I have conducted a few hundred of interviews for software engineers and DBAs in the last three years. I do want to see the passions of the candidates at the professions. It is a big plus in my eyes.
On the other hand, I am not impressed by "unequivocal statement". For seniors positions, I don't ask questions with a fixed answer. I prefer to listen to the candidates's opinions, to analyze the pros and cons of an approach, etc. I consider it low intelligence by any "unequivocal statement" for these questions.
Analytics is useful and important. It is utterly baloney, though, to use still pretty simplistic analytics at current stage to gauge a player for a team sport that requires players to move and have direct body contacts constantly, like basketball and soccer.
For example, defense. There are better analytics than several years ago when rebounds, blocks, and steals were the primary statistics. But basketball is a team sport. The contribution of a player in team defense includes how much space a player can cover, how much a player's presence can direct the offense's ball movement, how uncomfortable a player can make his opponent feel, both physically and psychologically, how good a player is at directing his/her teammates in a defense scheme, how good a player is at anticipating a potential issue and cover it, how much a player can make his/her teammates trust his/her defense so that the teammates can aggressively pressure the opponents, and so on. One has to really understand the game to evaluate a player's defense correctly and fully.
It is the same with offense. A simple example is that Duncan requires more help defense than Barkley. That creates more space for his teammates, thus more opportunities on higher percentage shots, which will translate to more wins. It also help gets teammates more involved, make teammates better, etc.
I really like Barkley, both as a player and as a person. But it is nonsense to compare him to Duncan in regard to the best PF in NBA history.
Enjoy it while you still can :). My children were the same way. Now they are making jokes at my pronunciations and grammars. [English is not my first language.]
> You're a different person than you were 5, 10, 20 years ago, and you'll experience the same book differently as a different reader
Besides, good literatures are of multi layers. One might miss some the deeper layers in the first few reads. There are also some subtle points the authors try to convey. They usually requires multi reads for most readers to get them.
My wife reads some classic novels many times. I think I am a very good reader. But she has much deeper understanding than I have, explaining to me what other meanings a plot conveys, why a sentence is constructed this way rather than another, although they sound the same for normal ears, etc. in literatures both in English and in our native languages. She always finds something new each time she re-read a classic.
My g/f is currently reading a lot of classics for the first time, and talking to her has reminded me of how little I have left of some of them after a decade or two (or three...) A good book is worth coming back to a few times at least, over the course of a long lifetime.
Location, location, location, to borrow it from real estate market. In places like Bay Area, there is a surplus of excellent talents competing for academic positions. If one can't get one in Standard, Cal, UCDavis, USF, etc, they will fight for the positions in the community colleges. So it is not uncommon to meet excellent professors in community colleges in such places. I know a few who taught in community colleges in Bay Area and eventually got positions in 4-year universities in Midwest. I bet they wouldn't hesitate to come back if they can get a position here.
It could be quite a different story in other places. I lived in a small town in the Midwest before. It has a big university, which one might think would provide some good staffs to the local community colleges. The reality was it was pretty bad. The only students who were interested in learning were middle-aged, who took the classes more for leisure. The teachers were aloof, most of whom were not qualified in teaching in a college, IMHO.
My personal experience makes me think that being minority might be one of the important factors, if not the most important one. Minorities are regularly ignored and interrupted in meetings while the majorities, unless explicitly reminded, don't feel it.
I don't mean the majorities do it intentionally. Some of them, when reminded they are interrupting, apologize sincerely. It could be deep in human's conscience.
Biology is enormous, as the author pointed out. Our understanding in biology at present might be less than 1% than the whole knowledge. For most biological research, it doesn't require any advanced knowledge in math beyond basic statistics.
For example, many labs have been studying an important gene and how other genes are functionally related to it for more than 10 years. The research involved are simply tedious, but indispensable, biological experiments. It is a waste of time to study math for this work, because it doesn't apply, except some basic statistics on data analysis.
Disclaimer: I have advanced degrees in both biology and computer science, and has multiple years of biomedical research experience. I have met exceptionally smart people working in both biology, CS, math, and physics. Yes, these smart biologists don't understand advanced topics in math and physics. I believe, however, if they had studied math or physics, they would have been excellent mathematicians or physicists.
IANAB. From what I understand, DNA research seems to have lots of still low hanging fruits for simple mathematical models to achieve big breakthroughs. Yamanaka won a nobel price for cell reprogramming that simply came from neglecting the previous brute force method to find the correct molecule combination that lead to many years of Biologists trying out combination after combination. Instead he basically deduced it through simple modelling and applying the scientific method. A quick google has brought the following thesis, which bases some more modelling on Yamanaka in order to refine stem cell reprogramming [1]. From my point of view, the really outstanding work in biology currently rather comes from outsiders that break out of the usual methods of biologists, such as the applied mathematician Erez Lieberman Aiden who showed how genome folding works and actually has an important function (activating / deactivating regions in order to program cell functions), purely through mathematical modelling of the signals we can get out of current instruments and throwing HPC at it. I'm pretty sure the field would benefit greatly from more cross pollination from other fields.
The study of biology is further complicated by the large number of confounding factors that muddle experimental results. Because of this, it is hard to know exactly when it is appropriate to bring in mathematics. Without a proper understanding of all the variables, math can only get you so far.
Another way to consider this is that biologists have not pushed back hard enough to mathematicians in the sense of asking for some tools which would allow for just slurping up a vast amount of unstructured, unprocessed data and getting something out of it.
It is certainly true that mathematical modeling as it is done now currently will indeed only get you so far.
But spirit of math in conjunction with physics has been to create tools that allow leaps and bounds. If we want to follow that spirit, it seems appropriate to ask for tools to help with messy things that now can't easily be dealt with. It may not be possible but it seems worthwhile to go all the way to the brick wall and pound on it.
Unfortunately biologist have been taken for a ride many times by people selling mathematical snake-oil. For example, the whole field of DNA microarrays [1] turned out to be an illusion woven out of applying complex statistical tools to "vast amount of unstructured, unprocessed data". There really is no way you can gain real understanding from poorly designed and unrepeatable experiments by apply obscure mathematical tools.
There are some other points to consider. First, the dataset sizes for most biomedical research are very small. Most advanced statistical methods don't apply. Due to curiosity, I took some advanced stat courses and tried to apply the methods to our lab's data. It didn't provide any significant improvement compared to basic ones, like linear regression, logistic regression, etc.
Second, biomedical research is highly collaborative these days. For some research that generate a large amount of data, either the researchers themselves understand statistics very well, or they collaborate with statisticians very closely. There is a field called biostatistics. Most biostatistics professors are either math or stat major, and many of them are adjoint professors in biomedical departments.
Biomedical research is really tedious and time-consuming. The professors I knew when I was doing biomedical research worked more than 60 hours a day, and they wish they had more time. One young woman professor came to the lab at 8am, left at 6pm, spent some time with her 4 children, and came back to lab at 9pm again, and worked until midnight, on every weekday. She brought her children to the lab on Saturday, and worked the whole day. IMHO, it is better for her to focus all her energy on the biomedical part, which she is best at, and collaborate with statisticians.
This is a very important point I think - only in very rare cases have I found my research actively improved by having a more sophisticated method available, and most projects have a statistician as a collaborator already. If not, they're readily available. It benefits a biologist to know what the statistician is talking about, and not just treating the analysis as a black box, but there's a reason we have subject matter experts. Sometimes, someone saying "Make sure to use robust variance" is enough information.
It depends on what research they're doing. It's also quite easy to be led astray and produce poor work by trying to throw the newest, shiniest thing at something when a much more basic technique will do.
For example, for much of the work I do, you could get away with never using anything more sophisticated than ANOVA.
I take the opposite stance - if biologists knew about advanced statistical methods they might be tempted to use them.
The general rule in biology is if you need to use statistics you did the wrong experiment. The reason for this rule is it is all too easy to use clever statistical methods to solve a flawed experimental design.
It should be noted that "Biology" also encompasses fields where you are limited to uncontrolled observational experiments, which often necessitate more advanced methods.
I agree that more advanced statistical methods would be useful. A surprising number of scientists have poor knowledge about statistics whilst being dependent on it to prove their research.
How do you prove that a medicine is safe and effective if not through large scale studies, which you then use statistics to show whether your hypotesus was correct or not?
I would argue it is extremely simplistic to compare languages to unit systems side by side.
In languages, it is both a way of communication and also a way of thinking, to say the least. Mandarin Chinese is my native language. My English is just ok, but it already gives me a new way of thinking, much more than a different perspective.
Also, there are many words and phrases in one language that you can't find a matched translation in another language, even between languages that are closely related, like English and German.
Different unit systems, on the other hand, can be converted to and from each other without losing anything. There could be some affinity attached to a system one grew up with. It is incomparable to languages, though.
Considering there has been an increasing larger percentage of the population going to college, the statistics might somehow get skewed by the population between age 18~23.
If one is in college, it is natural to assume that she or he is not fully employed, has low income, mostly not married, etc.
On the other hand, I am not impressed by "unequivocal statement". For seniors positions, I don't ask questions with a fixed answer. I prefer to listen to the candidates's opinions, to analyze the pros and cons of an approach, etc. I consider it low intelligence by any "unequivocal statement" for these questions.