Harini Srinivasan
Harini Srinivasan is a Senior Technical Staff Member in the IBM Sustainability Software organization. She currently leads a team of Data Scientists in building products incorporating advanced AI solutions using geo spatial and remote sensing data such as weather, satellite imagery and Lidar. In her 28 year career at IBM, she has also contributed significantly in the areas of Programming Languages and Runtimes, Performance Analysis and Tools, Social Media Analytics and Software Patterns. She has worked in IBM Research and IBM Software product divisions, published in major conferences and journals and has over 10 patents issued.
Sessions
The rarity and diversity of weather events and the large range of impacts of these events presents unique challenges in various phases of model building – feature engineering, model training, model evaluation and model selection. We discuss best in class approaches to optimize all relevant parameters and continuously improve model performance to deliver accurate actionable results via a highly scalable ML operational environment, enabling them to mitigate effects of climate change. We describe the challenges and approach using the Outage Prediction use case for Utility companies. These companies spend billions of dollars every year restoring power outages, majority of which are weather related. Climate change is creating more frequent and longer lasting power outages and making it harder to predict everyday weather events. Our approach has been used successfully in predicting weather caused outages that are then used to proactively mobilize the power restoration process.