> Very long baseline interferometry (VLBI) is a technique for imaging celestial radio emissions by simultaneously observing a source from telescopes distributed across Earth. The challenges in reconstructing images from fine angular resolution VLBI data are immense. The data is extremely sparse and noisy, thus requiring statistical image models such as those designed in the computer vision community. In this paper we present a novel Bayesian approach for VLBI image reconstruction. While other methods often require careful tuning and parameter selection for different types of data, our method (CHIRP) produces good results under different settings such as low SNR or extended emission. The success of our method is demonstrated on realistic synthetic experiments as well as publicly available real data. We present this problem in a way that is accessible to members of the community, and provide a dataset website (vlbiimaging.csail.mit.edu) that facilitates controlled comparisons across algorithms.
What strikes me as really amazing is the cross functional nature of these modern achievements. I did not realize that this image was created with statistical image models and a Bayesian approach.
Also, this link included -> http://vlbiimaging.csail.mit.edu/ introduces the field and offers a good explanation for those interested in learning more:
> Imaging distant celestial sources with high resolving power requires telescopes with prohibitively large diameters due to the inverse relationship between angular resolution and telescope diameter. However, by simultaneously collecting data from an array of telescopes located around the Earth, it is possible to emulate samples from a single telescope with a diameter equal to the maximum distance between telescopes in the array. Using multiple telescopes in this manner is referred to as very long baseline interferometry (VLBI).
Not trained in this field, but this reads like a certain mistype. Shouldn't resolution increase with telescope diameter?
It took me several rereads and reading the comments here to understand that we want low numbers for angular resolution.
I suppose it's fairly obvious for one well-versed in optics, but to the layman (like me) it's initially opaque.
Reconstructing Video from Interferometric Measurements of Time-Varying Sources
So maybe we will also see a video of a black hole, soon.
"Then they spent the two years parsing literal truckloads of data, some of which had to be shipped on hard drives from the South Pole and defrosted outside a supercomputer facility at MIT."
I'd love to read more about this if anyone has an article with more details.
Found it through this pdf: https://fskbhe1.puk.ac.za/people/mboett/Texas2017/Doeleman.p...
Do you understand Katie's explanation?
They have a sparse set of data that is part of an image. They have trained a model to look at the sparse set and make an educated guess about what the full image looks like. They do this by feeding it full images.
The full images you feed into the model thus have an effect on the final image generated. In order to see how large that effect is, they trained different versions of the model with different sets of complete images. Some were images of what we thought a black hole looked like. There is potential that this heavily influences the model and ensures that the output looks like what we expect it to, even if that isnt actually true.
They also trained the model with non-blackhole images. Since the output of the model was approximately the same, this indicates that the resulting output picture doesnt look like what we think a black hole looks like just because it was trained with black hole images. It likely really looks like that.
The model doesn't need to be told what a black hole looks like. The sparse measurements combined with knowledge of how sparse data can be combined to form a generic image is enough. The model learned that the sparse data is not likely pure noise, instead there are shapes and lines and gradients that relate the sparse data points to each other.
Her analogy of sketch artists is good. If you have a functionally complete description and give it to 3 sketch artists from different cultures who are used to different looking people, they will still draw the same person. However if your description isnt actually detailed enough, their sketches will significantly differ as they use their existing knowledge and bias to fill in the gaps with what they think is likely.
>They also trained the model with non-blackhole images. Since the output of the model was approximately the same, this indicates that the resulting output picture doesnt look like what we think a black hole looks like just because it was trained with black hole images. It likely really looks like that.
If you are feeding non-blackhole images in and getting blackhole results out, wouldn't that be indicative of an over-trained model? Her other analogy was we can't rule out that there is an elephant at the center of the galaxy, but it sounds like if you feed a picture of an elephant in you'll get a picture of a blackhole out?
They also showed that when they fed in simulated sparse measurements based on real full images of generic things, they got back fuzzy versions of the real image.  So if you put in a sparsely captured elephant (if for instance there was one at the center of the galaxy) you'd get an image of the elephant out, not this black hole.
To complete the artist analogy, imagine that the suspect that is being drawn by each artist is some stereotypical American. The description given to the artists doesnt say that, it just describes how the person looks. One of the three sketch artists is American and the others are Chinese and Ethiopian.
If the American draws a stereotypical American, how can you be sure that the drawing is accurate and thats not just what he assumed the person would look like because everyone he has ever seen looks like that?
You look at what the other two draw. If they both draw the same stereotypical American, even though they have no knowledge of what a stereotypical American looks like, you can be pretty sure that they determined that based on the description provided to them. The actual data.
They did still likely utilize some of their knowledge about what humans in general look like though. This is analogous to how the model uses its training on what a generic image looks like. For instance, maybe several sparse pixels of the same value are likely to have pixels of that same value between them. The model puts things like this together and spits out a picture of what we think a black hole looks like even though its never seen a black hole before.
Did they try to feed random noise into their trained image builder?
I suspect that the output of that trained image builder is always the same "black hole", even with random noise as an input.
I think if you trained with random noise you would get random noise output.
So I assume they're simulating what an input would look like of, say, a planet or astroid or elephant or whatever, given that it was viewed through the relevant type of sensor system. Then when they feed in the black hole sensor data, they get pictures that look like the black holes we imagined. Even if we never told the model what a black hole looks like.
What does training mean?
I thought that the training means to adjust Neural Network until it learns to convert our input into expected output of "complete image".
But if thaining means to teach the model to produce expected "complete image", then how is it possible that "the output of the model was approximately the same" [for different training "complete image"s]?
The output images are approximately the same because the model is "looking" at training images at a lower level that we do. The talk says they chop the images up into small pieces. So the model never "sees" the full shapes that are in the full images. It only sees small local features. I guess it turns out that these smaller pieces are pretty generic in that they are common between images of black holes and everything else. The curve of an elephant trunk looks similar to the curve of an event horizon if you cut it out in a small enough piece.
Perhaps if they didnt do this step, then the model would be more sensitive to the images its trained on.
They don't make a habit of posting the shitty TEDx talks to the main channel, I'm guessing. (And there's plenty of those.) This is definitely high quality relative to most TEDx talks, so I understand why it was upgraded.
If Katie was a man do you think people would be going through git histories and their published papers trying to determine if she is being over-credited for her achievements?
Edit: I just checked Twitter, apparently there are thousands of idiots who believe this "850,000/900,000 lines written by Andrew, therefore he wrote the algorithm" narrative. It's amazing how willing people are to eat up a low-hanging narrative as long as it confirms their world-view. All it takes is a very crude understanding of how software development works to see through this narrative.
> Andrew Chael wrote 850k out of the 900k lines of code. He was also the leader of the project. Michael D. Johnson wrote 12k lines of code. Chanchikwan wrote 5k lines of code. The woman? Only wrote 2.4k lines of code.
It's a little bit unbelievable that the author of this comment (/u/dragonballcell) nailed all of these fine-grained details (red herrings, perhaps?) and yet glossed over an incredibly important and superficial/trivial detail: that Andrew Chael did not "write 850k LOC", he generated hundreds of thousands of lines of data and committed them to the repo. Needless to say, I think this whole drama is incredibly pointless.
You might as well credit the Linux operating system to only a single man, whose effort is certainly largely responsible, but for who also does not in any way represent the whole of effort.
It's the ship of Theseus all over again.
That said, if Katie was a man, her story would not be as groundbreaking in a social context, and thus she would not be as celebrated.
Can you link the HN article with the "Mohawk NASA dude". Searching on "Mohawk NASA" gave me a 1 point article that didn't get a single upvote.
Searching on "Bobak Ferdowsi" gives zero articles on HN, and I could not find any article where even comments were celebrating the achievements of Bobak Ferdowsi, and obviously no 832 point upvoted ones.
No, I must conclude that there is no articled named "Bobak Ferdowsi, the scientist behind the Curiosity rover", and definitively not one that got just as much celebration as this one.
Unsurprisingly there is now research indicating that female candidates are now twice as likely to be chosen as equally qualified men for tenure track positions in university science departments.  And I'm sure my computer science class was no exception in that the generally ~three females in the class had about 90% of the rest of the class willing to do any and everything they possible could to help them, mostly being happy to just be able to spend time around a woman interested in CS. I have an inside track there as one of those three is now my wife!
I don't understand how people can think it would be harder to achieve as a female in STEM in the current environment (and neither does my wife for that matter). You get jobs easier, you get tenure tracks easier, you have enormous support networks, and so on. 30 years ago I'd agree with you, but I think we've long since radically overcorrected and, as you would say, that somebody can't see this does genuinely astonish me.
 - https://www.washingtonpost.com/news/morning-mix/wp/2015/04/1...
I guess we'll just both be astonished. Sometimes two people look at the exact same world and see different things.
I did not say women do not face discrimination. They do. And, to varying degrees, everybody does. This is true even in the most homogeneous of societies. The region of birth based discrimination in China is far more vigorous than any form of discrimination we've had in many decades. What matters of course are the consequences of such discrimination. Cultures, interests, and aptitudes vary among any selection of individuals. Even what seem to be completely 'agnostic' selection criterion such as height will yield extreme differences in distributions . So the presumption of equal opportunity leading to equal results is nonsensical. "Bias" is a loaded word, and not completely equal is not the same thing as biased, or at least the connotation of such.
I think there are two salient issues here:
1. There is a severe publication bias both against negative results and results that are not 'meaningful.' Negative results are results that indicate a hypothesis is not true. This sounds reasonable but it isn't in practice as it leads to the scenario we are currently in where finding evidence of discrimination is generally publication worthy. Yet, and this study notwithstanding, finding a lack of discrimination is generally not publication worthy. I expect the replication crisis, which is hitting the social sciences particularly hard, is in part driven by this. People need to publish something, and it generally needs to be shocking. That leads to...
2. Many people's careers and livelihoods depend upon the presence of discrimination. At one time astrology was a science at least as reputable and scholarly as psychology is today. And it's quite likely that a good number people who studied the field for decades had some inclining, perhaps buried deep in the back of their mind, that it was a bunch of crap. But of course they would quickly snuff such wrongthink out simply because such a possibility was unacceptable. After all, what are you to do when you've dedicated your life to something and you come to no longer see it as relevant? You go from a well regarded expert, to a master of nothing perhaps past thee point of being able to reboot your direction in life. No, such possibilities cannot be accepted. This is not to say discrimination is no more real than astrology. It certainly is. But rather I emphasize only that when people's livelihood depends upon finding evidence of discrimination, they will find it - whether or not it exists. "Science advances one funeral at a time."
 - https://en.wikipedia.org/wiki/Height_and_intelligence
Ok I don't have the energy to keep this conversation going, the distance between us is too great.
http://www.pnas.org/content/111/28/10107 - This is the exact sort of study I was referencing. It only shows that there is a different in result, not opportunity. It further shows that as the baseline competency standard increases (up to labs being operated by Nobel Laureates) - so does the "bias". It proposes explanations for this being either self selection by women, or bias by men. It ignores the most likely explanation which is that though the pool is split about 50/50 by gender, competencies are not.
http://www.pnas.org/content/109/41/16474.full.pdf - I was familiar with this study, and it's a good example of the ongoing issues with social psychology toy study. For reference the replication rate in social psychology is now at around 25%. Put another way, if a social psychology study tells you something - you'd generally be vastly more well informed if you assumed the opposite, or at least assumed what was stated, was not true!
This study offers a demonstration in a number of ways this has occurred. One major issue is that there was no effort to manage a response bias, other than in broad characteristics (race/gender) of applicants. Corinne Moss-Raucin  personally mailed a number of faculty asking them to respond and rate a variety of potential students. One glance at her faculty page will tell you what she's actually doing. So who voluntarily opts into this? In total just around 30% of contacted faculty chose to. I think there is a 0% chance that this is not a biased sample.
The questions were also framed in a context that seems to imply a potential personal "affinity" for an individual. One important nuance here is that the students offered up for consideration were all low quality. The questions to demonstrate bias included:
- "How likely would you be to encourage the applicant to continue to focus on research if he/she was considering switching focus to teaching?"
- "Would you characterize the applicant as someone you want to get to know better?"
Do you think you'd try to keep low performing Jennifer in your office, even if she was looking into teaching instead? Would you like to get to know her better? I mean come on this is just absurd, and a reason that the social sciences and especially social psychology is imploding in on itself. It's like if the "biases" went in the opposite direction our researcher was ready to write up an article about unhealthy professional attitudes towards females and female independence.
 - https://www.skidmore.edu/psychology/faculty/moss-racusin.php
I feel the same way about the the studies that form the basis of the article you linked. You don't seem sceptical about those results.
I'm not going hunting for a meta-analysis that addresses this, which is really what would be ideal.
I think you are off by orders of magnitude in terms of how much influence a person's physical body has over their interests, choices, and likelihood of success. I can't relate to that, I can't argue with it, you might as well be telling me that that the sky is made of cheese.
This is why I don't see the point continuing the conversation. We'd first have to agree on what the sky is made of.
Key findings are covered on page 153. Various highlights:
- The findings on academic hiring suggest that many women fared well in the hiring process at Research I institutions, which contradicts some commonly held perceptions of research-intensive universities. If women applied for positions at RI institutions, they had a better chance of being interviewed and receiving offers than had male job candidates.
- The percentage of women who were interviewed for tenure-track or tenured positions was higher than the percentage of women who applied.
- For all disciplines the percentage of tenure-track women who received the first job offer was greater than the percentage in the interview pool.
- Female tenure-track and tenured faculty reported that they were more likely to have mentors than male faculty.
- Women were more likely than men to receive tenure when they came up for tenure review.
It's the same story everywhere. Women are more than embraced in science and tech. The problem is not about equality of opportunity, but about equality of result: in spite of the very favorable treatment of women, they remain underrepresented.
 - https://www.nap.edu/catalog/12062/gender-differences-at-crit...
I think this is already getting quite long, but one other thing I'd also add is that you can find relevant studies from Scandinavia as well. Norway is generally considered the most gender equal nation in the world. And they too went through a phase of trying to push women into various roles generally filled by men. What they found is that there was a small and roughly constant bump in participation in these fields, as opposed to the self increasing bump you might expect if gender itself produced a strong feedback mechanism. And as soon as the push lapsed, everything went back to "normal" with a great rapidity. I think the thing this really emphasizes is that you can try to push people in one direction or another, much as with some effort you can form a sponge into nearly any shape, yet what happens when you stop pushing that sponge? It just goes back to its normal form.
I'm full on with you about ensuring complete and equal opportunity for any and all women who want to focus on STEM or whatever else, to do so. But in hindsight I sometimes wonder if we go too far with "encouragement." Now going on quite a number of years after graduation, I work with computers. My wife works with people. She was majoring in sociology before I, like the good egalitarian I thought myself to be, persuaded her to swap to computer science. It was probably still for the best overall (as computer science yields skills beyond just tech) but I've always found the irony thought provoking.
I have no idea why people are doing this to Dr. Bouman, if it's gender related or not. Just stating my experiences.
Just so I'm fair, my GitHub does suck.
Some companies addressed it directly and some let it happen. It also was not every co-worker at every job. Just a select few personality types mainly.
It's not that they don't deserve their fair share of credit, to be clear. It's that they do not deserve the level of overwhelming credit the media intentionally tries to bestow upon them, to create an idol that sells / generates clicks.
You pretty well see it in every thread regarding those two people. The hype train tries to give them credit, whether the media or fans, and other people get annoyed by it and call it out because it's obviously ridiculous to so overly credit such vast accomplishments requiring thousands of contributors to a given individual.
On the other hand, looking at git histories is basically how the social parts of engineering (e.g., money and power) at a place like Google works, at its fundamental level.
This has persisted for a very, very long time. I still remember when people would comment things like, "I worked with so-and-so unorthodox former Google employee, and he didn't commit code."
There are a lot of Googlers on HN. There are a lot of people who work at places that culturally align themselves with how that company runs.
It probably has something to do with why some women feel underpaid or unwelcome at these places.
It definitely has something to do with people commenting things like, "So is this the case of the product manager taking credit..." The tension between the product manager who "didn't do anything" and the engineer who "did all the work" and how the "org" sees that and measures "performance" are all swimming in the back of HN people's heads when they snipe some random academic.
Settling the score in a way so reductive is extremely appealing. But at least in duels, the other person gets to fire back.
In my experience, people don't start looking into these things without some other suspicion. In a work setting, that would be things like impressions of poor productivity, claimed output not matching perceptions of competency, etc. But those involve a ton of data points, based on direct interactions with the person. In this case, the article gives us the following demographic data points:
- 29 years old
- Computer Science doctorate from MIT
- Assistant professor of computing and mathematical sciences at the California Institute of Technology
Which of those data points suggests that her work output should be questioned?
I think people let their own personal biases destroy their impartiality. Replace her name with Musk, algorithm with science/engineering, and 'image of a black-hole' with reusable rocket. The article would (and does) read like something posted by a sycophantic fanboy. It wouldn't be doing him any favors, and certainly isn't doing her any favors. However, I also do not think this article is representative of her in any way, shape, or form.
For instance it tries to frame things in the most narcissistic way possible. They found one image posted where the developer stated, "Watching in disbelief as the first image I ever made of a black hole was in the process of being reconstructed." So she made that image. Not a team, not the project of a coordinate global effort, no - she made that image. Even the image framing itself is indicative. It's a tiny out of focus image of a laptop and a giant in focus image of her with an artificial pose. The article itself continues with a similar narrative in all the eye-catching spots such as the headline and image captions: "Katie Bouman designed an algorithm that made the image possible" "Katie Bouman: The woman behind the first black hole image", and so on.
But as mentioned, I doubt this is indicative of the developer herself. She's probably just being used by the media. She's attractive, young, and has the right genitalia = stories that'll get a million clicks and shares = $$$. When you actually read the very small number of quotes from her, they seem much more realistic and in stark contrast to the media sensationalism:
- "No one of us could've done it alone. It came together because of lots of different people from many different backgrounds."
- "We're a melting pot of astronomers, physicists, mathematicians and engineers, and that's what it took to achieve something once thought impossible".
Also if she was a man her story and contribution wouldn't be as sensationalized as has been done.
Well, I take part of that back. He did have some personal pieces about "he's the guy that's a bully of the project" (when he took a personal hiatus from the project)
So yes, that happens a lot.
>to the exclusion of the algorithm designer and the primary software author?
How can you possibly infer that from a git history?
I mean hard to imagine such a large project being taken up, for the benefit of public being able to see a picture of the black hole.
Would this kind of multi telescope effort be capable of producing surface images of extra solar planets for example?
And yes, this is one way of representing the data. I'm not sure exactly what your question is though, as actually getting this data is really important to confirm a variety of theories and also to potentially open up new avenues for investigation. And this cost orders of magnitude less than Hubble, whose purpose was also to generate photographs, seeing as how they simply connected together existing radio telescopes.
The point was to demonstrate that this technique is feasible. Now they can use it to image all sorts of other stuff and learn lots more.
So a photograph.
Is it wrong to say this is a logical improvement, extension of radio interferometry?
Or gather data that will help us study blackholes?
Because the press is largely focusing on the picture and not telling much else detail.
And is Boumans contributions to do with the making of this image?
As I understand it, the notability of the project is that it found a novel way to process data from coordinated data collectors scattered around the earth into a single coherent data set (with more resolution than any single collector could gather).
Katie's paper on VLBI reconstruction: https://arxiv.org/abs/1512.01413
This is how I learned about the topic and I think it's well suited for computery folks, since it was published in CVPR.
Similarly the title suggests she worked alone on the project. Which seems exceedingly unlikely given the need for telescope time and computing time and the wide range of disciplines I imagine the project covers ... did she work alone. That must be almost unique in experimentalism nowadays?
So I'm certain those authors did their part, so maybe yes, I should have linked this as "Bouman et al." but I wouldn't expect this to be six equal contributions either.
That all being said, she's certainly standing on the shoulders of a pyramid of giants there.
Edit: to the people downvoting the parent, maybe explain? I didn't take this as a bad faith comment. It can be genuinely confusing to someone who doesn't know the ins and outs of academic attribution...
It's interesting considering how many modern scientific endeavors are dependent on new innovative algorithms, software, and computing techniques in both experimental and theoretical work then its frequently just hand-waved away as "technology."
I'm not saying such contributions (typically, though they can,) lay groundwork for an experiment or theory in another domain, but I am saying active CS involvement/expertise is typically critical to many scientific endeavors' success these days. If a project is interdiscipinary, there's probably a computer scientist on the team helping out.
I think society implicitly assumes that there has been a tone of people backing up a single individual towards their main achievements and that the individual is humble enough to know and to try - ever so slightly - to show appreciation.
From my perspective, if in order to accomplish your work, you need consult or active collaboration with a computer scientist and otherwise could not develop/test your theory or conduct your experiment, then they almost certainly should be cited as an author/collaborator.
If you utilize something OTS outside the project that just works for you and don't need a computer scientist, then whatever entity created that OTS IP isn't really an author/collaborator, but it's likely their work should be cited/referenced if it's part of the critical methodology (as part of disclosure and repeatability).
If your project used Microsoft Word to write up a report, it's not important to the underlying science you conducted. You could have substituted it with TeX, other Word processors, or pen/paper and it wouldn't change the outcome of the underlying theory/experiment you developed. If you used an Ansys package to perform analysis for some purpose, you should probably mention that out of rigor but Ansys isn't an author or collaborator.
If on the other hand you need someone to architect a solution to handle processing your massive dataset, needed someone to write custom code because nothing could do what you needed, needed a new algorithm because you had no clue how to approach the problem, or even needed someone to modify source code significantly to something that existed but couldn't do what you needed, then they are certainly an author/collaborator. If you took existing code/algorithm and made it more efficient in order to accomplish a task that would have taken too long otherwise, you're a contributor/collaborator and should be listed as an author.
This has been a huge issue in academic research but it's been getting a bit better and researchers are starting even more to acknowledge/credit computing professionals as crucial contributors and authors, as they rightfully should be.
That's one of the reasons I said "I agree with the point you have raised".
I also think you have once again, raised some good points. Hopefully, others will use similar structures when writing and publishing their research.
Somehow though my facebook feed is already littered with images saying she was single-handedly responsible and no one's talking about her.
To clarify: I don't doubt women can do science, just empirically, they don't get to do it as often (at this level) as men.
On a slightly serious note though, I wonder how much productivity is lost in the scientific community due to poorly written and documented code?
I've heard stories of 40 year old Fortran code written by long deceased professors that was written to crunch physics numbers or whatever, and when it's come time to modify or add to it, nobody can make head nor tail of it and they have to write it from scratch.
There's a reason why in the non-academic world we have coding standards and code review. Code isn't written in a bubble, other people will look at it and work on it.
That's not to belittle or criticise the work done in the slightest. Cleanliness of code is orthogonal to functionality. You can have beautifully written, clean and documented code that doesn't do what it's meant to, and likewise you can have a complete mess of code that performs some genius function perfectly.
It's a toss-up. On the one hand, there's a loss due to dirty code, but a gain by a smaller group of people being able to do multidisciplinary work. In my own case, I'm a physicist outside academia, and in addition to code, I also do electronics and a variety of other things.
When you're doing exploratory R&D, as I am, there are downsides to getting things done by domain specialists. First, you have to find people with quantitative skills, and they tend to be in the greatest demand due to scarcity. Second, you have to manage the politics of getting them assigned and engaged. Third, you have to manage the interface between specialties. It becomes a project management exercise. And then, the way that code and project files are structured, it may be possible to read isolated sections of code, but very hard for a non-expert to find their way around the myriad of files that tend to form a modern code base.
In my own case, I do what I can to write good code. I try to keep up to date on good practices, and so forth. Could we do better? Sure. The quest to improve my coding is how I accidentally bumped into HN in the first place.
As long as it's published, if somebody wants to reuse it, reimplementing from the paper is the hardest part.
Industry professionals are forced to take the approach of "I need to write this code to be as maintainable and flexible as possible" because they have no idea what the business is going to want next and generally have no set timeframe for how long they may have to maintain any particular project.
If people have good resources I could pass to students about standards for Python code, for instance, let me know.
Most people in my field (materials engineering) are not programmers either they are lucky if they've done one intro course 10 years ago (which was probably done in a language like Java or Visual Basic).
Even then what gets taught in an intro course at university is not the type of code that is written "on the job". I did two semesters of programming courses when I was at uni (as electives) my courses were taught in Java and focused on stuff like object oriented programming and memorizing stuff about "the waterfall model"
There is a pretty big gap between this and my first experience which was being sat down in front of some 30 year old Fortran code which had no objects, no classes etc.
The goto at least in my org when people are trying to understand scientific code - write their own algorithms etc is the 30 year old "Numerical Recipes" (https://en.wikipedia.org/wiki/Numerical_Recipes) textbook. The explanations in this textbook are best and simplest I have come across by far.
I know I personally referenced this book heavily when I was writing code in C to do Spline interpolation/smoothing. I am unaware of any other reference for a lot of algorithms/techniques than this book.
Only other thing I am aware of is the GNU GSL library which in my experience is harder to understand for beginners - even it's example code is "for loop based"
If I had to convert this code to R (which I do know) or python (which I've never written) I'd probably write it this loop based style as well it's what I know and what makes sense to do me and the people in my org I'd expect to be interacting with my code. (the "Engineers can write Fortran in any language meme" is a real issue).
Maybe someone should write a new textbook on "modern" way to solve these sorts of problems if such a thing exists I am unaware of it but would certainly be welcome.
I worked with an old Fortran codebase at one point and there were comments in the documentation (a scan of a typewritten via typewriter document) throughout about switching "cards" and "decks"... took me a moment to realize it was refering to punch cards (and I thought I was old) which also led to the program structure fragmented in several individual smaller sub programs (so card reader could handle it) that now is a trivial matter to handle. Maybe they were just ready for the SOA and microservices trend.
In academia, pressure is often on publishing and pulling funding in through grants and contracts. I've done a lot of rapid prototyping in academic research environments and while writing clean software is always on my mind, often, sitting down and refactoring to be more cleverly efficient or taking time to focus on structure, long term maintainability, etc. isn't a priority and refocuses needed cognitive load from the high level research goal the software needed to achieve to instead focusing on production quality software.
I'm not concerned if it takes O(2n) vs O(n) or O(n log n) vs. O(n) time if I know the target scale is small. I'm not concerned that I can cleverly avoid using an extra data structure (and reduce space complexity) if I can do this operation in place on an existing data structure using some reasonably complex algorithm. Chances are I might remove this functionality entirely tomorrow or some student may have to figure it out later on, and I don't want to implement or explain to the student the Boyer-Moore majority algorithm when a brute force O(n^2) time is just fine here and a lot easier to adjust/maintain for a passer by scientist/student.
I'm aware there's a lot of problems and maybe my abstraction hierarchies aren't the best, I could probably make something better with more time.
You have some high-level complex process you're trying to represent and translate in to a program (maybe a simulation, maybe a complex model or set models, etc.). You're not always concerned about if there's a better way to write it or make extensive use of all the features of whatever language you needed to work in (which you may or may not have experience with since you needed to work from existing codebases to start with since time is tight), you simply want to use whatever requires the least time and cognitive load to think about and produce results so you can keep your eyes on the target of what you're developing.
Later on, when prototypes work (or if you hit performance bottlenecks stopping progress), then and only then do you start refactoring and looking at performance optimization--targeting the biggest bottlenecks first.
If everything works, then you can focus on overall refactoring and optimization and turning your Frankenstein into a supermodel (if you have resources/money to do that with--good luck), but you typically need a functional proof of concept to even have a chance of securing funding for that step.
If there's no money in that effort moving forward and you decide "well, maybe someone can use this" so let's release it, that typically has to get approval through a technology transfer office who are always in arms about protecting potential IP so it ends up on some disks rotting away never to be seen or used again.
If you're permitted to release the IP, you begin wondering how the development quality will reflect on you and your group, especially for those who see it and have no context of the constraints you worked with to produce that miracle functional Frankenstein. It's ugly as sin, but it fulfilled the goal to deliver the core research results and did so as quickly as possible and cheaply as possible.
Huh, I wonder how accurate this is. All the code is beyond me in any case, I'm in no position to judge the relative value of any of it.
* achael 566 commits 850,275 ++ 131,044 --
* klbouman 90 commits 2,410 ++ 1,265 --
However, at least at the level of reading the commit messages, Katie's are pretty math heavy:
"fixed bug in the fake briggs weighting"
"starting to fix chirp problems with polrep"
"made it possible to do a min uv cut on closure phase when adding it a..."
While Andrew's lean frequently toward code maintenance:
"updated some docstrings in imager_utils"
"moved imgsum to plotting.summary_plots"
That said, Andrew and others seem to have pretty good insight too.
But what could possibly qualify you to say that "Andrew is definitely smart (smarter than an average HN user) and his code is very important"?
It’s disappointing to see this celebration of an amazing technical achievement devolve into a contentious meta-analysis inspired by the USA’s broken politics.
I don't think there is a "secret feminist agenda" as such, but news outlets do over-egg the situation to try and create "women heroes of science". The way it's done appears to be sexist in an attempt at, so-called, positive discrimination; rather than being equalist.
You seem to consider my analysis to be abjectly errant, I would appreciate hearing why?
There are no woman scientists, science has no gender.
The article is sexiest not people who are curious what Dr. Bouman actually did to be honored to mention in BBC article.
This is a good article on the concept: https://everydayfeminism.com/2013/09/dont-see-race/
[Edit:] Or this, as a complementary one: https://www.mcsweeneys.net/articles/i-dont-see-race
Bad for you.
Thanks God I grew up in a society where every person no matter of gender and age can do hard science.
Do people like Linus deserve less credit now that he isn't the leader on the commit scoreboard ?
Writing code is easy, figuring out complex algorithms is something very different, and does not require coding knowledge
one commit with 524,306 additions. adding a model.
If I read it right, she mentioned and praised her team as well.
In particle physics, these practices evolved over decades, when specific individuals tried to claim credits for discoveries in an unfair way(Nobel dream by Gary Taubes gives a beautiful account of this). Many particle physics collaborations now have detailed constitution and guidelines on what images/graphs they can show to the public. Someone who first made the first Higgs mass plot which shows a 5 sigma evidence of Higgs observation could not have leaked that plot on social media.
However this narrative is inspiring and perhaps motivate many young woman to take up careers in science and promote a more welcoming atmosphere for women in STEM.
Jeez why the downvotes? It's a legitimate question I had.
For the people questioning if she’s receiving unfair attention because of her gender they view you comment as an attack on the establishment ala: “How dare they exclude her!? Is it just because she’s a woman?” And downvote you. For the people arguing that her media coverage is being unfairly criticized because she’s a woman they view your question as an attack on the assertion that she deserves the attention so they downvote you.
In either case I think it’s interesting to ask why the proclaimed “woman behind the image” wasn’t there for the unvailing of the image.
She’s not an astronomer?
I am a postdoctoral fellow with the Event Horizon Telescope and will be an Assistant Professor in the CMS department at Caltech beginning in 2019.
I just wish they had used a camera from this century
I didn't realize this was public code.
It looks like one "achael" is the author of this, though.
Never have men at large objected to such bias when women have cited theories and concepts discovered by men to publish papers and win medals in the field of mathematics. That is something naysayers should ponder over.
All the documentaries, autobiographies, and famous books that peered deep into the lives of those inspirational people always give proportionate credit to those contributors of success either by these people or the authentic researchers. Katie was no less enthusiastic when it was her turn.
But these news agencies play with people's emotions, desires,aspirations, etc. These news agencies are capitalistic and optimize over consumerism. These news agencies are shameless whores to betray the principles of intellectual honesty and journalistic ethics in dissemination of facts by kowtowing to the appeasement of the disgruntled - who happen to be majority of their viewers.
But? We, the layman, are hapless to (1) gain knowledge from immediate sources (2) draw immediate conclusions from these sources. We can't be blamed for not putting efforts to gain complete picture or check the veracity of middlemen called the media. We run forward the self fulling prophecy originating from media. The trust was put in reputed media and that is why the media should care for its reputation. That trust was put in the media because it was touted as fourth pillar of democracy who can't commit hypocrisy in its main endeavors to expose the truth.
Whereas otherwise, the organization Katie Bouman is working, official representatives such as MIT blogs, and TED talks have all credited to the development of original algorithm, though when it was at nascent stage, to THE Katie Bouman, while at the same time to her team for handling in subsequent parts.
I salute her. With relevant degree and using her education in imaging black holes, she set the discourse of the main branch that others picked up. If the idea and algorithm germinated in her mind, she should get credit for it, simple. All she needed is few people to delegate implementation of her ideas or modify it for sustenance. If somebody furthered her ideas enough that it can be versioned as 2.0 or 3.0, then they get equal credit and status as her in final mission. She can patent her invention rightly for conjuring the initial stages of algorithm using all of her own cognitive capabilities.
But we should go only so far.
Even women aspirants will get disheartened and show recidivism by wrongly strengthening the bias that they are somehow less capable in attaining pinnacles of STEM, when they learn that the achievements of women in reality is not what media portrays. This is why I consider the twitter photo of her being placed aside Margaret Hamilton as the efforts are no way comparable ceteris paribus.
Moreover, if lack of minority role models is enough of a reason to discourage that aspiring minority from their passions, then it would be no less effective in discouragement of non-minority's passions when there is lack of attention and acknowledgement to non-majority's achievements. I mean how did Katie meander through her success to begin with, if there were no role models to her in the field she is working, in the first place?
People say that men had plenty of men in annals of history to look up to, but I'd contend that women aren't in anyway stopped to take inspiration and pique their curiosity in men's achievements just like men take inspiration from Marie Curie or Hedy Lamarr apart from the sea of men.
 I mean Prof Falcke.
There have been countless threads over the years where a man gets the credit for something a team has worked on and there is practically never any comments about this. For once a woman gets credit and this thread is full of people complaining that there was an entire team.
Yes, there was a team, but that doesn't matter. For once a woman is getting credit for the great work they've done and this should be applauded. Stories like this help bring more women into STEM fields. Anyone who is complaining about the lack of fairness in this is making themselves look ignorant by ignoring the last thousand years of scientific progress.
I think it's just that many people feel threatened or inadequate when they (naturally) compare themselves to these people. It's tempting to put them down so that we feel better about ourselves. I think most of us here on HN like to think that we're clever but when people like Katie Bouman get under the spotlight suddenly most of us realize that we're not such hot shots after all.
It's probably worse when it's a woman/child/minority/... because it gives us the convenient excuse of "this is probably a PR stunt" to dismiss them. It's lazy and it's intellectually dishonest but it's also very human unfortunately.
This leads to people getting rejected thinking it's part of some culture war, when the truth is that most people get rejected, some of those people would have been brilliant in the role they got rejected for and it's exactly the same brutal industry that it was in the 1980s.
For an example of this that involves a male, the media has been hyping the Ocean Cleanup project because it provides them with a great prodigy story, but people on HN have been rightly pushing back against its merits.
Many people are intelligent enough, but are not going to work hard enough.
She became interested in this problem in high school and stuck with it all the way through. She is a genius, and also the genius who did the work that let this happen.
I think in this particular case, I have no problem with it. One, she obviously had a big part in it. Maybe it is blow back because they feel a picture of the inside of a black hole isn't a big deal and people are making it into something big? In my opinion, it is. I remember middle school teachers almost scoffing at the idea of a picture of a black hole and yet, 25 years later, here we are. Regardless, she in her twenties has generated something that researchers spend a lifetime trying to find so kudos to her. I'm sure there is a certain gendered element to it in both cases (for and against) and it'd probably be naive to think there wasn't.
Even if this were a smaller aspect of what these researchers were aiming for, I'd love to see a documentary series on what various team members worked on (and in her case, discovered). An image generated by radio waves and she (maybe with others?) was able to construct an image out of that? That's impressive. Probably not, but I'd be curious if this kind of thing could be localized in a way that it could be the "sound to visual model" element of a system so that blind people could make out the world a bit more directly (obviously, there'd need to be a means for them to consume said model. All of this is way above me and my pay grade).
Damned if you do and damned if you don't.
Anyway, I do not want to sidetrack from this amazing achievement.
I kinda hate the recent trend to focus more on gender or race if somebody achieves anything. Look what Morgan Freeman said about racism . Is it really important that she is a woman? Do people think a lot of women can't achieve these things? And if 1 woman achieved this, all women are better than men? Do everybody just expect men to be smarter and if they do something outstanding it's ok, but when a woman does it, it's extraordinary... Why focus so much on this?
In discussions like this we should really focus on the person (and also the team behind her/him, I doubt she could do it alone without the team), not the gender or race or whatever.
I really hope this positive discrimination hype dies out, it doesn't help anybody. Let the best person for the job get the job.
There are regularly posts here linking to articles about misattribution of credit in science and technology, the problem with the "great man theory," laments about the role of social media in creating hype, and there are plenty of male figures discussed here who engender bitter discussions about how credit should be assigned. I honestly don't see any difference between this discussion and any other discussion. I seem to remember similar discussions emerging about discovery of the Meltdown and Spectre hardware vulnerabilities, and many other physics discoveries involving large teams of researchers, just to take a few examples.
The way credit is assigned in science is a significant moral crisis in my opinion (as it is in work in general; cf. rampant income inequality), and it really doesn't matter what the genders of the individuals involved are. Strangely enough, I think attention is being paid to this argument here because of her gender. It's one of these unfortunate circumstances where I think two competing ethical goals are kind of conflicting, one being the better representation of women and minorities in science, the other being lack of fair representation for all in credit.
The sheer toxicity of many of the comments is something I haven't seen for a long time. They really hate that a women is getting credit and that others aren't getting the same level of attention.
I wonder how those same people think about Elon Musk, Bill Gates, Steve Jobs etc. They had huge teams behind them as well.
For every extraordinarily recognized academic/professional person, there’s always going to be many times more people who are never publicly recognized for their achievements.
Maybe they fly under the radar, maybe they picked the wrong subject to focus on or industry for career, maybe their timing is bad, maybe there’s nothing wrong with them.
I’m proud of this (stranger to me) girl for accomplishing something so large at this age. Being about the same age, I’m not jealous - but it is one more reminder that somewhere along the line my record-player skipped a few years. My 20s disappeared too quickly, or maybe I was focused on the wrong things (work) instead of passion.
As in engineering, it's helpful to use proper terminology with people:
if using a gender is necessary for the narrative …
- under 13: girl
- 13 – 18: girl / teenager / young woman (depends on context; 16? – 25?)
- 18 or over: woman
My wife absolutely loathes being called "girl". It is used to reinforce the toxic idea that women are less mature and capable than men. Same feeling from other women that I've discussed this with.
18 or over = woman.
This is why people say there's sexism in tech bud. A male MIT PhD who was the public face of their project would not be subjected to nearly this much doubt and accusations of being deadweight by insecure 4chan weirdos combing through git logs.
As someone who's been interested in astronomy my entire life, and considered getting a degree in it but only ended up with a minor since I sensibly prioritized CS and wanted to graduate in four years, this is an awesome, amazing, really clever accomplishment. And yet many of the comments here are just so negative, either outright sexist, picking nits and trying to argue that it isn't a big breakthrough or anything, or going through code contributions line-by-line trying to establish that really someone else had more to do with it.
All I know is, she must be insanely intelligent and hard-working. What an awesome PhD project, and at MIT no less!, an institution that I have enormous respect for and that I somewhat identify with because my dad attended and I've been there for many events. I'm jealous. This would've been the exact kind of thing I'd have gone into in astronomy for (because of my background in programming) had I seriously pursued it, but I know I'm just not diligent enough to have seen it through. And being honest, I didn't apply myself well enough in undergrad to have gotten good enough grades to get into a good grad school.
It sucks that so many people jump into "push people down" mode instead of "life people up" mode in these kinds of situations rather, because this is an amazing scientific accomplishment that deserves celebrating. One of the PIs in one of the press conferences said that this was the most important accomplishment in astronomy since 2014 [when Rosetta landed a probe on a comet], and I tend to agree. It's not just about this one image, but about establishing the feasibility of a virtual planet-sized radio telescope that is capable of imaging lots more than just black holes. A lot more discoveries are likely to come out of this technique, and guess who came up with the algorithm to make sense of all those petabytes of data?
1) Look back at any physic journal for similar stories of experimental success (example gravitational waves), you won't find news stories of focus pieces on a single team member because it is a COLLABORATIVE effort. The only cases were single people get recognition is for theorists like Prof Higgs, Hawkings etc, but not for the individual experimentalists at the LHC or other astronomical projects.
2) The idea of focussing on a single team member is a technique for creating a clear narrative that readers can follow. The story would get less interest if you were told about the live and works of all of the team members.
It's not all hate :)
I think it's friggin awesome to see women in science. But even if Bouman was male I would still be cautious of attributing so much of an international collaboration to one person in the form of "Meet the _____ behind the first black hole image". That phrasing disregards too much hard work. I see no reason to offer Bouman special treatment in this regard at the expense of others solely because of her gender. That isn't equality.
1/ She led the team and was first author on the image reconstruction paper
2/ She gave a Ted talk on the topic a while back
3/ There's a brilliant photo of her initial reaction to the image that captures the excitement of scientific discovery circulating on the internet
The first headline on Google for me when searching "black hole image" is this very BBC article.
It was clearly written to grab the reader's attention, and it grabs it away from the actual phenomenon as well as all of the other brilliant minds who came together to make this happen.
She led the CS team. But very-long-baseline interferometry has been around for half a century. Heino Falcke proposed the experiment. Shep Doeleman led the entire EHT initiative. Scientists around the world brought techniques to the table.
I imagine even Bouman takes issue with being labeled "the scientist behind the first image of the black hole". She is surely aware and appreciative of the massive international effort involved.
I never made any comment as to her gender being a distraction, either?
Your post is very mean-spirited, ignorant of the views I just expressed, and honestly I don't like your implication that I am not familiar with the accomplishments of women in the past, especially in my field. Or that I have a problem with their gender. Ada Lovelace and Joan of Arc are two of my greatest inspirations! Cut the obvious virtue signalling.
My entire point is that gender has no bearing on this discussion. It's a discussion about misattributing a massive group effort to one individual. The point is that gender should not play a role in either direction, because that would be sexist. Everything you've extrapolated upon you just pulled out of the aether and not my mouth.
I don't think I've ever seen so many people suddenly desperately concerned that the Little People get a mention, and I'm at least part-way convinced that gender (and maybe youth? she's 29...) has a good deal to do with it (apparently the other thing that triggers the "harrumph, what about the team" crowd are stories about child prodigies, according to another thread).
Imagine you had just had your invention create a picture of a black hole, you wrote the paper where you were the first author describing this and then the press came knocking: would you be as gracious as she has been? Or would you feel like the fucking rockstar you would, in fact, be?
Dr. Bouman is a talented, enthusiastic and no doubt indispensable force on the larger team responsible for this achievement. Her role is as a co-lead for one small team which is responsible for one algorithm (out of four) used for imaging, as well as for an imagine verification algorithm (with Dr. Bouman's focus more on the latter). The larger imaging group (about 45 people by my rough count, led by Drs. Michael Johnson and Kazunori Akiyama) is itself one part of the analysis group, which has three other working groups, and then the analysis group is one of a half dozen larger groups in the EHT project which produced this result.
So it's not a case of the project lead being presented as the face of the project, which is par for the course in academia (and the outside world). It is a postdoc one level above the grad students who form the least-senior rung of the project, and many levels from the top suddenly being misleadingly presented as the key figure in a major result.
Imagine you worked on a small team of a couple of postdocs and a few grad students near the bottom of a hierarchy of teams involving hundreds of people, and then came in one day and your colleague and co-lead at the same level as you was suddenly presented as the face and key contributor for not only your small slice of things, not even the larger component to which the slice belongs, but the entire project?
You'd probably be pretty happy for them, but also confused as to why the many people with the actual role as overall group leaders or the project leaders aren't mentioned. One might also note how distant the general public is from the machinations of the academic world that no one is asking how a 20-something CS postdoc ended up leading a multinational astronomy project involving top faculty from top institutions? In terms of notability and improbability, that would probably be a bigger story than any image produced by the group!
Explaining her actual position and contribution is not in any way to detract from her contributions: only to clarify the record in the face of an onslaught of misleading media articles, which seemed to largely sourced (transitively) from a few misleading tweets, themselves triggered by a viral image.
On top of that, none of this is doing Dr. Bouman any favors. Although they are mostly silent, no one in the EHT project is confused about her role, and none of the other people in her faculty or almost anyone else who matters will be under any misconception despite the headlines. If anything, academia is even more picky than other fields when it comes to attribution, so any type of misplaced credit can be viewed very negatively and can attach itself to a person indefinitely. Now she hasn't invited this or propagated this story, so one should consider her a blameless victim here: but not everyone in a position to care will necessarily remember that subtlety.
One needs to just look at another large thread that generated controversy to get an idea of the growing trend .
Which means one has to ask themselves: Is HN cultivating an environment that's only going to get worse? And personally, I think the answer would be yes.
She definitely did something amazing and unfortunately it turned political because it fits the narrative that some people love to push currently.
I'm not a fan of the liberal agenda of positive//negative discrimination. I really believe that it is making everyone worse off, especially women that are being treated like little kids that need to be shown the correct path.
We just want to show that you if you accomplish something in tech you aren't going to be diminished or dismissed simply because you're a woman.
How do you know?
In spite of this, I'm lucky to have an amazing network of other women in my field, and thanks to the internet and cultural exchanges, we don't feel so alone these days.
I worked at one of the most progressive / women-friendly companies in San Francisco, and as of last year, only 34.3% of our technical roles were filled by women (company size ~1,000). I'm eager to see this year's numbers, and hope they've improved, but there's undoubtedly a lot of room to grow.
You may feel uncomfortable knowing the hiring process is weighted. But I feel uncomfortable being in an office that doesn't have other women. If I can change that WHILE at the same time meeting my hiring standards AND not consciously turning away a clearly better candidate then absolutely, I'm going to use positive discrimination.
Women make up 48 percent of the total work force, yet only 24 percent of STEM workers
15% of engineering professionals are women
Women make up less than 10% overall in computer science and engineering
Surely, a man can be a role model for a woman in science (and vice versa) - e.g., if you are from the same small ethnic minority as the role model.
However, male/female lifestyle, upbringing, interests, challenges, etc. are quite different in general, even in otherwise very homogeneous (western) societies.
Therefore, the role model having the same sex/gender is very important.
(Just my view - I don't have evidence or experience in this regard).
I call bunkum on that. TBH it seems both sexist and racist to say one can only be inspired by people of one's own characteristics (in science).
In this case the sex and race are irrelevant to Bouman's contribution AFAICT.
If you were talking about someone like Payne-Gaposchkin, then she overcame a deal of sexism, fair enough.
The whole she did it and had ovaries, omg, seems so condescending and unnecessary.
> The whole she did it and had ovaries, omg, seems so condescending and unnecessary.
it's not omg she had ovaries, it's omg she did it, knowing she's going to get shit on by people (e.g. these comments) instead of applauded for what her and her team did for science. That's how it's inspiring to me.
That may be your personal definition, but that is not the actual denotation of "role model". Some definitions I found are:
"a person whose behavior, example, or success is or can be emulated by others, especially by younger people. "
"a person whose behavior in a particular role is imitated by others"
There is no mention or qualifier of it needing to be someone who shares one's background
Gender, like it or not, shapes the life experience of an individual. Why would you not want to have a role model that had a similar life experience to your own?
Except, inevitably, when a woman expresses that desire, it gets called "toxic feminism", and the justification is, wait for it: the personal anecdotes and experiences of a male.
I am shocked that in 2019 there is still so little self-awareness around this.
By that logic, why shouldn't I as a man say "fuck women in STEM", because apparently we will never be able to communicate about anything meaningful anyway. People who make it clear they don't care about my opinion, why should I want them in my life?
I stated my reasons why I think focusing on gendered role models is misleading and harmful. Fine, you may disagree. But calling it toxic and "mansplaining" - that's not furthering discourse, and frankly, if that is your attitude, STEM may be better off without you anyway. After all, science is about keeping an open mind, among other things.
Ah yes, the classic: "I'm not toxic, you are!".
Where in my original comment did I say the male perspective, anecdotal as it may be in a given context, counts for nothing?
What I did say was that a singular, anecdotal male perspective was not appropriate as a justification for depicting a woman desiring a similarly-gendered role model was somehow indicative of "toxic feminism".
>What makes women unable to have male role models, but men able to have female role models?
No one said they couldn't, but you're depicting what was said as far more benign than it really was. You didn't ask an open-ended question about it, you specifically categorized said desire as "toxic feminism".
>if that is your attitude, STEM may be better off without you anyway. After all, science is about keeping an open mind, among other things.
Maybe one of the STEM fields will be able to develop a device that can accurately measure the immense amount of irony bundled up in that sentence.
Same merry-go-round as usual in these threads:
Subtly patronizing comment(mansplaining if you will), someone points out "hey that's kind of toxic", original commenter retreats to victimhood and "I'm not toxic, you are! No one has an open mind about this kind of thing etc...", and around we go.
If you want to pretend like STEM doesn't have a centuries-long history of fairly uneven footing for other genders and minorities, and accuse everyone of suddenly being close-minded and toxic, fine, but you're going to have a hard time cashing in the victim card when someone points out the ridiculousness of it.
I did NOT say desiring a female role model is toxic feminism. Feminists claiming women need female role models is toxic feminism. There is a difference.
And that is what feminists claim, because they need this claim to support their victim narrative of why fewer women are in STEM.
No point commenting your other stuff, because you completely misrepresented what I said.
And by the way, you directly called ME toxic, whereas I made a general comment about feminism.
Like yeah I got some male role models too, but fuck I want some representation! Someone who I can relate to! Someone who I know went through what I did!
Which is what, exactly? What is so fundamental different abotu your experience? The immeasurable pain of being a minority in a group of people?
1. Your tone is excessively combative for Hacker News. If you're put off by my saying that, ask yourself what a non-combative way to take that in and reflect on it would be. As a concrete example, you said "What makes women unable to have male role models, but men able to have female role models?", in a thread after the OP had already replied to you that she had/has men as role models. It implies either that you aren't listening, or that you're being antagonistic for the sake of being antagonistic. Neither is welcome here.
2. Using phrases like "toxic feminism" make you sound intellectually feeble. Try to be more specific and concrete about what you're addressing without using charged words like that. Unironically using the phrase "toxic feminism" instantly undermines any argument you might make. Again, if your point really is to learn from / share with others, find ways to communicate that don't put up walls.
3. If you're legitimately interested in finding out about why representation matters — and I sincerely hope you are — this is a good piece on it: https://medium.com/@uxdiogenes/just-a-brown-hand-313db35230c...
From the abstract:
" Consistent with the importance of exposure effects in career selection, women and disadvantaged youth are as underrepresented among high-impact inventors as they are among inventors as a whole. These findings suggest that there are many “lost Einsteins”—individuals who would have had highly impactful inventions had they been exposed to innovation in childhood—especially among women, minorities, and children from low-income families."
Well at least according to the paper "The Gender-Equality Paradox in Science, Technology, Engineering, and Mathematics Education
Gijsbert Stoet, David C. Geary"
This is the life of all men in competence hierachies. All of these things happend to me within the last half year and have been since I was a boy. Doesn't matter in the least, you couldn't pry me away from my interests with a crowbar.
This is your real problem: generally, girls want to be invited, boys just do.
The reason I speak up at all and will take all the abuse and downvoting thats sure to follow is it irks me so much.
We got into PCs and didn't matter one damn if they came from space aliens or out of the dumpster. We sat at them, we sat at them and we got scolded for it and told to go outside and called nerds. Our status was absolute dogshit and few women would associate willingly with computing in any form.
I am old enough to remember that at parties we mumbled "something with computers" and smiled apologetically hoping the topic would move on. Yes, many of us spent years, decades even, feeling slightly ashamed of our profession.
Now that the best and brightest of us nerds literally reshaped the world into a place where your personal handheld computer became a status symbol here come the women.
And you know, it would be okay, we are very tame men overall, except now you claim your collective absence from this topic is because we hurt you. No, we did not, you all just didn't like computers.
Please don't. HN threads are supposed to be for people conversing, not copy-pasting.
However, she may be a superstar, no pun intended, and so her getting almost all of the credit is completely warranted, but graduate student and post-doc are training roles, and a lot of the time the post-doc won't really make a name for themsleves until they establish their own lab, because it is unclear who is producing the ideas.
Advisors often range from consultants/consulents to managers. Not because they’re not smart, but because they seldom have months of uninterrupted time to focus on a problem intensely enough.
She started working on this problem in high school, and worked on it across multiple institutions. If the PI should have gotten the credit, she wouldn’t be first author.
There was, however, an extra article about his rocket engineer on HN. Mabye that is more like it.