Dr. Pingfan Hu earned his Ph.D. in chemical engineering from Texas A&M University in 2023, an M.S. in chemical engineering from Johns Hopkins University in 2019, and a B.S. in chemical engineering from the University of Illinois at Urbana-Champaign in 2017. His research is related to devising, implementing, and refining machine learning and deep learning models to improve decision-making in health and safety domains. Currently, he serves as the National Data & Connectivity Manager at Atlas Copco Power Technique. He continues his research into developing predictive maintenance models to prevent equipment downtime and enhance health and safety, and utilizing machine learning to improve energy efficiency and reduce carbon emissions. His research has resulted in 14 peer-reviewed journal articles, 1 peer-reviewed conference paper, and 2 first-authored book chapters. His original research on nationally important topics, like mitigating carbon dioxide (CO2) pipeline and wildfire risks, enhancing natural product discovery and antibiotic synthesis, developing effective models for property and consequence prediction, and reducing COVID-19 transmission risk in indoor spaces and improving airway obstruction diagnosis, has been widely relied upon by his peers to advance their own research.
