5th HLF – Interviews with mathematics and computer science laureates: Stephen Smale
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| Number of Parts | 49 | |
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| License | No Open Access License: German copyright law applies. This film may be used for your own use but it may not be distributed via the internet or passed on to external parties. | |
| Identifiers | 10.5446/40127 (DOI) | |
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2
00:00
Internet forumComputerMathematicsSmale, StephenField (computer science)Musical ensembleStudent's t-test2 (number)Position operatorSelf-organizationQuicksortMultiplication signProcess (computing)Office suitePoint (geometry)RoboticsHypothesisUniverse (mathematics)Video gameMereologyMathematicsPhysicalismRoutingExtension (kinesiology)Semiconductor memoryInteractive televisionSoftware developerDifferent (Kate Ryan album)BitIdentical particlesComputer animationMeeting/Interview
07:38
Field (computer science)Smale, StephenStudent's t-testVideo gameMultiplication signMeeting/Interview
08:29
Field (computer science)Smale, StephenForm (programming)MathematicianMultiplication signContrast (vision)MereologyIntegrated development environmentWiles, AndrewInternetworkingMeeting/Interview
09:32
Field (computer science)Smale, StephenInternet forumInternetworkingSakokuRight angleMathematicianSource codeMixed realityType theoryWiles, AndrewMeeting/Interview
10:25
Internet forumSmale, StephenField (computer science)CoroutineMeeting/Interview
11:08
Internet forumField (computer science)Smale, StephenNeuroinformatikVirtual machineMathematicsPattern languageFields MedalMereologyQuicksortPattern recognitionMeeting/Interview
12:02
Field (computer science)Smale, StephenPosition operatorGreen's functionDifferential equationFields MedalStudent's t-testGroup actionDifferent (Kate Ryan album)MathematicsComputer scienceReal numberPhysicalismNeuroinformatikMultiplication signFreewareDirection (geometry)Physical lawFrequencyData structureStatisticsReading (process)PredictabilityGoodness of fitUniverse (mathematics)Integrated development environmentMechanism designTheoryMeeting/Interview
17:08
Field (computer science)Smale, StephenInternet forumMathematicsNeuroinformatikLattice (order)FrequencyMultiplication signMusical ensembleComputer scienceMeeting/Interview
Transcript: English(auto-generated)
00:22
First of all, is this your first HLF? No, I was here, I think maybe the second one. OK. Did you notice anything different between the two? Oh, my memory's not too great. I'm reminded of things you might say. I've forgotten a lot in the four years.
00:42
But it all comes back to me when I start walking here from the hotel and other things like that. And I have a bad memory. And you'll have to excuse me if I forget parts of your history. You were a professor or are a professor, yes?
01:01
Oh, more complicated than that. Most of my career was being a professor at Berkeley, University of California. But over 20 years ago, I retired. And then I got an offer and I took to be a professor in Hong Kong.
01:23
So that went on for, am I being recorded now? Oh, yes. And so I was in Hong Kong as a professor, some kind of university, distinguished professor even, at City University of Hong Kong. And then I left Hong Kong to go to Chicago.
01:44
I went to Chicago, Toyota Technological Institute and the University of Chicago. I had a position for about eight years. All this after I retired at Berkeley. And then at some point then, I got a very fancy offer
02:04
to go back to Hong Kong. And I resigned at Toyota Technological Institute to go back to Hong Kong. And that was in 2009. And so I was there until last year in Hong Kong.
02:22
And then I moved back to Berkeley. And that's where I am now. And now I'm emeritus at Berkeley, I have an office and everything. But not a job, but I do in some sense work via DARPA. DARPA gives me some kind of support. But it sounds like over the history of your career,
02:43
you've had a lot of opportunity to mentor people. You've had a lot of students. Well, it's not, yeah, it's mixed. I had essentially 49 or 50 PhD students, which is quite a lot.
03:01
So I guess that means a certain amount of mentoring of graduate students. But a lot of my positions have been with very small contact with students, small teaching. In the last 15 years, no teaching.
03:21
And so that diminishes my mentoring and contact with students. What is your style generally, either here at the HLF or in your university positions, when you have a PhD student and let's say they run into a wall?
03:40
Yeah, so I do not take care of my PhD students that much. I let them be independent, and some of them have left maybe because I didn't help them. I'm happy to give support, but they're on their own
04:01
to a great extent when they work for me. So I take this route where I let them be independent, succeed or fail. Why do you take that route? Well, that was my own route, for example. And also, I believe that too much nurturing is probably
04:23
not good for the long run success of a creative, important researcher. Well, let's talk about the students who are here, the young researchers. Yes. What's your interaction with them like? How does it compare, I suppose? Well, these are very short term,
04:40
and I've only been here a couple of days this time. And so some of them come up to me at dinner and so on. I've been talking to them. Yeah, so it's a good reaction, and they're interested in having their picture taken with me. We talk a little bit.
05:00
And so I was at one of the workshops. I was a mentor yesterday in a workshop, and I just sat there. I didn't organize any of it. I just sort of gave a quiet support to the organizer who was a post-doc. Have any of the students said anything or came up with ideas that really gave you new insight?
05:22
Through my whole years? Well, I meant at HLF. Oh, probably not so much, no. It's too short a time to expect otherwise. How about through your whole years where there's students? Oh, yeah, sure. I've written papers with my students, in fact. Yeah, and some students I've worked with for the last 50
05:47
years off and on, sometimes writing papers with them, some students, ex-students. So I keep a professional relationship with some of the students I've had. Now, looking back at your own development, you said that
06:03
when you were in, I would guess, your early 20s or so, you were mostly left on your own, and that worked fairly well for you. Could you sort of recap how that all worked for you? You can read this in things like Wikipedia about me, too. I had a very mediocre student life.
06:23
As a student, I was a B student in college, which is not too promising. I failed physics, in fact, in my senior year, and I switched to math. And then as a post-doc, I almost kicked out of graduate
06:44
school, because I wasn't doing that well. And then my prospective thesis advisor gave a course which attracted me, and also I was more ready for it. I got married, and so I was more ready to get more
07:01
settled down and write a thesis. And so I did that successfully. My thesis advisor, during the main year of writing my thesis, was gone to Princeton, so I wrote that mostly on my own. He suggested a topic, and the topic was good.
07:22
His suggested way of doing it was not good, but it was OK, I finished a thesis with Raul Bott, my advisor. It sounds like he was fairly important to you. Yeah, because he gave this very good course, maybe second
07:41
year of graduate school, when I was beginning to get more serious after I got married. Yeah, he was an inspiring teacher. What was his behavior? How did he interact with you? Oh, yeah, at that time he had lunch with only three
08:03
students in his course. The rest were all faculty. And so the three students he gave a little special attention by having lunch with him every week or two. So I'd have lunch with him every week or two during the course, and it was good. Any other mentors who stick out in your mind?
08:24
No, in fact, in my life I haven't had what you call mentors so much. I don't be serious mentors to anybody either. I like to teach by giving an inspiring example. That's what I do rather than teaching, telling people what
08:43
things are. I let them learn and try to set an example. The same for myself. I learn mostly on my own. That's been an interesting contrast I've noticed among the laureates. Some have said that math in particular is a team sport.
09:02
And then you have someone like Andrew Wiles, who is not part of a team. Yeah, and I don't fit either picture. Oh, really? Yeah. No, I don't go off and work by myself. Far from it. But I work in the environment. Nowadays I'm working as a biologist, so I use the
09:21
internet, Google, all the time. And I work with other people now. Most of my career I wrote papers by myself, but sometimes with other people too. So I'm more so I'm pretty social as a mathematician. Right? But my best paper is probably by myself, my best work.
09:43
But some very good joint papers too. So it's mixed. But I'm certainly not Andrew Wiles type by going off and some seclusion to work on a problem. No, it's not me. I like to be able to talk to people about it, even if
10:00
they're not working with me or ask questions. And now I ask Google. We would just become all of our best friends. So you can just write on the internet under Google, you can just ask a question. And they will immediately give you a reference to a few sources for answering the question.
10:20
So that's extremely handy. I don't read either, so I just browse. That's interesting. Yeah, I don't relate well to teachers. I do a lot of things, but almost always without a teacher on my own.
10:41
But sometimes, like with Raoul Bott, that was inspiring to have him teach this course. But I think I use teachers to get set up to learn by myself, my own way of thinking. I don't read or I don't follow a teacher's routine.
11:03
And I didn't either. That's why I didn't do so well in college, maybe. I'd like to move on to the five-year questions. Looking five years back and five years ahead in your field, what has changed in an interesting way? And then the second part of that is what do you think
11:21
will happen in the next five years that it's in there? My field changed five years ago. I became a biologist. Before that, I was working in computational mathematics. So I worked in computational mathematics. And then I switched over to working in pattern
11:40
recognition, machine learning for some years. That was coming out of my work in computation in mathematics. And then four years ago or five years ago, I started working in biology. Why did you make the change?
12:02
Well, it seemed some interesting, challenging questions in biology, which I could deal with with my background. And so this was at City University of Hong Kong. They'd given me a very fancy position with a huge amount of support, money. So I hired postdocs and put together a group.
12:26
We all learned biology together. Not from biologists, but usually Google. And some people in the group had some biology. So we learned biology. I did with them.
12:40
That was about five years ago, I guess. At first, it was computational biology. And then the last couple of years, two or three years, I switched over to, you might say, structural biology. So now I'm working on the structures of the genome,
13:00
which is not computational biology traditional. It's not my learning theory, but it's using a lot of mathematics to construct the laws of biology. That's the idea. So I'd like to see biology like physics, where mathematics plays a big role in the foundations of physics, mechanics, and so on.
13:21
Has a lot changed in this field in the last five years and the time you've been in it? Oh, it wasn't a field. It's not a field even now. No, my friends I work with, yeah, it's not a field. Because for one thing, biologists, they know
13:41
statistics, and computer science, and some math, but they do not know differential equations. And differential equations are the things that knit physics together with math. And I'm trying to do that in biology, so it's not a traditional field at all.
14:02
And biologists, almost all of them, cannot read what I write, because they don't know the mathematics. They don't know the differential equations. They know different kinds of mathematics. But there are some people who are on the edges of biology, or in biology, who do know something about
14:21
differential equations. And I communicate with them, and I manage to get my papers published. I work with one other person now, Ndika Rajapakse, Ann Arbor, so we communicate every day by Skype.
14:40
And he's got a biological lab. And he has a PhD in applied math. So anyways, it's not a traditional way of a subject as I relate to it at all. Well, it sounds like a real green field kind of thing. And that's one question that comes up between the laureates
15:01
and the students. It's like, especially computer science laureates, they say, yes, well, I got into it in 1962, and my professor had built the Mark 1, and so on. It was a green field. And the question is, what are the green fields for students now? It sounds like you really discovered one for yourself. Yeah, yeah. It's good, yeah.
15:21
In some sense, it's not so easy, because it's not traditional. And so I don't have this immediate support by the biologists in general. But a lot of especially young people who are beginning to get some education in differential equations and are
15:42
interested in biology, they're interested in working on these things. And I manage fine. Do you think, I mean, it sounds very exciting to be in at this time. Do you think it will engage you for the next five years? It's a good question.
16:00
I'm 87, so I don't know, five years, what I'll be doing. But I'm very involved right now. And also, I'm not a person who predicts. I'm against predictions, because so many things happen. But I see myself as working to finish
16:21
some of these problems. I see, I'm working on. Yeah, so I've changed my directions in my career so much that I just wouldn't look where I'll be in five years. The last thing I wanted to ask you was about the HLF itself.
16:40
So this is your second one. So clearly, you liked it well enough to come back. I missed a couple. So what brought you back? I don't know. I've had this free period of time during this period. And I enjoyed it. And it's a nice situation.
17:02
The hotel I see now, I remember now how great the hotel was, the Europäesch Hof. And it's a good environment with other colleagues, mostly from computer science, I think, rather than math, some math.
17:21
So that's good. All the young people, it's fun to talk with them at some of these meals we've had. Otherwise, yeah. So even though it's not enough time to really get involved with the young people, any personal young person that persists anyway for me, still, even this
17:43
short period is good. Anything else you'd like to add? Oh, I can say in the two days I've been here, because it's two days, it's been very pleasant.
18:01
Unjoyable for me. And I look forward to seeing the rest of the meeting. Same. Well, thank you. I don't think I have any other questions. OK.