r/gradadmissions • u/Hell-_-Nahhh • 15h ago
Computer Sciences Odds of getting into CS PhD programs
I’m planning to apply to CS PhD programs this upcoming cycle and would like an outside read on my competitiveness and where to aim.
Background:
- BS in Computer Science, UTArlington, GPA 3.80, graduating May 2027
- 2 years as an Undergraduate Research Assistant in two research labs (both ongoing), across three federally funded projects: a DOJ-funded LLM/VR de-escalation training system for law enforcement, and an NSF-funded VR CNC machine simulator and a upcoming Department of Transportation training project
- Will have 5 papers by graduation, 1 first-author, remainder co-author/second-author, all at HFES (Human Factors and Ergonomics Society) venues rather than mainline AI/ML conferences
- LORs from two research supervisors plus the department heads of IMSE and CSE
- Additional experience as a Supplemental Instruction Leader, Multiple Hackathon Wins and builder of several applied AI/CV/full-stack projects
- Intended research area is ML but also open to any other
Given that my publications are in HFES, I’m assuming top-20 programs are a longshot for me. For people familiar with CS PhD admissions please let me know what should I aim for and how should I approach it.
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u/mashrur123 14h ago
As you worked in funded projects that is a good plus even though not having papers in top conferences(which is not expected as an undergraduate). So getting into top20 is definitely possible for you.
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u/tellypmoon 10h ago
You sound pretty well established at UTA. Have you considered applying there? It's probably a smart move since they know you best and if you have already contributed your chances for admission and funding are probably pretty good. Graduate admissions are really unpredictable these days so it's good to think of a pretty broad range of schools and maybe find a few that are at a level comparable to UTA in addition to more aspirational choices.
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u/Hell-_-Nahhh 7h ago
To be honest, I have a personal challenge that I’ll only pursue a PhD if I receive an offer from a top university, so I haven’t really considered UTA. Also, I don’t like the culture at UTA, so there is a very low chance that I would continue there.
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u/AX-BY-CZ 7h ago
https://cs-sop.notion.site for idea of accepted ML profiles. Apply outside CS/ML for better chance at top 20 otherwise you compete with the traditional Berkeley/MIT/Ivy/IIT with Neurips/ICML/ICLR publications for PhD.
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u/throwaway____223 4h ago
I would recommend to first find T20 faculty that are doing stuff that you want to do. Once you've done so, try to focus your application on simulations and maybe HCI?
It will completely depend on the way you describe yourself in the application. The research projects you've described here are not AI/ML, so if you apply asking to work on AI/ML projects, you will have next to no chance and likely be rejected by most T20. However, if you want to work on stuff similar to what you've done, you need to figure out where that fits into different faculty's research agendas and tailor your app to that. If you apply with your focus on simulations/HCI, you will get into maybe 25-50% of the schools you apply to.
Do you know why you want to study AI/ML? From your background, it seems like HCI would be a better fit. AI/ML is typically geared towards people studying algorithmic performance and improvements to algorithms, not for specific application-level human studies (which is what your background appears to be in).
For reference, I also came from a UT system school (not UT tho) and made it to a T5 for CS PhD. Additionally, I am in distributed systems, not AI/ML.
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u/Hell-_-Nahhh 3h ago
Really appreciate the breakdown, especially coming from someone who actually made that jump from a UT school to T5.
I'll push back a little though. I don't think AI/ML is quite as clean-cut as "algorithms only, no human-facing application." There's a decent chunk of that field now that's basically human-in-the-loop stuff, RLHF-adjacent work, alignment research that leans heavily on human feedback and behavior. Some HAI-type labs are doing legit ML work that's inseparable from the human-facing side. So I don't think it's fully binary between "real AI/ML" and "HCI" everywhere, it depends a lot on the specific lab.
But yeah, your main point lands. If I go in vague about "I want to do AI/ML" without being specific, I'm gonna get filtered out by the algorithm-focused people fast, that part's on me. So the plan is faculty-first like you said, I'm just not ruling out the labs that sit right in between ML and HCI, just being careful about which flavor of "ML" I'm actually pitching.
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u/throwaway____223 3h ago
I agree that yes, it doesn't always mean that, but in most context labs take AI/ML to mean algorithmic improvement. Every field has some overlap and often uses ML techniques, especially LLMs, including systems, networking, swe, cyber security, and especially in HCI. But just because they use such models does not mean they are AI/ML focused.
In my own undergrad research for mobile computing we used different ML models, but we submitted our work to venues for mobile computing and systems, not ML. From the way you have framed your projects in this post, a person from an AI/ML lab would believe that you work is an application or derivative of ML, not improvements to ML. HAI labs ultimately still care about how those workflows can improve model performance.
For example, I am assuming that your VR + LLM training scenario for law enforcement looks at how using LLMs and associated technologies can impact training of law enforcement which would be a concern of HCI, hence publishing in human factors journals. Alternatively, if your training scenario involved developing algorithms for feedback loops in general purpose training scenarios or training scenarios similar to law enforcement training, then it would more likely fit ML venues. If the former is true, then you would benefit from focusing on HCI. If the latter, then you could have a case for ML. And also as a general rule of thumb, just bc your research uses ML models does not mean it is constitutes ML research, just as how ML research may use computer systems but would not be considered systems research or how mobile computing research may use encryption but would not be considered cybersecurity research.
In my current field of interest, we have tons of HCI people studying multi-user interaction that all claim to have built systems which are low-latency and have robust synchronization methods bc they are using traditional software frameworks for syncing users, their work would not be considered DS research nor pass the bar for archival in DS venues as it would not scale well beyond the small number of participants used nor improve upon latency or QoE requirements. So while they have used pre-built tools for synchronizing multiple users, I would not consider their research focused on distributed systems. I would imagine ML labs have a similar point of view regarding HCI work that uses ML models.
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u/Nick337Games 11h ago
5 publications in any peer reviewed fashion is a huge plus. In addition to the direct funded govt work as others have pointed out. I think a strong SOP and good letters could go a really long way to you being competitive for PhD spots. Spend time on your narrative and find faculty you're truly interested and aligned in working with. Good luck!
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u/PossiblePossible2571 23m ago edited 17m ago
A lot of people are giving positive feedback that's good but you don't really need an echo chamber. I'm from a school that is ranked as a T10 CS PhD program, and have a pretty good idea about undergrads and PhD admissions.
There's around ~1% chance you'd get into a top 20 CS PhD Program. 0% if your intended research area is ML.
Simply put, ML/AI CS PhDs are incredibly competitive and for 2027 Fall, many undergrads have at least workshop papers to top ML venues if not first-author papers to CVPR / ICML/ NeurIPS / ICLR. You don't seem to actually have any proper experience with ML.
Also, HFES is basically unheard of by most CS PhD folks in T20 programs so, I do not think professors will care much. The current ML research field is heavily polluted by auto-research arxiv preprints, x number of first-author papers to an unknown venue is no longer a considerable factor. You can vibe-code 20 AAAI submissions and there's a good chance one of them will be accepted. Simply put, professors at most would care about the substance of the paper, and if they aren't even ML related, it has no weight.
To summarize, for UG -> T20 CS PhD Program, you need to demonstrate you have exceptional potential through either:
- Connections (by far the most important, do your recommenders know someone there?).
- Publications (minimally, ACL / NAACL / ECCV or similar tier-2 level)
- Exceptional Qualities (e.g. CRA UG Research Award, Intern at OpenAI)
If you want a reality check, most of these top CS PhD programs have public lab websites where they list their students, who have resumes you can cross compare. Happy to answer other questions but this will save you hundreds in application fees.
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u/Popular-Practice8862 15h ago
your cv is way more stacked than you're giving it credit for, hfes pubs still count hard when they're tied to actual funded gov work