A Physicist and an Engineer Go Looking for Data Scientists

In early April we had a visit from two data scientists at Engage3, who came to speak in our Alumni Seminar Series. Toward the end we had some particularly interesting discussion about the value of a physicist as data scientist. I include that part of the conversation here, addressing the question: you need a lot of machine learning, probability and statistics, subjects never taught in physics departments, so why do you hire physicists?

Anup Doshi, Director of Data Science at Engage3

As background, in late March we had a physics department colloquium by the founder of Engage3, Ken Ouimet. According to the Engage3 website, "Ken earned a BS in Chemical Engineering from UC Davis and went on to UC Santa Barbara to pursue his PhD work in Chemical Engineering and Theoretical Physics. While studying Statistical Physics he realized he could utilize these same principles to model retail markets and optimize retail pricing decisions."

The colloquium title was "The Physics of Shopping and Algorithmic Trading in Consumer Marketplaces." The visit by Ouimet led to our meeting with the Engage3 data scientists. They were James Holliday (PhD in physics from UC Davis in 2007) and Anup Doshi (PhD in EE).


We pick up the discussion toward the very end of Holliday's presentation.

Holliday: We’re trying to hire people. I tend to look for physicists or people who have gone through a physics education. And the reason I do that is I believe that physicists have a way of solving problems and approaching problems that’s unique. I love the way that we’re taught to take a problem, a complex problem that we’ve never seen before, and break it down into fundamental blocks: things that we have seen before or things that we understand very well. And it can be a really complicated thing and maybe we have to make some approximations, but the ability to look at something that we have not seen before and come up with a way to solve it – it’s just wonderful and I think it’s unique to physics. 

LK: I’m curious for an engineer’s perspective. We can tell ourselves stuff like this all the time but I’m a physicist. You [Doshi] have physicists working for you, and you’re in the market for hiring talent. So I’m also really interested in your perspective on what’s valuable about a physicist.

“I think the key qualities that physicists bring to problem solving are the ability to approach a problem from first principles, mathematically model a problem from first principles, and then follow in some sense a scientific method to get all the way through the problem.”

Doshi: Sure. Just to preface that question: my background – I did a PhD in Electrical Engineering and since then I’ve been working in this field of data science for a number of years now. I’ve had bosses that are physicists, colleagues that are physicists, and folks that are working for me as physicists, and I always enjoyed working with all of them. I think the key qualities that physicists bring to problem solving are the ability to approach a problem from first principles, mathematically model a problem from first principles, and then follow in some sense a scientific method to get all the way through the problem. That's formulating the problem, doing background research, modeling, generating a hypothesis, doing experiments, doing tests, skills from high energy physics like Monte Carlo simulations, for example, solving great, tough optimizations, going to the whiteboard and actually writing out the optimization problem; working out better ways to solve this. And then beyond that just getting the results and interpreting the results and then communicating those back. Those skills are unique to I think the mathematically-oriented, scientific person, like physicists. You don’t get that necessarily in any other discipline, that I've seen.

Holliday: Exactly. I like to look for the physicists when I'm hiring data scientists. One thing that I do when I’m interviewing people is I’ll throw a problem – I’ll throw it very quickly – I’ll throw a very difficult problem that I don’t expect people to necessarily be able to solve; I don’t give them all the information they need to solve that because I want to see if they can ask the right questions to understand the problem to make progress on it. And I want to see how they think about it as they’re pushing forward; to see if they can’t work in those situations.  The ones we wind up hiring tend to be ones from the mathematical, the scientific-oriented fields, that can think through the problem. So I would encourage everyone as you’re pursuing science or whatever,  make a habit out of asking for clarifying information if you don’t understand something. That is the real world: sometimes you’re not given all the information you need; you need to get that information to make progress.

Questioner: Why don’t you guys hire from mathematicians rather than physicists?

Holliday: We have talked to a few statisticians, and we hired somebody recently with a statistics background; a statistician. 

Questioner: I think you’re thinking biased because for data analysis you need a lot of statistics and machine learning and probability, which are the courses that are never taught in physics departments. You have to spend a lot of time investing in some people to teach them those courses.

Somebody else: I think if you’re a physicist you’re assumed to know probability and statistics; that is the basic –

Original questioner: A little, but not as much as mathematicians need statistics. I am working on complexity, but in all interviews I say that I have a statistics background with probability and machine learning.

Holliday: Yeah, that’s very fair, and I appreciate the question. I suppose it is coming off like I’m saying I’m putting up a filter: only physicists apply. One nice thing, what I get when I assume that a physicist or data scientist is coming in, like we said, there’s the assumption that they have some of that mathematical foundation. If someone were to come with just a mathematical degree, I would be happy to interview. I would obviously be impressed with the math; there’s probably a lot that could be said about the problem solving, and we’d just have to see. 

Doshi: So if I could follow up on that: we see a lot of candidates come across our desk who have X, Y, Z background, and then they’ve got a Master’s in Data Science. And there’s lots of programs now. Data science itself is a big, growing field, and a lot of universities are offering the “Master’s in Data Science.” And they’ll teach you skills like basic statistics, basic machine learning, computational skills – learn python, whatever you need to learn – they’ll teach you that for a year or two, then pump you out with a degree in Data Science. You see a lot of those candidates coming across our desk. They’ll come across, and we’ll pose them one of the simplest problems, a Bayesian problem, and they won’t know how to approach it properly because it doesn’t fit into the things that they’ve learned.

Math questioner: I didn’t mean those. Because those are programs that you pay for them; you don’t get admitted to university for data science; it’s like an MBA.

“the key missing qualities there are that inquisitiveness and the ability to approach a problem from a first principles kind of concept.”

Doshi: Maybe, but there’s also courses – you can go out and learn by yourself whatever machine learning you want to learn, and so the key missing qualities there are that inquisitiveness and the ability to approach a problem from a first principles kind of concept. Even if you don’t know how to solve the problem the way it’s supposed to be solved, can you think about a solid approach, and can you formulate it in a way that, given your background, that will get you to a reasonable answer – a reasonable hypothesis even – a reasonable answer relatively quickly? And then can you follow through that logic? That kind of inquisitiveness and the ability to approach a problem correctly is much more valuable than actually having those skills because then if we grab the people that have that ability, then we can go out and say hey, here, read this book and come back. 

Other questioner: What are some of the things that you thought you needed to learn as you entered industry, that have helped you succeed in industry?

Holliday: So here’s the academic world versus the real world: in academia you spend a lot of time making sure that the calculations are correct, the foundations are right, the assumptions are correct; in industry there’s a whole lot of “I need something right now.” And that’s a little bit hard for me. And I could see how somebody who has definitely gone through the path of academia would not want to maybe compromise the ethics of the math in order to get a very sloppy calculation out now that we can give to the investor because we’re on the hook for something that needs to be delivered.

Kate Marvel: Physicist, Climate Scientist Part II

The California drought: does the climate change 'signal' stand out above the 

weather 'noise'? [Figure credit: Jeff Master's Wunderblog]

In the second half of this interview with Dr. Kate Marvel of the NASA Goddard Institute for Space Studies, we discuss her field of climate science. We cover uncertainty, the hunt for signal in noisy data, and the joy of seeing physics work. In her case that joy is a mixed blessing because her data are backing up models that can, at times, make somewhat depressing predictions. All views expressed here are her own.

LK: You’ve been in the thick of comparing different climate models, and seeing how well these models agree with each other, seeing how they’re doing in various tests. What would you want to say about the state of the art here, and the description of our uncertainty? 

KM: There is an incredible amount of uncertainty, and for me that is the scariest thing. It’s not true, but I think you could make an argument that wasn't that wrong if you were to say “We don’t know that much more than we did back in the 1800s.” We know that there’s a greenhouse effect; we know that carbon dioxide is a greenhouse gas. And just from looking at that we know that if you put a bunch of carbon dioxide in the atmosphere, the Earth is probably going to warm.

And then you see it empirically, in the historical record?

I do a lot of what’s called detection and attribution -- basically trying to figure out what climate change looks like. You might say this is easy: “Duh, it’s global warming.” But what does climate change look like in terms of changes to rainfall patterns or cloud cover, for example? And so we try to understand from basic physics what would happen, what is supposed to happen under climate change. And then looking at observational records; looking at the satellites or ground-based gauges or whatever and saying, “Okay, this thing that we expect to be happening; is it happening?” It’s this weird cognitive dissonance, because you get really excited when you can show when it’s happening, because it’s very elegant. You’re like, “This is what physics tells me to expect; this is what the models are saying; ooh look it’s happening!” Then you kind of realize, “Oh my god, it’s happening!” And that’s a little depressing.

The earth is super complicated, and there are a lot of things that could happen to either speed up or slow down the warming. For example, you melt the ice caps, and that’s a positive feedback – it speeds up the warming. Because you used to have things that were reflecting incident short-wave radiation and now it’s absorbing and re-radiating long wave. So that’s a positive feedback. Warmer air holds more water vapor, and water vapor’s a greenhouse gas, so you make the earth warmer, you get more water vapor, and that accelerates the warming.

But then there are possible negative feedbacks. So if you increase cloud cover down low, then that can kind of reflect more incoming solar radiation and slow down the warming. And it turns out we just don’t understand clouds. We’re making progress, but if you think about how to model cloud formation, that is something that’s really affected by very small scales. Like you put bits of dust up in the atmosphere and you can seed clouds. And that’s really hard to incorporate in a global climate model, because global climate models happen on really big scales, and these processes happen on really small scales. You can’t explicitly resolve them; you have to parameterize them. And it turns out that as a result, we just don’t understand the net effect of cloud changes in the future. And you know, I think we’re making some progress on narrowing that down, but it’s kind of the biggest source of uncertainty right now.

Well people must also be thinking then of seeding clouds; intentionally creating clouds.

Yeah. I mean, there’s a lot of work being done on what’s called Solar Radiation Management, which is either like “Let’s try to increase cloud cover,” or just like “Let’s put a bunch of junk up in the stratosphere so that we decrease the amount of solar radiation coming to the lower atmosphere and hitting the Earth.” People have done a lot of modeling studies, but I think there’s kind of a general consensus that nobody wants to do this. There are so many uncertainties and it would be so much better if we didn’t have to do this. But it doesn’t benefit anybody to be completely clueless about it.

I did a carbon audit on myself a few months ago; just on one of these sites where you can enter some simple information. And not surprisingly, my footprint is dominated by flying. And it’s something I’ve wrestled with: what do I do about that? And I’m wondering, so certainly you’re doing science; you benefit greatly from being able to talk to other people and there’s benefits to actually being there in person. How do you think about your own carbon footprint, your own contribution to this problem given the work you’re doing, what you’re focused on?

I try to draw a very firm line between what I do scientifically and what I do personally. Because I do know that there is a science of communication, like “What is the best way to talk to people about these things?” And telling people to stop having fun is not ever going to work. And so I would be thrilled if we had a conversation about what’s the best way to mitigate climate change. And I don’t know, I feel like there are a lot of really smart people thinking about this, people who have training in economics and sociology, etc. It’s not that I feel that it’s not my place, because I have my personal beliefs, but I don’t think we’re there yet. We’re still arguing about whether climate change is a thing and I would just be so thrilled if we stopped having that argument.

We’re still arguing about whether climate change is a thing? What do you mean by that?

I feel like a lot of the political discourse is talking about “Is this happening?” And I find that so frustrating because what should be arguing about it is “What should we do?” And to even be having the discussion would make me so happy. So I’m kind of trying to work on trying to shift the conversation from “Is climate change happening?” And my answer is yes. And “Are we responsible?” And the answer is almost definitely yes. And then trying to move that to “What should we do?”

“I’m kind of trying to work on trying to shift the conversation from “Is climate change happening?” And my answer is yes. And “Are we responsible?” And the answer is almost definitely yes. And then trying to move that to “What should we do?”

So how do you go about that? That sounds to me a) fundamentally important and b) really difficult, and fundamentally ripe with the possibility of tremendous frustration.

I think it is. I mean, there are some really smart people thinking about the best ways to communicate this. And I think that so far there is a consensus: the consensus is “You can’t just tell people more facts.” Like there’s this thing called the Deficit Model, which says “Well, people don’t believe science because they don’t understand it. So if you just tell them more facts, they’ll accept it.” And I think the consensus is that this approach is just not going to work. And part of it is, you have to restrain yourself. So when somebody says something like “There is no climate change,” or “Vaccines don’t work,” or “Evolution is clearly not happening,” then your first impulse as a scientist is to be like, “Well you’re wrong. You’re wrong and I’m going to tell you why you’re wrong.” And I think that’s just a human impulse – we don’t like it when other people are wrong. But you have to kind of suppress that impulse. It’s kind of like eating Krispy Kreme doughnuts: it’s going to make you feel really good in the short term, but you’re really going to regret it in the long term and it’s probably not worth that short term pleasure.

And a lot of it is tribal. A lot of it is, “I want to be the kind of person who believes in and does something about climate change, because it’s bound up with all of these other things that I accept politically.” Or “I don’t want to be the kind of person that does something about climate change because then I’ll have to accept all these things that I just don’t.” And I think it’s trying to come up with ways to say to people who don’t accept a lot of things which they associate with left-wing political ideologies, and to say, “No, there is room for you here. It is possible to create a narrative that includes you.” And I think that is more productive.

There’s a woman called Katharine Hayhoe who’s at Texas Tech. She’s an amazing climate scientist, but she’s also an evangelical Christian. So she is very good at bridging that gap and telling people that it’s okay; there’s room for people like us here. And I think a lot of what she does can then be undermined by a shouty atheist coming along and saying “No, accepting science immediately means that you have to give up all of your beliefs.” There’s nothing wrong with being an atheist; I don’t think there’s anything wrong with being a believer. And I think that there is room for everybody here, but that means that those of us who are scientists kind of have to suppress that urge to tell people that they’re being stupid when we think they’re being stupid.

I choose not to eat meat, because honestly I don't care for it, and I don't drive because I live in a dense city with decent subway service.  But I don't think my personal choices should necessarily be universal, and I don't think it helps to tell other people to give up things that make them happy.

“. . . When somebody says something like “There is no climate change,” or “Vaccines don’t work,” or “Evolution is clearly not happening,” then your first impulse as a scientist is to be like, “Well you’re wrong. You’re wrong and I’m going to tell you why you’re wrong.” And I think that’s just a human impulse – we don’t like it when other people are wrong. But you have to kind of suppress that impulse.”

There’s a book by Naomi Klein in which she argues that conservatives are scared of this for the right reasons, because meeting this challenge means we have to rethink fundamental things about the free market system. Do you care to comment?

I have no idea if that narrative is correct because I’m not an economist. I know that some people are saying, “Okay, we have to completely overhaul capitalism,” and some people are saying, “No, we just need to price externalities right and there’s a free market way to do that.” And I know which position I’m sympathetic to because of my own personal politics, but I think me talking about that would be like me talking about criminal justice or reproductive rights in the sense that I have my beliefs about this, but I don’t claim expertise on that, you know what I mean?

Yes, and I respect your reticence very much. 

Is there anything from the science you’re working on now that you want to tell me about? Questions that you’re digging into that are really fascinating you? You said you’re working on basically figuring out what the predictions are for things like rain patterns and then going and trying to verify those in the historical record, or see what you learned from what we know. Anything that was surprising in this process or really cool?

I just think it’s great. I think one of the great things about climate is that there are so many Big Questions to answer. So you can do these things like, “Okay, how much wind power can we extract from the atmosphere?” Or “What is happening to rainfall patterns?” You can ask these very big questions and I really like that. I’ve gotten really obsessed with clouds, which is ironic because I hate bad weather and I’m only happy when it’s sunny outside, but know your enemy, right? So I’ve been doing a lot of work on clouds. Because I think this is a really interesting question, and we’ve been able to show that you can actually see things happening in the observations in clouds. At least in a couple data sets, you can see clouds rising, which is what’s predicted under global warming conditions.

What do you mean, you can see them rising?

So we’ve got cloud satellites dating back to the early 80’s, and in the satellite data, you can basically see the fingerprint of human-caused climate change in the cloud records, which is really surprising, because they’re so noisy, and so difficult to get anything out of. But you can really start to see all of these patterns emerging and it’s amazing how coherent everything is.

So you mean the typical height of the clouds above the land is changing?

Yeah. So I mean the height of high clouds is changing. So these big thunderheads that you would see, like convective clouds in the tropics, those are rising, those are going higher in ways that are predicted very robustly by a lot of the climate models and some of the physics underlying them, which is incredible. If you look at what climate change is supposed to do to rainfall, there are two basic underlying physical concepts. One, warmer air holds more water vapor, so all else being equal, wet areas will get wetter and dry areas will get drier. Two, all else is not equal, and we expect changes to atmospheric dynamics, which means the locations of those wet and dry areas are moving. And if you look at the satellite records you can see that the wet areas are getting wetter and the dry areas are getting drier and the entire pattern is moving poleward in basically exactly the way that you think it should. So that’s kind of amazing to see theory go to models, go to observations, in quite such a straightforward way.

I can see why you would get excited.

Yeah, maybe like, “Oh man!” <laughs>

So wet areas getting wetter, dry areas getting drier, and then everything moving around, that final statement – there’s no meaning in it then, unless the predictions are quite specific on how things are moving around. And they are?

Yeah.

So we’re in what looks like the fourth year now of a drought in California. What does this work actually say about particulars like that? Is this likely associated with climate change? Is it just consistent with what we expect? How do I think about this drought in the context of climate change?

The smaller the scales you’re looking at, the more complicated the picture becomes. So if you’re just looking at the California drought: I think the story that’s emerging is that it’s consistent with things that have happened before naturally, so the California drought looks like natural variability. But that doesn’t mean that it’s not influenced by climate change. So I think the tricky thing to try to understand, and the tricky thing to communicate, is that there’s no such thing as weather independent of climate. It’s like personality and mood; weather is mood and climate is personality. But obviously your moods are affected by your personality. And so the California drought – things like that have happened before, things like that would probably happen even if we weren’t doing anything to change the climate, but the California drought is happening in the context of climate change. And so just because it’s consistent with natural variability, that doesn’t rule out a role for climate change. It’s just that the role of climate change is really difficult to disentangle, if that makes sense. There are some things that are more clear-cut; the extreme heat events they're experiencing in Australia are very unlikely to happen without systemic climate change. And so there are things that are more clear-cut than others, and the California drought is kind of in that non-clear-cut category.

“. . . It’s like personality and mood; weather is mood and climate is personality. But obviously your moods are affected by your personality. And so the California drought – things like that have happened before, things like that would probably happen even if we weren’t doing anything to change the climate, but the California drought is happening in the context of climate change.”

Well, it’s like that in cosmology, where there are certain signals we can only dig out of the noise by stacking, so I guess that’s the case here with climate in California: you look at the patterns globally and I guess that’s what you’re saying about dry areas getting drier, wet areas getting wetter. In particular areas you can’t separate out that signal from the weather variability noise. But more globally you’re seeing the signals emerge.

Yeah, exactly. I mean, that’s even how we use that language. We talk about things as a signal to noise problem. Just because there’s a lot of noise and you can’t pick out the signal, it doesn’t mean that the signal doesn't exist. But the signal of climate change is getting louder and louder, and we're starting to pick it up in more places.

Kate Marvel, Physicist and Climate Scientist

Kate Marvel hated science and math in high school. Then she took an astronomy for non-majors class at UC Berkeley, fell in love with the subject and boldly pursued it. Here Kate describes how she met the challenges faced by a former math/science-hater turned Physics and Astronomy double major. We follow her through the prestigious Cambridge Part III Mathematics program, teaching in Africa, a PhD in superstring cosmology, and her transition to climate science. We will discuss her work in climate science in a subsequent post. 

Dr. Marvel works at NASA's Goddard Institute for Space Studies in New York, NY and blogs at Kate Has Things To Say. All views expressed here are her own. 

I mentioned on Twitter and Facebook that I was looking for someone with a physics degree, working in climate science, and got a lot of suggestions. Why do you think there are so many of you?

When you do the physics degree, you have numerical skills, you have problem solving skills, and I think that just makes you really flexible. And for a long time there it was hard to study climate science, in particular, in graduate school. You had to do meteorology, or some schools had atmospheric science departments, but it wasn’t a big thing, and so a lot of people in the field just kind of drifted in from physics. But there’s a huge overlap between the skill sets, I think.

 “When you do the physics degree, you have numerical skills, you have problem solving skills, and I think that just makes you really flexible.”

Backing up some, what drew you into physics in the first place?

I feel like I’m a little weird in that respect; a little weird in a lot of respects. But I hated science and math when I was in high school. I was like, “This is so boring; I’m never going to use it; I can’t wait to not ever do it again.” And I still do not get excited about inclined planes. I feel like the way it was taught just kind of didn’t resonate with me and I was just like, “I cannot wait to not do this anymore.”

So I was going to be an actress. I was going to be a movie star, obviously, because that’s a great career choice. And I went to Berkeley and I took just Astro 10, Astro for non-majors, in my first semester. And I was like, "This is so cool. This is so amazing. And the only reason I’m not majoring in this is because I’m scared of math and I’m scared of physics. But if I can get over that fear, I can learn all this amazing stuff.” It was really scary, but I decided to do it. I was just like, “Well, I don’t really know what I want to do with my life, like probably being a movie star’s just not going to work out, so I’ll just see where this leads me.”

Well that’s really interesting. I was interested in science really early on, and then in second grade when science started being taught in school I became totally disinterested.

It’s such a shame, isn’t it?

Yes! How was that for you then? All of a sudden you’ve got to take calculus – how was it going into a physics class, with other people majoring in physics?

I mean, it was hard. I feel like there were a lot of people there who always knew that they wanted to do technical things – wanted to be engineers or physicists. And they just knew more than I did, and I was really intimidated. And I was like, “I’m never going to catch up.” But then I realized that I didn’t care. I didn’t care that I was the worst person in the class because I wanted to know this stuff.

I didn’t learn physics quickly, but I learned how to take exams quickly. I kind of learned that, okay, there’s only so many problems that show up on the exam, and if you just get good at pattern recognition, then you can kind of fake it. And so I faked it for a while. And then it finally started to sink in. I finally started to learn stuff. But no, it was rough. It was hard. I felt really out of place. I felt like I didn’t belong. But then I found a group of other people who also felt out of place, and that was really helpful.

"I didn’t care that I was the worst person in the class because I wanted to know this stuff."

Yeah, I was just going to ask about what resources you found to help with that. So just a group of others?

Yeah. So I double majored in the end, in Astronomy and Physics, and Berkeley’s just this giant school, but the Astronomy major is really small, and so it’s really tight-knit. And there’s this amazing lab class where they basically just throw you in a lab, with all this old telescope equipment, and tell you like, “Prove that the galaxy is a spiral,” and you have to teach yourself electronics; you have to teach yourself – I didn’t know how to program – you have to teach yourself programming – and it’s so much work but it’s so amazing, and you get so much help from the TA’s and the professors. And all the famous professors know you because you’re in the department all the time, because you’re working so hard. And that that was just an amazing educational experience, I think. I’m super grateful for that.

All that time in astronomy, and then you went to do very theoretical work at Cambridge.

Yeah. So again, I kind of didn’t know what I wanted to do after college but I applied for this scholarship never thinking I would get it and I guess they made the questionable decision to give it to me. So I had a scholarship to go to Cambridge for a year to do this crazy thing called Part III in Mathematics, which is basically like a really intense master’s program. And so you take classes, whatever you want, but I chose quantum field theory and general relativity and then you just take a bunch of exams at the very end. And so that was super intense but I had fun; I liked it; I was kind of burned out by the end of it, but I learned a lot, I think.

So you did that master’s degree in Part III, and then onto a PhD in physics?

Mmm-hmm.

Can you tell me more about that transition?

So after Part III I was kind of burned out, and I went to South Africa for a year to this place called the African Institute for the Mathematical Sciences (AIMS). So I taught there for a year, which was unbelievable. I met the smartest people I’ve ever met in my life. The students there, just incredible. I guess it makes sense. You get the smartest people on literally an entire continent all in the same building, and it’s phenomenal. So I was there for a year, and then – there’s a bit of a pattern – didn’t really know what I wanted to do. And then a professor at Cambridge I knew said, “I’ve got some funding. You can come back to Cambridge and do a PhD with me." So I did. And I think UK PhDs have the advantage that they’re done in only about three years. So that was an appeal as well.

I also didn’t 100% know what I wanted to do. I kind of suspected that I didn’t want to go the typical academic, theoretical physics route, but I did kind of feel like, at the very least, “I’ll learn a lot, I’ll get great mathematical skills, and there are worse ways to spend three years and you can call yourself ‘doctor’ afterwards.”

So how were those three years for you?

There were ups and downs, for sure. I struggled a lot, because I think in the UK it’s very much – you’re just kind of told, “Go away for three years and come back when you’ve figured something out.” And I think everybody’s experience varies, like some people get more mentorship; some people don’t, but I did kind of struggle a little bit. But I met great friends, and I feel like I did learn a lot. But there were definitely ups and downs. I wouldn’t say it was easy by any stretch of the imagination.

And then how did you make the switch into climate science? Were there other things you were considering as well?

So I was really lucky and I got a postdoc at Stanford in the Center for International Security and Cooperation, which has this great science fellowship program. And they basically said, “You can do whatever you want, as long as you’re doing something which is relating science and policy.” And that was just great for me. So I did a little bit of work on mathematical modeling of the electricity grid. So I published a physics paper on applying random matrix theory to understanding the network of the grid. And I did a little bit of work on nuclear energy; basically trying to figure out “How should I feel about nuclear energy? Is it a net good? Or is it a net bad?”

I did a little bit of work on that and then I realized – I was there for three years – and I realized in my third year that I really like doing science, I really like doing physics, and I’m just really interested in how the Earth works. I think of all the places in the universe, this is my favorite. And I just got really interested in it. So I started just talking to people. I was on the Stanford campus, just reaching out to people, and saying like “Hey, I don’t know anything, but can I come pick your brain?” And some people never replied, and some people said “No, I’m busy,” but a really large number of people said “No, come talk to me.”

So just by making contact, I ended up meeting somebody who works at the Carnegie Institute, which is an independent research center, but it’s on the Stanford campus. I ended up talking him into giving me a try, letting me be a postdoc for a little bit. And in that job, it was learning on the feet, learning how to use a climate model, learning how to modify it, and we ended up writing this crazy paper on basically how many wind turbines can you put in the atmosphere, before you either run out of wind or seriously alter the atmospheric circulation And the answer turned out to be, a lot. You go to crazy town here. But it’s a really interesting way to kind of look at the parameters of the atmospheric circulation. And that was just an amazing experience because I learned how to use a climate model and I learned the basics of atmospheric dynamics, and I got this very high-profile paper out of it. So that was amazing.

“I realized in my third year that I really like doing science, I really like doing physics, and I’m just really interested in how the Earth works. I think of all the places in the universe, this is my favorite. And I just got really interested in it.”

Can you back up a little bit? You said you were really lucky to get the postdoc at Stanford. How did that happen? 

In graduate school, I got really involved with an organization called Pugwash, which is kind of a silly name, but it’s actually a Nobel Peace Prize-winning organization. During the Cold War, they did a lot of bilateral negotiations between scientists in the US and in the Eastern Bloc. And now it’s kind of this general science and society organization devoted to trying to do good things through science. And so I ran the Cambridge chapter of that and I got to go to a couple of conferences and met people there. And so I met people who worked at Stanford through these conferences, which was great, yeah.

Basically, through an extracurricular activity that you really cared about.

Yeah.

Then you got this opportunity at the Carnegie Institute. You were actually working, you were learning how to use a climate model. Where did you go from there?

I went from there to Lawrence Livermore National Lab, which has a really good climate group and I did a postdoc there. So I was at Carnegie for like two months before this opportunity came up at Livermore. I’m still really blown away by how much help and mentoring and advice I got through the whole process. I showed up at Carnegie and then at Livermore being kind of clueless. But people were just so patient and really generous with their time. I’m incredibly grateful for that.

But at Livermore there’s this place called the Program for Climate Model Diagnosis and Intercomparison, which I don’t think is a word, but basically it’s a bit of a clearinghouse. So there are about thirty different independent climate modeling groups in the world, and in order to see the similarities and differences they all have to run the same set of experiments, so they all have to run like – “Okay, run your model with no external forcing whatsoever. Like no greenhouse gasses, no volcanoes, nothing. And then, okay, everybody run your model where you all of a sudden abruptly quadruple carbon dioxide in the atmosphere.” And so everybody has to do these same sets of experiments, and then send all of their results to the Program for Climate Model Diagnosis and Intercomparison at Livermore. And so it’s just kind of like being in a playground because you have all of these data to play with. And there are so many interesting questions you can ask. So yeah, that was great.

You said something in the beginning about the value of your physics degree and your training in physics. You also taught at AIMS for a year. Do you want to add anything now about the value of that training for what you’re doing now?

I personally think it was super-valuable. I’m really grateful that I kind of learned how to approach problems in kind of a systematic mathematical way. I’m really glad I have those skills. I never took a course on programming and I really wish I had, but at AIMS I had to teach programming and there’s nothing that makes you learn faster than having to teach. So I’ve had quite a few people talk to me about wanting to make the switch from physics into climate science, and I always say, “That’s the number one thing you need, on top of what you already have: solid programming skills.” And I don’t think I would have had those if I hadn’t taught.