Multi modal manifold learning and Medical Voice Monitoring

Welcome to hiCenter meetup!
In this event, organized in collaboration with DataTalks HFA,
we will present two different machine learning approaches to the study of medical signal processing.

First, Tal Shnitzer, a PhD student for signal processing at the faculty of electrical engineering, will present her recent work about multi modal manifold learning.
In this context, the goal is to characterize the relations between the different modalities by studying their underlying manifold.
The capabilities of the proposed method are demonstrated on a fetal heart rate monitoring application and several other examples.

Then, Daniel Aronovich, Co-Founder & CTO at Vocalis Health, will present the technology behind the Wave product that was developed at Vocalis, the first ever medical approved product that utilizes voice.
Daniel will present how Vocalis developed a convolutional neural network that can detect breaths and voiced parts in any given recording, both accurately and robustly, and he will talk about how this system was used for shortness of breath detection.

𝗔𝗴𝗲𝗻𝗱𝗮:
18:00 - Gathering, snacks & beer 🍕🍺
18:30 - Opening words from the organizers
18:40 - The Real Thing !