My research interests broadly revolves around robust time-series forecasting analysis with focus on uncertainty, scalability, and cross domain generalization. My current research interests include:
1.Foundational time-series models that are prettrained on multiple domain datasets and generalizable across wide range of domains and tasks.(LPTM ‘24, PEMS ‘23, LstPrompt ‘24, TimeMMD ‘24).
2.Scalable and efficient time-series forecasting models that can handle large-scale time-series data and provide robust and calibrated forecasts(HAILS ‘24, ProfHiT ‘23).
3.Probabilistic time-series forecasting models that can provide uncertainty estimates and are robust to outliers, missing data and novel scenarios.(STOIC ‘23, CAMUL ‘22, EPIFNP ‘21, B2F ‘22).