Hmmm

Congrats to my Ph.D. students Fiona Ryan (Fiona Ryan) and Max Xu (Max Xu) for both winning NSF Graduate Fellowships. 🎉🎉🎉 #ProudAdvisor Machine Learning at Georgia Tech Georgia Tech School of Interactive Computing

the prophet speaks

Next up at #INSAR2022 Max Xu James Matthew Rehg @Agamooz @ncrbrady applying a novel machine learning model to language development in autism. Discovering Novel Predictors of Minimally Verbal Outcomes in Autism through Computational Modeling. Thread by James Matthew Rehg (1/7)

Dense self-supervised learning from multiple 3D viewpoints → dense feature representations that generalize both to novel object instances and to novel categories of instances. Checkout our #NeurIPS2022 paper! arxiv: arxiv.org/abs/2211.15059 github: github.com/rehg-lab/dope_…

How can we fill in missing pulsative sensor data? Prior state-of-the-art fails in our novel setting, despite its well-defined temporal structure. Checkout our #NeurIPS2022 paper, PulseImpute, @ 4 pm CST! arxiv: openreview.net/pdf?id=x_kBZYi… github: github.com/rehg-lab/pulse…

Poster presentation alert 🚨 I will present the paper “Event-Based Contrastive Learning” in Poster Session B (Poster #156, 3:35-4:55PM) Machine Learning for Healthcare ! Link to camera-ready version of paper: arxiv.org/abs/2312.10308

Excited to be in beautiful Singapore for #ICLR2025! Come see our poster on RelCon, SOTA foundation model for motion data, led by stellar Max Xu with Apple Health Research. Poster 17 in session 6 (3:00 - 5:30 April 26 Hall 3+2B) arxiv.org/abs/2411.18822

🩺📈 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)

If you are working on AI in healthcare, please consider submitting to our Time-series 4 Health workshop at NeurIPS 2025!! We have prepared a great lineup of speakers and talks ⌚️🏥⏱️📈🧠🫀💃

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

this is my recent research at Google on learning #foundationmodels for #wearable sensors in real-world settings with real-world missingness and noise!!!

Only 10 days left to submit to the Time-series 4 Health Workshop at #NeurIPS2025 ! Call for Papers: timeseries4health.github.io/call_for_paper…

This year, submissions to the proceedings track will have the option to cross-submit to the #NeurIPS2025 Learning from Time Series for Health Workshop (timeseries4health.github.io). More details here: ahli.cc/ml4h/cross-sub… #ML4H