I am a Professor of Chemistry and Chair of the Department of Natural Sciences at the University of Michigan-Dearborn. My research lies at the intersection of nanoscience, surface chemistry, molecular interactions, and functional materials for catalytic, sensing, and biomedical applications.
My laboratory develops bottom-up strategies for fabricating surface-bound nanoparticle assemblies and functional nanostructured interfaces. A major focus of our current work involves the synthesis and organization of oxidation-resistant copper nanoparticle arrays on chemically functionalized surfaces for electrocatalytic and environmentally relevant reactions, including CO₂ reduction and nitrophenol transformation. Our research integrates surface functionalization, spectroscopy, electrochemistry, microscopy, and nanoscale materials characterization to understand how molecular-scale organization influences catalytic behavior and interfacial properties.
More recently, I have become increasingly interested in integrating artificial intelligence, machine learning, and data-driven methodologies into experimental chemistry and nanotechnology research. Modern materials and surface-science experiments routinely generate complex multidimensional datasets from spectroscopy, microscopy, electrochemical measurements, and imaging workflows. I am interested in developing AI-assisted approaches to analyze nanoparticle growth, predict structure–property relationships, accelerate materials optimization, and improve the interpretation of experimental data.
An important long-term goal of my research program is to help build interdisciplinary collaborations connecting chemistry, AI, materials science, engineering, and computational methods. I am particularly interested in collaborative efforts involving autonomous experimentation, scientific imaging, intelligent materials characterization, and predictive modeling for nanoscale systems.
In addition to research, I am committed to expanding interdisciplinary education and undergraduate research opportunities that bridge chemistry, data science, and emerging technologies across the University of Michigan ecosystem.
Please describe one or two of your most interesting projects.
One of my current research projects focuses on the bottom-up fabrication of oxidation-resistant copper nanoparticle arrays on chemically functionalized surfaces for catalytic and electrocatalytic applications. Copper is an attractive low-cost catalytic material, but controlling nanoparticle oxidation, organization, and long-term stability remains challenging. Our work combines nanoscale surface engineering, spectroscopy, microscopy, electrochemistry, and data-driven analysis to better understand how nanoscale architecture influences catalytic activity and interfacial behavior.
A growing direction of my research involves integrating AI-assisted analysis into experimental nanoscience workflows. We are interested in combining microscopy, spectroscopy, and electrochemical datasets with machine learning approaches to identify structure–property relationships in functional nanomaterials. The long-term vision is to accelerate materials discovery through intelligent experimentation and predictive modeling.
How did you end up where you are today? (Your research journey)
My research journey began in physical chemistry and surface science, driven by an interest in molecular interactions and interfacial phenomena. Over time, this evolved into broader work in nanotechnology, functional nanomaterials, and nanoscale catalytic systems. As experimental techniques became increasingly data-intensive, I became interested in how artificial intelligence and machine learning could help transform the way scientists analyze complex datasets, design experiments, and accelerate discovery. This convergence of chemistry, nanoscience, and AI now represents an exciting new direction for my research program.
What is the most significant scientific contribution you would like to make?
I hope to contribute toward the development of intelligent, AI-assisted experimental frameworks that integrate chemistry, nanoscience, imaging, and predictive modeling to accelerate discovery in functional materials and catalysis. I believe interdisciplinary collaborations between experimental scientists and AI researchers will play an increasingly important role in addressing complex scientific and societal challenges.
What makes you excited about your data science and AI research?
What excites me most about AI and data science is their potential to uncover hidden patterns and relationships in complex chemical systems that are difficult to recognize using conventional analysis alone. In nanoscience and surface chemistry, experimental data are often multidimensional and highly interconnected. AI has the potential to significantly accelerate scientific insight, improve materials optimization, and transform how experiments are designed and interpreted.
What are 1-3 interesting facts about yourself?
- My research combines chemistry, nanotechnology, and emerging AI-driven approaches to materials discovery.
- I am passionate about mentoring undergraduate researchers and building interdisciplinary collaborations.
- I am interested in developing educational initiatives that connect chemistry with data science and AI.