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Dimitris Spathis

@spdimitris

Research scientist @GoogleAI and visiting academic @Cambridge_Uni. Multimodal foundation models × AI for health. Past @Nokia @BellLabs, @Microsoft, @Telefonica.
Cambridge, UK dispathis.com Joined May 2009
772 Following 1.4K Followers 133 Posts

Latest posts 5

Dimitris Spathis @spdimitris
we announce SensorFM, a foundation model pretrained on the largest personal health dataset (1T+ mins, 5 mil people, 100 countries)

- embeddings transfer across 35 tasks via systematic performance scaling
- agents search for predictive heads
- grounding LLMs for clinical answers
Google Research Verified account @GoogleResearch Jul 9
Introducing SensorFM, a large-scale Sensor Foundation Model that learns from 1 trillion-minutes of unlabeled wearable data drawn from five million consented participants.

SensorFM learns a single, reusable representation of sensed human physiology that transfers across cardiovascular, metabolic, sleep, and mental health, as well as lifestyle and demographic factors.

More →goo.gle/4ycJvot
SensorFM is pre-trained on over a trillion minutes of multimodal sensor data via missing-aware masked reconstruction.
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Dimitris Spathis @spdimitris
Proud to be part of this work! We trained a foundation model on 40M hrs of wearable data where gaps from charging, motion artifacts etc. are inevitable. Rather than imputing or discarding, it uses a new attention masking mechanism.

Don't fight the messiness, embrace it!
Google Research Verified account @GoogleResearch Jul 22, 2025
Introducing LSM-2, our newest foundation model for wearable sensor data. LSM-2 uses Adaptive & Inherited Masking, a novel self-supervised framework, to learn from incomplete data & achieve strong performance without requiring explicit imputation. More → goo.gle/4kP6Ncc
Wearable devices sense an array of physiological and behavioral signals — making sense of these can be challenging.
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Dimitris Spathis @spdimitris
If you are working on AI for health×timeseries please consider submitting to our NeurIPS 2025 workshop below. The team has prepared a great lineup of speakers and topics — stay tuned for updates!
Hyewon Jeong @HyewonMandyJ Jul 18, 2025
🩺📈 The "Learning from Time-series for Health (TS4H)" workshop is BACK at NeurIPS 2025 🥳!

This workshop unites researchers across health time-series domains (from wearables to clinical systems) to tackle shared challenges. Details: timeseries4health.github.io
🧵 (1/6)
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