Denver, Colorado, United States
As a physicist with over 29 years of experience in both solid-state and biological physics, I have explored a diverse range of research areas. I received my Ph.D. in Applied Physics from the Colorado School of Mines (CSM) in 2007, where my research activities primarily involved investigating the electronic and optical properties of advanced polycrystalline semiconductor materials systems and device structures. At CSM, I developed and used novel characterization techniques to obtain a fundamental understanding of these advanced photovoltaic materials. After CSM, I continued my research at the National Renewable Energy Laboratory (NREL), where I shifted my focus to the pretreatment of biomass and characterizing biomass-degrading proteins. By developing and implementing novel data analysis and characterization techniques, I sought to better understand how fungal and bacterial cellulase systems break down diverse biomass feedstocks into fermentable sugars suitable for advanced biofuels and ethanol. In recent years, I have explored the use of machine learning techniques to gain new insights into complex biological systems. I implemented supervised machine learning to track wildlife in and around wind turbines, providing a better understanding of the impact of renewable energy on local ecosystems. I have also used unsupervised machine learning to analyze and quantify fluorescent microscopy images of biological systems, providing a more detailed understanding of how the distributions of both enzymes and bacterial cells on biomass. Currently, I am researching the immense potential of combining supervised and unsupervised machine learning techniques to enhance the process of microbial and fungal product generation. Additionally, I am actively involved in developing advanced machine learning models utilizing hyperspectral imaging too quickly and accurately characterize municipal solid waste. By leveraging the capabilities of machine learning, we can significantly expedite and optimize the production of valuable products derived from microbial, fungal, and municipal solid waste resources to efficiently generate a diverse array of valuable products, ranging from renewable energy to pharmaceuticals and beyond. Throughout my career, I have demonstrated my scientific writing skills by publishing numerous articles, project reports, and winning grant proposals. I am excited to continue exploring new frontiers in physics and biology, and to work alongside colleagues who share my passion for pushing the boundaries of what is possible.
Teaching Introduction to Energy, an undergraduate course covering the fundamentals of energy production, transmission, and efficiency. The course surveys various energy technologies, including steam, hydro, fossil fuels (petroleum, coal, and unconventionals), geothermal, wind, solar, biofuels, nuclear, and fuel cells. Students explore the feasibility of different energy sources through a matrix evaluating technical, economic, environmental, and political factors. Responsibilities include: Designing and delivering engaging lectures on diverse energy production methods and their real-world applications. Guiding students in critically assessing energy systems and emerging technologies. Incorporating case studies and current industry trends to highlight energy challenges and opportunities. Mentoring students in energy policy, sustainability, and system optimization. Facilitating discussions on the future of energy, including advancements in renewable technologies and energy storage. This role allows me to leverage my expertise in renewable energy, biomass conversion, and energy systems modeling to equip future engineers with the knowledge to navigate and innovate within the evolving energy landscape.
As the Principal Investigator of a cutting-edge project, I'm spearheading the development of machine learning and Artificial Intelligence-driven systems for thermal cameras. Our goal? To analyze bat behavior in and around wind turbines with lightning-fast efficiency, and help the Wind Wildlife community reduce the amount of time they spend sifting through thermal video data. With this project, we're taking a huge step towards making the world a safer and more sustainable place for both wildlife and humans alike. Imagine a world where municipal solid waste characterization is no longer a laborious and time-consuming process. Thanks to my collaboration with North Carolina State University, that vision is quickly becoming a reality! By combining machine learning and artificial intelligence with visual imaging and hyper-spectral imaging, we're able to rapidly characterize and segregate MSW for conversion-ready feedstock that's perfect for energy generation. Our DOE-funded project is the future of sustainable waste management, and I'm thrilled to be leading the charge! Fungal fermentations, biomass characterization and deconstruction, enzymatic digestion of biomass, and biophysical characterization of proteins - these are just a few of the research areas that I'm exploring. By delving into these diverse fields, I'm working to improve the degradation of biomass and unlock new pathways for energy generation. From the lab to the real world, my work is paving the way for a more sustainable future, one breakthrough at a time.
Played a key role in developing the front-end and back-end of the DOE's Energy and Materials Network and Geothermal Data Repository. This platform served as the primary submission point for all data collected from researchers funded by the U.S. Department of Energy's Geothermal Technologies Office. To improve user experience and functionality, I leveraged a variety of programming languages including Python, JavaScript, PHP, and Bootstrap. My efforts contributed significantly to the site's success, providing researchers with a streamlined, user-friendly experience while facilitating the flow of data to support important energy initiatives.
As a graduate student, I was on the cutting edge of research into advanced polycrystalline semiconductor materials systems and device structures. My focus was investigating the electronic and optical properties of these materials, with a particular emphasis on how grain boundaries affected the performance of thin-film polycrystalline photovoltaic devices. To do this, I designed and built a state-of-the-art near-field scanning optical microscope (NSOM) capable of optically and electrically characterizing these advanced semiconductor materials in a vacuum-compatible environment. Using this groundbreaking technology, I was able to perform near-field spatial and spectral photoluminescence as well as near-field spatial photocurrent measurements on the submicron level for both CdTe and CIGS thin-film polycrystalline photovoltaic devices. These highly precise measurements gave us unprecedented insight into the performance of these devices, bringing us one step closer to unlocking the full potential of solar energy as a sustainable and efficient source of power.