consortium in Amato" (listen here).
I also enjoy street photography and in particular playing with light—photography comes from Greek φως (light) and γραφή (writing), or drawing with light. A sample of my shots is on Flickr and one of my landscapes was featured in the Huffington Post.
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!