Recent & Upcoming Talks

This paper proposes aa framework to reprogram an existing LLM to perform time series forecasting.

This paper proposes a series of adaptations and enhancements around BERT to best work with IMU data in mobile sensing applications.

This paper proposes Health-LLM, a framework in the healthcare domain that aims to bridge the gap between pre-trained knowledge in current LLMs and consumer health problems.

This presentation presents self-supervised method that learns invariant representations rather than covariant ones.

This presentation presents self-supervised method to detect PD using wearable sensor data for BMI 310 undergrad students.

This presentation presents semi-supervised neural network for freezing of gait classification.

In this paper, various data augmentation methods for wearable sensor data are proposed. The proposed methods and CNNs are applied to the classification of the motor state of Parkinson’s Disease patients.

This paper presents SimCLR, a simple framework for contrastive learning of visual representations.