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Overview

Personal Profile

Peter Ciesielski's research focuses on integrating multiscale modeling, machine learning, and multimodal experimentation to design and optimize complex chemical, biological, and materials systems. His expertise spans biomass conversion, catalytic processes, bio-based materials, separations, and biohybrid systems. He is recognized for combining atomistic simulations, mesoscale reactive transport, process-scale modeling, and AI-driven workflows to develop innovative frameworks that connect insights from emergent physiochemical phenomena to actionable engineering solutions that advance energy efficiency, sustainable manufacturing, and supply chain resilience. 

Research Interests

Computational modeling

Artificial intelligence

Catalysis

Bio-based materials

Separations

Advanced imaging

Education/Academic Qualification

Bachelor, Chemical and Biological Engineering, Colorado State University

PhD, Interdisciplinary Material Science, Vanderbilt University

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Collaborations and Top Research Areas From the Past 5 Years

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