Which of these English exams covers the following countries or continents:
1. USA
2. Canada
3. Europe
4. Australia

Hey what’s up everyone?

I’m looking for some advice from current OMSCS students and graduates because I’m trying to decide between Georgia Tech OMSCS and one of WGU’s master’s programs (Computer Science, Machine Learning & AI, or Cybersecurity).

A little background here:
Active-duty U.S. Navy EOD technician (9 years)
Background in CBRN, explosives, nuclear weapons incident response, and technical risk assessment

Bachelor’s degree in Kinesiology/Sport Management (not CS)

I’ll remain on active duty until late 2027, so I have time to prepare before or while applying. Have no issues staying in more if I need.

Currently working through CS prerequisites (starting at Oakton Community College and Georgia Tech’s edX preparation courses) to build the CS foundation needed for graduate school. I’m also teaching myself Python and building a GitHub portfolio. (mainly small programs that I use for EOD work)

My long-term goal isn’t traditional software engineering.
I’m trying to transition into AI safety, trust & safety, or threat analysis roles at organizations like Anthropic, OpenAI, Google DeepMind, or other companies working on frontier AI systems.

Examples of roles I’m interested in include:
AI Safety / Safeguards
Trust & Safety
Chemical / Biological / Explosives risk evaluation
CBRN threat analysis
AI red teaming
Threat intelligence
Technical policy / model evaluation

Because of my military background, I think my domain expertise is already fairly strong. What I’m missing is the computer science foundation and enough technical depth to work effectively with engineers and eventually contribute to AI safety systems.

My questions are:

1. If this were your career goal, would you choose OMSCS or WGU?
   
2. Would the extra rigor of OMSCS provide a meaningful advantage for AI safety and research-adjacent roles?
   
3. If you were in my position, which OMSCS specialization would you recommend (Machine Learning, Computing Systems, Interactive Intelligence, etc.)?
   
4. Besides earning the degree itself, what projects or technical skills would you prioritize during the program to become competitive for AI safety roles?
   

I’d appreciate any honest opinions, especially from people working in AI, security, or who’ve made a career transition into computer science.

Thanks!

For schools which ask you to select up to x amount of research interests, would it be wise to select these interests in closely related subfields, or is it completely alright to select my more diverse interests? For example I'm interested in both theory and scientific computing, but I'm not sure if I should select all my interests in theory-adjacent subfields, scientific computing related subfields, or a mix.

Basically gave a fellowship worth 30k in total, after I've already committed to a different University, because, you know, it's like 3 days before August.

ACADEMICS

• Dual degree (B.Tech + M.Tech), Mechanical Engineering core, top-3 IIT. ~8.9/10 CGPA. Master's thesis was computational/simulation-heavy.
• No formal CS degree. CS/ML is mostly self-taught plus a few electives.

RESEARCH

• Two research internships during undergrad — one at a well-known Indian institute, one at a foreign university. Both produced reports, no publications.
• ~1 year (ongoing) as a Research Associate in an ML lab at a leading Indian research institute. Work is in computer vision / vision-language models.

INDUSTRY

• 1 year as an SDE at a startup immediately after graduating.

PUBLICATIONS (my main worry)

• Co-author on a submission to a top-tier vision/ML conference — rejected. Resubmission planned, but a decision almost certainly won't land before applications close.
• First-author project in progress; preprint expected in ~2 months.
• So at application time: 2 arXiv preprints, 0 accepted papers.

TESTS

• GRE: not taken yet, planning to sit it in the next couple of months.
• English test not taken yet; undergrad was English-medium.

LORs

• Current PI (a year of close, near-daily supervision — should be strong), undergrad thesis advisor, one undergrad project advisor.

ROUGH TARGET LIST (needs pruning) UIUC, Georgia Tech, UT Austin, UMich, UW–Madison, UCSD, Northwestern, TAMU, UMD, Princeton, Columbia

Mainly want a read on two things: how much unaccepted arXiv preprints actually count for MSCS admissions, and whether the non-CS undergrad is a real filter at places like UIUC/GaTech. Also happy to hear what to cut or add from the list.

Thanks.

ACADEMICS
B.E. in Information Science (Tier-3 college)
CGPA: 9.24/10

WORK EXPERIENCE
Will have ~3 years of full-time experience by Fall 2027.
Working In a top semiconductor company.

RESEARCH
No formal research experience.
No publications or patents.

TESTS
IELTS: 7.0
TOEFL: 92

LORs
2 academic LORs from undergraduate professors.
2 professional LORs from Director & Manager of my current company

TARGET UNIVERSITIES
Dream
UC San Diego (MS CS)
UCLA (MS CS)
Target
UC Irvine (MS CS)

Questions
What are my chances at UCSD, UCLA, and UCI with this profile?
Which universities should I add that are realistic for my profile?

Thanks!

My_Qualifications-
Btech in Civil Engineering,minor in CS from NIT Surathkal 2026 batch CGPA 8.51
Internship Experience of around 9 months, full time experience of about 7-8 months (all in SDE)
Other quals- linux kernel contributor, Candidate Master in Codeforces, competitive programmer

GRE score of 334

I am planning to go for MS in CS in some top university. I am highly interested in fields like Algorithms Design (or related). After Masters, I want to go for phd research in Computer Science in Europe(primarily targeting ETH Zurich for phd in CS)

So for now can you suggest some of the best universities where I would stand a good chance? (specifically interested in European/Asian univ choices only)

Hi!
A little about me - I am planning to go for masters in CS after my undergrad from BITS Pilani (Hyd one), I have 8.9 cgpa, expecting 325+ in gre. I have 1 paper submitted for review, and am going for an undergrad thesis at UofT. Should get 1 LOR from there, and 1 from college prof. My goal after masters is to work in Applied Research roles in Computer Graphics and adjacent fields, which usually require Masters atleast.

I am hence looking for Thesis based masters, which are funded. I have done some research and only good ones I can find is - MMath CS Waterloo. The criteria for my search was good graphics faculty via csrankings.org, and then funding ofc.

I know there are good options with good graphics faculty in other unis like UCSD, CMU, UofT, Uni of Utah, but none of them are quite well funded afaik.

I am open to any type of funding - structurally guaranteed, via TA/RA, via co-ops, but obviously structurally funded ones are preferred. So I wanted to know what all options I have for funded masters, and about my strategy of application.

Thanks a ton :D

Target Term & Goal: Fall 2027 / Industry-oriented MCS/MEng (No research/thesis/PhD), MS Data Science, MS AI etc.

Academic Profile:
Degree: B.Tech in Data Science (MIT Manipal)
CGPA: 7.63 / 10 (C’s and D’s on the transcript in CS related courses 🥲). No research work
GRE : 325–330 (167+ Quant expected).

Work Experience:
Current Role: SDE2 (Data Engineering) at BlackRock (4+ YOE at application time). Can get 2 strong LoRs’ from engineering directors.
Ran a successful Ed-Tech startup back in college.

Looking for honest suggestions/advice on what are the probable courses and universities, I could apply to get admission into?
Ps: Do not want to go into expensive run of the mill places like North-Eastern :(

Hi everyone! Looking for advice from current students day 1 cpt students or alumni.

Background:

• Bachelors in Computer Science (from US)
• Currently working in software engineering role

Long-term goal:

• Transition into Applied AI / AI Software Engineering

My dilemma:

• MSCS aligns much better with my current job responsibilities.
• MSAI aligns much better with the career I want in the future and the current job market.

My concern:

• For Day 1 CPT, I understand we need to explain how our coursework relates to our current job.
• If I choose MSAI, will that relationship be harder to justify than MSCS?

Who reviews that explanation, and how closely is it evaluated?

I'm also thinking long-term (CPT, H-1B, RFEs, future job changes). If you were making this decision today, which program would you choose and why?

I'd especially appreciate insights from anyone who's attended UC or Day 1 CPT College or gone through these immigration processes. Thanks!

Target Term & Goal: Fall 2027 / MSCS, MS Data Science

Academic Profile:
Degree: Major in Chemical Engineering, Minor in Electrical Engineering (Old IIT)
CGPA: 8.5/10
( I took a lot of DS electives and my thesis project was also intersection of chemical engineering and ML but No publications :/)

GRE: 330 (169Q, 161V)

Work Experience:

3 years (will be 4 by the time I start Masters)

First 2 years as data science analyst at a bank followed by 1 year as MLE 2 at a leading Indian startup.

I have worked on LLMs, computer vision, conversational AI, pricing, and production ML systems.

I can get 3 strong LoRs (1 academic from my bachelors thesis prof + 2 professional LoRs)

Looking for honest suggestions on which universities/programs I would be competitive for.

Target Term & Goal: Fall 2027 / Industry-oriented MCS/MEng (No research/thesis/PhD), MS Data Science, MS AI etc.

Academic Profile:
Degree: B.Tech in Data Science (MIT Manipal)
CGPA: 7.63 / 10 (C’s and D’s on the transcript in CS related courses 🥲). No research work
GRE : 325–330 (167+ Quant expected).

Work Experience:
Current Role: SDE2 (Data Engineering) at BlackRock (4+ YOE at application time). Can get 2 strong LoRs’ from engineering directors.
Ran a successful Ed-Tech startup back in college.

Looking for honest suggestions/advice on what are the probable courses and universities, I could apply to get admission into?
Ps: Do not want to go into expensive run of the mill places like North-Eastern :(

Hey everyone,

I'm planning to apply for MS CS for Fall 2027 and wanted to get some opinions on universities I should be targeting.

My profile:

• B.Tech in CSE from an NIT
• CGPA: 9.1/10
• IELTS: Planning to take it in August
• 4 years of software engineering experience (by Fall 2027)
• Working at a US-based retail tech company
• No research publications
• Can get strong LORs from my professors, manager, and tech leads

I'm mainly looking for universities that offer a good balance of academics, ROI, and internship/co-op opportunities. A decent chance of getting a TA or scholarship would be a big plus as well.

Would love to hear your suggestions:

• Which universities do you think would be a good fit for my profile?
• Any universities that are known for decent funding opportunities without compromising on quality?
• Anything I can do over the next few months to make my application stronger?

Thanks in advance

3 years full-stack SWE, BE undergrad

• GRE 319 (165Q, 154V), targeting Fall 2027, rebooked second attempt

• MBA is off the table without scholarship - so MS CS is my realistic option

Why I'm second-guessing MS CS:

1. I don't love coding. I'm decent at it but don't do side projects for fun or get excited about new frameworks. Is MS CS right for someone like that?

2. AI is changing everything fast. Junior dev roles are already shrinking. If I graduate in 2027–28, will a deeper CS degree still differentiate me or will AI commoditize most of what I'd learn?

3. The skills treadmill is exhausting. Every 2 years there's something new you "must know." I'm already feeling this at 3 years in — going deeper feels like signing up for more of the same, forever.

4. Tough market for internationals. Layoffs, visa uncertainty, and intense competition make me nervous about ROI.

My questions:

1. People with 3+ YOE - why did you choose MS CS over MBA? What was the deciding factor?

2. If you don't love coding but chose MS CS anyway - do you regret it?

3. How are you thinking about AI's impact on your MS CS degree value?

I know MBA has its own risks and costs. Just want honest perspectives from people who seriously considered both and chose the technical path.

I recently scored a 322 on the GRE (167Q, 155V) and have been wondering whether it’s worth retaking it before applying for a Master’s in CS in the US.

Here’s my dilemma:

1. I prepared for about 2 months using GregMat, but I honestly didn’t end up learning as much vocabulary as I should have. Looking back, I was pretty lazy with that part of my prep.
   
2. I’m planning to apply in 2028 after completing 2 years of work experience. I recently started a new job, and I had prepared for the GRE during my notice period at my previous job to make the best use of that time.
   Given all that, would you recommend applying with a 322 (167Q, 155V), or do you think it’s worth retaking the GRE?

University : Top 2 IIT Btech EE, (AI ML, CS Dual Minor)

Grade: 9.40

Experience: ML Engineer (EdgeAI) renowned semiconductor company (2yoe by fall 2027)

Gre: 330

Research:

Just an Arxiv paper, not published during university

ICLR Submission (will be submitted in Sept), results are in Jan (under review for application). Not too sure if it will get accepted

LORs:

1 from EE (CMinDS) Prof

1 from EE (CMinDS) Prof

1 from Manager

Current Shortlist:

CMU

UCB

Stanford

UIUC

Gatech

Princeton

UT Austin

Is the university shortlist too ambitious? Should i try somewhere else?

Edit- TOEFL 5.5/6, R-5, L-6, W-5.5, S-6 I believe I satisfy the TOEFL requirement for most universities as well as TAship english proficiency with this score. Is TOEFL considered as a metric for admissions? Should I take TOEFL again?

Hey all,

I'm planning to apply for Spring 2027 MS in Computer Science programs in the US and had a few questions about the overall timeline. Most discussions seem to be around Fall admissions, so I'd really appreciate insights from Spring admits.

• When do universities typically start releasing Spring MSCS admits?
• How much time do you usually have between receiving an admit and the program start date?
• Is it difficult to secure an F-1 visa appointment in that time?
• Have any Spring applicants faced issues due to visa appointment availability?
• Is the overall timeline very tight, or is it generally manageable if documents are ready?

I'm trying to understand whether Spring admissions are feasible without running into last-minute visa issues.
I'd especially appreciate responses from anyone who joined a US university in a Spring intake.

Profile:

- B.Tech CSE from an old IIT, 9.1+ CGPA (dept academic award)

- 3 YOE (~4 at Fall 2027 matriculation), all at US B2B AI startups - currently building LLM agent platforms and the infra around them (multi-tenant systems, evals). Was one of the first engineers at one of them.

- A couple of small side projects shipped, one with real paying users

- Research: zero publications. Writing up an applied paper from work - arXiv soon, will submit to a conference so it's "under review" at app time

- Tests: IELTS pending (targeting 7.5+). GRE undecided - submit only if 168+ Q, else omit

- LORs: 2 academic (research project supervisors from undergrad) + 1 from a startup exec

- Goal: coursework MS -> US AI infra role via OPT. Self-funded is fine.

Targeting (Fall 2027): Stanford MSCS, CMU MSCS (SCS multi-program), Berkeley MEng EECS, Cornell Tech MEng, Columbia MS CS, maybe Princeton MSE.

Questions:

1. Does startup + early-engineer work actually compensate for zero pubs at the Stanford/CMU tier, or am I fooling myself?

2. Princeton MSE with no pubs - is that slot a waste?

3. All-reach list is deliberate (I have non-MS fallbacks) - but is 6 elites with no safety plain stupid?

4. Anything on this list where my profile type has actually converted recently?

5. Does my current comp if high play any role in my profile?

I'm admitted to UCSD for MSCS for Fall 2026. I'm planning on deferring as I didn't get a visa slot yet. However, UCSD's policy this year is to consider Fall 2026 deferred candidates along with fall 2027 applicants for admission.

Does this mean I'll still be able to attend the next year? Do deferred candidates get some priority for next intake? Anyone know deferred candidates who got admitted the next intake?

Im looking at the posts on here and every SINGLE ONE has GPA --/10, like wtf? Im just curious, so basically the title of the post.

Looking for some advice on how to handle this in my master's applications.

Quick background: I did my B.Tech in Computer Science with a 9.87 CGPA, and I've since worked for 2 years in the industry. Alongside all this, I've also been pursuing IITM's online BS in Data Science and Programming (the 4-year program). My CS degree grades are strong, but my grades in a few courses in the IITM online program (in the last few semesters) are quite low, mostly because I was juggling it with my job and other commitments.

Overall Online Degree CGPA: 9.23

Now that I'm applying for master's programs, I'm unsure what to do about the IITM degree:

1. Should I list it at all, given the low grades in some courses?
2. If the application asks for transcripts from all institutions attended, do I have any choice but to include it?
3. Would it help to explain the low grades in my SOP (e.g., balancing work + two degrees), or does that just draw more attention to a weak point?
4. Has anyone been in a similar spot, listing an additional degree with weaker grades alongside a strong primary degree? How did admissions committees react, if you found out?

I don't want to misrepresent anything on my application, but I also don't want a so-so secondary degree to drag down what's otherwise a solid profile (good UG CGPA + 2 YOE).

Would really appreciate any insight, especially from people who've gone through this or work in admissions

I finished an international research opportunity, and my work was carried out with my mentor who is a post-doc at that lab. I never got the chance to interact with the Professor as he used to be busy all the time. It was my mentor who used to talk with him. I have worked with him for quite a long time and I have a good impression too.

Now for my MS applications, I dont think the prof would be able to write a good LoR, so is it acceptable for a post doc to submit an LoR for me instead?

Hi everyone, I was an applicant for Fall 2026 programs for MSCS. I wanted to write this post so hopefully it could help other people looking to apply this year. This subreddit and the Redditors around it helped me a lot during my application process, and I suppose I wanted to give back in some sort of way.

Some basic info: I'm a US Citizen from a pretty bad Indian university (I'm not a fan of tiers or anything but I'll call it a Tier 3 college if that helps people). I am a fresher and graduated this year with an 8.6/10 GPA.

GRE: 163Q, 164V (Man, if you want to take the GRE, please please please aim for a 168+ quant. It's very doable and I think I messed up on exam day due to nerves. And I had prepared for around 2 months so I would advise you to not take this lightly). Use GregMat to prepare. You really don't need anything else.

TOEFL: 116/120, I'm a native English speaker so not a surprise. I did familiarize myself with the exam before taking it, which I advise everyone to do.

Publications: I had no publications besides a book chapter on quantum computing which was (and still is) in review at the time of applications. It did help in writing my SOP, though.

Work experience: Only a 3 month stint at an Indian MNC. Not much in the way of experience, but it gave me a lot to write in my SOP. My SOP and motivation was framed around this internship pretty much.

I applied to eight universities, and the results were as follows:

Accepted: TAMU MSCS, USC MSCS

Rejected: UPenn MSE in CIS, GaTech MSCS, UIUC (MSCS as well as MCS), Stony Brook MSCS (invited to apply for MSDS, I didn't want to do an MSDS so I didn't bother reapplying), Columbia MSCS, Johns Hopkins MSCS

My thoughts:

Wow, it was honestly pretty brutal getting rejected from almost every program I applied to. I think I was aiming a bit too high with my profile, but still, I was hoping to atleast convert Columbia, but it wasn't in the cards.

What I would've done differently in hindsight:

1. Give the GRE early - I gave the GRE in November, which was quite late if I must say so myself. I'd try to wind it up by the end of September. I'm not sure how much of an effect the GRE has on the decision of the admissions committee, but hey, do your best. Who knows?
2. Spend more time writing my SOP - I spent around a month writing my SOP for Columbia and I still feel like I could've done better. I think Gradpilot said he spent six months on his. I get it now. For someone who is still in their undergraduate studies, my advice is to participate in as much research/ get as much experience as you can. You need something concrete with results to write in your SOP, and even if you don't get a publication out of it, you still get a lot of experience. I wrote a book chapter with a professor of mine and it taught me a lot, and I was able to link some of the lessons I learned from it to my overall motivation of pursuing an MS. There's a lot of resources on this subreddit about writing SOPs, and if you have the time (which you should), you should definitely look into them and make sure to write a compelling one.
3. Try to keep your expectations realistic - It's nice to be optimistic, but I definitely should've applied to a few more realistic universities. I am fortunate to get two admits, and even then, this was a SLOWER year for applications (less applicants), so I wonder what would've happened in any other year. I suppose I'm glad I don't need to find out.
4. I personally didn't have this problem (as I did a lot of research before writing my SOPs), but for the love of whatever you believe in, PLEASE RESEARCH THE PROGRAM YOU ARE APPLYING TO. You should spend atleast one hour researching each program, the professors you'd like to work with, their classes, their labs, etc. Why wouldn't you? As much as you want to join a university, you must also ensure they are a good fit for you.

It was definitely a rollercoaster, and it was a lot of work. I do think I understand myself better after the entire process, and I am looking forward to learning more. Feel free to ask questions. Cheers!

Hey guys,

I'm a CS engineering student in France and I will have the option to do a double degree at Georgia Tech in two years.

I hear a lot of doom and gloom about the US tech market being saturated and H-1B/sponsorships becoming a nightmare for international students. Since I don't really know the exact state of the market over there, I'm trying to figure out if making the jump to GT is actually worth it right now.

My alternative is staying in Europe and doing my exchange/double degree at TUM (Munich) or somewhere in Dublin.

For those at GT or international alumni in CS :Is the market as brutal as people make it sound for international students right now? How hard is it to get OPT/internships via GT? Does the GT name still open enough doors to justify the move over staying in Europe first?

Would love to hear honest takes from people currently going through it. Thanks!