A chemometrician's guide to transfer learning

Domain adaptation (DA) and Transfer Learning (TL) are terms coined by the machine learning community, particular computer vision. However, the chemometrics community has been working on similar problems (with chemical and spectroscopic contexts) for much longer and these techniques go under the moniker of calibration transfer and maintenance (CTM). Both the machine learning and chemometrics communities often encounter the same problem: their prediction models have a tendency to rely too much on the distribution of the data on which they have been trained. In practice, we are constantly updating a predictive model on data that evolves over time. Ramin Nikzad-Langerodi received his MSc in Biochemistry and Biophysics from the University of Zurich, Switzerland and his PhD in Pharmaceutical Sciences from the University of Vienna, Austria. He currently leads a Data Science research group at the Software Competence Center Hagenberg (SCCH), Austria. His research interests are in analytical chemistry, chemometrics and process analytical technology (PAT). He has published several scientific papers at the intersection between chemometrics and artificial intelligence. Erik Andries resides in New Mexico and currently wears many hats: mathematics instructor at Central New Mexico Community College, visiting research scientist at the Center for Advanced Research Computing (CARC) at the University of New Mexico (UNM), and an active consultant for biomedical start-ups in the Albuquerque and San Francisco Bay areas. His interests revolve around the intersection of chemometrics and numerical linear algebra, particularly the incorporation of chemical and spectroscopic domain knowledge into analyte prediction models. Unsupervised domain adaptation (i.e., calibration transfer and maintenance using secondary samples without reference measurements) is his current obsession. Previously, Erik Andries was a research scientist at InLight Solutions working on data analysis algorithms for non-invasive glucose sensing. Before that, he had a joint postdoctoral fellowship at the UNM Department of Pathology (spatio-kinetic Monte-Carlo methods and stochastic differential equations) and Sandia National Laboratories (biomolecular imaging). In 2004, he received a Ph.D. in applied mathematics from UNM, where he was a research assistant at the UNM Cancer Research Center. He is originally from New Orleans, Louisiana, USA.