<div dir="ltr"><div>FYI -- this might be of interest to many working on multi-modal data..</div><div><br></div><div><p class="MsoNormal">Additionally, here are the available meeting slots with the speaker. Feel free to sign-up: <u></u></p><p class="MsoNormal"><br></p><p class="MsoNormal"><a href="https://docs.google.com/spreadsheets/d/13OLRRRRjbA9ivtLi-dECB3xTOje4evN8H27PX7LJ-TY/edit?usp=sharing" target="_blank">https://docs.google.com/spreadsheets/d/13OLRRRRjbA9ivtLi-dECB3xTOje4evN8H27PX7LJ-TY/edit?usp=sharing</a></p></div><div><br></div><div><br></div><div>Aayush</div><div class="gmail_quote"><div dir="ltr"><br><div class="gmail_quote"><div dir="ltr" class="gmail_attr">---------- Forwarded message ---------<br>From: <strong class="gmail_sendername" dir="auto">Christine A Downey</strong> <span dir="auto"><<a href="mailto:cdowney@andrew.cmu.edu" target="_blank">cdowney@andrew.cmu.edu</a>></span><br>Date: Mon, Nov 4, 2019 at 10:47 AM<br>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"<br>To: <a href="mailto:vasc-seminar@cs.cmu.edu" target="_blank">vasc-seminar@cs.cmu.edu</a> <<a href="mailto:vasc-seminar@cs.cmu.edu" target="_blank">vasc-seminar@cs.cmu.edu</a>><br></div><br><br>
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<p><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:#1f497d">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
<b>Gates Hillman 6501</b>. Refreshments will be served. Details are as follows:</span><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:#1f497d"><u></u><u></u></span></p>
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<p><b><u><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:#1f497d">Title</span></u></b><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:#1f497d">: </span><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:#1f497d">Hybrid
Methods for the Integration of Heterogeneous Multimodal Biomedical Data<u></u><u></u></span></p>
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<p><b><u><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:#1f497d">Abstract</span></u></b><b><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:#1f497d">: </span></b><span style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:#1f497d">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<u></u><u></u></span></p>
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<p class="MsoNormal"><b><span style="color:#1f497d">Bio: </span></b><span style="color:#1f497d">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.<u></u><u></u></span></p>
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<p class="MsoNormal"><b><span style="color:#1f497d">Homepage:</span></b><span style="color:#1f497d">
</span><a href="https://www.cics.umass.edu/people/fiterau-brostean-madalina" target="_blank">https://www.cics.umass.edu/people/fiterau-brostean-madalina</a>
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