For this week's last DIRO talk, we have Ina Fiterau, a post-doc from Stanford giving a talk on Friday Feb 23 at 10:30AM in room AA1360.
This talk should combine a variety of different modal topics in a seamless way, which sounds bloody exciting!
Michael
TITLE
Hybrid Machine Learning Methods for the Interpretation and Integration of Heterogeneous Multimodal Data
KEYWORDS Biomedical ML, Deep + Shallow Learning, Human-in-the-loop, Mulit-modal
ABSTRACT
The prevalence of smartphones and wearable devices and the widespread
use of electronic health records have led to a surge in multimodal
health data that is noisy, non-uniform, and collected at an
unprecedented scale. This talk focuses on machine learning models that
learn expressive representations of multimodal, heterogeneous data for
biomedical predictive models designed to interact with domain experts.
In the first part of the talk, the focus is on techniques for
partitioning data and leveraging low-dimensional structure to enable
visualization and annotation by humans. The latter part addresses the
construction of hybrid models that combine deep learning with random
forests, and the fusing of structured information into temporal
representation learning. This array of methods obviates the need for
feature engineering while improving on the state of the art for diverse
biomedical applications. Use cases include the classification of alerts
in a vital sign monitoring system, the prediction of surgical outcomes
in children with cerebral palsy, and forecasting the progression of
osteoarthritis from subjects' physical activity.
BIO
Madalina Fiterau is a Postdoctoral Fellow in the
Computer Science Department at Stanford University, working with
Professors Chris Ré and Scott Delp in the Mobilize Center. She has
obtained a PhD in Machine Learning from Carnegie Mellon University in
September 2015, advised by Professor Artur Dubrawski. The focus of her
PhD thesis, entitled “Discovering Compact and Informative Structures
through Data Partitioning”, is learning interpretable ensembles, with
applicability ranging from image classification to clinical alert
prediction. Madalina is currently expanding her research on
interpretable models, in part by applying deep learning to obtain
salient representations from biomedical “deep” data, including time
series, text and images. Madalina is the recipient of the Marr Prize for
Best Paper at ICCV 2015 and of Star Research Award at the Annual
Congress of the Society of Critical Care Medicine 2016. She has
co-organized NIPS workshops on the topic of Machine Learning in
Healthcare in 2013, 2014, 2016 and 2017.