Machine Learning

I’m in a similar position to you, altho I have publications in the medical domain with applied ML. I am currently pursuing the path of SWE ML. My idea here is that if I want to do research I can move into that slot pretty easily provided I do well in my initial position for a bit

Dude I’m also a PHD dropout don’t even worry about it. Just enjoy life and use your skills.

You’re not a failure until you give up. You haven’t accomplished your goals yet, but you still can write/submit papers and apply to your dream job. Persistence will get you there eventually if that’s what you really want.

Just get a job at a different company. Not every place demands fancy publications. Many just need you to know what you're talking about.

I spent five years in an engineering phd and finished with zero publication. Afterwards, I went into management consulting. Clients care about the degree and maybe the school, they don’t have the attention span to check out your publications.

After a few years of that, I went back into academia, but as an administrator who focuses on technology commercialization. I make significantly more money than academic scientists with glorious publications at my age in the same institution. I’m cofounding a company with two of said academics.

Where is your advisor in all of this? If your advisor is not helping you navigate this they are not doing their job...

Build your own startup and make it successful using your knowledge/ skills. Once the startup gets traction, you get to hire people with publications...

Edit: happy to chat if you need a nudge

I never understood the logic peer reviewed conferences. For example take 2 top conferences in any sub field of ML, now why are these 2 conferences top conferences? Do they become top conferences just by limiting the number of papers they accept? The people who review for these conferences are also the same so what makes them different. Also the capabilities of each reviewer are different so how are scores from each reviewer given the same weightage? A incompetent reviewer might give a bad paper very high score whereas a competent reviewer might give a good paper average score.

You will certainly face an uphill battle when trying to compete in ML these days. A huge percentage of the people getting into top tier ML programs already have 1-3 papers in those conferences so you would need an incredible connection to even make it through most screening levels for an internship. I definitely wouldn't say it was a failure or waste but I think you set yourself up poorly and then allowed that to compound when it didn't work (by repeating the action/goal rather than resubmitting and getting publications in other conferences and journals). If you wanted to get into one of those careers I would suggest staying in your program for another year or doing a post doc but if finances are important right now then you have to prioritize. Nobody can really tell you what is important to you so I would say it's about deciding what is important and choosing that rather than letting life happen to you.

I have a PhD in material science. No publications. They didn't even ask if I had any. Probably not a issue. They might value the fact that you were able to get a PhD to start with.

I believe that research starts after completing phd. Doing phd makes you learn how to do research so phd is just a beginning of a long journey

I still haven't published yet and I've been here 4 years now. I'm part-time though so I can barely find time.

Tbh its really uncommon to produce something worthwhile in a 3 year PhD, most work you see in respected journals take 5-6 years minimum with a lot of post doc. I'd say you did not lose any time, if anything the title alone is worth more than a measly 3 year study

Program requirements vary, but I would expect 6 or more publications in an IEEE/ACM sponsored conference from a student prior to scheduling examinations. A good way for students to demonstrate they are extending the field of study is through peer reviewed works.

The advisor-student relationship is perhaps the most important part of the process. It’s not what you did, it’s how much could you do, and did you meet your potential. The process is not technical training, but guidance on how to conduct research and how to be a good mentor. Not all people spend their life in research and teaching, but this is the historical intent.

Your advisor, and to a lesser extent your committee, has as much of a responsibility for your success as you do. If you gave it your all, and could have done more, the failure, if there is one, is shared with your advisor.

Most people feel a bit of fear and loathing when completing their academic work, this is normal. Take what you have enjoyed and learned to enjoy and move on. Your work, even if not peer reviewed is likely to open doors.

You should consider yourself blessed the way your life went. Quant finance is maybe the most prestigious thing you can do in the US or any country. The job is interesting and rewarding. In a few years you will be looking DOWN on those machine learning snobs at Google. Now Google wants to be seen for their AI accomplishments but don’t even want to hire suitable specialists like you. Only the top of the crop. What a miserable outlook.

Later, once you made it into a quant trader role at a hedge fund or prop firm, you will get 20-50% of the profits your algos generate. Which can be millions per year. Quant trader is a job where only your performance matters, which is a good thing, not stupid papers that some biased reviewers didn’t like.

Remember: the path of least resistance is always the best. Don’t swim against the stream. Bend with the wind like a Willow tree and you shall stay whole.

I think there's a lot of bias in how you're looking at the data. In particular, for someone trained to deal with noise, you're attributing your observations to signal, not noise. What's the acceptance rate at these conferences these days? It's so low it beggars belief. The review process exists during the same duration as ever (the briefest in academia), but the raw number of submissions has exploded. There's no serious way to stack rank that much data without multiple evaluations, and that's too hard / expensive. So the end ranking is largely noise, probably only weakly correlated with the "true" ranking that would be determined by a million ML profs doing nothing but reviewing papers all the time.

You have failed in the narrow sense that you didn't earn the laurels you needed to achieve your career goals. But that isn't to say that someone else, with your fortune but their ability, would have done differently. You gambled and didn't win the grand prize.

Please don't buy into the myth that those of us who've gotten luckier are so well-served by propagating - that this field is a serious meritocracy. There's just way too little signal and way too much noise to take that belief seriously.

At least you got something. You'll get about the same money, but fewer tshirts and snacks. And you'll have to dress better.

Which area of ML are you working on?