From IP management to machine learning and audio diagnostics – highlights of interdisciplinary response to the pandemic

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• Frank Tietze, IfM and Cecilia Mascolo (Dep Computer Science and Technology)
• Thursday 02 July 2020, 14:00-15:00
• Webinar (via Zoom online)- link to follow.

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Cambridge Network and Maxwell Centre are delighted to invite you attend a webinar featuring:

Frank Tietze, University Lecturer at the Institute for Manufacturing (IfM)

Title: Unpacking Intellectual Property challenges during the Covid-19 pandemic

Have you ever wondered what intellectual property (IP) got to do with a crisis? When the Covid-19 pandemic started to emerge we started asking this ourselves, particular my team at the Innovation and IP Management (IIPM) Laboratory. When unpacking what IP got to do with the Covid-19 pandemic we found quite a number of IP associated challenges. Probably, the most obvious one is related to the IP associated with any future vaccine. However, we learnt that this is by far the only IP associated challenge in such highly turbulent and dynamic times that decision makers in companies and governments should be aware of. During this talk I will share with you the insights we gained during the past months, such as IP challenges resulting from changing industrial organisational structures during the pandemic caused by new constellations and partnerships being formed because of Covid-19 manufacturing repurposing. The talk provides initial recommendations for decision makers that consider IP as a potential policy tool, such as the Open Covid Pledge (www.opencovidpledge.org). A number of technology corporations and university have already pledged to make their IP freely available during the pandemic.

Cecilia Mascolo, Professor at the Department of Computer Science and Technology

Title: Mobile Health Diagnostics through Audio Signals

Audio has been used for centuries by doctors as diagnostics technique. In this talk, I plan to reflect on the challenges and opportunities that using audio sourced from mobile and wearables could offer in terms of automated diagnostic tools for disease and progression. I will use examples from my group’s ongoing research on exploring devices collecting audio signals (as well as other more traditional signals) from the human body to understand, track and diagnose health, in particular a case study on COVID -19 (covid-19-sounds.org) we have been working on since the outbreak. I will also talk about the machine learning and data analysis challenges imposed by this sort of data using examples from our collaborations with epidemiologists and clinicians. 

This talk is part of the From discovery science to industrial applications series.

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