Topic of Research Seminar: i-Talks: Design of experiments via Bayesian optimization

Abstract: Machine learning (ML) can be used to speed up the development of novel materials, as well as to relate variables in general, such as processing parameters or composition to the final properties of the material. In this talk, Dr. Albuquerque will show how to use ML methods to design new epoxy resin materials with as few experiments as possible. In addition, he will discuss different ML approaches to relate the polymer composition to its glass transition temperature.

Dr. Rodrigo Albuquerque has done his PhD in Theoretical Chemistry in Brazil and have worked as a researcher in Switzerland, Germany and England where he investigated different materials (e.g. Supramolecular polymers, nanoparticles, coordination compounds) by means of computational chemistry and machine learning techniques. Recently he became guest lecturer at Technical University of Munich to teach and apply Machine Learning techniques. Currently, Dr. Albuquerque participates in the digitalization of the Department of Polymer Engineering, where he applies Machine Learning models to different problems, besides providing hands-on training about the same topic.

Subject Field of Topic: Drug Discovery | Structural Biologist | Inflammatory diseases | Targeted protein degradation

Name of Speaker: Dr. Rodrigo Albuquerque

Professorial Rank of Speaker: Data Scientist / Research Assistant

Affiliation of Speaker: Department of Polymer Engineering, University of Bayreuth, Germany

Date and Venue: February 14, 2023, at 1200 hrs, School of Interdisciplinary Engineering & Science (SINES) Auditorium, NUST Islamabad Campus