I have been using this app for about 6 months:
I have found it to be very, very beneficial in terms of my productivity and focus*. It's based on the pomodoro technique, which follows the basic pattern of working in 25 minute chunks. Whenever you do a work chunk, it can be automatically synced with google calendar. At the end of the day, it's easy to see how many work chunks you have done. Additionally, it can be set up so that you can easily create preset categories for the type of work you are doing. I have categories for different projects at work, hobbies, and exercise. Before I set the timer, I simply select which category I'm doing my work in, and then hit go. When I look at my google calendar, I have an honest breakdown of how I'm spending my productive time.
A word of warning - the app is a little rough around the edges. Every now and then (twice a week?), it will crash, and syncing with google calendar requires a little troubleshooting to start. This wouldn't be that difficult for anyone on LW, and once it's set up, syncing takes care of itself. Crashing is an insignificant issue, and at worst means you don't record the work done during that chunk.
*I now measure my productivity (or at least my work ethic) in how many chunks I have done in a day. I do substantially more now than when I first started, which could be partly because I have gotten into the habit of measuring myself. Still, I feel that I am actually working more. If anyone has any suggestions on how I could more fairly compare my productivity now vs. in the past, I'd be happy to give it a try.
On top of Cambridge/Boston, Research Triangle Park, DC, Bethesda, I'd also suggest Houston (I live here, so I suppose I could have some level of desire to justify that decision. I don't think it amounts to much, but fair warning). I think the city is largely underrated, because it has been a late bloomer and has had some issues in the past. However, it has the largest medical center in the world, renowned scientists and clinicians, and a quickly growing biotech sector. It also rates as one of the best cities in the country for young professionals, and the economic hardships of the recent past have largely skipped over Houston The economy is energy and health, which are basically the last things people stop spending money on.
It is cheap to live here. Incredibly so for a city this size. Sharing a full house with two roommates, I live 2 miles from where I work in the medical center, and 2 miles from downtown, for about $500 a month. This is also near the rail, which can get you either place pretty fast, but I prefer to bike. I know people living with roommates for $300/month, a little farther away from downtown, but still only a few miles away from the medical center. 9 months of the year, the weather is very pleasant. If you absolutely hate the heat, summer will suck, but it's not too hard to deal with.
I would second the notion of using grad school interviews as a way to travel around. Good grad schools will also pay for you to go, and even forward you some money if you need it to set up. They also often have people that will help you set up in the new city. In Houston, you can live very well on a graduate stipend. This is not true everywhere you go - NYU pays the same as my school, but life would be a lot harder there.
I don't even know if you are still paying attention to LW, but I just found this response. Yes, there is some semblance of a LW group in Houston, mostly focused around the Rice/Med center area, unsurprisingly. Right now, we're on temporary hiatus. There are three regular members, including myself. We have been going over ET Jayne's "The Logic of Science", but our collective workloads have gone up recently, and we were no longer able to put in the effort required to get something out of it. Hopefully we will get started back again in the summer when we all have more time.
It also looks like there will be a club focused on the singularity and surrounding issues at Rice soon. There are a dozen or so people interested in learning neural networks, computational biology/neuroscience, bayesian stat, etc. I plan to help with that a lot, and it's a good possibility that a Houston LW group might be tightly integrated with that group. One of our problems was lack of people, and this would be a good pool to draw from. I'll send you more information about it if you want.
I recall seeing opposite advice regarding extraverts and introverts on some TV show about dinner parties a long time ago. They suggested alternating them when arranging a table - the extroverts should be close enough to talk, but they have to talk around introverts. Introverts would have the opportunity to fluidly join conversations going on around them and have an easier time disengaging because someone else beside them will fill in the gap. Obviously, this would be miserable for an extreme introverts, but you can put those people in a corner.
Again, this is a half remembered piece of information, but it stuck with me because it seems to make sense.
One additional point - I think it has been made above, but it is good to emphasize: If you want to do any biological science at a high level, focus on hard subjects in undergrad. It's still difficult to learn organic chem or neuroanatomy in grad school, but it's much more difficult to add mathematical skills to your repertoire if you're doing time consuming wet lab work in grad school. Mathematics/Engineering/Physics will get you into better schools, and bio programs will be happy to have you. Every time I talked to a professor, they became much more interested me when they learned of my physics background.
My own history is majoring in physics, psychology, and philosophy. Neuroscience fell out of that for me. If you really want neuroscience, and a head start will really help with a career, then I would suggest a similar undergrad course - some hard field paired with psychology or neuroscience. Make friends with a professor in the softer field, and do lab work there. That will look good on the resume, and it will let you know if you actually like the day to day work. The softer field also helps with point 8 below, which I wish I knew earlier in college.
I am currently in my second year as a grad student, focusing in theoretical/computational neuroscience. Here are my observations on the matter:
1) Neuroscience is a hard science, but as in many things, there’s a continuum. The computational folk are at the hardest part, while the fMRI researchers are considered the softest. In general, the larger the structure you study, the softer it is. Exceptions exist, and within the field there are controversies as to how solid some of the theoretical frameworks are for even the most rigorous parts of the profession.
2) My own observations on average time spent on the job is about 50 hours a week, with some fairly extreme flexibility. Almost all my work is reading and programming, so most days I don’t have to I don’t even have to come in (I do, but that’s because I’m more productive in an office). People on the biology side of things do not have quite that flexibility. If you take human subjects, particularly hospital patients instead of college students, then your life will revolve around them.
3) My program has no requirement to TA, which is a huge time suck. I’m unsure how prevalent TAing is in neuroscience. My general impression is that the better your program, the less likely you are to have to do it. Similarly, if you go to a primarily graduate university, you will spend less time teaching. If you can find a program that lets you avoid that, then do so. Being a TA seems to be the leading cause of misery in grad school.
4) Currently, my pay is $29K/year as a grad student, and post doc pay tends to be in the $40Ks. Full professorship pay (at least in computational) is >80K. As to how likely a full professorship is, see my comments on job prospects. Obviously, location is going to make a difference. My stipend means I live very well in Houston. If I studied at NYU, which has a comparable stipend, I would effectively be much poorer.
5) If all you want is $40K/year, then you’ll be fine. You can be an eternal post-doc and manage that. That being said, I suspect you will revise your estimates up. It can be really hard to see your friends getting other professional degrees with the same or less work and raking in substantially more than you. It’s also hard knowing that you can quit at any time and make substantially more money within a year or two of education. As much as it sucks, knowing about opportunity costs can really dampen your enjoyment of life. You have to resist that, but it takes effort. To keep up with the engineering/M.D./business Joneses, you have to advance in the ranks.
6) High level job prospects in academia: Yeah, you know this isn’t great. Neuroscience fairs better than most sciences because there is so much low hanging fruit, but still, you’re facing an uphill battle. You have to be fully willing to move around until your 40s, possibly to other countries. Many signs point to a decrease in governmental funding and an increase in competition. Business is not picking up the slack. Tenure is going away. I’m aiming for this, and my heavy computational focus has better odds than most, but I have backup plans.
7) High level job prospects outside academia: Better than academia, but depends a lot on how fast the field progresses. You’ll reach your prime around 15 years from now. Neural prosthetics might (that’s a big might) come online by that time and be a big industry. Some types of neural enhancements will hit the market within 30 years, so it could be very profitable if you position yourself right. Integrated computer chips based on neural architectures are beginning to be mass-produced now, so knowledge of existing and highly functional architectures (brains) might make you very valuable. You will become very knowledgeable about people, decision making, and modeling if you do the theoretical branch, so I don’t see much difficulty spinning that into business and stock consulting, especially if grab an MBA. Medical equipment design is always lucrative. Medical consulting is a possibility. Also, the better the industry outside of academia, the easier academia gets (more funding/ less competition)
8) Neuroscience is cool. Don’t underestimate how nice it is to have people want to talk to you about your subject of study. Being an engineer, mathematician, physicist, etc. can really suck sometimes. Subjects like that are really difficult if not impossible to get people interested in. I mention neuroscience in passing, and a huge number of people are interested in what I do. You do have to put up with stupid comments (“What if like your brain is a particle and a wave and collapses the universe, man?”), but I’ve done physics. Socially, dealing with stupidity is easier than obscurity. Economics might be similar, but because of its relationship to politics, it breaches the mindkiller zone and I suspect it would be much less fun to talk about.
I'll answer any other specific questions you might have too.
[Edited for clarity]
I have been told that math would also help in English and other humanities as well. Statistical analyses of literature, as well as things like procedurally generated narratives, are beginning to take root. Literary/artistic criticism through the lens of neuroscience/cognitive psychology is also ready to take off, so many of the scientific fields listed above would be useful.
This is not to say that I think that a focus in humanities will lead to the greatest personal utility, but if you feel that you must do something in that vein for a career, then a background in hard science/math would be a good thing. It will make you quite unique and valuable, something that you will need if you enter that horribly over saturated market.
I'm not sure I understand the leap in logic there. If people have a reasonably comfortable minimum income regardless of what they do, how does that induce runaway speculation? Would venture capital firms not be as hesitant to hand out money to people who consistently failed to return on investment? Granted, VC firms could still get caught up in fads like in the dot-com bubble, but I don't foresee a minimum income really driving (very rich, well above the minimum income level) VCs into higher risk taking behavior.
I brush up against the field. I'm a grad student in computational neuroscience, and work with modeling how the brain's neural networks might be structured for certain tasks. Right now, I'm focusing on issues involving timing at the seconds/minutes level, as well as the neurological architecture involved with perceptual discrimination (Weber's law and the like, if anyone is interested). That may expand in the future, depending on how productive my current line of research is.
Yes, date typo. It is today. Sorry for any confusion.