[AI Seminar] Fwd: [graphics] VASC - 11/11/19 -- Madalina Fiterau, an Assistant Professor at UMass Amherst, College of Information & Computer Sciences, presenting "Hybrid Methods for the Integration of Heterogeneous Multimodal Biomedical Data"

[Aayush Bansal]

FYI -- this might be of interest to many working on multi-modal data..

Additionally, here are the available meeting slots with the speaker. Feel
free to sign-up:


https://docs.google.com/spreadsheets/d/13OLRRRRjbA9ivtLi-dECB3xTOje4evN8H27PX7LJ-TY/edit?usp=sharing


Aayush

---------- Forwarded message ---------
From: Christine A Downey <cdowney at andrew.cmu.edu>
Date: Mon, Nov 4, 2019 at 10:47 AM
Subject: [graphics] VASC - 11/11/19 -- Madalina Fiterau, an Assistant
Professor at UMass Amherst, College of Information & Computer Sciences,
presenting "Hybrid Methods for the Integration of Heterogeneous Multimodal
Biomedical Data"
To: vasc-seminar at cs.cmu.edu <vasc-seminar at cs.cmu.edu>


VASC - 11/11/19 -- Madalina Fiterau, an Assistant Professor at UMass
Amherst, College of Information & Computer Sciences, will be giving a
seminar on "Hybrid Methods for the Integration of Heterogeneous Multimodal
Biomedical Data", on 11/11/19, from 3:00-4:00 in *Gates Hillman 6501*.
Refreshments will be served.  Details are as follows:



*Title*:   Hybrid Methods for the Integration of Heterogeneous Multimodal
Biomedical Data



*Abstract**:  *The prevalence of smartphones and wearable devices for
health monitoring and widespread use of electronic health records have led
to a surge in heterogeneous multimodal healthcare data, collected at an
unprecedented scale. My research focuses on developing machine learning
techniques that learn salient representations of multimodal, heterogeneous
data for biomedical predictive models. The first part of the talk describes
the construction of hybrid models that combine deep learning with random
forests, and the fusing of structured information into temporal
representation learning. These methods obviate the need for feature
engineering while improving on the state of the art for diverse biomedical
applications. Use cases include the prediction of surgical outcomes for
children with cerebral palsy, and forecasting the progression of
osteoarthritis from subjects' physical activity. The focus of the latter
part is on hybrid methods for the integration of images and
multi-resolution, irregular time series data for disease trajectory
modeling, developed with my students at UMass Amherst. Multi-FIT, a unified
model for the construction of flexible temporal representations, is
designed to handle missing values and irregularly collected samples in
multi-resolution, multivariate time series. Multi-FIT outperforms the
state-of-the-art for patient survival prediction on the PhysioNet Challenge
2012 ICU data. FLARe is a model that provides more informative modeling of
the temporal relationships between patients' history and the disease
trajectory by generating a sequence of latent representations of patients'
health status across the time horizon. FLARe improves on the
state-of-the-art on forecasting the progression of Alzheimer's disease from
brain MRIs and contextual information from the ADNI dataset



*Bio:  *Ina Fiterau is an Assistant Professor in the College of Information
and Computer Sciences at UMass Amherst. She has completed a PhD in Machine
Learning from Carnegie Mellon University (Fall 2015), and a Postdoc at
Stanford University (Fall 2018). Ina is currently expanding her research on
interpretable models, in part by applying deep learning to obtain salient
representations from biomedical unstructured data, including time series,
text and images. She 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. Madalina has co-organized the NeurIPS
workshop on Machine Learning in Healthcare.



*Homepage:*  https://www.cics.umass.edu/people/fiterau-brostean-madalina
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mailman.srv.cs.cmu.edu/pipermail/ai-seminar-announce/attachments/20191106/293a5970/attachment.html>