In this talk, I will explain how we are using machine learning to improve materials synthesis using concrete examples and by making an analogy to chocolate chip cookie baking. In addition to describing our scientific efforts, I will also give a brief overview of our DOE Genesis project and how the Genesis program is operating.
Stephanie Law | Materials Science & Engineering
Artificial intelligence is transforming science and society, but it is also driving unprecedented growth in the energy needed to power data centers. In this talk, I will show how physics-based digital twins help us understand and optimize these complex systems, and how they provide the foundation for the next generation of Physical AI.
Wangda Zuo | Architectural Engineering
Millennium Café is coming back this fall—mark your calendar for September 2026!
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Much research has explored the impact of different teaching methods (e.g., traditional lecture vs. active learning). But what about interpersonal dynamics? Things instructors do or say in class; do they impact student success? Let's talk about it!
Nate Brown | Mathematics
Scientific discovery creates the greatest impact when it reaches beyond the laboratory. The Penn State Center for Science and the Schools (CSATS) partners with researchers to design and implement education and broader impacts programs that connect cutting-edge research with K-12 classrooms. By equipping educators with authentic scientific knowledge and research-informed teaching tools, CSATS strengthens the STEM workforce pipeline and helps translate university research into lasting societal benefit.
Kathleen Hill | College of Education
Evolving regulations and consumer expectations are driving the food packaging industry to adopt new can coating chemistries, but evaluating how these coatings interact with food ingredients remains slow, empirical, and limited in mechanistic insight. We combined optical profilometry (microscopy) with unsupervised AI to automatically detect and classify coating defects across 1,000+ unlabeled images, linking defect types to the food ingredients that drive them and showing that image-derived features can predict results from conventional analytical techniques -- building predictive tools as a step toward rational design for next-generation food contact coatings.
Stiphany Tieu | Materials Science & Engineering