## Wednesday, December 11, 2013

### Intelligence in Astronomy: The Growth of My Intelligence

When I was in graduate school at UC Berkeley, I had a very rough first year. I started astronomy graduate school with a B.S. in physics from a small mid-western school and zero preparation in astronomy. I didn't use a telescope until I was 21 years old, I hadn't taken an astronomy course as an undergrad, and upon my arrival at Berkeley I couldn't tell you why the moon went through phases. Seriously. I learned moon phases as a TA of Astro 10.
 Campbell Hall at UC Berkeley. My office was next to the dome on the right side. The building was torn down a few years ago.
I remember very clearly heading down to the sixth floor of Campbell Hall for my Stellar Structure class, taught by Prof. Frank Shu. As I walked down the stairs with the other students, two of the second-years, Jason Wright and Erik Rosolowsky, were engaged in an intense discussion the likes of which I had never heard before from students. They were discussing whether the forward-scattering of light is the same as inverse Compton scattering, and under which conditions one description is better than the other (or at least this is how I recall the conversation).

I remember chuckling and thinking, "Yeah, right, they're seriously discussing physics outside of class like they're professors. Hilarious!" But then it sank in: they were dead serious. The joke was on me, and it was clear that intellectually I was a long way away from these high-power second-years. They were Smart. I was not.

Interactions like this continued at more or less a steady state in my first year, and I felt less and less capable compared to my peers. Two of my classmates came from Caltech and Harvard, respectively. The third came from Maryland, and he had completed most of his graduate course work as a PhD student there before transferring to Berkeley. I came from the University of Missouri. At Rolla. People generally don't even know how to pronounce Rolla. (BTW, UMR is now Missouri S&T)

The second-years were taking classes with me in the morning, but working on mysterious-looking astronomy data late into the evening and talking in a foreign tongue while doing so. I knew classroom physics, but my fellow students had taken the next step and could describe how all of that problem-set physics applied to things such as interstellar dust grains and the structure of the Sun. I was lost, and I was feeling increasingly stupid.

What saved me was a class taught by a postdoc named Doug Finkbeiner. As a former BADGrad and newly-minted Berkeley postdoc, Doug decided to teach a late-night, unofficial course on astronomical computer programming using this new and exciting scripting language called IDL. About a dozen students gathered in the seventh-floor undergraduate laboratory to use the new Sun workstations that a donor had purchased (oddly, first-year students used Sun SPARC workstations essentially as dummy terminals to login to faster computers. The undergrads had the real computing power with their SunUltra 10s.)

Doug taught a decidedly untraditional class. Each week he would teach a couple new concepts (e.g. array-based math operations), and he'd hand us some data and have us analyze the data using the programming techniques he just described. Because of my extensive past computer science experience I took to the analysis problems like a duck to water. Plus, the vector-based nature of IDL really meshed with the way my brain thinks about the world. Suddenly, all of those classroom lessons on Linear Algebra and even calculus were coming to life on my screen as I manipulated astronomical images. Fourier transforms took on a much deeper meaning after Doug gave us a radio telescope time series of the Crab Pulsar. Not to mention the fact that we were looking at the Crab frikkin' Pulsar!

I took my new-found IDL expertise and applied it to my Stellar Structure and Radiative Processes course work. IDL's plotting tools were just what my visual-manipulation brain needed to see past the confusing, abstract mathematics. Multi-dimensional integration, which to this day is often incomprehensible to me on the written page, became my best friend first using IDL's TOTAL() function and later my own numerical integrators (oooohhhhh, that's how the trapezoidal rule works!).

Doug's fly-by-night class gave me the tools I needed to not just get by in my courses, but start to excel in grad school. HW sets were no longer a slog, but were rather exercises that strengthened my physical intuition, built on my growing problem-solving toolset, and increased my vocabulary. With these traits growing in strength, I was able to start engaging in the science discussions that my older classmates were having, and as I did so my vocabulary, problem-solving sense, and intuition also grew. This positive feedback process led to an exponential growth in all of the traits that eventually led me to become a tenured professor at Harvard.

My intelligence grew exponentially.
 Figure by Nathan Sandars on Astrobites.org. For more see Nathan's interview of yours truly.
More importantly, I didn't quit in the face of difficulty. Why? It's tough to say. I was truly on my way out the door during my first year. One important ingredient was taking a class that was taught in a way that really resonated with the way my brain works. Another ingredient was having Doug Finkbeiner as a teacher. He was, and is, an astrophysical god in my mind, a wizard of the highest order. Yet his approach to science was so humble and down to Earth. He had these sayings that I use to this day. When tackling a tough, complicated problem, he'd say "Just do the stupidest possible thing first." If that doesn't work, start increasing the sophistication slowly until you find a solution. Astrophysical problems can at first seem daunting. But they can all be reduced to sophomore-level physics to get a first-order solution that can be implemented in code. And given the nature of most astrophysical data, first-order is often good enough!

Another ingredient was vividly seeing how the effort I invested paid off in direct proportion in my ability to do astrophysics. More effort in, more growth out. I may not be smart right at this moment, but give me a week or two and I'll be just as smart, if not smarter than you. More importantly, I saw my classmates working their asses off. I figured out that Jason Wright didn't arrive at Berkeley with his encyclopedic knowledge. He arrived with perhaps a quarter of an encyclopedia. At any given moment, I could walk into his office and find him reading an article or book, talking science with his office mates, doing math at his whiteboard, or programming. He worked. Hard. If I wanted to be like Jason, and I most certainly did, I'd have to put in the work.

My intelligence grew exponentially. And it's still growing because when challenges come my way, or when I encounter something that "I should know because I'm a Harvard professor," I don't back down. I don't try to find an easy path around it. I find someone who knows, like Smadar Naoz or Ruth Murray Clay or Avi Loeb, and I ask them "dumb" questions. I ask them to send me to the chalk board so I can struggle through basic problems. And you know what? They don't seem to judge me as being stupid. They help me learn.

At this pace, it's absolutely scary to think of how intelligent I'll be in 10 years.

Epilogue

This year Doug Finkbeiner and I both became tenured professors at Harvard. Doug was tenured jointly in Astronomy and Physics, and I was tenured in Astro. I'm still learning from him and it's an honor to work in the same department as him.

## Tuesday, December 10, 2013

### The simple power of presence in even modest numbers

 Shirley Jackson, the first African-American female Ph.D graduate of MIT. She is now the president of the Rensselaer Polytechnic Institute
Upon arriving in Cambridge I've had the pleasure of getting to know Prof. Chris Rose, who is an engineering professor at Rutgers, currently visiting MIT as an MLK Scholar. We've been talking about diversity in the sciences, with a particular focus on increasing the footprint of what Chris refers to as "the Greater Us," referring to the small community of Black folk among the American science professoriate. Sadly, "small" in this case means epsilon-small.

 Prof. Chris Rose (Rutgers)
Even in 2013, there are only of order 10 Black professors at top-40 astronomy institutions according to this poll taken circa 2007. That's about 1% of all astronomy professors in the US, compared to the 12.6% representation of Blacks in the US population. The same order-of-magnitude discrepancy in representation persists across all science disciplines, from physics to chemistry to comp sci. Decades after the Civil Rights era, the overwhelming majority of all US science professors are white (and male).

That's the bad news. The good news is that increasing the absolute numbers with the addition of ~10 individuals results in a 100% change in the fractional representation of the Greater Us in science in general, and  the astronomy community in particular. And such a change brings benefits that go well beyond the warm fuzzies associated with the mention of progressive concepts of "diversity."

## Monday, December 9, 2013

### Intelligence in Astronomy: Compendium Thus Far

I realize now that I should have referenced previous posts in my Intelligence in Astronomy series in some of my more recent posts. Read in isolation, I suppose my past couple posts would be confusing otherwise. Here is a list of posts so far:

Intelligence: Nature or Nurture? Both, together!
Preview 1 (with some motivation for what follows)
What you think and why it matters
What is Intelligence (Part 1)?
What is Intelligence (Part 2)?

Stay tuned for more to come!

### Intelligence in Astronomy: The Fixed Mindset and the Cult of Smart

 Image credit: here
(For my previous posts in this series see this handy compendium. In particular, if you missed it you should check out this post on fixed vs. growth mindsets)

The key feature of a fixed mindset is that intelligence is a fixed, inborn property that does not change in time for a given individual. Those with fixed mindsets tend to see outcomes such as success and failure as a result of these fixed, personal traits. "He didn't get the job because he's not smart" or "I didn't pass the test because I'm not smart" or "she's not a good scientist because she's not smart." I'm sure there are other personal qualities that people could focus on other than smartness, but in the realm of science for many people with fixed mindsets it comes down to who is and is not "smart."

I like to refer to this fixation on smartness as the Cult of Smart. Somewhat pejorative? Yes, indeed. Apropos? Big-time. Primarily because this stance is based more on faith than scientific evidence.

Members of the Cult of Smart can be found in all astronomy departments and, sadly, their voices are
 "Nope! Not good enough." says Prof. Cowell thoughtfully
quite loud. Whereas other people try to used nuance in explaining why others are excellent, or in predicting future success, members of the Cult are certain in their black-and-white evaluations. They're quick to use short, few-sentence evaluations that not only convey their point of view, but also tend to squelch further discussion by making everyone else in the room feel insecure.

"How about candidate A? He had a really interesting recent result that I---"

"NOPE! Not good. Doesn't even know GR. No way, not good enough."

"But, he gave a great talk at a recent conference I was at, and he really showed a deep understanding during the Q&A. What I particularly liked was---"

"Oh, come on! Seriously? Are you crazy? Where's the fundamental physics? This guy doesn't know anything."

(Note that this is not an actual conversation, but it is based on many real conversations I've been involved in over the years. It's similarity to the opinions of specific individuals is purely coincidental, but not unlikely.)

Why do people act this way? Well, think about their fixed mindset. To their minds intelligence is a fixed trait, and guess who has it? They do! It's their birthright and what sets them apart from everyone else. They're special, and there are only a chosen few who are like them. Unless the person they are evaluating exhibits the signs of the excellence they see in themselves, then those people aren't useful for much. They're not good now and they're not gonna be any better in the future.

Keep in mind that this is just one, particularly pathological manifestation of a fixed mindset. Even if you aren't a pompous blow-hard, as I'm sure you, dear reader, are not, you can still suffer from other side effects of a fixed outlook. If you believe that intelligence is innate and immutable, then what does facing an intellectual challenge tell you? If you're stuck working a tough problem, then what's the message? The message is fairly clear and not very positive: This is as far as you can go. You're not that smart after all.

After hitting this point and having those types of thoughts, students often drop out of contact with their advisors. "Oh no! I can't let the Prof. know that I'm stuck. She'll think I'm an idiot. Maybe I am an idiot! No! I can't let her know. Maybe if I sit here for a month or two the answer will drop from the sky. Maybe my intelligence is just temporarily suspended somehow." Weeks go by and the professor starts wondering where that bright-eyed, young student research went. Did they run off and join the Peace Corps? Did they get hit by a train?

This phenomenon of the disappearing student occurs regularly even at the most elite universities and research institutions. It happens with students who are smart by traditional definitions, and those who are not. It happens for students with good grades and bad, both high and low GRE scores.

Another manifestation of fixedness is a difficulty in taking criticism, whether constructive or not. This is how my fixed attitudes are manifested. When someone gives me specific yet critical feedback, rather than taking it for what it is---advice on how I can improve one specific aspect of my research or personal behavior---I sometimes take it as a global assessment of my self-worth.

Person: "You know, it would help to include a few more references on your slides."

Me thinking to myself: "What?! Doesn't this person realize how much work I put into this talk. Just who do they think they are, telling me that I don't give good talks. I cite so many people in my talks. Are they saying I'm a self-centered, selfish, ungrateful person? How dare they! Screw them and their crappy advice!"

 A Smart crashing. Credit here
Okay, this is a little exaggerated. And to my credit, I've been consciously working on this over the past few years. I've even come up with a standard set of responses that I practice saying ahead of time. Things like, "Thank you very much for that feedback. I can see what you mean and I also see how that will improve my talk in the future." I'm actively countering my fixed mentality in certain areas.

But what about those judgmental people? Are they judging you as being smart or not smart right now? Yup, they sure are and they're not likely to go anywhere anytime soon. This is something that we all have to deal with, just like we have to deal with rainy days, bad drivers, rude people at the grocery store, crying babies on airplanes, etc. It's a part of life in general, and academic life in particular, that we'll be judged rather frequently, often by people with fixed mindsets.

The only question is: How will you deal with it. Is your identity and worth tied up in others reaffirming your inborn talent and intelligence? If so, it'll be rough for you in the coming years as those fixed-minded individuals in higher positions pass judgement on you. You'll waste precious brain-CPU cycles ruminating on what others think about you and your smartness. Every comment sent your way will run through a filter that transforms off-handed remarks into judgements of your personal worth. You'll have a tough time.

Or will you overcome your fixed-mindset tendencies and start marginalizing out the pompous blow-hards and start working with me to form a vocal contingent to push back on the old ways of thinking?

## Thursday, December 5, 2013

### Intelligence in Astronomy: What Is Intelligence? (Part 2)

One night in Cambridge, England in the late 1970's, two astrophysics postdocs were sitting at a table outside of the Ft. Saint George Pub. One of the astrophysicists was Ed Turner (Princeton) and the other was Scott Tremaine (Institute for Advanced Study). As my good friend, Ed Turner, tells the story
At some point we fell to debating which of our famous senior colleagues was the best scientist.  Ostriker, Rees, Peebles, Lynden-Bell and others appeared in the conversation. We failed to find a compelling case for any one of them or even for comparing any two of them; generally there were arguments for many or both alternatives re who was the best.  I can't recall whether we discussed only theorists or also some observers.
Anyway, at some point we noticed that while it was very hard to say whether X was better than Y or vice versa as an overall scientist, it was often relatively easy to say which was better at some particular aspect of science...like who had the most extensive and detailed knowledge or who was more creative or who picked the best problems etc.  I recall making some analogy to comparing baseball players; it is hard to say who is the best overall but relatively easy to say who has the highest batting average, hits the most HRs, steals the most bases etc...
From this point it was only a short hop into science nerdery as they imagined the various components of excellence and traits of successful astronomers as basis vectors in a multidimensional hyperspace, which they termed the 7-Dimensional Scientist Hyperspace (7DSH, pronounced "seven-dish," I guess :). The seven dimensions of excellence that they identified was some version of the following according to an email Scott Tremaine sent me in response to my inquiry:

Taste  - Ability to identify an important question that can be addressed with the skills that you possess.
Intelligence -   Adeptness at the basic problem solving, calculating, perceptual skills needed to work the problem.
Grit - Ability to do the hard extended work needed.  Ability to maintain attention.  Ability to complete. The ability to face struggles and push through.
Knowledge - Breadth and extent of the corpus of knowledge needed to solve the problem and bring in interesting external information.
Curiosity - Alertness to interesting paths, byways, anomalies, etc.
Luck - Intuitive ability to expose oneself to, select for, and respond to constructive paths.

I really like these dimensions. Note, however, that they do not necessarily form an orthogonal basis set. One cannot be lucky or creative or curious without gaining the necessary knowledge. One cannot communicate well without good taste in selecting the right questions.

Note also that these traits are not static qualities of an individual, and smartness is only one component of success (mostly closely aligned with a combination of knowledge and intelligence). Even if you don't think you are getting smarter in time, and many people doubt that they are, one's knowledge increases monotonically throughout their lives, curiosity comes through effective communication with others, which generates ideas that can lead to asking important new questions. First-year students don't arrive on campus with this sort of software bundled and pre-installed. These are things that need to be learned, and successful graduate programs focus on training students and helping their projections in these various dimensions grow in time.

 Moving from one-dimensional "smartness" to multi-dimensional excellence
After thinking on 7DSH for a few months now, I've devised my modified 7-dimensional hyperspace of scientific excellence (M7DHSE), which draws upon the Turner & Tremaine conception as well as Sternberg's Successful Intelligence:

Creativity - The ability to successfully deal with new and unusual research problems and situations by drawing on existing knowledge and skills. The ability to connect disparate concepts to devise solutions to outstanding problems.

Curiosity - Alertness to interesting paths, byways, anomalies. The ability to identify important questions that can be addressed with one’s skills.

Basic intelligence - The ability to quickly identify the correct solution to academic, problem-solving tasks by drawing upon fundamental physical concepts.

Knowledge - Breadth and depth of the corpus of information one possesses that can be used to solve problems

Productivity - The ability to understand what needs to be done in a specific setting and then do it at  rate that contributes to the advancement of knowledge throughout one’s field

Communication The ability to advance ideas; generate needed input through positive interactions  with others; and disseminate results in oral and/or written form so that others can use them to advance the field.

Pedagogy - Abilities related to the effective training of the next generation of excellent scientists through teaching, advising and mentoring. The ability to adapt to different backgrounds and learning styles in order to help others learn how to be excellent

How does your ability vector, $\vec{A}$,  project into this hyperspace? What is the magnitude of your vector, $|\vec{A}|$? An most importantly, what is the time derivative of your vector, $d\vec{A}/dt$ and what are you doing to accelerate that growth?

## Tuesday, December 3, 2013

### Intelligence in Astronomy: What Is Intelligence? (Part 1)

In my previous post we saw how you, the readers of this blog, see intelligence as a key to success. The vast majority of you also see intelligence as something that can increase in time. But a question was left lingering: What do we mean by intelligence?

Is intelligence encapsulated in standardized tests? How about the IQ test? Here's what a famous psychologist, Alfred Binet, had to say about the IQ test:
“The scale [the IQ test], properly speaking, does not permit the measure of the intelligence, because intellectual qualities are not superposable, and therefore cannot be measured as linear surfaces are measured.”
"With practice, training, and above all method, we manage to increase our attention, our memory, our judgement and literally become more intelligent than we were before."