Biography:
I am an Assistant Professor in the Department of Bioproducts and Biosystems Engineering at the University of Minnesota Twin Cities, where my research focuses on Data Science in Agricultural and Ecosystem Sustainability. My work integrates artificial intelligence, machine learning, sensing, and data analytics to address challenges in sustainable agriculture, bioenergy, environmental systems, food security, and resource recovery. I am particularly interested in developing data-driven methods that translate complex sensing and environmental data into practical tools for decision-making and sustainable resource management.
Prior to joining the University of Minnesota, I was a Postdoctoral Associate at Idaho National Laboratory (INL), where I conducted research at the intersection of artificial intelligence, advanced sensing, and sustainable resource utilization. My work included the integration of hyperspectral imaging and near-infrared (NIR) spectroscopy for biomass characterization and biofuel applications, AI-assisted sensing and sorting systems, and the detection and recovery of rare earth elements and other critical materials. I also contributed to research applying AI and data-driven approaches to challenges in food safety, food security, and sustainable agriculture.
I received my Ph.D. in Electrical and Computer Engineering from Texas A&M University. My doctoral research focused on developing error and uncertainty estimation methods for small and sparse datasets, with broader research interests spanning machine learning, deep learning, computer vision, reinforcement learning, natural language processing, and large-scale data analytics. During my doctoral studies, I served for multiple semesters as a Graduate Assistant Lecturer and instructor in the Department of Electrical and Computer Engineering, mentoring senior capstone design teams working on projects involving embedded systems, microcontroller programming, database and web development, and PCB design.
I also previously worked as a Statistical Researcher at Bayer, where I applied machine learning and statistical modeling to investigate relationships among soil characteristics, environmental conditions, agricultural treatments, and crop yield. This experience helped shape my broader interest in combining engineering, artificial intelligence, sensing, and agricultural and environmental science to develop scalable solutions for complex sustainability challenges.
My research ultimately seeks to bridge data science and physical systems, with an emphasis on trustworthy AI, multimodal sensing, uncertainty-aware machine learning, precision agriculture, sustainable bioresource utilization, and intelligent decision-support systems for agricultural and ecosystem applications.