ANNOUNCEMENT: The registration is now closed since the limit of participants has been reached. Thank you for your interest in participation in our event!!! 

The integration of Machine Learning (ML) into spectroscopy is revolutionizing how we analyze and interpret complex spectral data, but the potential of this approach has yet to be fully explored. This conference seeks to bridge that gap by bringing experts in spectroscopy, data science, and ML to collaborate on cutting-edge methodologies.

The focus will be on AI-driven spectral analysis, advanced classification techniques, and data-driven prediction models applied to various spectroscopies, including Fourier Transform Infrared (FTIR), Raman, near-infrared (NIR), fluorescence, and core-electron techniques like X-ray absorption (XAS), photoelectron (XPS), and fluorescence (XRF) spectroscopy. Deep learning approaches for analyzing vibrational properties, light-driven processes, and excited state dynamics will also be central to discussions. Key goals include strengthening collaborations and exploring ML applications in spectroscopy, including vibrational properties of materials, fluorescence, optical absorption, and excited state dynamics.

Keynote talks will be delivered by leading experts in the field, providing valuable insights and fostering engaging discussions. These sessions will cover the latest advancements and future directions, setting the stage for in-depth exploration and collaboration.

ANNOUNCEMENT: The registration is now closed since the limit of participants has been reached. Thank you for your interest in participation in our event!!! 

Participation in the conference is entirely free of charge (no registration fees)!!!

Registration is required from all participants, including those who are not presenting.

Applicants are invited to register (registration section to the left) and then submit an abstract for a contributed talk or poster using the template below:

Abstract Template

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