3. Read blogs like "The Morning Paper" (https://blog.acolyer.org) but skip fields that are outside your scope. You don't have time to follow more than one or two (or three) major fields.
4. Use Google Scholar to find the most cited papers, or to find papers that build on papers you think are good.
5. Keep an eye out for the conferences where these papers were presented. Then read the other papers that were also presented.
6. When you come across an amazing paper, read other papers by the same authors or supervisors.
7. If you're lucky you might also find good "survey" papers that cover and reference the state of the art.
8. Lecture notes from Stanford or MIT or another university can also be a great way to get a big picture of the evolution of techniques for a given data structure or problem. For example, these lecture notes are just brilliant for getting started with stuff around memory hierarchies: https://www.eidos.ic.i.u-tokyo.ac.jp/~tau/lecture/parallel_d...
These are a few tricks that I find useful. What else?
Yeah similarly “review” “survey” in google scholar will work. Identify major authors (they keep cropping up). Find the big textbooks. See who those kinda people cite, follow that trail.
Start with the foundational paper(s) on the topic. Then use google scholar or your bibliography tool of choice to see who's cited it. It takes a little work but just burn down the list reading abstracts as well as noting how heavily each of these papers has in turn been cited. Those are likely the most influential derived works. It's common for grad students to do survey papers as well, so keep an eye out for those as they often give you a great roadmap to the current state of things.
Also pay attention to authors. If someone has done an influential paper on a topic, it's likely there will be additional work or tech reports in that area on their homepage or with their research group.
To find conferences/venues just note where the more recent papers are published.
1. Add "abstract" to your search query to surface papers.
2. Search for "... reading list". For example, Heidi Howard maintains https://github.com/heidihoward/distributed-consensus-reading...
3. Read blogs like "The Morning Paper" (https://blog.acolyer.org) but skip fields that are outside your scope. You don't have time to follow more than one or two (or three) major fields.
4. Use Google Scholar to find the most cited papers, or to find papers that build on papers you think are good.
5. Keep an eye out for the conferences where these papers were presented. Then read the other papers that were also presented.
6. When you come across an amazing paper, read other papers by the same authors or supervisors.
7. If you're lucky you might also find good "survey" papers that cover and reference the state of the art.
8. Lecture notes from Stanford or MIT or another university can also be a great way to get a big picture of the evolution of techniques for a given data structure or problem. For example, these lecture notes are just brilliant for getting started with stuff around memory hierarchies: https://www.eidos.ic.i.u-tokyo.ac.jp/~tau/lecture/parallel_d...
These are a few tricks that I find useful. What else?