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

Michael Noukhovitch mnoukhov at gmail.com
Ven 23 Fév 10:04:22 EST 2018


Reminder this talk is in 30 minutes

On Tue, Feb 20, 2018, 13:39 Michael Noukhovitch <mnoukhov at gmail.com> wrote:

> 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.
>
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