Music, ok everyone, welcome to the Microsoft Research colloquium. Today, we have Tamara Broderick, a professor of computer science and statistics at MIT, and also a longtime friend of mine. We actually have a history of following each other around the country. We were undergrads together at Princeton, and then Tamara followed me to Berkeley for grad school, and then I followed her back to Cambridge to work full time. I'm delighted that she's here today to tell us about her work on variational Bayes. Let's give her attention. Thank you. You know, actually, on that note, when I was in high school, I went to this Science and Engineering Fair, and my friend Liz told me that I had to look out for this supercool guy, Lester Mackey, who was gonna be there. Unfortunately, I didn't meet you there, but then, as an undergrad, I did. Anyway, today I'm going to talk about how we can quickly and accurately quantify uncertainty and robustness with variational Bayes. So, before I get into the details of that statement, let me just start by talking about our motivating data example in this work. We've been looking at microcredit, and more specifically, our collaborator Rachel Meeker, who's a fantastic economist, has been looking at microcredit, and we've been working with her. So, if I say anything wrong from the economic side, that's totally me, it's not Rachel. She's got data on microcredit, these small loans to individuals in impoverished areas, and the hope is to help bring them out of poverty. She's got data from seven different countries, Mexico, Mongolia, all the way through Ethiopia, and there are a number of businesses at each of these sites. So, there's one site in each of the countries, there's between 900 and 17,000 businesses, and so there...