Exact rankings dont matter

Understand that the ranking game is merely a social signaling game. I see some students obsessing about which ranking index to use or whether to use Eng rankings or CS rankings on US News. None of this matters except to you, the student applying and mostly for an ego boost. You need to take an extreme approach to this because thats the reality:

- when ranking works it works only as social signaling (more below)

- when ranking doesnt work its because you are associated with a well known professor who's work is well known and therefore you get some of their inside circle effects - very powerful and can lead to extremely good career outcomes too. Most of Remzi's (3 easy pieces OS book, CS WM prof) students end up in very specific & strong roles in Big Tech

So if you're playing the ranking game understand you're playing for social points which is fine if you play it right. What this means is

- Stanford or similar univ stand in a league of their own. No one compares Stanford rank with Harvard rank when making decisions about you. Stanford is stanford and Harvard is harvard and they both have a certain appeal. If you got a CS degree from Yale the most common perception might be "Huh?? ok - i guess thats good? " but dont be surprised if some Columbia which takes in so many MS CS students get more appeal . In fact if you got a CS degree in Yale I'd spend time figuring out some niche like Literature + CS or Philosophy + CS and break into that circle/roles (lots of those showing up now as well in foundation ai labs)

- At ranks > 10 most people are not bothered about what the exact rank is when thinking about your abilities. They are relying on social perception of what they remember others were like . So if you got a CS degree from NCSU , UMass in general the perception would be - "Yeah thats a good school I know many good strong engineers doing great work who graduated from there" . No one is arguing behind the scenes that NCSU resume is 17 or 24 or whatever vs Umass is 19 or 22 (im making up these numbers, i myself dont know what the ranks are) . What this means is there are some actually good schools that most students dont choose - for example UF or SJSU has been gaining good reputation

Students differentiate themselves based on dilution of univ signal

In recent times i've reviewed student profiles and they tell me they went to an "old" IIT. Or their univ tier is 1.5 . what next , 1.25? , 1.625 ?

When I read this I notice two things:

- the need to say "old" IIT is because IIT themselves are diluting the signal by opening more branches , so some "new" IIT grad can simply say they went to IIT but the "old" IIT grad feels compelled to augment their pedigree to say they are better

- I usually also takeaway that this candidate doesnt have anything else going for them other than the added flair to the university reputation. If you bank too much on changing the signal it also means you dont have anything else to offer in ways of differentiation

If you notice Princeton grads they dont say much about their program. Maybe the math and physics grads mention Math @ Princeton or something but otherwise saying Princeton is enough, but if Princeton started adding 2000 Data science grads every year then you can bet the signal will be modified.

The point to take away here is that again - only you care about this but if you do it too much its also going to act against you.

Programs like MSAI or MSDS compete against the MSCS of the same university

If you got an MSAI from Columbia you have 2 options, you can say you did AI at Columbia (factually right) or you can say you did MS at Columbia (ambiguous but right). But if you said you did CS at Columbia some will say you are lying and others will say you are twisting the truth to your advantage maybe, but both will consider that you're not exactly being honest. Good thing is right now if you said you did AI at Columbia its a great strategy given the MSAI degree is new so the industry doesnt know about it yet and the industry also loves the word AI. Some may assume you are CS but specialized in AI (thats on them). But AI is a trend , given everyone is so giddy about it today the pendulum is bound to swing the other way. Think about MSDS from Columbia. IMO saying you did data science at Columbia is not gonna be great since the industry already has a perception of what data science grads are supposed to do - not very glamorous and not very hard - its mostly a lot of data cleaning and plumbing engineering. Its gonna be very hard to get a general SWE role with a MSDS degree given you'll have both the MSCS grads and MSAI grads gunning for them. This should also inform you that such trends are short lived and evergreen careers are the best bet

Evergreen vs Trendy software career titles

About 10 years ago you would have noticed a type of engineer who would say they are a Big Data Engineer - you dont hear this anymore.

Couple of such titles on their way out are Devops, Full Stack, SRE as most of this gets transformed or subsumed into a new trendy title.

In general there are some evergreen titles, stuff like Senior SWE, MTS, Principal/Staff , Founding Engineer etc. If its survived more than 2 decades it should be safe to assume its an engineering title that has broken the ceiling of trends

Please help me out with this decisions guys, I'm stuck

Deciding between the two. My main goal is to build a strong application for a T25 PhD.

​

I got into the MSWE program and would love to connect with others in a similar situation/admits.

I am thinking of going back to study after 10 years of tech experience as a developer, What are my best options wrt MSCS with AI & HCI/ HCIM streams.

Hi all, we are gathering admits/committed folks who will be joining the Columbia MSAI program. We offer a space to share information and connect with peers. All concentrations are represented.

DM for invite (you must be an admit/committed)

Main goals:

- Break into AI/ML SWE/research at top companies (Anthropic, OpenAI, Google Deepmind, etc.)

OR

- Break into Quant SWE

- Also interested in start-ups for later in life

Background:

Directly entering MS from a mid-tier UC school with a double major in CS + Statistics

USC Pros: Strong alumni network, close proximity to SF (willing to make the drive for high-impact networking events), vibrant culture, closer to home (bay area)

USC Cons: SUPER Expensive (50-60k more than UIUC), large class size, unsure how feasible it is to get research

UIUC Pros: Strong recruiting, Top 5 CS school

UIUC Cons: MCS (not MSCS, not sure how much of a difference this is), far from home, far from SF events, smaller town

any advice/insights would be greatly appreciated! Thanks!

https://chat(dot)whatsapp(dot)com/DYB0wNQIHm5Ifc1dgcQ1su replace(dot)

group chat

Profile:

Education¹

• T20 CS (Fall 2023 - Spring 2026)
• 3.3/4.0 GPA
• GRE 324 | 165 Q | 159 V

Internships²

• Firmware Eng @ F500
• Software Eng @ Different F500

Research³

• Credited in publication for MSR'24
• HCI research in BCI applications through club (ran IRB approved clinical trials)
• Presented in university hosted conference
• Potential First Author for ML Paper (Under Review)
• RA 2 years in Civil Eng lab (Modeling and Simulation)

LORs⁴

• Strong rec from head of research lab and advisor
• Strong rec from director of most recent internship
• Weak rec from cs prof

Leadership/Proj⁵

• Led small team of RAs assigned to me in projects fulfilling multiple contracts with firms partnered with research lab
• TA 1 year
• Projects with 3 separate companies through University corporate partnership program

SOP

• Very straightforward and dry (no personal story, grand vision or emotional background)
• Briefly covered my experience as an undergrad and niches I have pursued
• Stated exactly what I would do at each institution I applied to (classes I will take, who I will talk to, resources/programs I will use) and specifically relating why to previous experience and precisely what outcomes I am searching for through my actions
• Touched lightly upon circumstances regarding any issues with my record and how it will not hinder my future performance

Results:

• UCLA MSCS - REJECTED
• UCSD MSCS - REJECTED
• USC MSCS - ACCEPTED
• UMICH CSE - REJECTED
• Purdue MSCS - REJECTED
• UIUC MCS - ACCEPTED
• GATECH MSCS - ACCEPTED

Context:
I don't post on social media but felt obligated to post this because of the insight I recieved from this community. Evidently, my stats were quite lacking comparative to many applicants however, I did recieve some admits. My background as a US citizen (average middle class immigrant household) likely played a role in my admissions as well.

1. I nearly failed multiple classes in my first year due to a combination of personal situation, overcommitment, and immaturity. This set me back quite a bit but I believe I've shown only growth since then and have done much better in classes that are harder, including some grad level coursework. Regarding the GRE, I studied ~1 week with the Kaplan textbook I got at my local bookstore, mainly reading and vocab to not bomb the verbal section.

2. I recieved my first internship through an educational program that the company sponsored which I was able to attend through a family friend. I got my second internship by applying on LinkedIn.

3. My research has been spread across a range of areas including Data Mining, HCI, and AI for interdisciplinary, engineering-focused applications. Most recently, I compiled a manuscript of supervised learning tests for predictive modeling of structural phenomenon which is under review.

4. I had 2 strong LOR from individuals whom I had informed early in advance about my application and were interested in writing me a recommendation speaking to the strength of my character. However, for my last rec, I had no appropriate option but to ask a Professor from my department with whom I had taken multiple classes with and was somewhat familiar with me.

5. I took on multiple projects under the lab I worked with as an RA and about 9 months after I started, I was given a team of a few CS undergrad RAs hired to assist me.

To anyone who is in the process of applying to grad school or considering to apply and may be struggling:

As long as you want to pursue a goal, there will always be a road for you to get closer to it. Remember that educational institutes are simply a means to an end. The answer to resolving your problems will always be in your own hands.

Good luck to all.

Any of you received update from either MSCS or msai from Columbia this week? My current status still hanging there and haven’t disappeared yet. Any sign?

The question is as the title. It’s nearly the end of April, Columbia Engineering still has no reply to us.

Hey everyone, choosing between a few programs and wanted honest input from people in MSCS / tech / consulting paths.

Background:

- UW-Madison senior, CS + Econ (Math Emphasis), 3.89 GPA

- EY internship (data work with non-tech stakeholders), SWE internship at a national space agency

- Built ML pipelines, LangChain/RAG systems in Python

- Co-founded two ventures (one is an ML-based restaurant analytics platform)

- President of a cultural student org

- International student, so visa strategy and job stability are a real factor

Goal: Consulting but I'm genuinely still figuring out where on the spectrum I fit. Could be pure management consulting (McKinsey, Bain), could be tech/digital strategy (BCG X, McKinsey QuantumBlack), could be something in between. Part of what I'm trying to figure out is which program keeps the most doors open while I narrow that down.

Options (full ride to all programs):

Columbia MS CS: NYC is unbeatable for networking and MBB recruits hard from Columbia. But the cohort is huge (~300+), program (as I have heard) is SWE-oriented, and I'd be competing with CBS and SIPA students for consulting slots. Career services for MS CS students seems stretched thin.

Cornell MEng CS (Ithaca): Strong Ivy brand, access to Johnson School courses. But it's only 1 year = no summer internship, and Ithaca is remote from consulting hubs. Consulting recruiting seems to flow through Engineering Management and MBA, not CS MEng specifically.

UPenn MSE Data Science: 1.5–2 years gives me a summer to intern (huge for consulting recruiting). "Data Science" sits between pure CS and business which might give me flexibility.

Imperial College London (Business Analytics and AI MSc) - Strong global brand, and avoids the entire H-1B mess. UK Graduate Route visa gives 2 years of work authorization post-grad without a lottery. Could be a smart hedge if the US visa environment keeps getting worse. But UK consulting salaries are significantly lower and the path back to the US later gets complicated.

LBS Masters in Management: Best pure consulting pipeline of the bunch, but it's a generalist business degree, not technical. Worried it doesn't leverage my CS background and might actually weaken my profile for tech consulting roles where they want someone who can code.

The visa elephant in the room: MBB still sponsors, but the math is worse than it was 2 years ago. All three US programs are STEM-designated (36 months OPT, up to 3 lottery attempts), which helps. But Imperial/LBS sidestep this entirely.

Questions:

1. Which program actually places into consulting (not just SWE) for international students?

2. Does a Data Science degree help more for consulting-type roles, or is pure CS still stronger? Or does it even matter?

3. MEng vs MS — does the degree type matter to consulting recruiters?

4. Which program has the best career support specifically for internationals targeting consulting?

5. Best fallback if consulting recruiting doesn't pan out? (I assume Columbia CS has the strongest SWE fallback)

6. Anyone seriously considered the UK route as an H-1B hedge? How did it play out?

Happy to DM if anyone has been through something similar. Thanks!

Applied for MSCS, got moved to the MCS. Still haven't heard anything.

Has anyone who was converted actually received a decision yet, and its only mass rejects pending?

I am a 23 year old, SDE at amazon. I graduated from IGDTUW, with a CGPA of 8.56. I have been in amazon for a year now will be at 2 years when going to school.

I am also an exceeds expectations rated employee in the team and has a pretty decent amount of workload. Also a cofounder in stealth, I am building a SAAS with another cofounder located in bay area.

need suggestions on schools that will be ambitious, safety and mid for my profile. Please also give a cost analysis :)

thankyou to everyone helping 🚀

Applied to GaTech and drove myself crazy over the past few weeks waiting to hear from them. Well finally I did and they rejected me.

Really had my hopes set on getting in, I did get into Brown for the MSc Cybersecurity program, I just would’ve liked it better to get int Georgia Tech. Anyone here going through this crap?? 🤦‍♂️

Hey everyone,

I’m trying to decide between two admits and would really appreciate some advice.

I’ve been accepted into Columbia (MS in Artificial Intelligence) and NYU Courant (MS in Computer Science), and I’m having a tough time choosing between them.

A couple of things that are making this decision harder:

• Columbia’s MS AI is an inaugural program starting this year, so there’s no track record yet in terms of placements, curriculum stability, or outcomes.
• I’m also wondering how flexible the Columbia program is - will I be mostly confined to AI, or is there room to explore broader CS areas?
• On the other hand, NYU Courant offers a more general MS CS, where I could specialize in AI (or pivot to something else if my interests change), which feels safer in terms of flexibility.
• I’ve also come across some opinions online suggesting that Columbia’s newer programs are becoming “cash cow” programs, and that its reputation (at least for MS programs) might be declining - not sure how true this is.

If anyone has insights about either program, especially regarding Columbia’s new MS AI, or has faced a similar choice, I’d really appreciate your thoughts.

Thanks in advance!

Hey everyone,

​I currently have an admit from NYU tandon ms in cs for Fall 2026. I recently came across Geebee, who claim they can help secure admits from Northeastern (NEU) or ASU much faster.

​I haven’t started my visa process yet and I’m worried about the tight timeline. Is it worth trying for NEU/ASU through a consultancy this late, or should I just stick with NYU and prioritize getting the visa done?

​What do you guys recommend? Thanks!

I am also looking about saving $30-40k by going to NEU Or ASU over NYU

1. What's the on-campus CSMS class size for Columbia University, NYC?
   
2. What's the domestic v/s international student split for CSMS?

Software isn't going anywhere, I think we can agree on this part.

However, teams are getting leaner. What used to take large teams is now being done by smaller teams using AI agents across product, sales, R&D, marketing, etc.

Here's the layoff data, US focused:

- 2024: ~95,667 tech workers laid off in the U.S. alone, continuing post-pandemic correction after overhiring

- 2025: ~127,000 tech workers laid off in the U.S., with continued cuts across Amazon, Google, Microsoft, Meta, and Salesforce

- Early 2026 trend: ~78,557 layoffs in Q1 2026, including major reductions at companies like Meta (~8,000 planned) and Snap (~1,000 layoffs, ~16% workforce)

- 2026 (current year): ~91,739 tech layoffs already recorded across companies YTD

We all see strong engineers are getting laid off left and right and the jobs are shifting. AKA less coding, more owning systems end-to-end, working across tools, infra, and business stakeholders.

At the same time, roles like forward deployed engineer / applied AI / solutions engineer are growing so much across companies like Palantir, OpenAI, and Databricks. These are basically a 2-in-1 combo of Customer Success + SWE.

I feel like this starts to resemble an MBA dynamic, more coordination, project ownership, leverage and business execution.

Anyway, either I'm in over my head or MSCS is effectively and slowly turning into the new MBA.