Category Archives: Science

The 10-step program to quitting astronomy

I left astronomy formally 18 months ago and recently finished up a computational modelling job in public health. With a little bit of time and perspective I now present to you my 10-step Program to Quitting Astronomy. Or any line of work. I’m not arguing that you *should* leave. This is simply about the process. Some of the details are specific to my aims, but I’m also incorporating a range of advice from my friends and colleagues, who each have their own unique goals. These steps don’t have to happen in exactly this order, but you’ll want or need to do most of these at some point. Good luck!

The 10-step program

  1. Acknowledge your feelings
  2. Identify your skills and interests
  3. Decide what to do next
  4. Find a mentor
  5. Go public
  6. Form a study group
  7. Re-train and re-brand
  8. Transition
  9. Settle in
  10. Mentor others

Let’s break these down.

1.
So: you’re thinking about quitting astronomy. Why are you considering leaving? Are the problems specific to the job? Or a consequence of unusual circumstances? Could they exist in all workplaces? What future do you picture if you continue? What do you expect if you leave? What are the risks of leaving? What are the risks of staying? What are you afraid of? Do some honest soul-searching; talk to friends; read some quit-lit if you like. I had never planned to leave, so I struggled and this stage took me a whole year. At the end of the day, you might decide to stay. That’s OK. But your mental health is your health, so make that a priority whatever you decide.

2.
If you make the decision to leave, then recognising your own skills and interests is crucial, especially when you come to re-branding (see Step 7). Make a mental list of intellectual interests and priorities (incl. workplace environment, autonomy, opportunity for progression, big picture, salary & benefits, flexibility & stability, family & relationships). Which of these are dealbreakers? My original list had astronomy near the top, and I had to gently but purposely delete it so that I could acknowledge its existence but then turn my attention to everything else.

3.
Decide what to do next *generally* speaking, both in terms of your career and other life choices (based on Step 2). Take every opportunity to hear from friends and acquaintances outside your regular professional sphere about what they do and who they work with. Ask whether they hire people with your background and skills. My first resource was the JobsForAstronomers website, which – back in 2012 – introduced me to the now famous Insight Data Science Fellowship. Insight has since spawned several other satellite initiatives and similar data science programs are popping up everywhere. At that point, however, what mattered wasn’t the programs themselves, but rather realising that my PhD would really be useful and valued elsewhere, that I wouldn’t be wasting my achievements, and that a path existed somewhere to mediate my transition. At first I was drawn to data-science, but later decided (after going back to Step 1) that I still wanted to do some academic work as an applied statistician but in a different discipline; now I sit somewhere in between.

4.
Seek out a mentor or three. Find ex-astronomers who are already working in a field you’re interested in joining (preferably people who you can relate to), ask them about what they do day-to-day, good companies/institutes to work with, where/how to look for jobs, and CV/application/interview tips. People are almost always willing to step up: advice is free and it’s flattering to be asked. In the last few years, a few Facebook groups have been formed to help with this. It’s OK to cold-call someone to ask for advice. I did.

5.
At some point you need to let people know your plans and where you’re looking to go. Partly for logistical reasons (so you can ask for reference letters) and partly for your own sanity (you can stop pretending that your sole purpose in life is to get a faculty job). As a bonus, once people know you’re looking for a job, they might just tell you about potential mentors and any openings they hear about. So go public with positivity. There’s no need to be defensive: you’ve made a (big) choice for valid reasons.
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Channel your inner career-transition spirit animal and explore new identities

6.
Find a few other people preparing to make a similar move and suggest that you form a study-group that doubles as a support-group. I’ve met these people either in my own department or at astro-conferences (these days people discuss post-astro careers at every single event; thankfully this is no longer a taboo subject). You can swap tips and discuss all the practicalities of steps 7 & 8. You will find out about twice as many job opportunities. Most importantly, you will not be alone in this.

7.
Re-training is simple; we do it all the time, like reading up for new projects or teaching ourselves new programming languages. But re-branding feels like a bit of a dirty word for a researcher. The fact is we’ve always been one-person companies: creating a product, marketing, consulting, HR, all wrapped up in a human package. Re-branding  means consciously influencing your potential professional community’s perception of you, communicating your expertise, what values you stand for, and ultimately if and where you would fit in with their team. Both re-training and re-branding could involve applying to fellowships and internships, completing MOOCs and kaggle-competitions, attending short courses & workshops, conferences & hackathons, meetups & career seminars, blogging demo-projects, and taking to social media.
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Also, take some decent professional photographs – you’ll need them.

8.
Beginning the transition involves a few things: job hunting, re-designing your CV, learning new interview skills, and time. Your mentors and support-study group will be crucial here so make the most of them. A good recruiter can do wonders, and networking is key. I found social-media to be very useful for serendipitous job ads, although I rely more on job-listing websites and newsletters. Interviews can be quite different to what you’re used to, so treat each one as a learning opportunity. Are companies reluctant to hire astronomers? I can’t speak for all companies, but I can tell you that to improve your chances you should avoid being seen as a risky hire: do your research, adapt to speak their language and understand their stakeholders’ needs. So… go out and score that job!
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Optional extra: transition between countries…

9.
Adjust and settle into your new job & new life. Get used to answering the question: “So, what do you do?”. I still almost always mention astronomy. No job is perfect, but sometimes it’s not until after we’ve had one job outside our previous career that we realise what we’d taken for granted. That said, nobody I know regrets their decision to leave. It’s OK to miss your research topic. I miss gravitational lensing terribly – the research, my colleagues, and the conferences. I still like to share major science news with my family, friends, and bus drivers. But why not get involved with other science disciplines? Why not the tech industries and start-ups? Why not help shape policy? Now that I’ve freed myself from the shackles of lifelong career goals, I have the opportunity to be part of any of it. I am also free, it turns out, to continue doing research astronomy as a hobby, at my own pace, on whatever I’m inclined to do.

10.
Congratulations! You’ve moved on – with some help. Now it’s time to pay it forward. From the moment you go public (step 5), you can expect requests for mentorship or at least a one-off chat. One year on, I was asked to speak on a panel to junior academics about alternative career paths (keep an eye on emails from your previous department and volunteer when you see calls for speakers). Your ability to help others transition will be useful for at least a few years, so go out and do your duty. Maybe write a blog about your experience.

An Astro-interlude

I decided to spend two weeks hopping from continent to continent to take part in back-to-back astro-statistics-tech events: the COIN Residency Program and AstroHackWeek. A year after having left the field, formally speaking, I’ve chosen to make astronomy my hobby, taking “leave” to do research. It’s maybe not entirely sensible, but I’m doing this on my own terms. This blog is a report on things I learned that sleep-deprived mostly-barefoot fortnight.

First, a little background about the events.

The Cosmostatistics Initiative (COIN) is a collaboration that began in 2014 as a section of the International Astronomical Association (IAA) and brings together people across the Astronomer–Statistician spectrum to do some left-of-field research introducing new data analytic, statistical, and visualisation techniques to the astronomy community. The Residence Program happens once a year: we hang out in an apartment for a week, do some intense work on 2-3 projects well into the wee hours, write-up half the papers, and still get some sun. This year we found ourselves in the lovely, warm, city of Budapest.

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Some of COIN on our day off to go sightseeing around Budapest. Credit: Pierre-Yves LaBlanche

AstroHackWeek (AHW), on the other hand, is a free-form event with elements of a workshop (pre-defined lectures) and a lot more making-it-up-as-we-go-along. Early on, 50 participants suggest topics they would like to learn about, identify one expert amongst the group and allow them to become teacher for an hour to a class of 10-20 (learning collectives are a brilliant idea!). Hack projects are the highlight, and are proposed both before and throughout the event; many of us will work on 2-4 at once. AHW also started in 2014, and was held this year at the Berkeley Institute for Data Science (BIDS).

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AstroHackWeek getting settled in at GitHub HQ, San Francisco.

For completeness, I’m also going to mention dotAstronomy, a similar out-of-the-box unconference that started way back in 2008/9. It has evolved over the years, but by the time I attended dotAstro7 in Sydney in 2015, it had become a combination of idea-lectures, just one day of hack-projects, and a lot of unconference group discussions. More of the emphasis is on software/tech and education/communication.

OK, so here’s my brain-dump:

Mixture models

Mixture models are the result of combining models for different sub-populations or classes. This makes them relevant to both clustering classification routines and for dealing with outliers. You can never really tease the subpopulations apart; the point is to model the combined dataset. And maybe provide a probability for each data-point that it belongs to a specific class.

Hierarchical models

Some parameters of the model will be relevant to different subsets of the group. For example, for supernova data one needs to model individual light-curves (layer 1), properties of supernovae type Ia (layer 2), and cosmology (layer 3). I’m now convinced that at least half of all models are actually hierarchical, just not recognised and named as such.

Probabilistic Graphical Models

Probabilistic Graphical Models (PGM) are diagrams that are very helpful for communicating parametrizations of models. You have to learn the “notation”, but once you do, they make great visual aids (see an example in this paper). Parameters are described as distributions, data or constants. Relationships between parameters are noted. This is particularly good for describing hierarchical models.

Gaussian Processes

Making your covariance matrix Gaussian is the first step to modelling correlated errors. This is a complicated subject, and GPs certainly have limitations (maybe Gaussian isn’t appropriate!) but it’s better than just diagonal matrix, and besides, they have useful properties that make things easier to calculate.

Jupyter (IPython) Notebooks

This was the first time I actively used Jupyter Notebooks for writing python code, and I was pleasantly surprised by the interactive features and formatted commenting. Perfect for small pieces of code and teaching/demonstration. However, I do have some questions/gripes (please let me know if there are solutions) :

  • can you import a package/module written in a notebook? Sometimes we end up with a notebook version for development, and then a standard python file for importing.
  • can’t use all emacs commands meaning I have to do more clicking with the mouse, which is why I tend to avoid interactive editors in general.
  • how does one work collaboratively on the same notebook? Can git handle that?

To be fair, I have an old version of ipython notebook, so maybe these gripes no longer apply. I should talk to the Jupyter crew, one of whom I met at AHW.

Parallel programming in Python

I had thought that parallel programming wasn’t really possible in python: you could run code on multiple threads yes, but not really multiple cores. People use multiprocessing sometimes, but now I need to look into mpipool. Could be useful, if you have the mpiexec job launcher set up on your cluster.

Natural Language Processing & Web-scraping

Despite being astronomers-by-trade, you’ll often find us talking excitedly about everything fascinating from outside our field. At a hack-week, we’re happy to give anything a shot. So after free dinner and drinks at GitHub HQ , we dreamt up the Happiness Hack (under a different name) and within 2 hours, created this.

It was going to end there, but the next day, we drummed up interest from the group and ended up extending the hack to grab** and analyse participants’ commit messages, as a bit of a joke, I guess, but here you go.

**beautiful-soup : holy crap!! So powerful, so beautiful…

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Mock Turtle sings “beautiful soup”. Snippet of the drawing by Sir John Tenniel

Failing efficiently

Pair coding has been part of my life for the last few months, and I totally appreciate how it can really be more efficient despite the extra person investment. Just enough cooks. The small collaborations formed at both events worked wonderfully together, and several papers have been spawned. But really the big lesson, particularly from hacking at AHW, is that we benefit from learning to fail efficiently, because that sets us free to explore high risk projects. One person could hack away for weeks or months at an idea, while two or three people could declare it a lost cause in a mere day or two. Besides efficiency, this system prevents frustration and burn-out. Trying and failing was actively encouraged at AHW, and, better yet, demonstrated by senior participants.

Career transitions & Imposter Syndrome

Every time I meet with astronomers these days, the discussion turns to the process of leaving astronomy and imposter syndrome. The global community only really started talking about these on open forums about three years ago, and now it’s a recurring theme. At hack days/weeks, in particular, imposter syndrome is rife. Trying to prove your skills and worth and produce something spectacular on a short timescale is a recipe for mental health disaster. The pressure to dazzle with our hacking skillz certainly got to me back at dotAstro, but not as much this time, partly because the organisers made it a point to tackle the problem head-on (thank you!) and make the most of everyone’s diverse skill-sets, and partly because this time I knew better and put more emphasis on play and fun, and less on achieving goals.

 

So yeah, amongst the astronomy, statistics, computing, collaborating, hacking, and playing, I managed to learn a ton of stuff, see lovely places, and make new friends, which made the trip very worthwhile. My most important lesson, however, was:

Try not to doze off while on your laptop on the sofa near your colleagues, otherwise you end up with photos of creepy teddy bears watching you sleeping…

Seven Sisters

Every year, the Art Gallery of NSW (Australia) features the Archibald Prize for portraiture. Alongside this exhibition are the Wynne Prize for Australian landscapes, and the Sulman Prize for … everything else.

While the Archibald tends to get most of the attention, the Wynne finalists are pretty impressive, and this year’s winner was worth a special mention. The acrylic painting is a collaborative work by five sisters from the Ken family, who live in a remote Aboriginal community in South Australia. The Seven Sisters depicts a Dreaming story** of seven young sisters escaping the advances of a man from ‘another skin group’. They eventually land in the heavens as a small bright group of stars; the man follows them into the sky, forever in pursuit but never able to catch them. The songline for the story extends from south/central Australia all the way to the west coast, and so comes in several variations and languages.

**Aboriginal works such as this are an expression of identity – through representations of country and traditional stories – so sometimes I wonder why they wouldn’t be classed as portraits. But I digress.

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Seven sisters by Ken Family Collaborative (acrylic on linen), winner of the Wynne Prize 2016 for landscape, at the Art Gallery of NSW.

The group of stars that the seven sisters become is known to many as the Pleiades, an open star cluster that lies 400 light years away within the constellation of Taurus the Bull. It actually contains not seven, but over 3000 stars, and can be seen from both the Southern and Northern hemispheres. Blue reflection nebulae surround some of the stars; these are the result of carbon dust grains reflecting blue light from the stars themselves. The man in pursuit is sometimes thought to be Orion the Hunter, or just one of the stars in said constellation.

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M45: The Pleiades star cluster (image credit & copyright: Robert Gendler)

Interestingly, this star cluster is associated with mythology across many other cultures: Indian, Greek, Native North American, Maori, and Japanese to name a few. As an aside, the Japanese name for the star cluster is Subaru, which is why the eponymous car manufacturer uses a stylised image of the cluster as its logo. In most of the myths and legends, the Pleiades represent seven sisters. So it’s no surprise that the Astronomical Society of Australia has chosen the Pleiades as the name for the Award that recognises institutions that actively advance the careers of women in astronomy.

Risk and rationality

I had the privilege of attending the 2016 Australian Academy of Science Theo Murphy High Flyers Think Tank in Canberra just recently. I’d only heard about it via a single tweet the day before applications were due, but with the topic of “An interdisciplinary approach to living in a risky world”, my response was: yes please.

We were also asked to choose our preferred topic for breakout-group discussion, and I got my obvious favourite, the technical theme of “Uncertainty, ignorance and partial knowledge”, which turned out to have some focus on decision theory. The session would chaired by Prof. Mark Colyvan, a professor of Philosophy at my alma mater, The University of Sydney, who had recently responded to Luke Barnes’s recent fine-tuning of the universe talk. Some of the recommended reading got me thinking about matters we didn’t get to cover (like how much I don’t like maximin), but I’ll discuss with Mark, and I’m sure I’ll blog about that later. In the meantime, our breakout group spent a couple of hours throwing around our thoughts and ideas and have begun to craft a report and recommendations for the Academy regarding decision-making and risk communication in the face of uncertainty.

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Wrap up from chair Prof Hugh Possingham

My fellow delegates were such interesting people from diverse backgrounds like health, maths, stats, philosophy, history, law, geology, ecology, microbiology etc, and absorbing ideas from these amazing people over the two days provided a complete mental recharge. It was like NYSF for grown-ups. Even the conference dinner speech by emergency doctor David Caldicott was so stimulating, leaving my laughing and crying, I’d dare say it was the “best event speech ever”.

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Slide from Prof Terry Speed’s talk at the AAS Think Tank

Actually one of the things I most enjoyed at the Think Tank was finding out people’s thoughts on rationality during tea break, as always. As it turns out, most people I spoke to (about this topic, sample size ~5) were adamant that people are at heart, irrational creatures. Only one person (besides myself) thought otherwise. I’ve been told I have to read Daniel Kahneman’s Thinking Fast and Slow to hear more arguments against the assumption of rationality. Apparently there are tests for this sort of thing…

Outliers are people too

I confess: I like Tom Stoppard because his plays highlight all the intellectually stimulating but somewhat pretentious (aren’t they all?) discussions I’ve had over the last 15 years. His latest, The Hard Problem, was no different. It follows Hilary, a psychology student who we meet as she applies for a job at the Krohl Institute for Brain Science, hoping to inject some humanity into their research. As always, Stoppard treats us to some witty banter, this time about altruism, animal behaviour, coincidence, consciousness, ego, evolutionary biology, morality, neuroscience, religion, and the worlds of academia and finance. The Hard Problem is perhaps less clever and fresh than Arcadia or RosenGuild, but fun and thought-provoking nonetheless. Some of the characters are true to the bone while others, disappointingly, feel typecast, but there is definitely some familiar truth in all. Overall, I’m pretty happy with the brain-lit Hytner production that we saw streamed live from the National Theatre in London – worth seeing.

Parth Thakerar (Amal), Vera Chok (Bo), Lucy Robinson (Ursula), Rosie Hilal (Julia), Olivia Vinall (Hilary) and Damien Molony (Spike) in The Hard Problem by Tom Stoppard @ Dorfman, National Theatre. (Opening 28-01-15) ©Tristram Kenton

Parth Thakerar (Amal), Vera Chok (Bo), Lucy Robinson (Ursula), Rosie Hilal (Julia), Olivia Vinall (Hilary) and Damien Molony (Spike) in The Hard Problem by Tom Stoppard @ Dorfman, National Theatre.
(Opening 28-01-15)
©Tristram Kenton

Rational Agents

Recently I attended the second ever Bayesian Young Statisticians’ Meeting (BAYSM`14) in Vienna, which was a really stimulating experience, and something pretty new for me, being my first non-astronomy conference. I won a prize for my talk too, which was pretty sweet!

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Swanky BAYSM`14 venue at WU Vienna designed by architect Zaha Hadid

During the two-day overview of theory and a variety of applications by the newest people in the field (read about the highlights over at the blogs of Ewan Cameron and Christian Robert), we heard from a few Keynote Speakers including Chris Holmes. In his talk, he mentioned the world of rational decision makers as envisioned by Leonard J. Savage in his 1954/1972 tome The Foundations of Statistics (adding that on my ‘to read’ list), and went on to describe the application of a loss function and minimax to avoid worst-case scenarios. Minimax isn’t the only approach to decision-making; I think other approaches  are more relevant to our behaviour, as I’ll describe later.

“If you lived your life according to minimax, you’d never get out of bed” – C. Holmes

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We are all made of stats

Children are very good at science. They start with broad priors (anything is possible) and learn through collecting data (see picture below) what conclusions are supported best by the evidence. They experiment, make mistakes, and test the variations on a theme. They learn what is dangerous; they learn what is tasty; they learn how to speak.

Kids doing science

Kids doing science

Our responses to experiences are very similar to Bayesian reasoning. Take trust as an example. If some dudette off the street – let’s call her Margaret – were to recommend a movie, say Moon, we might not heed her words since we have no reason to think we’d have the same taste in movies as her, but if upon watching Moon we found that we quite enjoyed it – we’d be more likely to rely on Margaret’s next tip, say Wadjda. And if Wadjda was also to our liking, we’d probably trust Margaret’s advice when she suggests Fast & Furious 6 (oops). But that blunder would reduce our confidence in her next recommendation, etc. If we define our experience of the movie in binary terms such as “liked” and “disliked”, the situation resembles the classic coin-toss experiment in which one tries to determine if a coin is biased by flipping it many times.
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