> <i>While it’s easy to point to areas in computer science that might be over-researched (after all, Machine Learning conferences often get more papers than they can effectively review), there are still areas that are neglected with respect to their potential benefit.</i><p>It would be worth considering what drives people towards researching particular areas, even if it might seem kinda obvious. I.e. it might be tempting that it's visions of fame, loot & prizes, but I think to most people it is obvious on some level that they personally won't get any prominent position in these fields. I think it's partly a question of discoverability (say I'm a student, how do I learn about these topics, and that there are practical ways to work on them?), partly perceived prestige.<p>Also, getting into a <i>particular</i> PhD or similar currently means thinking years in advance -- I'm not talking learning/studying here, but connections, bureaucracy and applications, having paper "proofs" you know something etc. You notice some interesting area towards the end of your studies. It's too late to move even not that much from what you're doing (e.g. move from cognitive science to computational neuroscience) without wasting additional precious years. And that for entering not particularly rosy world of academia.<p>Myself (not academically nowadays), I see myself searching for a middle ground between overcrowded fields (where I will probably do relative "grunt work" at best) and fields that are so obscure as to be not viable. The fear of having no steady income is too real.