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With hindsight I would suggest the following: (01) Most PhD Research is unremarkable. Do some research in your field, list all PhD theses over the last 30 year

by yannis 17y ago

With hindsight I would suggest the following: (01) Most PhD Research is unremarkable. Do some research in your field, list all PhD theses over the last 30 years and give them a score. How many made a mark on the field? My guess possibly 1/1000. You can do the same with journal papers, possibly 1/3000. (02) Citations is not a measure of success of a research paper/topic. Is this research applied today? Is it useful? (03) The Dons in the UK had one measure of success of a PhD. It had to advance the field and be original. Most Science advances this way. There is one major breakthrough and hundreds of small steps to advance it forward. (this would probably fall within your second group). (04) Do a social graph on 3-5 topics. Who is working where? If you want to land in say one of the big Research Institutions, make sure that there is on-going research in this area by that Institution. (05) Decide who you want to get married to. Your thesis supervisor would be the most important person for you over the next few years. Is he interested in the research you are interested in? My late professor used to advise that your PhD is you license to carry out research and not your major work. If I had to do it all over again, I would do the social graph and go from there. I would also try and get into an area that scales rather than purple cows or black swans. At the moment anything with a prefix bio is still virgin territory as far as I am concerned. Perhaps if you could let us know your field, people might be able to give you some ideas. You could also try reposting the query as If you had three-five years to do research in Computing/field, what would you choose as a topic and why?

eob 17y ago
Thanks yannis, these are some great comments. With regard to the social graph one, I've heard advice akin to this from several people: that the person is more important than the topic when it comes to advisors. When you look at the PhD as a license to carry out research, rather than a major work itself, this makes sense. But it is sure hard to force yourself to adopt that attitude when you're in the middle of it. From the student perspective, it is tempting to make the argument, "This professor doesn't fit my personality, but I really want to work on this particular topic." Regarding scalability, what you say makes sense. Indeed, I am a bit jealous of people with a bio- prefix; they are in the same spot computer scientists were in a few decades ago. As far as my personal background, I am a Computer Science guy with a background in the systems community (semantic web, databases, web architecture) who is interested in getting more into machine learning. I'd love any advice on where you think the exciting momentum is in that area. EDIT: Re-worded for clarity.
- yannis 17y ago
  I think there is both large scope to do research as well as exciting momentum in Large-Scale Machine Learning. As data in many domains arrives faster than we are able to learn from it, we need to switch from the traditional "one-shot" machine learning approach to systems that are able to mine continuous, high-volume, open-ended data streams as they arrive. Establishing the theoretical basis of such an approach, is still virgin territory.

by yannis 17y ago

eob 17y ago

Thanks yannis, these are some great comments. With regard to the social graph one, I've heard advice akin to this from several people: that the person is more important than the topic when it comes to advisors. When you look at the PhD as a license to carry out research, rather than a major work itself, this makes sense. But it is sure hard to force yourself to adopt that attitude when you're in the middle of it. From the student perspective, it is tempting to make the argument, "This professor doesn't fit my personality, but I really want to work on this particular topic." Regarding scalability, what you say makes sense. Indeed, I am a bit jealous of people with a bio- prefix; they are in the same spot computer scientists were in a few decades ago. As far as my personal background, I am a Computer Science guy with a background in the systems community (semantic web, databases, web architecture) who is interested in getting more into machine learning. I'd love any advice on where you think the exciting momentum is in that area. EDIT: Re-worded for clarity.

yannis 17y ago
I think there is both large scope to do research as well as exciting momentum in Large-Scale Machine Learning. As data in many domains arrives faster than we are able to learn from it, we need to switch from the traditional "one-shot" machine learning approach to systems that are able to mine continuous, high-volume, open-ended data streams as they arrive. Establishing the theoretical basis of such an approach, is still virgin territory.