Helping AI models to meet the real world

Systems using artificial intelligence to enhance forecasting, planning, and decision-making in businesses have been proliferating in recent years, but in many cases, they lack the detailed, specific information about the organization itself, limiting the usefulness of those tools. Devavrat Shah, a principal investigator at MIT’s Laboratory for Information and Decision Systems (LIDS), faculty member with the department of Electrical Engineering and Computer Science (EECS), and member of the Institute for Data, Systems, and Society (IDSS), has been focused on how to design methods that can handle second-by-second decision-making using limited computational resources. “In a sense, with a small amount of resource, you have to do a lot of heavy lifting,” he says.

As a researcher, “my interest is in the ability to develop methods that can extract information from data at scale in as effective a manner as possible.” The Andrew (1956) and Erna Viterbi Professor has been teaching at MIT since 2005. In 2019, he also co-founded a spinoff company called Ikigai Labs. Ikigai built a foundation model for tabular, time series data based on years of research in…