TAIGA-IceLab Pitch Event with Martin Rosvall and Laura Bacete

September 14, 2023 @ 12:00 - 13:00

TAIGA-IceLab Pitch Event with Martin Rosvall and Laura Bacete

TAIGA’s pitch events in September seek to provide a forum for researchers with ideas where AI could be involved. They aim to provide an interdisciplinary platform where you can connect with potential collaborators or find inspiration for the next step in your project. TAIGA will provide support to projects that focus on artificial intelligence and its development, application, implementation and impact on society throughout the process, from idea to completed project application, with follow-up events in the autumn. Take the chance to discuss in a relaxed and creative environment.
 
 
Place: MIT Seminarierummet
Time: Thursday 14 September at 12:00.
 
 

Pitch 1: Martin Rosvall: Deciphering epigenetic dynamics to explain why some people experience long-term effects of COVID-19

Professor, IceLab, Department of Physics, Molecular Infection Medicine Sweden (MIMS)
 
Abstract:
 
We have found that COVID-19 leaves epigenetic marks on our genome. However, the origin, dynamics, impact on severity, and potential for long-term effects such as long-COVID-19 syndrome remain unknown. To complement our expertise, we are looking for experts on deep autoencoders or other non-linear dimensionality reduction techniques. As part of an interactive team, you will gain experience in our extensive longitudinal biobanks and network models, helping analyze unique clinical data, decode complex epigenetic dynamics, and predict patient outcomes. We also offer postdoctoral scholarships in IceLab.
 

Pitch 2: Laura Bacete: Decoding the Biophysical Language of Plant Cell Walls: A Multidisciplinary Approach

Assistant Professor, Umeå Plant Science Center, Department of Plant Physiology
 
Abstract:
 
Plant cell walls depend on mechanical and biochemical signals for functional integrity. However, the intricate dialogue between these remains largely unexplored. We aim to separate signals from noise and establish causality, a task well-suited for computational biology. By integrating diverse data, we hope to develop algorithms that can predict changes in cell wall integrity and their impact on plant fitness. While in our team we have a robust understanding of cell wall integrity and methods to assess plant cell wall characteristics, we lack expertise in comprehensive data integration, extending only to basic logical models. If you have skills in data science, modelling, or bioengineering, your collaboration could be extremely beneficial! A better understanding of plant cell walls can lead to more resilient crops, efficient bioenergy production, and improved environmental sustainability. Join us to see how a multidisciplinary approach can make this possible.

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