- Hey! Who are you, what are you currently doing and what is your background?
- How did you decide that you wanted to pursue an MS in the US?
- How did you start your application preparation; can you throw some light on profile building, GRE, LORs and SOPs?
- What colleges did you apply for and what was the result?
- Can you tell us more about the course you chose and what the learning outcomes are?
- What is the cost of tuition and cost of living for the program?
- What does the future after master’s look like?
- Any final words of advice for anyone who aspires to be where you currently are?
- Resources Suggested by TheGradPost
- Connect
Hey! Who are you, what are you currently doing and what is your background?
Hi everyone, my name is Nishit Anand, and I’m currently pursuing my Master’s in Computer Science at the University of Maryland, College Park. I completed my Bachelor’s from Jaypee Institute of Information Technology, Noida.
After my bachelor’s, I worked as a Research Engineer for a year at IIIT Delhi, contributing to its autonomous driving project. Following that, I spent another year as a Research Fellow at IIT Delhi at its Vision & Graphics Lab.
During my B.Tech, I did a software engineering internship at PepCoding, a startup focused on teaching coding, web development, and machine learning. Apart from my research roles, I also interned at two startups—one as an ML Scientist Intern at Radien and another as a Founding Machine Learning Engineer at Ananas Labs.
My work has primarily been in machine learning research. The roles at IIT Delhi and IIIT Delhi were full-time, on-site experiences after my graduation, giving me a total of two years of professional experience.
How did you decide that you wanted to pursue an MS in the US?
During my B.Tech, I studied basic computer science subjects, but my interest in machine learning really developed when I took a course on Machine Learning at Coding Blocks. That course led me to explore ML further through various projects. However, I felt that I needed to study it in greater depth, and pursuing a Master’s seemed like the right path.
I was also influenced by my seniors; seeing their journey inspired me to consider the same route. After completing my Bachelor’s, I realized that while I had a foundational understanding, I wanted to study machine learning in much more detail.
I chose to pursue an MS in Computer Science instead of an MS in Data Science because CS is broader, allowing me to explore a wider variety of subjects beyond just ML. While machine learning is a major area of interest, I wanted the flexibility to study other topics as well and gain a more well-rounded education.
How did you start your application preparation; can you throw some light on profile building, GRE, LORs and SOPs?
During my final year of B.Tech, I decided that I wanted to pursue a Master’s. Initially, I received an on-campus offer as a Software Development Engineer (SDE) at Oracle, but just a few months before graduating, I got an opportunity as a Research Engineer at IIIT Delhi. I had to weigh both options carefully, but I ultimately chose IIIT Delhi because I found the work far more interesting than traditional software development. Since I was already inclined towards machine learning and had been working in computer vision for two years, I felt that my research experience at IIIT Delhi would strengthen my Master’s application. Additionally, I wasn’t particularly interested in software development, so I decided to forgo the SDE role and focus on research instead. After making this decision, I began my GRE preparation.
GRE
For the GRE, I used Jamboree’s material along with various free online resources. I scored 168 in Quant, 155 in Verbal, and 4.0 in AWA, with a total score of 323. I started my preparation in July and studied through August and September, but I took the exam quite late, in mid-November. Ideally, I would recommend taking it earlier—by October at the latest—so that scores are available in time for applications. Fortunately, I began working on my Statement of Purpose (SOP) in late September and continued refining it throughout October and November.
SOP & LOR
I believe the SOP is more important than the GRE, especially now that many universities have made GRE scores optional. A strong SOP, along with well-written Letters of Recommendation (LORs), plays a significant role in securing admissions. The quality of the LORs depends on who writes them and how well they articulate their assessment of you.
For writing a strong SOP, I structured it by first giving a brief introduction about myself and my academic background. I then highlighted two or three major projects or experiences that shaped my decision to pursue a Master’s, discussing what I did, what I learned, and how these experiences influenced my decision. Finally, I concluded by explaining why I wanted to study at that particular university and mentioned specific courses or areas of interest relevant to my goals.
Regarding my LORs, I received one from my university professor, who supervised my final year project, and the others from the professors I worked under during my time at IIIT Delhi and IIT Delhi.
Timelines
Coming to my application timeline, I had already decided to pursue a Master’s during my final year of undergrad. In 2023, I finalized my plan to apply for Fall 2024 admissions. I started preparing for the GRE in mid-to-late June, but my serious preparation took place in July and August. In September, my focus was split between studying for the GRE and beginning my SOP. By October, I shifted my focus primarily to refining my SOP. I took the GRE quite late, in early November, and received my scores around mid-November. At the same time, I requested LORs from my professors and mentors.
Most application deadlines were in December and I started receiving admits in late February, with ASU being one of the earliest. However, most responses, including from UMD and NYU, came in March and even into April. Some universities released decisions as late as April 10th to 15th.
What colleges did you apply for and what was the result?
I applied to 15 universities as below.
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Can you tell us more about the course you chose and what the learning outcomes are?
Out of my admits, the best options for research were Johns Hopkins, University of Maryland and NYU. I explored CSRankings and went through professors’ profiles to understand their research areas. After shortlisting these three, I felt that UMD had the biggest and best CS department among them. I liked the research happening there, which ultimately led to my decision to choose UMD.
The Master’s cohort size at UMD has increased over the past few years. This year, the MSCS intake was around 70 students. However, more than half—about 38 to 40 students—were BS/MS students who had already been pursuing their undergraduate degrees at UMD and were transitioning into the MS program. The two-year MS students like me made up around 30 students.
Regarding coursework, we need to complete 30 credits in total. Out of these, 24 credits come from coursework, while the remaining 6 credits are research-based. These are distributed across various courses throughout the program.
What is the cost of tuition and cost of living for the program?
The tuition cost for the entire degree is around $60,000. When you include living expenses, the total cost comes to approximately $100,000 for the two years.
For living expenses, rent in my area is around $700 to $750 per month, with utilities adding another $100. So, overall, the monthly cost for rent and utilities comes to about $800 to $850.
Regarding funding, TA and RA positions used to be more accessible for Master’s students in the past. However, it has become more challenging to secure these roles. Whether you get a TA or RA position depends on factors like reaching out to professors, their interest in your profile, available funding, and passing an interview. Some MSCS students manage to secure funding, but it’s quite rare for other programs.
What does the future after master’s look like?
The job market for Master’s students isn’t very strong right now. However, at my college, many Master’s students are interested in converting to a PhD. The PhD students from our college have gone on to work at top companies like Google and Meta.
Before COVID, the job market may have been better. Some students from the previous year managed to secure internships at well-known companies like Samsung and Amazon, while others interned at smaller startups. There were also students who didn’t land an internship but are now trying for full-time positions.
In terms of finding jobs and hiring, the career fair isn’t that useful. Most students have to find opportunities on their own. Since the university is close to Washington D.C., a few government and defense companies attended the fair, but they typically only hire U.S. citizens. Other major companies like Google also showed up, but they were mainly promoting their general job portals rather than conducting on-campus hiring.
As for job roles after graduation, most students without prior experience start as Software Development Engineer 1 (SDE-1) or Machine Learning Engineer. Those with strong research backgrounds sometimes secure roles as Applied Scientists. These are the most common designations for graduates in AI/ML-related fields.
Salary expectations vary widely depending on the company, but from what I have seen, they range anywhere between $70,000 and $150,000. However, I don’t have a large enough sample size to provide an exact figure.
Any final words of advice for anyone who aspires to be where you currently are?
If you are applying to UMD or considering it, my advice would be to focus on research early if your goal is to get into an MSCS program. Prioritize gaining research experience, whether with a professor or in the industry, and try to get some publications. Having publishing experience strengthens your profile significantly. Additionally, craft your Statement of Purpose carefully and ensure your Letters of Recommendation are strong. The GRE isn’t very important anymore—most universities have made it optional.
Looking back, I wish I had started the application process earlier. That would have given me more time to refine my application. My biggest strength during the application process was the amount of time I spent perfecting my Statement of Purpose. I also had solid research experience from two different places, which I was able to showcase effectively in my application.
For those coming to UMD, my advice is to make the most of your time here from day one. You won’t realize how quickly time passes, so start exploring opportunities as soon as you arrive. Connect with other students—both Master’s and PhD—collaborate on projects, and engage with professors. Whether your focus is ML, Data Science, or any another area, start working on it early and build your profile proactively. Take advantage of every opportunity that comes your way.
Resources Suggested by TheGradPost
For GRE/GMAT, especially for acing it in the quant session, try out Target Test Prep (TTP).
Air travel and temporary accommodations – MakeMyTrip
Help with a simpler transactions process – HDFC, SBI, ICICI Credit Cards
Connect
Linkedin – Nishit Anand
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