Dimitris Spathis

🦜PaPaGei: an open foundation models for the most widely used biosignal🦜 It is pre-trained on over 57,000 hours of PPG signals (like those from smartwatches & pulse oximeters) from publicly available sources. Paper: arxiv.org/abs/2410.20542 Models/code: github.com/nokia-bell-lab…

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!

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!

I'll be at #ICLR2025 this week in Singapore and would love to connect with researchers interested in foundation models, representation learning, multimodal pretraining, & health sensing! Feel free to catch me at any of these events or ping me here to schedule a time to chat.

Great news from Vancouver, happy to share that 🦜PaPaGei received the 🏆 Best Paper Award at the Time Series in the Age of Large Models (TSALM) workshop in #NeurIPS2024! Huge congratulations to @ArvindPillai10 and the team. Stay tuned for the v2 of the paper with more results.

We'd love to hear from researchers and developers who try it out! Your feedback will be invaluable in improving and expanding its capabilities. Tremendous work by @ArvindPillai10 and team @malekz4deh @raswak!

🦜PaPaGei: an open foundation models for the most widely used biosignal🦜 It is pre-trained on over 57,000 hours of PPG signals (like those from smartwatches & pulse oximeters) from publicly available sources. Paper: arxiv.org/abs/2410.20542 Models/code: github.com/nokia-bell-lab…

Some news: after 2.5 years at @BellLabs, I’m moving on. It was a truly rewarding experience, letting me experiment with the latest in AI during a seismic shift. Huge thanks to @raswak and the team. I've now joined @GoogleAI where I'll be working on AI for Health and Fitness.

Exercising regularly can be hard and there's often conflicting information on the best ways to measure progress and fitness levels. But one stat that's increasingly seen as important to track is 'VO2 max' and Bloomberg Prognosis explains why.... trib.al/Tswcryv

@inthefade Neil Hannon Stuart Murdoch Mitski Colin Meloy Matt Berninger Julia Holter

@judyhshen @rajiinio @irenetrampoline Excellent work & analysis! It's refreshing to see more studies exploring these issues (& thanks for acknowledging our eICU-OOD). We somewhat understated the heuristics experiment at the end, great to see a proper formalization with score metrics (& highly actionable guidelines).

More training data isn’t always better! We (joint work w.@rajiinio and @irenetrampoline) will present our new work: The Data Addition Dilemma, at MLHC 2024 this week! (1/5)

Check out this recent article on the tokenization problem - a quite accessible read for anyone interested in how LLMs represent inputs. Happy to see our work featured alongside cutting-edge research 😊 x.com/TechCrunch/sta…

You can read more on the full paper: Paper: doi.org/10.1093/jamia/… PDF: api.repository.cam.ac.uk/server/api/cor…

🤔Why do Large Language Models like ChatGPT struggle with temporal data? Our new work published in JAMIA w/ @raswak explores LLMs' difficulties when processing time series data, particularly from sources like mobile sensors or medical records.