Reto Häring
Organisation: TOPTICA Photonics Ag Role: Senior vice-president R&D
Reto Häring was appointed senior vice- president of R&D at Toptica in April 2022, heading a scientific and engineering division of 95 people. He said Toptica’s driving force over
the next 12 months will be the quantum computer: ‘As a provider of laser sources, we are working on the next generation of light sources for quantum. Thereby the laser itself becomes more and more a component and the systems for control of coherence, wavelength, locking and timing become more and more important.’ The big question around quantum
Nicholas Harris
Organisation: Lightmatter Role: CEO and founder Based in: Boston, Massachusetts, USA
As communication data rates between photonic integrated circuits and advanced- node chips increase rapidly – currently at the few Tb/s level, but moving towards 100Tb/s and beyond, Harris believes it will force careful co-design and extreme packaging technologies. ‘Ultra-tight integration between advanced- node compute chips and silicon photonics is the next big thing. Laser solutions and fibre packaging costs hold the silicon photonics industry back. These are hard problems to solve, but we have some of the best minds in the industry working on them.’
Education: PhD, Electrical Engineering and Computer Science, MIT
Harris, who didn’t apply to university until pushed by mentors, recommends photonics researchers develop a deep understanding of electromagnetics and solid-state physics: ‘My understanding of electromagnetics was strengthened by quantum theory. When you go deep into physics and look back at lower concepts, they’ll make even more sense. There are only a handful of foundational concepts and the better you understand them, the more creative power you will have.’ You can find Harris online at
twitter.com/ theanalognick. He plans to attend CLEO 2023.
Julia Hartung
Organisation: Trumpf Laser Role: Currently pursuing a doctorate degree with the Karlsruhe Institute of Technology
In her recent research, Julia Hartung has been applying machine learning (ML) technology to smart manufacturing where it can be harnessed to provide insights into complex processes without requiring deep domain expertise. It’s work that has quickly helped gather attention. In a paper released in 2022, Hartung and
her colleagues used deep learning algorithms to determine a 3D reconstruction from a single 2D grayscale image. 3D reconstruction can be used for quality control of laser-welded components
Based in: Karlsruhe, Germany Education: Master's in applied informatics, Hochschule Esslingen, University of Applied Sciences
because the height values contain relevant information that is not visible in two- dimensional data. In this recent project, the researchers
compared three deep learning methods, namely stacked autoencoder (SAE), generative adversarial networks (GANs) and U-Nets to see how they compared when reconstructing a 3D image from a 2D grayscale image of laser-welded components. You can find Hartung online at
linkedin.com/in/ julia-hartung-523a5416b/ or
researchgate.net/ profile/Julia-Hartung-2.
Based in: Munich, Germany Education: PhD
computing for Häring is around whether researchers can manage to solve real-world applications. He said: ‘That means not only that a challenge can be solved, but also that it is economically attractive. That makes the difference between a promising technology and a solution.’ He believes that interaction and communication between teams is vital: ‘Inclusion of different disciplines in physics and engineering are key to success.’ You can find Häring online at
www.linkedin. com/in/reto-haering-4213a56/ and he is a regular attendee at Photonics West and Laser World of Photonics.
28 Photonics100 2023
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