Harshit Gujral is a PhD candidate in Computer Science at the University of Toronto whose work sits at the intersection of climate, air quality, and public health. He develops computational models that link large-scale environmental and health datasets to evaluate how technologies, such as electric vehicles and data centers affect respiratory outcomes, equity, and emissions. His recent work provides the first national evidence that electric-vehicle adoption can reduce childhood asthma in the United States and offers systems-thinking tools for air-quality governance. In parallel, he studies rebound effects and “digital sufficiency” in sustainable HCI and data centers, and responsible data practices for high-stakes machine learning. His research has appeared in Environmental Research, Environmental Modelling & Software, Science of the Total Environment, NeurIPS, FAccT, and ACM Computing and Sustainable Societies, and has been recognized by AGU, the John R. Brown Award, and U of T’s Data Science Institute.