Finding Your Dream Master’s Program in AI
A values-based approach to program choice and networking.
Last year I applied and was accepted to 12 graduate programs in Artificial Intelligence, including the Korea Advanced Institute of Science and Technology (KAIST), Columbia, and University College London (UCL).
While researching best practices for applying to graduate programs I came across resources that advocated for selecting your program based solely on how a university looks on paper. Though a good indicator of a school’s overall standing, this method does not take into account many factors including what type of program you want to apply to.
My remedy for this? Values-based decision-making and active networking.
In this article, I will provide an overview of how I found my dream AI graduate program, and subsequently networked with their faculty to secure a research position before the admissions cycle even began. By following the process outlined here, I am sure that you, too, can find your dream AI graduate program.
Values-based Program Choice
Values-based decision-making is useful for streamlining any decision-making process. Deciding on which graduate programs to apply to can help you wade through the sea of programs quickly and efficiently.
Before you start looking for programs, you first need to identify your values as related to pursuing a Master’s degree. Determining my values was instrumental in helping me find the perfect program that aligned with my personal and professional goals.
Even though everyone’s values are different, some are more prevalent than others. These include values such as financial stability, time commitment, and social life among others. If you are not sure where to start, I would recommend reading this article as a starting point.
I identified my values as; career mobility, personal interest, education, motivation, location, and advancement opportunity. While all were consequential in my final decisions, three stood out to me as being the most important: motivation, education, and location.
Motivation
If you’re reading this, then chances are that you have already found your why and decided that grad school is for you. Even still, before you find a master’s program you should have identified your motivation in going to graduate school.
Are you planning to do a PhD? Do you want a higher starting salary? A better job? Just more time in the classroom? There is no such thing as a “good” or “bad” motivation for wanting to go to grad school, but the lack of one is something that you should be wary of.
My motivation in going to graduate school is to bolster my knowledge of AI and to learn more about how it can be applied in the fight against climate change.
Education
AI graduate programs generally fall into three main categories: MSci, MSc, and MRes. Though all three will teach you similar concepts related to AI, they will also teach you different ways to utilize that knowledge. The table below shows a high-level breakdown of these programs.
One important thing to note is the difference between an MSc and MSci degree. Sometimes called an “integrated Master’s,” an MSci is an extended undergraduate degree, whereas an MSc is a standalone Master’s program.
In the end, I decided that I would seek an MSc program as this would give me a good mix of experience in both research and applied AI.
Location
By now you already know what you want to study, but you should now think about where you want to study. Do you want to attend your alma mater? An entirely new university abroad?
Your answer to these questions depends entirely on how geographically mobile you are. If you are unable to move away from your current location, either due to personal, financial, or familial reasons, then you should limit your search to local programs. Alternatively, if you are geographically mobile, then you can expand your search to programs elsewhere in your home country, or even internationally.
Since I am geographically mobile, I decided to look into programs both in the US and abroad.
Background research
Now that you have determined your values-based approach, it’s time to use them to find your dream AI master’s program.
Finding a School
By far the best CS-specific tool for identifying accredited universities is CSRankings.com. CSRankings ranks universities on how “actively engaged” the faculty members are in conducting research in computer science and related fields. A high ranking on this list is a good indicator of industry relevance.
To filter my search, I utilized the values outlined above to match my personal criteria, while also constraining the All Areas menu to just “Artificial intelligence” and “Machine learning.” You can view the search results here.
Though the number varies, one source recommends identifying four to six programs to apply to. However, if you have some extra time and additional funds to cover application fees you can apply to as many as you would like. In my case, I spent two-months researching and applying to programs throughout the Summer of 2022, which is why I opted to apply to a slew of programs.
By scrolling through this list, I identified the programs I wanted to look into. Of those, my top three were KAIST (4th), Columbia (41st), and UCL (59th).
Finding a Program
Once you have your list of schools, the next step is to go to that school’s website and look for their list of graduate programs.
My top choice was KAIST, so I navigated to the College of Engineering’s website and found their list of programs. I then chose the program most relevant to me — MSc in Artificial Intelligence.
You should repeat this for every school on your list, while also making note of any other programs that pique your interest. For example, UCL had two programs that I was interested in; MSc Data Science and MSc AI for Sustainable Development. However, some universities restrict graduate applicants to just one application per cycle. In this case, it is important to find out the university’s policy on multiple applications.
Program-specific Considerations
Now that you have found a program that aligns with your values, the next step is to look at program-specific information to ensure that it is a good fit for you.
In the end, these considerations are equally as important as your values when it comes to choosing your Master’s program, so it is a good idea to do your due diligence before applying to anything.
When in doubt, ask! Reaching out to a school’s Office of Admissions is generally a good way to quickly answer any program-related questions.
Important Program Information
Some of the most important things to look for are financial support, application deadlines, and housing situations to name a few. Thankfully, most universities have already compiled this information for prospective students in the form of an “Application Guide” or “Student Handbook.”
While looking at the program page above I found the KAIST Applicant Guide which outlines everything mentioned here in a PDF. Alternatively, some schools, like Columbia, list all relevant information on their website.
Funding Methods
One of the most notable considerations when it comes to program selection is the funding methods. While some schools offer competitive tuition scholarships and stipend packages, others do not. It is a good idea to vet the school on account of its financial feasibility in terms of your personal situation.
If you are planning to pay out of pocket for your tuition, then you have a bit more flexibility in terms of what program you choose. However, given that most people will not be paying for their program by themselves, identifying funding opportunities is paramount to finding a suitable program. This is also a good time to ask your employer if they have any programs in-place to finance employees’ higher education.
There are numerous funding opportunities in the US that are backed by both private organizations and the government. However, other countries also offer similar programs, like GKS in South Korea and DAAD in Germany.
Some universities also offer need-based funding opportunities for those who are unable to finance a Master’s degree on their own. Here is an example from Columbia.
In my case, KAIST offered a competitive financial aid package which weighed heavily in my decision. Most international students attend KAIST tuition-free and are given a monthly stipend to assist with living expenses.
Networking
Once you have narrowed down your options for prospective programs, taking the initiative to network with faculty members can give you valuable insights and help you tailor your application. Networking in this respect can take many forms including attending information sessions, touring the campus, or reaching out to faculty members. In my search, I opted for the latter.
By doing this I was not only able to make inroads with professors but also ask exactly what kind of student they were looking for, allowing me to tailor my application to each specific program. Though not feasible for every program, my goal was to connect with two faculty members per school.
When I was networking with faculty members, I wanted to understand the program beyond what I could find online. Specifically, with every email I sent, I wanted to answer three questions:
- What can I do to make my application stand out?
- What research opportunities are there for students?
- Can this professor advise me during my program?
I went through the faculty pages for the programs I was interested in and selected a few professors whose research interests aligned with my own. Then, I created a template email and a spreadsheet to track who I contacted and when.
Active networking like this can be as time-consuming as it is beneficial. To expedite the process I crafted my template email in such a way that I only required three pieces of information about each faculty member: their name, research interests, and what school they teach in (i.e. School of Computing, School of AI). This information was used to fill in my template email, which was then sent to them along with my CV and unofficial transcript.
Hi Professor [NAME],
My name is Simon Aytes and I am a prospective graduate student at [UNIVERSITY]. Currently, I am looking for full-time programs starting in [TERM].
I was researching different programs at [UNIVERSITY] when I came across you on the faculty page. I would be grateful for the opportunity to speak with you more about [UNIVERSITY] and [SPECIFIC RESEARCH OR TOPIC]. I would be more than happy to accommodate your schedule. For your reference, I have attached my CV and transcript to this email.
I look forward to hearing from you. Thank you for your time.
Regards,
Simon Aytes
This template can be further modified to include faculty-specific information regarding their research, work experience, or active grants.
By following this process, I sent emails to over 30 professors at the 12 programs I was applying to. Of these, I connected with 15 via email and had a virtual “coffee chat” with a few more. As a direct result of this networking effort, I was able to secure both an advisor and a research position at my first-choice university.
Final Thoughts
In the end, values-based decision-making and active networking are only one piece of the puzzle. There are a multitude of factors that determine whether or not you will be accepted into the programs you’re interested in. Things like work experience, GPA, and standardized test scores (i.e. GRE) all weigh heavily on your qualifications for a program.
Finding the perfect AI graduate program requires a combination of self-reflection, research, and active networking. By following the process outlined in this article, you can give yourself a strong head-start on the competition going into the formal application process.
Simon Aytes is currently graduate student at the KAIST Graduate School of AI in Seoul, South Korea. He has previously worked with organizations such as NASA, Penta Group, and Columbia University.
Want to hear more? Visit his website or reach out to him on LinkedIn!
