How I got CS PhD interviews from 6 schools without any publications
Today, I would like to write a post about how to maximize chances of getting interviews and in turn, admissions from PhD programs without a related publication under my belt, an undergrad from a Ivy League or any prestious school, and an outstanding GPA.
If you have at least one of them, don’t even bother reading this post as it is catered to the audience who feel they do not have enough qualifications to confidently apply for.
To start off with giving a bit of my background, I transitioned from business into Computer Science(CS) which I have finished in two years, and went on to grad school studying Masters in Telecommunications.
Compared with a typical candidate of CS PhD applicants, I was not at all in an advantageous position. After self-assessing my candidacy, I realized I had to come up with a damn good strategy in order to get my feet into the door. Otherwise I would miserably end up with exactly what I experienced through the my Masters application process.
Researching schools and advisors for your PhD program is an essential part of your application process. In this regard, targeting schools considering both feasibility and research fit and finding advisors that could make you thrive were the key ideas of my strategy.
After opening the results, it turned out to work pretty well and I was fortunate to have received 6 interviews out of 11 schools I applied for, which were UC Santa Cruz, Michigan State University, Virginia Tech, University of Maryland-College Park, University of Florida, and Oregon University.
Of course, strong recommendation letters from research advisors and a research statement clearly stating the motivations for pursuing a PhD and the research areas of interest based off of months of thorough contemplation would have played critical roles.
However, I would like to attribute my success more on the my two key ideas, which led to successful application results. Although this strategy and observation might be applicable only to my discipline(CS) or engineering, I hope it could be helpful for audience who are applying for PhDs in other disciplines as well.
Choosing the right schools
Although there would be many criteria for choosing schools, but one thing for sure is that we care about overall reputation of the school. Obviously, everyone wants to get admitted to prestigious schools in their field. However, we must be down-to-earth and realistic when it comes to applications. The more popular it is, the more difficult it is to get in.
This was the bitter lesson I had learned when I was applying for my Masters. I only received one offer among 10 programs and that 1 program was even not the program I originally applied for, but the one that I have been transferred to. My application strategy was too idealistic to be considered realistic as I remember myself applying almost to top 10 schools ordered from top to the bottom. However your desire to get into good schools are strong, there is of no use as long as you can’t get admitted to it.
The gist of this strategy is to finding the schools that are low-ranked in general, but relatively high-ranked in your research and sub-research area.
Below shows an example of how I viewed school rankings in 3 dimensions(general, research area, and sub-research area) to find the right schools that would favor my chances of getting accepted.
General Area of Study
These rankings will be based on CS schools since I was applying for a PhD in CS. These rankings were based on CSrankings, as of 3/19/2021).
Computer Science
- UMD: 10
- GIT: 10
- Ohio State: 34
- UCSC: 43
- BU: 43
- JHU: 45
- VT: 48
- Oregon State: 48
- UCF: 52
- Rochester: 55
- MSU: 60
Electrical Engineering
- UMD: 14
Research Area — AI.
What is interesting here is that the general CS and specific research area rankings differs by quite a lot. This shows that there are many schools that the general perception is undervalued, but their reputations in AI research are much highly regarded than their face-values.
- UMD: 7 (+3)
- GIT: 8 (+2)
- JHU: 23 (+22)
- Oregon State: 35 (+13)
- BU: 36 (+7)
- UCF: 42 (+10)
- MSU: 42 (+18)
- UCSC: 43 (+0)
- Ohio State: 46 (-12)
- VT: 48 (+0)
Sub-Research Area — Computer vision
If you dig deeper into your sub-area, computer vision in my case, it might give you more interesting results as the variability of the rankings more increases than you would have expected. While researching those schools, I was surprised at the fact that many schools have a trend that the more granularity pertaining to your interest, the higher their rankings become.
- UMD: 5 (+5)
- UCF: 12 (+40)
- GIT: 13 (-3)
- BU: 16 (+27)
- Oregon State: 18 (+30)
- MSU: 22 (+38)
- JHU: 23 (+22)
- VT: 48 (+0)
- Ohio State: 64 (-30)
- UCSC: 67 (-24)
If you are applying for a BS or MS program, I would definitely recommend schools with higher rankings in general if you believe in yourself that your candidacy is good enough. However, Ph.D is a completely different game. Practically, you need to get into a school which excels at your very specific research area because that’s what you are gonna be doing in-depth and that’s why you’re going to a Ph.D program to become an expert in that area. Nothing else should matter to you.
I would like to call these schools ‘blue ocean schools’ in that these rather undervalued universities are growing at a noticeable rate, especially in the research field you are interested in. Additionally, they are relatively easy to get admitted as less students are likely to apply and compete for. I believe it is truly a nice strategy to focus on blue ocean schools rather than your so-called dreams schools from a realistic perspective.
What I learned from the process of conducting school research was that general CS rankings are not the only criteria that you should be holding onto as the concept of rankings are highly variable depending on your needs and interests. Even the rankings based on your sub-research field would not fully reflect how good the research environment is like.
To sum up, if you are not that kind of super-smart guy that could aim at getting admissions from multiple top 10 schools, be mindful of the 4 takeaways below:
- Rankings are quite subjective and variable depending on what criteria you set with the exception of top 10 schools which are superb in nearly every aspect. If you are targeting schools roughly based on rankings, broaden the range of your applications applying from high-to-low ranking universities.
- If rankings are still important to you, it would be practical to research the rankings that truly reflect the university’s academic achievement in the specific field that you are willing to conduct research. I personally don’t think general rankings means much in PhD programs.
- Notice the case of University of Central Florida and Oregon State University. Although their general rankings were low, they were renowned in their specific research field. I was able to find so many distinguished professors from those universities. Finding schools with these characteristics will increase your chances of getting admitted and finding an advisor that really does well in your research interest at the same time.
- Don’t miss out researching universities with low rankings even in your research area. For example, UC Santa Cruz ranked 67th in computer vision, but when I looked into other factors, it was a very nice fit with me.
Now that you might as well have found some schools based on rankings, you should find an advisor who is guiding a decent amount of students and either looking for or open to hiring new students in roughly selected schools to finalize it feels just right for you to apply for. Due to time constraints, I will continue writing about the second idea in the next post. Stay tuned!
Thanks for reading this post and if you have any questions regarding the Ph.D application process: feel free to email me at aspiringtechsavvy@gmail.com or leave a comment below.
References
[2] https://premium.usnews.com/best-graduate-schools/top-science-schools/computer-science-rankings
