Digital twins are more than 3D models; they are dynamic testing grounds for improving how we operate and secure complex systems. In our research, we use them to train incumbent workers, study human-AI collaboration, and simulate cyber threats in safe but realistic environments. I’ll discuss what we’ve learned so far and where cross-campus collaboration could help unlock their full potential.
Jessica Menold | Mechanical Engineering
The World Food Forum Youth Food Lab North America at Penn State advances global food security by empowering young innovators through a collaboration with the UN FAO’s World Food Forum Global Youth Action Initiative. Designed for early‑career leaders ages 18–35, the Youth Food Lab bridges the innovation gap by providing mentorship, networks, and technical support for promising agrifood and climate solutions. Centered on a youth‑supporting‑youth model, the program reduces barriers to translating new technologies by pairing Penn State’s technical expertise with the WFF’s global innovator network, enabling entrepreneurs to access hands‑on support while students apply their training to real‑world challenges—all in support of the mission of good food for all.
Maria Theresa Spencer | Agricultural Economics, Sociology, and Education
About a decade ago, our team began studying how a tiny backyard insect produces microscopic, hollow particles—called brochosomes—that control how light reflects and scatters, enabling natural camouflage. We uncovered how the shape of these particles governs their optical behavior and, most recently, developed the first biomimetic strategy to manufacture brochosome-like particles at scale. This talk will highlight how this platform is opening new opportunities across sustainable materials, biomedical devices, and advanced optical technologies.
Tak Sing Wong | Mechanical Engineering
My approach to mentoring and advising PhD students has remained largely unchanged over the past decades, with a focus on developing critical thinking, challenging published work, forming and testing hypotheses, and advancing knowledge and discovery. However, accomplishing these goals requires continual adaptation to the technologies students use to obtain and process research information, which have changed dramatically over the past 30 years—from the internet and search engines such as Google to today’s AI-based tools like ChatGPT. To remain effective, PhD advisors must recognize how deeply AI has been integrated into the way that students access, interpret, and synthesize information, and how this integration necessitates changes in mentor–student interactions. In this talk, I discuss the challenges and opportunities AI presents for making advising and mentoring time more efficient, while avoiding pitfalls and preserving the central goals of doctoral training: cultivating independent critical thinkers and enabling meaningful, impactful discoveries rather than following a path toward bland accomplishments.
Bruce Logan | Director, Institute of Energy and the Environment
In nature, symbiosis is a survival strategy. In science, it is an innovation strategy—but it rarely happens by accident. At the One Health Microbiome Center, we are not just studying ecosystems; we are engineering one. I will discuss how we intentionally fuse distinct disciplines, linking a unified vision with the data scientist’s algorithm, the ecologist's field trial, and the technologist’s state-of-the-art platforms. By "designing" this collision and training bilingual, industry-academia PhDs through our NIH T32 BIOMS program, we are creating a culture where technology, biology, and scholars co-evolve. This is symbiosis by design, ensuring that our collective output is far greater than the sum of our individual labs.
Seth Bordenstein | Biology & Entomology | One Health Microbiome Center
The brain, as a physical substrate, exploits rich dynamics arising from neuronal coupling through dynamic synapses, rather than centralized control or symbolic logic. In my group, we explore how similar principles can be realized in engineered materials by embedding computation directly into networks of soft, ionic, and biomolecular components. We create neuromorphic tissues—networks of physically coupled material nodes that process information through intrinsic nonlinear dynamics, fading memory, and signal propagation. By tuning coupling strength, connectivity, and material properties, these systems perform real-time temporal transformations without clocks, software, or hard-coded logic. Experiments and modeling show that these tissues can serve as physical substrates for reservoir computing, enabling the prediction and classification of highly nonlinear and chaotic temporal data.
Joseph Najem | Mechanical Engineering | LiMC2