Nanoscience Colloquia
Open, advanced talks on nanoscience
The Nanoscience Colloquia – from the latin word ”loqui” that means talk – are a series of advanced talks on nanoscience, open to everyone within and outside academia. Welcome to join the conversation – see you in k-space at the Physics Department!
2024 Colloquia
Nanoscience colloquia are on Thursdays at 15:15 (unless otherwise stated).
Colloquia scheduled
11 January 2024 14:00, k-space, Physics Department
Very welcome to this mini-symposium with with Giovanni Volpe and Pawel Sikorski!
Agenda
14:00-14:45 Pawel Sikorski: Nanotechnology meets bioengineering with some examples from nano-, microfabrications and biomaterials.
14:45:15:15 Coffee Break
15:15-16:00 Giovanni Volpe: Deep Learning for Imaging and Microscopy
Speaker information and abstracts
Pawel Sikorski, Professor at the Department of Physics, Norwegian University of Science and Technology (NTNU)
Nanotechnology meets bioengineering with some examples from nano-, microfabrications and biomaterials.
In this presentation, I aim to highlight connections between bioengineering, biomaterials, and nanotechnology. I will introduce nanoscale effects that can be important for bioengineering research and technology development. Nanotechnology has the potential to contribute to biomedical research by providing new tools and new experimental methods. Compared to traditional approaches, these techniques often allow for miniaturization and better control of the experimental system. In our research, we are interested in the fabrication of nanostructured and microstructured surfaces that are easy and inexpensive to make, and that are compatible with typical workflows in biomedical research. I will describe two different fabrication approaches and how they are used to study biological systems.
Giovanni Volpe, Professor at the Department of Physics, University of Gothenburg
Deep Learning for Imaging and Microscopy
Video microscopy has a long history of providing insights and breakthroughs for a broad range of disciplines, from physics to biology. Image analysis to extract quantitative information from video microscopy data has traditionally relied on algorithmic approaches, which are often difficult to implement, time consuming, and computationally expensive. Recently, alternative data-driven approaches using deep learning have greatly improved quantitative digital microscopy, potentially offering automatized, accurate, and fast image analysis. However, the combination of deep learning and video microscopy remains underutilized primarily due to the steep learning curve involved in developing custom deep-learning solutions. To overcome this issue, we have introduced a software, currently at version DeepTrack 2.1, to design, train and validate deep-learning solutions for digital microscopy.
Recommended reading
https://pubs.aip.org/aip/apr/article/8/1/011310/238663/Quantitative-digital-microscopy-with-deep-learning
(open access so everyone can download the fulltext in pdf)
Extra note for students enrolled in the Nanoscience Breadth Course: the talk by Giovanni Volpe is included in this course and the enrolled students will be informed separately by Jonas Johansson.
Future colloquia will be organized by Sara Blomberg, so if you have ideas for speakers that you would like to invite, please contact Sara.
Sara Blombergs research profile and e-mail