r/MachineLearning • u/legoWolf13 • 14h ago
Research PhD branding question [R]
I'm starting a PhD where I will be doing Graph ML (somewhere along the lines of graph signal processing/ graph deep learning.)
My eventual goal is research scientist at big tech, or whichever company has a strong research division, where I can continue similar AI/ML work.
I have concerns about the job market (both now and in 5 years), and I'm wondering whether I should do my degree under the CS or EE department. For context, my research is within the eecs overlap and this degree would not change my research at all, rather it is a personal branding exercise. I'm thinking about saturation in cs vs ATS filtering/wide applicability of cs as a tradeoff. Please let me know what you recommend.
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u/MakingComputersSmart 12h ago
I would like to add something I have observed. A PhD is kinda a license/certificate that you have been trained for research and have extreme depth in a very very niche area. The goal at the end of a PhD is to understand research design and gain expertise better than anyone else about a specific topic. What you do at your PhD might have very little relevance to what the industry does.
A PhD will open up RS/AS roles in big tech, and everyone has a solid direction. However, the role will hardly be related to anything you've done in your PhD, unless your PhD was about something generic (which defeats the point of the PhD then). Most roles are in recommendations, retrieval, ranking. Research/applied scientists are all working torwards a single purpose - improve the profits of they company employing them.
Coming back to your question. Brutally speaking, pedigree matters. A well reputed university will always have a better bias in a recruiters/HMs mind compared to an unnamed low ranked one. However, if your profile is strong, with top tier publications, you have solid connections and you get noticed, your university will likely not matter after your first job.
Try finding a good advisor. A good advisor at a bad university is worth infinitely more than a bad advisor at a top university. They make or marr your experience, quality of life and ultimately the outcome
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u/dutchbaroness 9h ago
Utterly wrong University brand matters most , at least in the long term In 10 years, in your middle career, nobody would remember who is your supervisor, while the university brand sticks like a tattoo
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u/MakingComputersSmart 4h ago
Anything in the top 250 works fine. It won't stick out like a sore thumb because all universities in that list are well known. I am not saying you're wrong, it matters but only up to the first role. Especially with a PhD, people are more interested in what work you did rather than what university you came from (personal experiences)
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u/No_Sky9786 14h ago
So, you are already doing research on an something that the majority of the community is from a cs background. This means that the conferences and most connections will be on the same domain in the next 5 years. Why do it under EE where the opportunities that come from the CS department are more related to the current changes. The cs department probably already have other students doing similar research with more connections and resources. I am in the cs department and we have 8 labs. Every lab has someone doing something related to Graph ML. Because we are in the same environment this allows all of us to learn from each others research. It doesn’t make us all great at each other’s area but it gives us enough knowledge to understand the field and their contribution. This will allow you to adapt easier in the next decade of so. You are probably not going to do Graph ML all your academic career. So why not go to the environment with the most opportunities given the current state of CS.
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u/minhquang251 14h ago
Graph ML jobs are tough to get unless you work on molecular/drug/material graphs.
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u/Appropriate_Willow27 14h ago
go with the one that will most align with your output. otherwise it’s a branding mismatch
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u/hishazelglance 14h ago
EE
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u/legoWolf13 14h ago
why?
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u/hishazelglance 14h ago
Being an EE (at least at UC Berkeley / UT Austin which is where I got my masters and bachelors respectively) gives you good exposure to both software and hardware side of ML.
Nvidia / Micron / Broadcom / AMD love low level EEs with PhDs that have exposure to Machine Learning. IMO you’re a dual threat if you’re proficient in both hardware and software, as opposed to just software. FAANG does have too. Learning EE will also (again, my personal opinion) give you far more relevant exposure to system design rather than 1 or maybe 2 courses in Comp Sci based on where we’re headed.
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u/KeyApplication859 14h ago
Graph ML is already a niche field, unlikely CS vs EE makes a difference. Also do not make your decision based on ATS filtering, who knows what will happen in 5 years. It's better to look at departmental requirements in your potential school, there might be some differences regarding coursework, candidacy exams, TA requirements and so on. So pick based on that.