After several tests, the idea seemed to work. I am a PhD for more than 3 semesters already. Within the first two I followed every word of my supervisor, working 12 hours a day, to deliver on his suggestions. This failed, his ideas failed, I have failed.
I think I can blame him for the ideas, but I am to blame for my naivness to blindly follow something, that he and I knew nothing about. The last semester was all machine learning and AI and 12 hours a day in a lab to make my own testing setup.
Now I am facing a failure, or at least a very intensive feeling of failure. What to do? Please, notice the word "forward" in the title means I am opened to any point of view. I really don't know. Other PhDs cooperate together, but since there is a language barrier, I am left out. It is hard to admit, but in this and previous situations I had break downs. Crying in my room, in my office or anytime when I realized, what failures I had been through, and that there is probably no one that could help.
Failing is part of research! We all fail; those that haven't are surely not trying hard enough. First of all, failing is normal and happens all the time in academia. We get our papers rejected, we get our grants rejected, or our awesome idea is later revealed to have a deep fatal flaw by a colleague.
This is all a very normal part of academia. Though this does not seem like it in the moment, you are in the safest possible place to fail in academia.
You are a student. There is a strong expectation that you are learning and will make mistakes. Now, if you were an assistant professor at the 4th or 5th year of your tenure review period So as far as next steps, learn what you can from your failure, pick yourself up, and work on another problem.
You are in an awesome field if you are working on machine learning. There is so much low hanging fruit. So many interesting applications of the technology. Sorry that you feel like you're in a bad situation and are not going forward with your PhD.
I will add that you have been doing exactly what you should have been for the first few semesters of it - getting really confident with the broader topic, related work, old and new literature in the field. So, you really didn't lose as much, and even more, you started getting your own ideas even if some of them fail, being able to formulate an idea already requires a deeper understanding of the material , which means you are progressing.
However, you say you feel like your supervisor has little understanding of Machine Learning and the data you are using. This is a very bold statement for a PhD student at the beginning of their research career. It takes a lot of courage, and can often seem like it's coming from frustration rather than a real place.
I don't know all your details - I just made conclusions based on some things you wrote - but I will assume your assessment of your supervisor is actually correct. As a Master student you expect to come to a PhD and work with leading experts in the field. Sometimes, it happens that they are not. Sometimes, it happens that a student and a supervisor are simply not a good fit with one another clashing work styles, personalities, cultures It happened to a friend of mine, and it took her well over a semester to come to terms that it is not her research that is going badly, it is the unfitting supervision in her case, also bad supervisor, but I am trying to allow for the case of simply "not suitable for me".
Some symptoms:. So, assuming you have taught carefully about all of the above, and decided you are being badly supervised, I am going to give a different suggestion from all other answers here. There is of course a way to try and increase the quality of your PhD by your own efforts alone, by networking with other people and attempting to attend conferences and workshops whenever the opportunity arises.
But, if you really thing you could do better research if you got more quality supervision, look for another PhD programme. In the grand scheme of things, 3 semesters is pretty quick to come to terms with bad supervision - it's not something you expect, so it takes a while to diagnose. While doing that, learn from your current experience and try to asses some of the things that are currently lacking before you accept the offer:.
Getting into a second PhD programme might be harder, and you'll need a compelling and tactful explanation of why you quit the first one it is generally not recommended to hide such things during admissions , it is a hard decision and you need to consider this option well. But staying in a bad one might mean that you too put down by the end of it to finish at all, or that you finish with a publication record far below your capabilities, and invalidate some of your career plans.
You are not alone in the feeling of being a failure. This is called impostor syndrome and it's extremely common. You should google it and read about it. It will make you feel much better. It is perfectly normal. On the contrary, people who claim to be doing so well in graduate school are typically the ones with real problems. The fact that you are worried about failing just means you're intelligent, sensitive, and concerned about your future.
Those are good qualities of a PhD student. Not all doctoral advisors are awesome. Some of them are terrible. I was lucky enough to have a great one, but many, many people switch advisors or drop out altogether because of bad advisors. Just remember, advisors are people, and not all people are great leaders or even great academics, despite having a PhD.
You should also google doctoral advisor abandonment and read The Guardian's article on "When your relationship with your PhD supervisor turns toxic. They will generally sympathize. You will soon discover that every single PhD on earth went through this, and they will definitely make you feel better. Try to solve easier problems.
Remember, in undergrad, you learned knowledge that already existed. In graduate school, you are trying to produce new research and make a unique contribution to human knowledge. You will not always succeed and that's fine.
Failure is part of the plan here. Make failure your friend. I wrote half my thesis on a particular principle, and then discovered some evidence that contradicted my research. I absolutely freaked out. I thought I'd wasted the last two years of my life. And then I realized that I could alter my arguments and realign my thesis with the evidence, and not all was lost. Just some of my arguments and conclusions evolved.
This is not a bad thing. Follow the evidence. You are going to be okay. Even if you ultimately decide you don't want to finish the PhD. A PhD will not make you any happier in life. It just adds three letters to your name and opens up a few job prospects. Hate grad school? You will eventually get into the swing of things. You'll find a groove. There is a pattern to graduate school, and once you figure it out, there will be no more surprises. You'll just be looping through the same challenges every year until like your third or fourth year depending on where you are.
Find the patterns, anticipate them, remind yourself that you've been here before, surmounted these obstacles before, and you are here to be challenged. You are here to sweat and cry and stay up late. You are an academic soldier.
And you've already made it 2 years I am a postdoc working in AI. The situation you're describing is nowhere near close to "failing PhD". During PhD you are doing research, which means that you are up against the unknown.
Failure is expected. And not just the failure of understanding the system, you're up against yourself. You will make mistakes and they will come back to haunt you. Science is hard. In some fields you can get into the situation when you are steadily rewarded for your effort, but that's generally not the case. And it is definitely not the case in AI, since that's the field where general methodology hasn't been worked out yet.
It is also an engineering field, meaning that people are not interested in failure that much, meaning that it is way harder to publish a negative result. While pure probabilistic machine learning is somewhat tractable, AI is considered by many to be harder than theoretical physics. Regarding your advisor - out of all AI researchers I met maybe one person had all screws in his head properly tightened, and maybe that's because I don't know him well enough.
I find this to be wonderful, but that also means that you have to take absolutely anyone's opinion with a grain of salt. As others have suggested failing is normal in this area. I haven't done a Ph.
Initially, it was fun to get to know the subjects and everything but as soon as the research work started it was really bad. My mentor gave me some previous research works to get my research started but after struggling with it for several months as it was completely new to me I picked up another topic in which I worked in my major project but in that topic, most of the work was already done so I had to drop that too.
After this, it was really bad and I started having doubts as whether or not I would be able to complete my work with the given time if not my course would be extended to another semester. But finally I decided to start from scratch and started looking for potential work that's when I found something I was interested in and after months I was able to submit my research work and it really felt so good. So, I suggest you that instead of "Leaving Ph.
First of all, you have to bear in your consideration if your supervisor is giving you false ideas is pretty common among students and not a big issue, however, what is really important that you have started realizing that those ideas fail and that means you are in your way to be an independent researcher.
For solving your problem, you have to know that you are going to be an expert on this topic, not your PI, so you have to wrap a plan to learn very well and practice more diligently , failure is a part of learning, I would be worried if everything sounds perfect to you, so you have to stress on learning and being more knowledgable and don't depend on your supervisor. By the end of the third year, a typical Ph. Of course, some students go too far with the related work search, reading so much about their intended area of research that they never start that research.
Advisors will lose patience with "eternal" students that aren't focused on the goal--making a small but significant contribution to human knowledge. In the interest of personal disclosure, I suffered from the "want to learn everything" bug when I got to Ph. I took classes all over campus for my first two years: Arabic, linguistics, economics, physics, math and even philosophy.
In computer science, I took lots of classes in areas that had nothing to do with my research. I only got away with this detour because while I was doing all that, I was a TA, which meant I wasn't wasting my advisor's grant funding. Perfectionism is a tragic affliction in academia, since it tends to hit the brightest the hardest. Students that polish a research paper well past the point of diminishing returns, expecting to hit perfection, will never stop polishing.
Students that can't begin to write until they have the perfect structure of the paper mapped out will never get started. For students with problems starting on a paper or dissertation, my advice is that writing a paper should be an iterative process: start with an outline and some rough notes; take a pass over the paper and improve it a little; rinse; repeat.
When the paper changes little with each pass, it's at diminishing returns. One or two more passes over the paper are all it needs at that point. Procrastinators should check out my tips for boosting productivity. Early on, the advisor should be hands on, doling out specific topics and helping to craft early papers. Toward the end, the student should know more than the advisor about her topic.
Once the inversion happens, she needs to "go rogue" and start choosing the topics to investigate and initiating the paper write-ups. She needs to do so even if her advisor is insisting she do something else. Going rogue before the student knows how to choose good topics and write well will end in wasted paper submissions and a grumpy advisor. On the other hand, continuing to act only when ordered to act past a certain point will strain an advisor that expects to start seeing a "return" on an investment of time and hard-won grant money.
Advisors expect near-terminal Ph. They should be capable of selecting and attacking research problems of appropriate size and scope. Solving problems and writing up papers well enough to pass peer review demands contemplative labor on days, nights and weekends. Students that treat Ph. It's important for students to maintain contact with committee members in the latter years of a Ph. They need to know what a student is doing.
It's also easy to forget advice from a committee member since they're not an everyday presence like an advisor. It doesn't usually happen, but I've seen a shouting match between a committee member and a defender where they disagreed over the metrics used for evaluation of an experiment. This committee member warned the student at his proposal about his choice of metrics.
Another student I knew in grad school was told not to defend, based on the draft of his dissertation. He overruled his committee's advice, and failed his defense. He was told to scrap his entire dissertaton and start over.
It took him over ten years to finish his Ph. This could be in the form of annual reviews, quarterly interim reviews or regular meetings. The majority of students also have a secondary academic supervisor and in some cases a thesis committee of supervisors ; the role of these can vary from having a hands-on role in regular supervision, to being another useful person to bounce ideas off of.
These frequent check-ins are designed to help you stay on track with your project. For example, if any issues are identified, you and your supervisor can discuss how to rectify them in order to refocus your research. In addition, the thesis you submit to your examiners will likely be your third or fourth iteration, with your supervisor having critiqued each earlier version. As a result, your thesis will typically only be submitted to the examiners after your supervisor approves it; many UK universities require a formal, signed document to be submitted by the primary academic supervisor at the same time as the student submits the thesis, confirming that he or she has approved the submission.
Despite what you may have heard, the failing PhD rate amongst students who sit their viva is low. You can find a detailed breakdown of all viva outcomes in our viva guide, but to summarise — the most common outcome will be for you to revise your thesis in accordance with the comments from your examiners and resubmit it. Therefore, while the breakdowns represent the current known data, the exact breakdown may differ. By using our data in combination with the earlier statistic provided by HEFCE, we can gain an overall picture of the PhD journey as summarised in the image below.
To summarise, based on the analysis of 26, PhD candidates at 14 universities between and , the PhD pass rate in the UK is Of the The above statistics indicate that while 1 in every 5 students fail their PhD, the failure rate for the viva process itself is low. If you believe you had a valid case, you can try to appeal against your outcome.
The appeal process will be different for each university, so ensure you consult the guidelines published by your university before taking any action. While making an appeal may be an option, it should only be considered if you genuinely believe you have a legitimate case. Most examiners have a lot of experience in assessing PhD candidates and follow strict guidelines when making their decisions. Therefore, your claim for appeal will need to be strong if it is to stand up in front of committee members in the adjudication process.
If you are unsuccessful in being awarded a PhD, an MPhil may be awarded instead. Finding a PhD has never been this easy — search for a PhD by keyword, location or academic area of interest. Hopefully now knowing the overall picture your mind will feel slightly more at ease.
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