Okay.

As a child, were you good at mathematics?

Like, was mathematics a natural thing for you?

Yeah, it was very natural.

And I always liked it.

I liked counting.

I liked continually multiplying things by two.

Although by the time I got to a thousand twenty-four or whatever it is, I had enough of it.

But I liked math.

I discovered as a very young kid, maybe four, something called Zeno's Paradox.

Did you ever hear of Zeno's Paradox?

My father told me that the car could run out of gas.

And I was disturbed by that notion.

It never occurred to me.

But then I thought, well, it shouldn't run out.

It could always use half of what it has, and then it could use half of that and then half of that, and it could go on forever, and so it would never run out.

So, now, it didn't occur to me, yes, but it wouldn't get very far either.

But the idea that, in principle, you didn't have to run out of gas was kind of a profound thought for a very little boy.

Did it feel like this was where your career was going to take you, or were you like another boy who dreamed of being a baseballer or something?

Oh, no, no, no.

The only thing I thought about was I would be a mathematician, whatever exactly that meant.

I didn't know quite what it meant, except that mathematics was the only subject I really liked.

They wanted me to go to Berkeley and get away from MIT, meet some new faculty, because I was quite close to the MIT faculty, and they thought, I think they didn't want to get rid of me so much as they thought I was probably pretty good, so I should get exposed to a certain guy named Churn, who was just coming to Berkeley that year.

And I got this very nice fellowship, and I went there.

I was very eager to work with Churn, meet Churn, except he wasn't there.

He celebrated his first year at Berkeley.

He had just come to Berkeley, and he celebrated that first year by taking a sabbatical, so he wasn't there.

So I worked with another guy, which was fine, and by the time Churn came in the second year,

I was already pretty far along with the thesis project.

I was giving a seminar at the beginning of my second year at Berkeley, and in walked this tall Chinese guy, and I said to the guy next to me, who's that?

He said, oh, that's Churn.

I said, that's Churn?

I didn't know he was Chinese.

I thought Churn was probably short for Chernowski or something, or he was probably some Polish guy who had shortened his name to Churn.

If it had been Chen or Chan, I would have known it was Chinese, but Churn with the R, I didn't know.

But anyway, so I met Churn then, and we became friends.

Of course, I was much younger than he, but we became friends, and later collaborators.

So we worked together, and we came up with these results, this whole structure.

In fact, that's the slides of the presentation that Churn made at the International Congress in the early 70s.

It was very nice geometry.

I pushed on with it, and we defined some things called differential characters, which was another chapter, working with a guy named Cheeger.

But the Churn-Simons invariance, about 10 years later, the physicists got a hold of it, and it seemed to be very good for what ailed them, or whatever might have ailed them.

And it wasn't just string theory, as I subsequently developed.

It was kind of all areas of physics, including condensed matter theory.

Even some astronomers seemed to want to look at those terms.

That's really what's great about basic science, in this case mathematics.

I mean, I didn't know any physics.

It didn't occur to me that this material that Churn and I had developed would find use somewhere else altogether.

This happens in basic science all the time, that one guy's discovery leads to someone else's invention and leads to another guy's machine.

Actually, in the middle of my mathematics career, which ended when I was about 37 or 38, was that I spent four years at a place called the Institute for Defense Analyses, down in Princeton, which was a super-secret, government-based,

National Security Agency-based place for code cracking.

But I also learned about computers and algorithms, and I did one thing there that was quite good, because I can't tell you what it was.

It's all classified.

So I had a good career there, both doing mathematics and learning about the fun of computer modeling.

My father had made a little bit of money, and I had the opportunity to try investing it.

And that was interesting, and I thought, you know, I'm going to try another career altogether.

And so I went into the money management business, so to speak.

So you started with some of your dad's money, and that got you a taste for an interest in it?

Yeah, some family money.

And then some other people put up some money, and I did that.

But no models. No models for the first two years.

So what were you doing then?

You were just using cunning and, you know, just like normal people do.

Like normal people do.

And I brought in a couple of people to work with me, and we were extremely successful.

I think it was just plain good luck, but nonetheless, we were very successful.

But I could see that this was a very gut-wrenching business.

You know, you come in one morning, you think you're a genius, the markets are for you.

We were trading currencies and commodities and financial instruments and so on.

Not stocks, but those kinds of things.

And the next morning you come, and you feel like a jerk, the market's against you.

It was very gut-wrenching.

And in looking at the patterns of prices,

I could see that there was something we could study here, that there were maybe some ways to predict prices, mathematically or statistically.

And I started working on that, and then brought in some other people, and gradually built models.

And the models got better and better, and finally the models replaced the fundamental stuff.

So it took a while.

I would have thought, with your background, a mathematician, this would have almost occurred to you immediately, like you would have straightaway seen this.

What was the two-year delay?

Well, two things.

I saw it pretty early, but...

And I brought in a guy who was a wonderful guy, also from the code-cracking place.

And he was...

I thought, together we'll start building models.

That was fairly early, but it wasn't right away.

But he got more interested in the fundamental stuff, and he says, the models aren't going to be very strong, and so on and so forth.

So we didn't get very far.

But I knew there were models to be made.

Then I brought in another mathematician, and a couple more, and a better computer guy.

And then we started making models, which really worked.

But, you know, the general...

There's something called the efficient market theory, which says that there's nothing in the data, let's say price data, which will indicate anything about the future, because the price is sort of always right.

The price is always right, in some sense.

But that's just not true.

So there are anomalies in the data, even in the price history data.

For one thing, commodities especially used to trend, not dramatically trend, but trend.

So if you could get the trend right, you'd bet on the trend, and you'd make money more often than you wouldn't, whether it was going down or going up.

That was an anomaly in the data.

But gradually we found more and more and more and more anomalies.

None of them is so overwhelming that you're going to clean up on a particular anomaly, because if they were, other people would have seen them.

So they have to be subtle things.

And you put together a collection of these subtle anomalies, and you begin to get something that will predict pretty well.

It's what's called machine learning.

So you find things that are predictive.

You might guess, oh, such and such should be predictive, might be predictive, and you test it out on the computer, and maybe it is, and maybe it isn't.

What discipline of mathematics, or disciplines, is it multidisciplinary, or are we talking...

It's mostly statistics.

It's mostly statistics and some probability theory.

But I can't get into what things we do use and what things we don't use.

We reach for different things that might be effective.

I would imagine lots of people want to be financially successful.

Most people want that, of course.

I suppose.

And lots of people are good at mathematics and know a lot about computers, at your level, I would imagine.

Why did you do it? Why didn't someone else do it?

I don't know.

Well, first of all, some other people have done it.

I think that our firm is better.

But nonetheless, I'm pretty sure of that.

But nonetheless, other people have done some very good modeling, and so we're not alone.

But it's not easy to do, and there's a big barrier to entry.

For example, huge data sets that we've collected over the years, programs that we've written to make it really easy to test hypotheses and so on.

The infrastructure is exceptionally good, so everything is tuned right.

It took years to learn how to do that.

I know you guys made the model, so you do have the ownership of it and feel proud of it.

But is it hard to follow the model religiously?

Is it hard for your ego to think all the success is because of the computer?

And I just sat there and watched?

No, the computer is just a tool that we use.

A good cabinetmaker doesn't say, it's all because of my wonderful chisel.

You may have great film equipment, but that's not why you're a success at doing what you're doing.

You're working with good equipment, but another guy would make a mess of it with the same equipment.

So no, we don't feel, oh, the computer is doing everything.

The computer does what you tell it to do.

Given that you will put some of it down to luck, what are you more proud of, all of this and the business, or the mathematics from that first half of your career?

Oh, to the extent that I'm proud,

I think I'm proud of both.

I think, you know, I've done some mathematics, and some of it's had a positive effect, so I guess I'm proud of that.

And I've built a nice business, and I'm proud of that.

I don't say I'm prouder of one than the other.

And now for the last number of years,

I've been working with my wife on this foundation, which she started actually in 1994 with my money, but nonetheless she started the foundation, and then I joined.

I got more and more involved with the foundation as time went on, and now that's my main thing, so I'm very proud of the foundation.

Let me focus you more on the mathematics versus the business then.

Would you trade any or all of your business success for being the man who cracked the Riemann hypothesis, or something like that?

No, that's a good question.

Would I trade that for...

Well, I'd probably trade some of it,

I mean, for the Riemann hypothesis.

It would certainly, I guess, be a thrill to solve the Riemann hypothesis.

I'm pleased mostly with the way my career has gone, so would I trade part of it for something?

I don't know.

I've never looked back and said,

I wish, at least in business,

I wish I hadn't done that, or I wish I had done this and not that, whatever it is.

I've never looked back that way.

The foundation is focused on support of scientific research, primarily basic science, but not completely because we have a large autism project, which involves a lot of basic science, but treatments are a goal.

But the rest is support of mathematics, physics, computer science, biology of all sorts, neuroscience, genetics.

We support basic science, and that's what we like to do.

There's a certain amount of outreach.

We have Math for America Pro.

We spend maybe 10 or 15 percent on outreach, outreach and education, but 80, 85 percent is support of basic science.

You put a lot of money into mathematics, and you've got some right to comment on it.

How are you feeling about it?

Oh, I think mathematics is really going quite well worldwide, the research end of it.

A lot of new ideas are coming up.

New fields sort of are flourishing.

It feels like a pretty healthy enterprise to me.

What's not healthy is the state of mathematics education in our country.

It's very unhealthy for young people.

That's why we started this thing called Math for America and so on.

But we don't have enough teachers of mathematics who know it, who know the subject, and even of science.

And that's for a simple reason.

30, 40 years ago, if you knew some mathematics enough to teach, let's say, in high school, there weren't a million other things you could do with that knowledge.

Oh, yeah, maybe you could become a professor, but let's suppose you're not quite at that level, but you were good at math and so on.

Being a math teacher was a nice job.

But today, if you know that much mathematics, you could get a job at Google, you could get a job at IBM, you could get a job at Goldman Sachs.

I mean, there's plenty of opportunities that are going to pay more than being a high school teacher, right?

There weren't so many when I was going to high school, such things.

So the quality of high school teachers in math has declined simply because if you know enough to teach in high school, you know enough to work for Google.

And, well, they're not going to pay you that much in high school.

So how do you redress that?

How do we redress that as a country?