The Data Science Initiative at NC State hopes to raise awareness of the breadth and depth of data science across campus as well as to continue to grow and engage the data science community at NC State by sponsoring RED Talks, Where Data Meets Science. RED Talks will consist of an hour long, data science related talk from local and national leaders in data science. In addition to promoting data science at NC State, our aim is to also spur interdisciplinary collaboration through a series of networking events, following the talks, where faculty and researchers from across campus, and more importantly across disciplines, can meet and engage with each other as well as with the larger data science community within the Triangle. Further, one RED Talk per semester will also include additional activities to promote existing research on campus and cultivate new interdisciplinary approaches.
Note to CSC and Statistics Graduate Students – these lectures have been approved to count toward the required lectures for graduate students. For Statistics grad students, there will be a sign-in sheet at the front. Download CSC seminar attendance form.
Co-Founder of Climate Change AI
Date: October 12, 2021
Time: 2:00 PM
Talk Title: Tackling Climate Change with Machine Learning
Climate change is one of the greatest challenges that society faces today, requiring rapid action from all corners. In this talk, I will describe how machine learning can be a potentially powerful tool for addressing climate change, when applied in coordination with policy, engineering, and other areas of action. From energy to agriculture to disaster response, I will describe high impact problems where machine learning can help through avenues such as distilling decision-relevant information, optimizing complex systems, and accelerating scientific experimentation. I will then dive into some of my own work in this area, which merges data-driven approaches with physical knowledge to facilitate the transition to low-carbon electric power grids.
riya Donti is a co-founder and chair of Climate Change AI , an initiative to catalyze impactful work in climate change and machine learning. Her work focuses on machine learning for forecasting, optimization, and control in high-renewables power grids. Specifically, her research explores methods to incorporate the physics and hard constraints associated with electric power systems into deep learning models. Priya is a recipient of the MIT Technology Review Innovators Under 35 award, and best paper awards at ICML (honorable mention), ACM e-Energy (runner-up), PECI, the Duke Energy Data Analytics Symposium, and the NeurIPS workshop on AI for Social Good. She is a Ph.D. student in Computer Science and Public Policy at Carnegie Mellon University, and a U.S. Department of Energy Computational Science Graduate Fellow.