The Machine Learning for Health and Well-Being (MLwell) Lab is a research lab at the Bio-Medical Engineering department at Tel-Aviv University. Our vision is to create the technology to allow everyone and everywhere access to personalized medicine and precision psychology that is: (i) effective (ii) respects the biological, cultural and behavioral differences between people (iii) respects privacy and other ethical requirements (iv) affordable. Our mission is to improve the state in the art in machine learning algorithms for personalized medicine and precision psychology.
Congratulations to Omri Armstrong for defending his thesis
Congratulations to Omri Armstrong for successfully defending his Master's thesis - the last step towards receiving his Master's degree.
Marginal Contribution Feature Importance -- an Axiomatic Approach for The Natural Case
The paper studies way to assign feature importance in order to gain insights about data.
Congratulations to Mor Zukin for graduating
Mor Zukin has graduated her undergrad studies and she is an engineer now. We are proud of her and will miss her in the lab.
Presentation at the Summer School on Cyber Computer Security
Please join us to a talk about privacy, security and machine learning at the Technion Summer School on Cyber Computer Security on September 1-th 2020. Visit the event page here.
Joy Ventures Grant
We are very thankful for the generous grant awarded to us by Joy Ventures to allow us to study "Improved stress coping using just-in-time mental preparedness exercises"
Webinar: Technology, Artificial Intelligence and Privacy
Please join us to a webinar with leading experts from the social sciences and engineering for a webinar on Technology, Artificial Intelligence and Privacy. The event will be live streaming on March 24th 2020. Visit the event page here.
The Automated Copywriter: Algorithmic Rephrasing of Health-Related Advertisements to Improve their Performance
Our paper was accepted for publication in The WebConference (WWW 2020). The paper describes techniques for improving public health advertisments in search advertising. We use deep learning techniques to “translate” existing ads into more effective ones.
On Jan 27 we will participate the Y-Data meetup in Tel-Aviv presenting our work Privacy Preserving Machine Learning. In this talk we will present how cryptography can be combined with machine learning to provide privacy and security and allow cloud services to opperate on private and sensitive data such as medical and financial data.