WEQA: Wearable hEalth Question Answering with Query-Adaptive Agentic Reasoning
Yuwei Zhang, Tong Xia, Bianca Emmerich, Yu Yvonne Wu, Dimitris Spathis, Xin Liu, Daniel McDuff, Cecilia Mascolo
Dimitris Spathis
I am a research scientist at Google and a visiting academic at the University of Cambridge, focusing on building foundation models and adaptive agents for health. My work bridges the gap between fundamental research and real-world application, developing models that are currently used by millions of people worldwide. I am particularly focused on the following areas:
- Multimodal foundation models & agents: Building large-scale models and agents for health data, including SensorFM (pre-trained on 1T+ minutes of wearable data), LSM-2 (handling incomplete multimodal data), PaPaGei (first open foundation model for biosignals), WEQA (query-adaptive agentic reasoning), and Time2Lang (bridging timeseries and LLMs).
- Real-world product impact: I've led model training, evaluation, or post-training improvements for some of Google Health's core physiological models. This includes the heart sensing and activity recognition models powering the Pixel Watch and Fitbit Air devices, as well as powering 'blood pressure' and 'insulin resistance trends' features via SensorFM and other models.
Previously, I was a senior research scientist at Nokia Bell Labs, leading efforts in AI for multimodal health. Before that, 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 at 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 New York Times, BBC, CNN, Guardian, Washington Post, Forbes, and Financial Times (see more in Press).
Selected Works View all publications →
SensorFM & LSM-2: Foundation Models for Wearable Data
Generative models trained on up to one trillion minutes of wearable sensor data, capable of handling incomplete multimodal inputs.
PaPaGei: Open Foundation Models for Optical Physiological Signals
The first open foundation model for biosignals (PPG) pre-trained on 57,000 hours of data, published at ICLR 2025.
Why do LLMs struggle with temporal data?
Analyzing the pitfalls of representing and tokenizing temporal data for Large Language Models, particularly from sources like mobile sensors or medical records.
Looking for audio biomarkers in respiratory recordings
COVID-19 Sounds analyzed respiratory recordings for digital respiratory screening. Released large-scale datasets at NeurIPS 2021.
Estimating a key longevity biomarker through passive wearable data
Models that predict VO2max/cardio fitness using wearable sensor data in large cohorts, published in Nature Digital Medicine.
Self-supervised learning for wearable sensor data
We developed some of the first pre-trained models using self-supervised objectives and applied them to various health-related downstream tasks.
News
aug 2026
The announced Health Guardian tool includes 'blood pressure' and 'insulin resistance trends' and is based on our SensorFM and other models. You can read more here.
jul 2026
We released SensorFM, a foundation model trained on the largest personal health dataset. You can read more here.
jun 2026
With Cambridge, we released WEQA: Wearable hEalth Question Answering with Query-Adaptive Agentic Reasoning.
may 2026
We published a paper describing the heart sensing model and system that runs on the Pixel Watch.
We announced Google Health, the Google Health Coach, and a new screenless device. You can read more here.
Invited talk at the Machine Intelligence for Health Conference (MI4H), Coventry, UK.
apr 2026
New review in AI fairness in personal and mobile sensing that reviewed the literature from the past 8 years. You can read more here.
Invited talk at the RADAR-base Symposium by Wellcome Trust & King’s College London.
mar 2026
We released a perspective discussing that Wearable Foundation Models Should Go Beyond Static Encoders.
Invited lecture at UCambridge on Foundation Models for Personal Health Signals.
dec 2025
Serving on the Advisory Committee for the Learning from Time Series for Health Workshop at NeurIPS 2025.
sep 2025
Invited talk at AI4Health Industry Day 2025 @ Imperial.
aug 2025
We released a review paper in biosignal foundation models.
jun 2025
We released the paper of Large Sensor Model 2 (LSM-2), a foundation model trained on 40M hours of wearable data.
may 2025
We are organizing a new workshop EvalComp @ Ubicomp'25 focusing on the future of evals, consider submitting your relevant works!
apr 2025
Time2Lang, a new method to use timeseries foundation models with LLMs, was accepted at CHIL 2025. I also gave an invited talk at Singapore Management University on foundation models for personal health.
feb 2025
Our model averaging for label noise mitigation work was published in Scientific Reports.
jan 2025
🦜 PaPaGei was accepted at ICLR 2025! Our music work was also featured in a Guardian article.
dec 2024
Our 🦜 PaPaGei work received the best paper award at the NeurIPS’24 workshop on Time Series in the Age of Large Models. I also gave an invited lecture at the Aristotle University of Thessaloniki on the topic of foundation models for personal health. Our SoundCollage paper was accepted at ICASSP'25.
nov 2024
I joined Google in London, working within the Consumer Health Research team.
oct 2024
We released 🦜 PaPaGei, the first open foundation model for biosignals (PPG). You can read more here. I was also interviewed by Bloomberg on a newsletter about VO2max.
sep 2024
I was a panel speaker at Cambridge Tech Week. You can watch the segment on Youtube.
aug 2024
StatioCL, a new non-stationary self-supervised model for timeseries was accepted to CIKM 2024.
jul 2024
Our work on how Large Language Models struggle with temporal data was published at JAMIA, and was covered by Techcrunch and LG AI Research. You can read more on this post.
jun 2024
Our work on how Self-Supervised Learning improves fairness was accepted at KDD 2024. We released the paper, code, and a project website. I was also interviewed by Runner's World magazine on a feature article about VO2max - you can read more here.
may 2024
My MedAI talk from earlier this year is now available on Youtube.
apr 2024
I was interviewed by the New York Times for an article on cardio fitness and wearables. Also launched a new Short Papers section at IEEE Pervasive journal - consider submitting your works! In addition, my first patent from a few years ago became public; you can read more here.
mar 2024
The collection of accepted papers at the Human-Centric Representation Learning workshop is available as an Arxiv index.
feb 2024
Co-chaired the Human-Centric Representation Learning workshop at AAAI 2024 in Vancouver, with a great set of papers and keynotes - you can read some highlights of the day at AIhub.org. I also gave an invited keynote at the Health Intelligence workshop of the same conference (here are the slides of the talk).
jan 2024
Gave an invited talk at Cambridge Biomedical Campus as part of the MedAI seminar series.
dec 2023
I authored a corporate blogpost describing our team's recent research. I also joined the editorial board of the IEEE Pervasive Computing journal.
Publications
I have published over 60 papers in top-tier venues including NeurIPS, ICLR, KDD, Nature Digital Medicine, UbiComp, and ICASSP. You can also see the full list on Google Scholar.
2026
Towards a General Intelligence and Interface for Wearable Health Data
Girish Narayanswamy, Maxwell A. Xu, A. Ali Heydari, Samy Abdel-Ghaffar, Marius Guerard, Kara Vaillancourt, Zhihan Zhang, Jake Garrison, Levi Albuquerque, Dimitris Spathis, Hong Yu, Hamid Palangi, Xuhai "Orson" Xu, David G. T. Barrett, Joseph Breda, Jed McGiffin, Yubin Kim, Yuwei Zhang, Naghmeh Rezaei, Samuel Solomon, Karan Ahuja, Tim Althoff, Jake Sunshine, Ming-Zher Poh, Benjamin Yetton, Ari Winbush, Nicholas B. Allen, James M. Rehg, Isaac Galatzer-Levy, Yun Liu, John Hernandez, Anupam Pathak, Conor Heneghan, Yuzhe Yang, Ahmed A. Metwally, Pushmeet Kohli, Mark Malhotra, Shwetak Patel, Xin Liu, Daniel McDuff
Pixel Watch: Robust Heart Rate Sensing from Multipath PPG and On-Device Deep Learning Trained on 10,000 hours of Free-Living and Fitness Data
Daniel Roggen, Megan Walker, Yojan Patel, Shyam Tailor, Dimitris Spathis, Matt Wimmer, Brennan Garrett, Dan Howe, Abhinuv Pitale, Hamed Vavadi, Tien Le, Steve Diamond, Oleksiy Vyalov, Vik Sharma, Pete Richards, Tracy Giest, Erika Siegel, Tuan Phan, Sam Mravca, Derrick Vickers, Benjamin Stone, Katarina Vukosavljevic, Justin Phillips, YongSuk Cho, Stefanie Hollidge, Antony Siahaan, Soren Brage, Shwetak Patel, Robert Harle
Wearable Foundation Models Should Go Beyond Static Encoders
Yu Yvonne Wu, Yuwei Zhang, Hyungjun Yoon, Ting Dang, Dimitris Spathis, Tong Xia, Qiang Yang, Jing Han, Dong Ma, Sung-Ju Lee, Cecilia Mascolo
(Un)fair devices: Moving beyond AI accuracy in personal sensing
Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, Fahim Kawsar
2025
Reliable wrist PPG monitoring by mitigating poor skin sensor contact
Hung Manh Pham, Matthew Yiwen Ho, Yiming Zhang, Dimitris Spathis, Aaqib Saeed, Dong Ma
Foundation Models for Biosignals: A Survey
Xiao Gu, Yuxuan Shu, Jinpei Han, Yuxuan Liu, Zhangdaihong Liu, James Anibal, Veer Sangha, Edward Phillips, Bradley Segal, Hang Yuan, Fenglin Liu, Kim Branson, Patrick Schwab, Danielle Belgrave, Lei Clifton, Dimitris Spathis, Vasileios Lampos, A Aldo Faisal, David A Clifton
Human Factors and Behavioral Sensing in AI Applications
Marios Constantinides, Dimitris Spathis, Sofia Yfantidou
Human-Centered AI: An Illustrated Scientific Quest, 573-591
Time2Lang: Bridging Time-Series Foundation Models and Large Language Models for Health Sensing Beyond Prompting
Arvind Pillai, Dimitris Spathis, Subigya Nepal, Amanda C Collins, Daniel M Mackin, Michael V Heinz, Tess Z Griffin, Nicholas C Jacobson, Andrew Campbell
Conference on Health, Inference, and Learning (CHIL 2025)
Learning under label noise through few-shot human-in-the-loop refinement
Aaqib Saeed, Dimitris Spathis, Jungwoo Oh, Edward Choi, Ali Etemad
Scientific Reports 15 (1), 4276
LSM-2: Learning from Incomplete Wearable Sensor Data
Maxwell A. Xu, Girish Narayanswamy, Kumar Ayush, Dimitris Spathis..., Xin Liu, Daniel McDuff
🦜PaPaGei: Open Foundation Models for Optical Physiological Signals
Arvind Pillai, Dimitris Spathis, Fahim Kawsar, Mohammad Malekzadeh
International Conference on Learning Representations (ICLR'25) Workshop Best Paper Award (Top 1%)
SoundCollage: Automated Discovery of New Classes in Audio Datasets
Ryuhaerang Choi, Soumyajit Chatterjee, Dimitris Spathis, Sung-Ju Lee, Fahim Kawsar, Mohammad Malekzadeh
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP'25)
Learning under label noise through few-shot human-in-the-loop refinement
Aaqib Saeed, Dimitris Spathis, Jungwoo Oh, Edward Choi, Ali Etemad
Time2Lang: Bridging Time-Series Foundation Models and Large Language Models for Health Sensing Beyond Prompting
Arvind Pillai, Dimitris Spathis, Subigya Nepal, Amanda C Collins, Daniel M Mackin, Michael V Heinz, Tess Z Griffin, Nicholas C Jacobson, Andrew Campbell
Conference on Health, Inference, and Learning (CHIL'25)
Human Factors and Behavioral Sensing in AI Applications
Marios Constantinides, Dimitris Spathis, Sofia Yfantidou
2024
WellComp 2024: Seventh International Workshop on Computing for Well-Being
Ting Dang, Shkurta Gashi, Dimitris Spathis, Alexander Hoelzemann
ACM Intl. Joint Conf. Pervasive and Ubiquitous Computing (Ubicomp 2024)
FairComp: 2nd International Workshop on Fairness and Robustness in Machine Learning for Ubiquitous Computing
Lakmal Meegahapola, Dimitris Spathis, Marios Constantinides, Han Zhang, Sofia Yfantidou, Niels van Berkel, Anind K. Dey
ACM Intl. Joint Conf. Pervasive and Ubiquitous Computing (Ubicomp 2024)
The first step is the hardest: Pitfalls of Representing and Tokenizing Temporal Data for Large Language Models
Dimitris Spathis, Fahim Kawsar
Using Self-Supervised Learning Can Improve Model Fairness
Sofia Yfantidou, Dimitris Spathis, Marios Constantinides, Athena Vakali, Daniele Quercia, Fahim Kawsar
International Conference on Knowledge Discovery and Data Mining (KDD'24)
CroSSL: Cross-modal Self-Supervised Learning for Time-series through Latent Masking
Shohreh Deldari, Dimitris Spathis, Mohammad Malekzadeh, Fahim Kawsar, Flora Salim, Akhil Mathur
ACM Conference on Web Search and Data Mining (WSDM'24)
Kaizen: Practical self-supervised continual learning with continual fine-tuning
Chi Ian Tang, Lorena Qendro, Dimitris Spathis, Fahim Kawsar, Cecilia Mascolo, Akhil Mathur
IEEE/CVF Winter Conference on Applications of Computer Vision (WACV'24),
StatioCL: Contrastive Learning for Time Series via Non-Stationary and Temporal Contrast
Yu Wu, Ting Dang, Dimitris Spathis, Hong Jia, Cecilia Mascolo
ACM International Conference on Information and Knowledge Management (CIKM'24)
OptiBreathe: An Earable-based PPG System for Continuous Respiration Rate, Breathing Phase, and Tidal Volume Monitoring
Julia Romero, Andrea Ferlini, Dimitris Spathis, Ting Dang, Katayoun Farrahi, Fahim Kawsar, Alessandro Montanari
Intl. Workshop on Mobile Computing Systems and Applications (HotMobile'24),
Balancing Continual Learning and Fine-tuning for Human Activity Recognition
Chi Ian Tang, Lorena Qendro, Dimitris Spathis, Fahim Kawsar, Cecilia Mascolo, Akhil Mathur
AAAI Human-centric Representation Learning workshop (HCRL @ AAAI'24),
2023
The State of Algorithmic Fairness in Mobile Human-Computer Interaction
Sofia Yfantidou, Marios Constantinides, Dimitris Spathis, Athena Vakali, Daniele Quercia, Fahim Kawsar
ACM International Conference on Mobile Human-Computer Interaction (MobileHCI'23),
Human-centred artificial intelligence for mobile health sensing: challenges and opportunities
Ting Dang, Dimitris Spathis, Abhirup Ghosh, Cecilia Mascolo
UDAMA: Unsupervised Domain Adaptation through Multi-discriminator Adversarial Training with Noisy Labels Improves Cardio-fitness Prediction
Yu Wu, Dimitris Spathis, Hong Jia, Ignacio Perez-Pozuelo, Tomas I Gonzales, Soren Brage, Nicholas Wareham, Cecilia Mascolo
Machine Learning for Healthcare (MLHC'23)
Conditional Neural ODE Processes for Individual Disease Progression Forecasting: A Case Study on COVID-19
Ting Dang, Jing Han, Tong Xia, Erika Bondareva, Chloë Siegele-Brown, Jagmohan Chauhan, Andreas Grammenos, Dimitris Spathis, Pietro Cicuta, Cecilia Mascolo
International Conference on Knowledge Discovery and Data Mining (KDD'23)
Recent Advances, Applications and Open Challenges in Machine Learning for Health: Reflections from Research Roundtables at ML4H 2022 Symposium
Stefan Hegselmann, Helen Zhou, Yuyin Zhou, Jennifer Chien, Sujay Nagaraj, Neha Hulkund, Shreyas Bhave, Michael Oberst ... Dimitris Spathis, Jun Seita, Bastiaan Quast, Megan Coffee, Collin Stultz, Irene Y Chen, Shalmali Joshi, Girmaw Abebe Tadesse
Evaluating Listening Performance for COVID-19 Detection by Clinicians and Machine Learning: A Comparative Study
Jing Han, Marco Montagna, Andreas Grammenos, Tong Xia, Erika Bondareva, Chloë Siegele-Brown, Jagmohan Chauhan, Ting Dang, Dimitris Spathis, Andres Floto, Pietro Cicuta, Cecilia Mascolo
A Summary of the ComParE COVID-19 Challenges
Alican Akman, Harry Coppock, Christian Bergler, Maurice Gerczuk, Chloë Brown, Jagmohan Chauhan, Andreas Grammenos, Apinan Hasthanasombat, Dimitris Spathis, Tong Xia, Pietro Cicuta, Jing Han, Shahin Amiriparian, Alice Baird, Lukas Stappen, Sandra Ottl, Panagiotis Tzirakis, Anton Batliner, Cecilia Mascolo, Björn Wolfgang Schuller
2022
Longitudinal cardio-respiratory fitness prediction through wearables in free-living environments
Dimitris Spathis*, Ignacio Perez-Pozuelo*, Tomas I. Gonzales, Yu Wu, Soren Brage, Nicholas Wareham, Cecilia Mascolo (*equal contribution)
Nature Digital Medicine Altmetric Top 5% of all research outputs
Sounds of COVID-19: exploring realistic performance of audio-based digital testing
Jing Han*, Tong Xia*, Dimitris Spathis, Erika Bondareva, Chloë Brown, Jagmohan Chauhan, Ting Dang, Andreas Grammenos, Apinan Hasthanasombat, Andres Floto, Pietro Cicuta, Cecilia Mascolo
Breaking away from labels: the promise of self-supervised machine learning in intelligent health
Dimitris Spathis, Ignacio Perez-Pozuelo, Laia Marques-Fernandez, Cecilia Mascolo
Detecting sleep outside the clinic using wearable heart rate devices
Ignacio Perez-Pozuelo, Marius Posa, Dimitris Spathis, Kate Westgate, Nicholas Wareham, Cecilia Mascolo, Soren Brage, Joao Palotti
Exploring Longitudinal Cough, Breath, and Voice Data for COVID-19 Progression Prediction via Sequential Deep Learning: Model Development and Validation
Ting Dang, Jing Han, Tong Xia, Dimitris Spathis, Erika Bondareva, Chloë Brown, Jagmohan Chauhan, Andreas Grammenos, Apinan Hasthanasombat, Andres Floto, Pietro Cicuta, Cecilia Mascolo
Universals and variations in musical preferences: A study of preferential reactions to Western music in 53 countries
David Greenberg, Sebastian Wride, Daniel Snowden, Dimitris Spathis, Jeff Potter, Jason Rentfrow
Journal of Personality and Social Psychology Altmetric Top 5% of all research outputs
Looking for Out-of-Distribution Environments in Multi-center Critical Care Data
Dimitris Spathis, Stephanie Hyland
Machine Learning for Health(ML4H'22)
Turning Silver into Gold: Domain Adaptation with Noisy Labels for Wearable Cardio-Respiratory Fitness Prediction
Yu Wu, Dimitris Spathis, Hong Jia, Ignacio Perez-Pozuelo, Tomas I Gonzales, Soren Brage, Nicholas Wareham, Cecilia Mascolo
Machine Learning for Health(ML4H'22)
Investigating Domain-agnostic Performance in Activity Recognition using Accelerometer Data
Apinan Hasthanasombat, Abhirup Ghosh, Dimitris Spathis, Cecilia Mascolo
UbiComp workshop on Human Activity Sensing Corpus & Applications (HASCA @ UbiComp'22)
2021
COVID-19 Sounds: A Large-Scale Audio Dataset for Digital Respiratory Screening
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'21)
Self-supervised transfer learning of physiological representations from free-living wearable data
Dimitris Spathis, Ignacio Perez-Pozuelo, Soren Brage, Nicholas Wareham, Cecilia Mascolo
Conference on Health, Inference, and Learning (CHIL'21)
Exploring Automatic COVID-19 Diagnosis via voice and symptoms from Crowdsourced Data
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'21)
SelfHAR: Improving Human Activity Recognition through Self-training with Unlabeled Data
Chi Ian Tang, Ignacio Perez-Pozuelo*, Dimitris Spathis*, Soren Brage, Nicholas Wareham, Cecilia Mascolo
Proc. on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT/Ubicomp'21)
The INTERSPEECH 2021 Computational Paralinguistics Challenge: COVID-19 Cough, COVID-19 Speech, Escalation & Primates
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'21)
Digital Phenotyping and Sensitive Health Data: Implications for Data Governance
Ignacio Perez-Pozuelo, Dimitris Spathis, Jordan Gifford-Moore, Jessica Morley, Josh Cowls
Anticipatory Detection of Compulsive Body-focused Repetitive Behaviors with Wearables
Benjamin Searle, Dimitris Spathis, Marios Constantinides, Daniele Quercia, Cecilia Mascolo
ACM International Conference on Mobile Human-Computer Interaction (MobileHCI'21)
Evaluating Contrastive Learning on Wearable Timeseries for Downstream Clinical Outcomes
Kevalee Shah, Dimitris Spathis, Chi Ian Tang, Cecilia Mascolo
Machine Learning for Health (ML4H'21)
Federated mobile sensing for activity recognition
Stefanos Laskaridis, Dimitris Spathis, Mario Almeida
ACM International Conference on Mobile Computing and Networking (MobiCom)
Wearables, smartphones and artificial intelligence for digital phenotyping and health
Ignacio Perez-Pozuelo, Dimitris Spathis, Emma Clifton, Cecilia Mascolo
2020
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, Cecilia Mascolo
International Conference on Knowledge Discovery and Data Mining (KDD'20) Oral presentation
Learning Generalizable Physiological Representations from Large-scale Wearable Data
Dimitris Spathis, Ignacio Perez-Pozuelo, Soren Brage, Nicholas Wareham, Cecilia Mascolo
Exploring Contrastive Learning in Human Activity Recognition for Healthcare
Chi Ian Tang, Ignacio Perez-Pozuelo, Dimitris Spathis, Cecilia Mascolo
2019
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'19) Oral presentation (Top 6%)
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'19)
Pre-PhD (2013-2018)
Interactive dimensionality reduction using similarity projections
Dimitris Spathis, Nikolaos Passalis, Anastasios Tefas
Fast, Visual and Interactive Semi-supervised Dimensionality Reduction
Dimitris Spathis, Nikolaos Passalis, Anastasios Tefas
Diagnosing Asthma and Chronic Obstructive Pulmonary Disease with Machine Learning
Dimitris Spathis, Panayiotis Vlamos
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
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
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
Glocal News: An Attempt to Visualize the Discovery of Localized Top Local News, Globally
Dimitris Spathis, Theofilos Mouratidis, Spyros Sioutas, Athanasios Tsakalidis
Theses
Machine learning to model health with multimodal mobile sensor data
PhD thesis
Learning to interact with high-dimensional data
MSc thesis
Patents
Methods for training foundation models for processing optical physiological signals
US20260093979A1 (filed 2025, published 2026)
Training a machine learning model
US20260119980A1 (filed 2024, published 2026)
Apparatus & method for federated learning
US20250209343A1 (filed 2024, published 2025)
Reuse of data for training machine learning models
US20250156763A1 (filed 2024, published 2025)
Audio notifications
GB2635388A (filed 2023, published 2025)
Apparatus & method for generating feature embeddings
US20240273404A1 (filed 2023, published 2024)
Apparatus, method, and computer program for transfer learning
US20240127057A1 (filed 2022, published 2024)
Academic service
Leadership & organizer roles:
- Advisory Committee for the Learning from Time Series for Health Workshop at NeurIPS 2025.
- Organizer of EvalComp workshop at UbiComp 2025, Espoo, Finland.
- Area Chair at the Web Conference 2025, Sydney, Australia.
- Organizer of FairComp & WellComp workshops at UbiComp 2024, Melbourne, Australia.
- 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.
Expert reviewer & advisory roles:
- External PhD examiner, King's College London (2025).
- Expert reviewer, European Research Council (ERC) (2025).
- Expert reviewer, Research Council of Norway (2024).
- External advisor, UK Information Commissioner's Office (2022).
Program Committee Member: AAAI, IJCAI, KDD, FAccT, SIAM SDM, Sensiblend @ Ubicomp.
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.
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)
Team
Over the years, I've had the privilege of supervising and closely collaborating with a talented group of researchers, both through industry internships and university PhD co-supervision. We focus on building intelligent and personalized agents for health. Here are the amazing researchers who have worked with me:
- Gautham Krishna Gudur (University of Texas at Austin): Personalized agents for health data
- Hyungjun Yoon (KAIST): Personalized agents for health data
- 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
- Arvind Pillai (Dartmouth College): Foundation models for physiological signals
Invited talks & lectures
may 2026
Foundation Models for Personal Health Signals
Machine
Intelligence for Health Conference (MI4H), Coventry, UK
apr 2026
Foundation Models for Personal Health Signals
RADAR-base
Symposium, Wellcome Trust & King’s College London, UK
mar 2026
Foundation Models for Personal Health
Signals
University of Cambridge, UK
sep 2025
AI4Health Industry Day 2025
Imperial
College London, UK
apr 2025
Foundation models for personal health
Singapore Management University, Singapore
dec 2024
The era of foundation models – AI for personal health as its ultimate use case
Aristotle University,
Thessaloniki, Greece
sep 2024
Evidence from industry – what are you really using AI for? (panel)
Cambridge Tech Week,
Cambridge, UK
feb 2024
Multimodal AI for Real-World Signals and the Role of Language
AAAI'24 Health Intelligence workshop,
Vancouver, Canada
jan 2024
Multimodal, data-efficient, and robust AI for real-world biosignals & the role of generative
models
Cambridge MedAI Seminar
Series, Biomedical Campus, Cambridge, UK
nov 2023
Multimodal AI for real-world signals – does the key to specialized models lie in
language?
Microsoft AI & Pizza talk -
Cambridge ELLIS Unit, Cambridge, UK
mar 2023
Human-centric AI for health signals with applications in fitness and activity
modeling
Cambridge Public Health symposium,
Cambridge, UK
feb 2023
Self-Supervised Learning for Health Signals
Rising Stars in AI, KAUST, Saudi Arabia
nov 2022
Representation learning for cardio-fitness prediction in free-living environments
King's College London, Precision Health Informatics Data Lab, London, UK
jun 2022
AI-powered Wearables Transforming Mobile Health
AI Summit, London Tech week, London, UK
mar 2022
Self-supervised learning for health signals
Feinstein Institutes of Northwell Health, New York, USA (remote)
mar 2021
AI to model Human Behaviour and Health
Jesus College Postgraduate Conference, virtual event,
UK
mar 2019
Deep sequence learning for large-scale inference of human behaviour from mobile sensor
data
MRC Epidemiology Unit, University of
Cambridge, UK
oct 2018
Fast, Visual and Interactive Semi-supervised Dimensionality Reduction
Facebook PhD Open House, London, UK
Press & media coverage
AI for physiological sensing, Pixel Watch & Fitbit Air: PCMag, Wired, Engadget, 9to5Google, ZDNet, Tom's Guide, The Verge, NYT Wirecutter, DC Rainmaker, Engadget, Android Authority, CNET, Men's Health, PCMag, Wired.
Large Language Models for timeseries: Techcrunch, LG AI Research.
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, Psychology Today, El Pais, RAI, Corriere della Sera, Focus, DerStandard.
AI for VO2max: Cambridge University (1), (2), New York Times, Bloomberg, VentureBeat, Business Insider, Runner's World, Communications of the ACM, Daily Mirror, Bicycling Magazine, Owkin, Spektrum.de.
Data-driven music psychology: Cambridge University, The Times, Washington Post, CNN, The Telegraph, Sky News, Guardian, ITV, DailyMail, Inc., CTV, ZDF, Der Tagesspiegel, ABC.ES, ABC.AU, ELLE, Cosmopolitan, RTBF, TEDx.
Interviews: IndiaAI.gov
Playground
“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
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!