Chilling rain, swirling gray clouds and blustery winds rolled into Southern California on Sunday as the strongest winter storm of the season geared up to deliver near-record rainfall and life-threatening flash flooding in the region through Tuesday. The slow-moving atmospheric river was gathering strength Sunday afternoon, with the National Weather Service in Oxnard warning that "all systems are go for one of the most dramatic weather days in recent memory." Forecasters said the brunt of the storm appeared focused on the Los Angeles area, where the system could park itself for an extended time over the next few days. The storm could drop up to 8 inches of rainfall on the coast and valleys, and up to 14 inches in the foothills and mountains. Snowfall totals of 2 to 5 feet are likely at elevations above 7,000 feet.

Tensor factorization has received increasing interest due to its intrinsic ability to capture latent factors in multi-dimensional data with many applications such as recommender systems and Electronic Health Records (EHR) mining. PARAFAC2 and its variants have been proposed to address irregular tensors where one of the tensor modes is not aligned, e.g., different users in recommender systems or patients in EHRs may have different length of records. PARAFAC2 has been successfully applied on EHRs for extracting meaningful medical concepts (phenotypes). Despite recent advancements, current models' predictability and interpretability are not satisfactory, which limits its utility for downstream analysis. In this paper, we propose MULTIPAR: a supervised irregular tensor factorization with multi-task learning. MULTIPAR is flexible to incorporate both static (e.g. in-hospital mortality prediction) and continuous or dynamic (e.g. the need for ventilation) tasks. By supervising the tensor factorization with downstream prediction tasks and leveraging information from multiple related predictive tasks, MULTIPAR can yield not only more meaningful phenotypes but also better predictive performance for downstream tasks. We conduct extensive experiments on two real-world temporal EHR datasets to demonstrate that MULTIPAR is scalable and achieves better tensor fit with more meaningful subgroups and stronger predictive performance compared to existing state-of-the-art methods.