Date(s) - 02/15/2022
3:00 pm - 4:00 pm
Lilo D. Pozzo, Ph.D.
Boeing-Roundhill Professor of Chemical Engineering
Interim Chair of Materials Science and Engineering
University of Washington
Dr. Lilo Pozzo’s research interests are in the area of colloids, polymers and soft-matter systems. Her research group focuses on controlling and manipulating materials structure for applications in health, alternative energy and separations. Her group also develops and utilizes new advanced measurement techniques based on neutron and x-ray scattering. Prof. Pozzo obtained her B.S. from the University of Puerto Rico at Mayagüez and her Ph.D. in Chemical Engineering from Carnegie Mellon University in Pittsburgh PA. She also worked in the NIST Center for Neutron Research as a post-doctoral fellow and is currently the Boeing-Roundhill Professor of Chemical Engineering at the University of Washington where she has served since 2007. She also chairs the Department of Materials Science and Engineering at UW. In addition to her research activities, she is also dedicated to improving engineering education with course development in areas of entrepreneurship and service-oriented global engagement.
The increasing adoption of data science, machine learning and artificial intelligence within chemical engineering and materials science promises to drastically accelerate materials discovery and technology translation. In order to continue advancing data-driven materials developments, high-throughput experimentation (HTE) and automation throughout complete laboratory workflows must be developed and widely adopted by both new and established researchers.
In this talk, I will outline examples and experiences showcasing how researchers in our group are developing and adapting hardware and software infrastructure to accelerate the pace of materials discovery in soft-matter systems (i.e. colloids, polymers, complex fluids and nanomaterials). The talk will highlight recent research examples related to the implementation of HTE for colloidal formulation/synthesis and electrolyte design. It will also highlight outstanding challenges that emerge when transitioning from traditional ‘wet-laboratory’ practices to HTE, and AI-driven experimentation. These include adapting specialized experimental methods to HTE, developing key skills within the research workforce, adoption of good data stewardship practices, financial and infrastructure obstacles, needs for autonomous data treatment, algorithms for automatic modeling and analysis, and others.
Conversely, I will also highlight the numerous opportunities that emerge for enhancing virtual collaboration, enabling open data/hardware/software sharing, tackling challenging irreducible problems (e.g. optimization of complex formulations), and the promise of implementing autonomous ‘self-driving’ laboratories.