Harshith has a Master degree in physics from the Indian Institute of Science Education and Research, Pune, India, where he submitted a Master thesis in optics, whose results can be found here.
In his PhD, he will focus on microscopy and deep learning.
Our idea Lucero, has reached the semi-final for the SPIE Start-up challenge, where will pitch in front of a jury at Photonics West in San Francisco, CA, USA on the 4th of February 2020.
Lucero will compete, among other 41 semifinalists, for cash prizes and business support.
In addition, Lucero was awarded one of the three Early Stage Entrepreneurship Travel Grants to attend the semi-final.
The start-up is aiming to make cutting-edge laser technology easy to use and available to anyone by combining it with commercial microscope. The product and software combo utilizes optical tweezers in a brand-new way – and bridges the gap between physics and other scientific fields that would greatly benefit from easier access to this tool.
In December, Lucero was ranked among the best 5 business ideas in West Sweden.
Team components: Christopher Jacklin, Rich Zapata Rosas, Felix Mossberg, Falko Schmidt, Alejandro Diaz Tormo and Martin Mojica-Benavides.
Links: Lucero Homepage
On Monday, the 16th of December, Falko Schmidt, will visit Frank Cichos’ lab at the University of Leipzig. He will present latest research on a nanoscopic particle in a harmonic trap.
Falko Schmidt and other researchers at the University of Gothenburg, in collaboration with Business students at the Chalmers School of Entrepreneurship, have received early acclaimfor their Start-up idea “Lucero Bio”.
Lucero Bio was ranked among one of the top 5 business ideas in West Sweden by Venture Cup Sweden. Out of the 376 ideas that were submitted to the competition, nearly half came from the western region of Sweden.
The start-up is aiming to make cutting-edge laser technology easy to use and available to anyone by combining it with commercial microscope. The product and software combo utilizes optical tweezers in a brand-new way – and bridges the gap between physics and other scientific fields that would greatly benefit from easier access to this tool.
Team components: Christopher Jacklin, Rich Zapata Rosas, Felix Mossberg, Falko Schmidt, Alejandro Diaz Tormo and Martin Mojica-Benavides.
More information:
Press release, in Swedish.
Top 20 list of the 2019 winners, in Swedish.
On Friday, the 13th of December, Falko Schmidt, will visit Juliane Simmchen’s lab at the Technical University of Dresden. He will present results on Liquid-Liquid-Phase Separation a project that is currently in collaboration with their lab.
Light-induced phase separation power novel micro machines
Falko Schmidt, and Giovanni Volpe
Light at the Nanoscale Conference, Chalmers University, Gothenburg, Sweden
5 December 2019, 16:30-18:30
Phase separation is a phenomena that commonly exists in nature, from the freezing of ice to the intrinsic mechanism of the cell to order matter. We are exploiting phase separations to produce new types of miniaturised machines, in particular micron and nano sized engines1as well as to form self-assembled colloidal molecules2. We control their behaviour using only light and varying its ambient temperature making this a simple tool to study complex matter3. This will enhance the development of future medicine where nano robots deliver drugs specifically to the local infection side.
References:1. F. Schmidt et al. Microscopic engine powered by critical demixing, Phys Rev Lett 120, 068004, 2018
2. F. Schmidt et al. Light-controlled assembly of active colloidal molecules, J Chem Phys150, 094905, 2019
3. S. Bo et al. Measurement of anomalous diffusion using recurrent neural networks, Phys Rev E 100, 010102(R), 2019
Optics & Photonics News has picked Saga Helgadóttir and Aykut Argun’s work on deep learning for particle tracking (DeepTrack) as a top break-through of the year.
“This has been a really good year for me, research-wise. My publication, presenting a new AI method, garnered a lot of attention,” says Saga Helgadóttir, PhD at the Department of Physics.
The research article in question, which is now included in Optics & Photonics News’ best-of-2019 list, identifies a new way of implementing neural networks and machine learning in order to track particle motion and study surrounding microenvironments.
After the publication in mid-April, Saga Helgadóttir was contacted by both national and international press to talk about her discoveries. She has also been invited to visit research groups abroad and was a speaker at the AI in Health and Health in AI conference held in Gothenburg in August.
Currently, Saga Helgadottir is collaborating with a group of scientists at Sahlgrenska’s Wallenberg Laboratory. They are working on new ways of using deep learning in the medical field.
“I started my PhD research studying bio-hybrid microswimmers, but ended up more within the area of artificial intelligence and optics. I like this work a lot, and the positive response to my publication earlier this year has allowed me to establish myself in the AI-field.”
Text: Carolina Svensson
List of highlighted research from 2019: Optics in 2019
Saga Helgadottir’s featured summary: Deep Learning for Particle Tracking
Original press release about the research: She has discovered a new method of using AI
Machine learning as a tool for the natural sciences: Opportunities and challenges
Giovanni Volpe
Invited Talk at BRC Day “Biomaterials meets AI”, University of Gothenburg, Gothenburg, Sweden, 12 November 2019
Abstract: Data-driven machine-learning methods are more and more widely used in the natural sciences. Machine learning offers unprecedented opportunities, but it also poses unexpected practical and fundamental challenges. Most importantly, machine-learning methods often work as black boxes, and therefore it can be difficult to understand and interpret their results. Here, we present an overview of the current state of the art of the adoption of machine learning in active-matter research. Finally, we discuss the opportunities and challenges that are emerging, highlighting how active matter and machine learning can work together for mutual benefit.
Bio: Giovanni Volpe is Associate Professor at the University of Gothenburg, where he leads the Soft Matter Lab (http://www.softmatterlab.org/).
He has published more than 80 articles on diverse topics including optical trapping, active matter, neurosciences, and machine learning.
He has co-authored the book “Optical Tweezers: Principles and Applications” (Cambridge University Press, 2015).
He is the recipient the ERC Starting Grant ComplexSwimmers, coordinator of the MSCA Innovative Training Networks ActiveMatter, and the KAW research grant “Active Matter Goes Smart”.
He is one of the chairs of the Conference Emerging Topics in Artificial Intelligence at the SPIE Optics & Photonics Meeting held annually in San Diego (CA).
My research and my career
Giovanni Volpe
SNF Outreach Lecture
Chalmers University go Technology, Gothenburg, Sweden
7 November 2019
Machine learning for active matter
Giovanni Volpe
Introductory Talk at CECAM Workshop “Active Matter and Artificial Intelligence”
CECAM-HQ-EPFL, Lausanne, Switzerland
30 September – 2 October, 2019
Data-driven machine-learning methods are more and more widely used in the natural sciences. Active-matter research is no exception and has recently started experiment- ing machine-learning approaches. Machine learning offers unprecedented opportunities, but it also poses unexpected practical and fundamental challenges. Most importantly, machine-learning methods often work as black boxes, and therefore it can be difficult to understand and interpret their results. Here, we present an overview of the current state of the art of the adoption of machine learning in active-matter research. Finally, we discuss the opportunities and challenges that are emerging, highlighting how active matter and machine learning can work together for mutual benefit.
