Vibrational Spectroscopy-based Machine Learning Applications section specializes in leveraging vibrational spectroscopy techniques, such as Raman spectroscopy and mid-infrared (MIR) spectroscopy, in conjunction with advanced machine learning algorithms, to address a diverse group of research objectives. The projects include various applications, including human phenotyping from human nails, drug screening from human tissue and body fluids, studying the mechanisms of enzyme inhibition by organophosphate compounds using Raman spectroscopy, and screening for different diseases affecting both human and wildlife populations.

At the core of our research are instruments like the DXR3 Raman microscope and the Nicolet iS20 mid-infrared spectrometer. These instruments enable us to obtain high-resolution vibrational spectra from a wide range of samples, allowing for detailed analysis and characterization. By combining these spectroscopic techniques with sophisticated machine learning models, we aim to extract valuable insights and patterns from complex datasets, ultimately advancing our understanding of various biological, environmental, and biomedical phenomena.

Faculty Contacts:

Dr. Lenka Halamkova