From academia to hedge fund

(upbeat music) (upbeat music continues) - When I met Ben Lorica, I knew there was something special about him. Not only is he a formally trained mathematician, but he was one of the earliest to realize that AI was going to have a resurgence. He literally laid the foundation for today's AI movement with his conferences, and he's been an early advisor to some of the hottest companies in the data space, including Databricks and Anyscale. Thank you for joining us, Ben. - Thank you, man, this is great. - How did you go from such a formalized training in mathematics to being so applied and making that transition? And really, what does that tell people who are trying to go through a similar journey how to almost cross the chasm? - I think I've mentioned this in public before, but even when I was in grad school, I actually was reading, believe it or not, business magazines a lot in the library. So I was secretly kind of yearning. I mean, it was clear I was going to go into academia, so that was going to be the immediate path. But I think the fact that I was spending a lot of time reading "Businessweek" and all these magazines was telling me something, even at that point in grad school. And then secondly, I was much more in the, I would say not completely applied, but somewhat applied partial differential equations, so where you've had to maybe do simple numerical experiments, so there were the beginnings of working with data there. And then, at the risk of dating myself, DJ, back then, of course, the exit strategy, if you wanted to get out of academia, was becoming a quant. - In the financial sector. - Yeah, yeah, financial engineer. And another tale is that in my last year of grad school, I took a whole year in stochastic PDEs. And you know, it was not directly related to my thesis, but maybe I think in the back of my mind, this might be good to know. - You basically wanted to how could I solve black shoal style equations? - Yeah, yeah, and more than that, right? So, but then I think in the back of my mind I was thinking, okay, so if this academia thing doesn't work out, then I have a path. So there were, I think, while I was in math, and I really enjoyed math, and I still respect a lot of people who are in math, I think I was probably not completely all bought in. And the other thing I have to confess on this recording is I don't know about you DJ, but I've talked to enough fields medalists that I knew that this is not a, I am never going to be, this is not about the amount of work you put in. So there's only certain level of potential you can reach in this profession. - It resonates with me similarly, you meet this caliber of mathematician, and you realize, uh oh, I'm not that caliber, so I better go, I got to have a plan B. - Yeah, yeah, exactly. - So what was that moment then when you realized, quant, all these other things, that there was something special happening with data, that there was like something new here? - I knew early on that data was essential to everything, because a lot of the signals that we were developing depended on data, either bringing data from the outside or taking existing data and somehow uncovering some new patterns out of it. But I would say I was doing data science, but that wasn't the term that we were using.