[Lisa_seminaires] [DIRO Talk] Ina Fiterau (Stanford) Fri Feb 23 10:30AM AA1360

Michael Noukhovitch mnoukhov at gmail.com
Mar 20 Fév 13:39:31 EST 2018


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.
-------------- section suivante --------------
Une pièce jointe HTML a été nettoyée...
URL: http://webmail.iro.umontreal.ca/pipermail/lisa_seminaires/attachments/20180220/0848c2a2/attachment-0001.html 


Plus d'informations sur la liste de diffusion Lisa_seminaires