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I am a senior research scientist at Nokia Bell Labs and a visiting researcher at the University of Cambridge. My work enables AI to handle the messiness of the real world through human-centric, data-efficient, and robust machine learning. I am particularly interested in the following areas:

• AI for Sequential & Multimodal Data: I develop new ML algorithms that make the most out of high-frequency person-generated data through self-supervised learning [CHIL'21], multimodal fusion [WSDM'24], forecasting [KDD'19 oral], and knowledge distillation [UbiComp'21].
• Accessible Health Sensing: I build AI systems that detect vital health information without specialized equipment, with applications to disease monitoring [NeurIPS'21], cardio fitness [Nature Dig. Medicine'22], sleep disorders [Sci. Reports'22], and more.
• Robust & Trustworthy AI: I develop reliable ML algorithms for high-stakes applications, focusing on out-of-distribution generalization [ML4H'22, ACLw'17], addressing forgeting [WACV'24], fairness [AAAIw'24], and ethical considerations [JAMIA'21].

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, and Ocado. I also helped start COVID-19 Sounds, one of the largest studies in audio AI for health.

My research has been published in top venues in artificial intelligence, AI for health, and human-centered signal processing while recent projects have been featured in international media such as the BBC, CNN, Guardian, Washington Post, Forbes, and Financial Times (see more below).

CV

📖 Publications

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2024

2023

2022

2021

2020

2019

Pre-PhD (2013-2018)

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'18), 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'17), 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

Theses

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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

🧐 Academic service

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Leadership & Organizer roles:

• General co-chair of HCRL workshop at AAAI 2024, Vancouver, Canada.
• Editorial board member of Nature Digital Medicine (2023-).
• Editorial board member of IEEE Pervasive Computing (2023-).
• General co-chair of FairComp & WellComp workshops at UbiComp 2023, Cancun, Mexico.
• Session chair on Industry Perspectives at MobileHCI 2023, Athens, Greece.
• Co-organizer and track chair of CHIL 2023, Boston, USA.
• Senior panel/roundtable chair at ML4H 2022, New Orleans, USA.
• Chair of WellComp workshop at UbiComp 2022, Cambridge, UK.
• Session chair on data science for rich data types at KDD 2021, Singapore/online.
• Co-organizer of the Federated sensing tutorial at MobiCom 2021, New Orleans, USA.

Program Committee Member: AAAI 2021-2024, IJCAI 2020, KDD 2020-2023, FAccT 2023, SIAM SDM 2022, Sensiblend @ Ubicomp 2021, Mobiquitous 2022.

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

📢 Invited talks

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Multimodal AI for Real-World Signals and the Role of Language

• 📍 AAAI'24 Health Intelligence workshop, Vancouver, Canada — February 27, 2024

Multimodal, data-efficient, and robust AI for real-world biosignals & the role of generative models

• 📍 Cambridge MedAI Seminar Series, Biomedical Campus, Cambridge, UK — January 30, 2024

Multimodal AI for real-world signals – does the key to specialized models lie in language?

• 📍 Microsoft AI & Pizza talk - Cambridge ELLIS Unit, Cambridge, UK — November 30, 2023

Human-centric AI for health signals with applications in fitness and activity modeling

• 📍 Cambridge Public Health symposium, Cambridge, UK — March 27, 2023

Self-Supervised Learning for Health Signals

• 📍 Rising Stars in AI, KAUST, Saudi Arabia — February 20, 2023

Representation learning for cardio-fitness prediction in free-living environments

• 📍 King's College London, Precision Health Informatics Data Lab, London, UK — November 24, 2022

AI-powered Wearables Transforming Mobile Health

• 📍 AI Summit, London Tech week, London, UK — June 16, 2022

Self-supervised learning for health signals

• 📍 Feinstein Institutes of Northwell Health, New York, USA (remote) — March 22, 2022

AI to model Human Behaviour and Health

• 📍 Jesus College Postgraduate Conference, virtual event, UK — March 5, 2021
• 📍 Barclays Eagle Lab, Cambridge, UK — March 12, 2020

Deep sequence learning for large-scale inference of human behaviour from mobile sensor data

• 📍 MRC Epidemiology Unit, University of Cambridge, UK — March 5, 2019
• 📍 Ocado, Barcelona, Spain & Hatfield, UK (remote) — July 10, 2019

Fast, Visual and Interactive Semi-supervised Dimensionality Reduction

• 📍 Facebook PhD Open House, London, UK — October 25, 2018

🗞️ Press

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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, Daily Mirror, Bicycling Magazine, Owkin, Heidelberg Laureate Forum (Spektrum.de).

Data-driven music psychology: Cambridge University, The Times, Washington Post, CNN, The Telegraph, Sky News, ITV, DailyMail, Inc., CTV, ZDF, Der Tagesspiegel, ABC.ES, ABC.AU, ELLE, Cosmopolitan, RTBF, TEDx.

Interviews: IndiaAI.gov

🎒 Mentoring

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I enjoy collaborating with PhD and thesis students, usually as part of an internship in our lab. Here are some recent research projects I supervised:

• Chi Ian Tang (University of Cambridge): Self-supervised and continual learning
• Benjamin Searle (University of Cambridge): Capturing compulsive behaviours w/ wearables
• Kevalee Shah (University of Cambridge): Benchmarking contrastive learning algorithms
• Chuen Low (University of Cambridge): Attention models for timeseries
• Yu Yvonne Wu (University of Cambridge): Weakly-supervised and self-supervised learning
• Shohreh Deldari (UNSW Sydney): Multimodal self-supervised learning
• Sofia Yfantidou (Aristotle University): Machine learning fairness
• Francesco Pase (University of Padova): Self-supervised federated learning
• Aashish Kolluri (National University of Singapore): Multimodal adapters for large models
• Ryuhaerang Choi (KAIST): Data-centric multi-task learning

I have also been a teaching assistant for the following undergraduate courses:

• Machine Learning & Real-World Data (U. of Cambridge)
• Mobile & Sensor Systems (U. of Cambridge)
• Scientific Computing (U. of Cambridge)
• Numerical Analysis (Aristotle University)

🎠 Playground

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“The next big thing in technology often starts off looking like a toy”

Quantifying name-dropping

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.

Map out your music taste on Spotify

Visualizing my favourite songs on Spotify with dimensionality reduction and anomaly detection. Data essay published in Cuepoint Magazine, Medium's premier music publication.

Children books and childish language?

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

Anonymize kids' faces before posting online

Mobile app with face recognition, age estimation, & emotion recognition to blur kids 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 it is now defunct due to API discontinuance.

Composing music and text with Recurrent Neural Networks

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.

🕳️🐇 Personal

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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!