 Good morning everybody. Good morning. You all are achieving the first objective of anybody in this lecture room, which is to spread yourselves randomly around the room. I apologize already for the heat in this room. We're in room 100 and they somehow think that's the temperature the room is supposed to be. And I would like to tell you I will fix it, but I can't. So we'll just see how it goes. Maybe it will correct. We have two temperatures in this room, 100 and 32. So we'll see which one we end up with over the course of the day. So welcome to the University of Michigan, to the University of Michigan Law School, to the Center on Financial Law and Policy, conference with the Office of Financial Research, Big Data in Finance. I'm thrilled to be here with you today. I should say I'm Michael Barr. I teach here at the Law School and in the Ford School of Public Policy, and I'm the faculty director of the Center on Financial Law and Policy. I am going to in a moment introduce Dick Burner, but just let me start by welcoming you on behalf of myself and Dick to this conference today. As all of you know, the financial crisis of 2008 crushed both the U.S. and many economies around the world and caused millions of Americans, their households, their livelihoods, their jobs, their businesses, their incomes. And in the wake of the financial crisis, there's been a lot of progress in making the system somewhat safer and somewhat fairer, but we all know that there's still a long way to go. And there are lots of things that we do not know, and one of the purposes of this conference is to discuss how to make progress on that. So I think it's useful to take a step back and think why is it that we're meeting on Big Data in Finance? Big Data can help us through a better understanding of financial stability. And to do that, we need some set of theory of financial stability that we're going to be discussing over the course of the next two days. We need data itself, and as it turns out, as we'll talk about in many of our panels, getting that data is not as easy as one would hope. Even if you have an office that is called the Office of Financial Research and the federal government seems very powerful. I think Dick will explain to you it's hard to get data. We need better risk analytical tools, which I'll say about more in a minute. We need better risk management within firms and in the federal government. So just having the data and understanding it is not enough. We need to embed that data and that understanding in systems and institutions that enable us to act on that data. And then lastly, of course, we need both the legal authority and the political will within firms and in the government to act on the basis of that understanding. And none of these really, I think, should be taken for granted. As I said, there has been a good bit of progress on analytics. We're going to hear about some of these methodologies over the course of the day integrated into the panels. So I hear quite a bit about how to use data using new approaches, complex systems modeling, agent-based modeling, dynamic stress tests, a crowdsourcing of data and analytics, and analogies from other systems like biology, public health, epidemiology that I think will help us grapple with data in new and interesting ways. At the same time, there are lots of things we don't know, lots of things that we don't know. So I'm going to give you five examples of things that we don't know as a way of getting us started on today. The first category of things are things that we used to know but have forgotten. So what do I mean by that? I mean, we like in educational institutions and in general to think of knowledge as being forward progress that only knowledge only moves in one direction. But we all know through studying history that sometimes we forget things like we forget that home prices can decline nationally in the United States. So we forgot that and then we remembered it again. Just because we remembered it again doesn't mean we won't forget it again. And so thinking about how to embed systems so that our forgetting happens with, happens less often I think would be good. And also I think we need to keep in mind as we're studying these questions that the question of forgetting is not only a matter of accident or increased risk tolerance and the like but it's also a kind of forgetting that can be bought and paid for. So we need to remember the role of politics in shaping our understanding of reality. The second category of things are things that we know we could get access to that is they're knowable but are kept hidden. And by this I mean categories of information that are concealed by information arbitrage by firms or by a refusal to share nicely among regulatory agencies. So things that are knowable but kept hidden. Third category of things we don't know are things we know but don't know what it means. So we have a lot of data but we don't have the intellectual capacity or firepower to make sense out of the data in ways that are useful and predictive of the future. The fourth category of thing perhaps more common to those of you in this crowd but I think useful to remember are the things we don't know that we don't know. The unknowable unknowables. The imponderable imponderables for those of you who have religious bents. So a big category and of course we don't know what they are. And lastly and this is I think a continuum of continued importance what we should do about the things that we actually do know. So it's often the case that we have the data. We have the analytic tools. We have the understanding but we don't know what to do about them because the choices involve very very difficult trade-offs and that applies to basically everything. So figuring out those trade-offs and which direction to go I think are critical in the future. So we have I think a terrific lineup today to talk about these issues. We have people today, scholars, regulators, practitioners from a wide range of backgrounds. We're going to begin with a discussion after Dick's talk a discussion of data privacy and security. How can we ensure that data remains private, secure, accessible and useful? Again presenting that trade-off. We're then going to segue into a panel on data quality, data gaps, information arbitrage. We're going to then go to lunch where you'll have a chance to talk informally together just across the quad. Hopefully the rain will have ended by then if it hasn't already. And after lunch, we're going to have a panel which I will share on big big questions about big data. Ethical, legal, political, moral questions about a big data who owns data and the like. And we'll conclude today's session with a keynote by Sendil Malanathan from Harvard University. And then tomorrow we're going to dig deeper into some of the practical challenges to data sharing, data transparency within government and across, inside and outside of government. We're going to have a panel on computer science and engineering techniques in financial data modeling and mapping. And then tomorrow at lunch, SEC Commissioner Cara Stein will be providing a keynote address and we'll close with a panel on data integration and visualization. So we have quite a full two days of activities with all of you. I want to just take a moment to thank many people who have been helping put this conference together over many, many months. First, the planning committee for the conference. Matt Reed and Mark Flood and Miriam Ockdenberger from the Office of Financial Research, John Burge from Chicago, H.V. Jagadish, Michael Wellman and Rume Shseigel from the College of Engineering here at Michigan, Amitash Pernanandam from the Ross School of Business, Matthew Shapiro from the Economics Department, Jeremy Kress from our Center on Finance Law and Policy. And I especially want to thank Christy Baer who's sitting down here. You'll see quite a lot over the next two days who is the who runs the Center on Finance Law and Policy. And also Law School Events Manager Jenny Ricard who you met on the way in and her team, Sheri Fittich in my office. Let me also thank our financial supporters for the conference, the Smiths Richardson Foundation, Omijar Network, the Law School Engineering School, the Ross School of Business, the Michigan Institute for Data Science. And we're also grateful for the support at the Center which indirectly supports this conference for that support of John Loomis, Paul Lee, Bill Marqueau, Stefan Tucker and Ron Glantz. So that's a wonderful set of support groups here. So my last job is to introduce Dick, which is always a delight and a pleasure to do. All of you know Dick Burner, who is the Director of the Office of Financial Research. And Dick has been the Director of OFR since 2011. Prior to that, Dick was the co-head of Global Economics at Morgan Stanley. He served as Chief Economist at Mellon Bank, a Senior Economist from Morgan Stanley, Solomon Brothers and Morgan Guarantee. He's worked on the Fed's research staff and received numerous forecasting awards in his private sector life from Blue Chip Economic Indicators, the Wall Street Journal, Market News, and the National Association for Business Economics. Anybody who's been in and around Washington knows that it is very, very, very, very hard to start a new agency. And I think we are all deeply in Dick's debt for having started the Office of Financial Research, building it from scratch into quite a serious enterprise in Washington. Dick has had to negotiate with other countries on complex issues of data collection and standardization, and even harder he's had to negotiate with his fellow agencies in the U.S. government. So this is a huge task. Dick has met it with extraordinary skill and grace, and I think we all should be extremely grateful to him for his service thus far. So please join me in welcoming Dick Burner.