The Data and Predictive Analytics Center (DPAC ... Her research interests also include machine learning, computational statistics, microarray gene expression data analysis, survival analysis, and ...

While traditional data science practices have paved the way for critical insights and informed decision-making, AI has ...

While the course covers theories of Machine Learning and tools such as R, the focus is on using them for solving data-driven problems. The students will be introduced to several real-life problems ...

Accelerated Climate Modeling AI-driven models, particularly Google's NeuralGCM, are leading a revolution in climate modeling.

Filling gaps in data sets or identifying outliers—that's the domain of the machine learning algorithm TabPFN ... therefore more likely to make correct predictions than the standard algorithms ...

While ML models are powerful tools for predicting diabetes, their lack of interpretability presents a major challenge for clinical adoption. Healthcare professionals require AI models to not only be ...

Connected AI can help you "listen" to the voice and behavior of your customers, your competitors customers and your team.

With the increasing reliance on ML to predict outcomes and optimize agricultural processes, the lack of diverse, high-quality datasets has emerged as a significant barrier. According to the study, ...

In an era of economic disparities and growing demand for equitable access to goods and services, pricing strategies are undergoing a transformation. Karthik Ramakrishnan, a researcher specializing in ...

Study: A machine learning approach to leveraging electronic health records for enhanced omics analysis ... with omics and EHR data. First, COMET was applied to predict days to labor onset in ...