lessons in academia from the summer

i learnt a lot about what it means to be in academia from my professor. it was an eye opening experience for me because nobody in any of my immediate family has any academia experience.

the age of the professor rotting away inside a messy laboratory looking at things under a microscope for 20 years is over. professors in today’s world run labs with large scores of graduate students and undergrads. thus, for a professor, it’s no longer about you yourself being great at research, but rather about you knowing how to lead everyone else to do research and knowing what the next thing to research is.

i guess the surprising part in working in ben’s lab is realizing how much of his work is just replying to emails, trying to keep up with the current research and guiding his grad students on what to do next. bear in mind also though that computational bio is a very collaborative and fast moving field. the purpose of computational bio isn’t really to find some universal laws that hold true in any situation but it is more about generating a model, refining it and applying it to some real world data. sure, the right models/answers are the ones that are backed up by biological data but there’s so many exceptions in bio that you could say that there isn’t a really wrong answer.

most of what I will list below are paraphrased from the group retreat where ben talked a lot about the nature of academia. a good professor in any fast moving computational field like compbio has to be acutely aware of the hype curve, because the acceptance of a paper into a journal highly depends on whether the topic is hot or not. obviously, it’s not the best way to produce research, to only research on what everyone else is too. but i think it’s somewhat necessary because these hype curves depend on basically what technology is available.

i also ended up learning a lot about the conceptual framework needed for data analysis this summer just by sitting in weekly meetings where my professor critiqued the methodologies and presentation styles of the graduate students.

what doing any type of investigative work, or research, two things are critically important. motivation and the objective function in the algorithm.
in cs 181, ben always prefaced his lectures with the motivation of the algorithm that he was going to teach. the motivation is important because it is the thing that you have to keep thinking about when you think of a new algorithm, or a new approach. this idea is the same to having the content of your essay be directly answering your thesis. off topic doesn’t cut it in technical research.

the other thing is the importance of showing the objective function you’re trying to optimize in your algorithm. because papers can be very long, and derivations can be extremely confusing, you need to be able to clearly explain the objective function in your paper so that people can decide whether it is worth their time to try to understand it.

one of the best things about working with ben is that he is so smart that he can really quickly grasp almost any algorithm. the best part of listening to him talk is when he says “somehow the insight of this is that ….” somehow in those words, he gives you a very clear picture of what’s going on. not only is he intelligent but also has superb communication skills.

working in computational biology also showed me how much cross-referencing there is between different fields. obviously, there is stronger relation between bio and chem, physics and math, but computation is like the new thing which every field is trying to get to

the links below are just sort of how the different fields are related to teach other, in terms of citations and paradigms respectively


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