27

I have read many PhDs have a feeling of failure during their studies. At the moment I am seeking a reflection, from someone who is/has been a PhD and maybe a similar situation.

I found myself in a group of around 10 PhDs under 1 supervisor. All of them are non-native speakers, struggling with English, and speak their native language (which I do not speak) amongst each other. Meetings with my supervisor deteriorated into short, few minutes long conversations, where he presents his ideas, that are unprecedented in literature, or his previous research.

I have decided to detach myself from his suggestions and try ideas that I can reference in previously published literature.

To give the full story, I have followed his advice and suggestions, but this lead me to a dead end, twice. After quite a hard time, realizing that I am fighting my supervisors advice and undoubtedly a fear of another failure, I have stood up and started again.

A new idea, new approach. I took all the relevant courses and read literature. After several tests, the idea seemed to work.

But as of yesterday, I found a flaw and a possible problem that discards my 4 months or 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. Leaving PhD (temporarily/cancelling), starting new PhD in English speaking country or working. I really don't know.

P.S. The lady at counselling is very nice, but far from offering real help/career advice. Other PhDs cooperate together, but since there is a language barrier, I am left out. My supervisor has little / no understanding of machine learning or data I am working with.

P.S. 2 I am temporarily in a bad mood. 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.

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  • 3
    I find these statements contradictory: "I followed every word of my supervisor, working 12 hours a day" + "last semester was all machine learning and AI" + "My supervisor has little / no understanding of machine learning". How can that be? – rg_software Feb 20 at 8:45
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    @rg_software Thank you for reading about my problem. I have started my PhD listening and fully trusting to everything my supervisor said. After hitting dead end and realizing my supervisor has little understanding on the topic, I have slowly diverged. Since then I put all his suggestions into perspective of previous research and I slowly found out he doesnt understand the concepts of what he is proposing. I am not sure, where exactly the contradiction is. Is it about the following and then diverging from my supervisors advice ? – Martin G Feb 20 at 9:22
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    I do completely understand your problem. Is this program in the US or Europe? – user103209 Feb 20 at 11:43
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    @rg_software Yes. I was naive and believed that following his advice exactly will lead to success. I spend most of the time programming, rather than reading or studying data. I was naive and the positive feedback reinforced my belief that I doing great. Until I hit a dead end, where he didnt understand what is going on. – Martin G Feb 20 at 11:48
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    @Monkia China/Hong Kong. All other PhDs are locals. There is no interest in speaking English and we have no group meetings (I meet my supervisor with 1 other local PhD, that also struggles with English) – Martin G Feb 20 at 11:51

13 Answers 13

32

Failing is part of research! We all fail; those that haven't are surely not trying hard enough.

If you're failing too frequently, then

  • Search for easier problems
  • Discuss ideas with peers
  • Find a mentor
  • Collaborate with another student
  • Work more closely with your supervisor
  • Attend seminars and conferences
  • Study textbooks on the research process at PhD level
  • 16
    - look something else than a PhD? Honestly, many people waste their time therein for wrong reasons or talked into it by a professor – user48953094 Feb 20 at 12:07
  • 1
    I fear quitting PhD gets downvoted on a site full of profs ;-) so I leave it up to your anser. So there is a difference when one should consider quitting when started with a bachelor/master/diploma imho – user48953094 Feb 20 at 12:14
  • 3
    @MichaelSchmidt profs push/recommend/suggest PhD students to quit – user2768 Feb 20 at 12:25
  • 1
    I am contemplating on finding a mentor. But I struggle to set my expectations. May I ask, what you would recommend as a top mentor characteristic ? – Martin G Feb 20 at 13:32
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    @MichaelSchmidt a step short (though still serious) of quitting a PhD is finding a new supervisor. I fear from the stories I hear from friends who stayed in academia, that the students who would most benefit from a new supervisor are the ones that never hear of the idea, because the supervisor themselves would never suggest it (perhaps for the same reasons that they are not the best supervisor...). The motto of industry is that "people don't quit jobs, they quit managers" and there is definitely a lot of truth to it. – mbrig Feb 20 at 19:11
20

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...thats a bad time for failure.

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.

  • At my university there ist a fast-track programme for CS and machine learning, but I have to admit the fruits hang so low (getting PhD in 3 years for often optimizing an algorithm) that I wonder why someone in CS is allowed to earn in 3 years after bachelor a PhD, while every physicist spends the double time inclusive master degree!? It doesn't speak for the value of the PhD in CS, especially when you pursue academic career and don't belong to top 5% students, and my university is the best among the german ones concerning this topic... – user48953094 Feb 20 at 13:53
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    @MichaelSchmidt Back when I was applying for graduate school I was warned away from CS because "a lot of the low hanging fruit has been plucked." Most of the work being done now is incremental improvement on existing algorithms. – anonymous Feb 20 at 16:21
  • To quote Edison: "I have not failed 700 times. I’ve succeeded in proving 700 ways how not to build a lightbulb." – Ister Feb 21 at 11:46
  • @anonymous That is always the "word on the street" with computer science and statistics. The low hanging fruit was plucked until convolutional neural nets...then it was plucked until GANS....then it will be plucked until the next advanced. To be honest, AI is moving so quickly that its difficult to find all useful applications to a current technique before a new technique is developed. AI has not even really made headway into the social sciences yet for one. – JWH2006 Feb 21 at 13:13
  • @JWH2006 With regards to the first part, I'm not sure I would entirely agree that CNN, GANS, and so forth are really "low hanging fruit" in the sense of major advancements like Dijkstra's algorithm was. A good case could be made that they are more incremental improvements. With regards to the second part, computational social science is growing as a field but there are also a lot of reasons why social scientists are reluctant to use AI / ML to conduct research. – anonymous Feb 21 at 14:59
11

Sorry that you feel like you're in a bad situation and are not going forward with your PhD. I will however echo what some other answers already said - 3 semesters is very short, PhD programmes last 5+ years in some places, it is quite common not to produce publications in the beginning of 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:

  • supervisor struggling with basic concepts in the field (in my friends case, the supervisor was refusing to take a baseline measure. In machine learning, it could be basic concepts such as cross validation; or understanding accuracy is a bad measure for unbalanced data)
  • you are repeatedly asked to perform experiments for which you can find no justification; i.e. they do not seem to produce any results worth reporting nor allow you to analyse any interesting properties of the methods you are using, and this does not change with time nor does the purpose become clear after performing experiments.
  • you are asked to perform experiments which you can not concisely place within the current literature, i.e. they neither seem to build on, complement, replace, or fit in any way with the current body of literature, nor is there any importance given to placing ideas within the current state of the art. Of course, the ideas which we work on should be new, but they can not be created in a scientific vacuum. The appearance of said vacuum could be a red flag.
  • the scientific output of your advisor in term of peer-reviewed publications is very low. A good researcher with only lateral knowledge of the field might still be a good advisor, if he is guiding you in quality writing, pointing you to good conferences and high-impact journals and in general imparting approaches to doing good, solid, repeatable science.
  • communication problems. Your supervisor is supposed to be somewhat of your "scientific parent". If you can't communicate ideas between you, and moreover if you can't occasionally discuss your (professional) doubts, career plans, etc., you are simply not getting adequate supervision. I personally would expect even a minimal personal investment from my supervisor (the creative work we do, again, does not exist in a vacuum, so shocking personal circumstances like i.e. death in the family could easily impede our work, and I would expect to be comfortable sharing such a circumstance with my supervisor)
  • they do not facilitate a collaborative environment in their lab. It is the supervisors responsibility to form their research groups. A lot of quality work is produced through discussions with your peers. If the environment is not supportive in that sense, be it because of the language, cultural or some other barrier, it is diminishing the quality of the PhD programme.

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:

  • discuss the proposed topic. It doesn't have to be perfectly fleshed out, but it should be something you are interested in, and you should be able to gauge if the potential supervisor has plans on how to develop the topic and the ideas. If you strongly disagree on the approaches, it is not for you.
  • look through the potential supervisors previous publications. Are they in good conferences/journals? Are they well written? Are they cited?
  • look at the previous students of the same supervisor. Did some of them end up in career paths you want to take?
  • if possible, talk to the some students from the lab. Can you establish a conversation easily or do you seem to be struggling to start chatting?
  • if at all possible, have a face-to-face talk with the potential supervisor, even if it is just as a video chat. You need to be able to communicate well with your supervisor; if you can not you probably are not the best fit for each other.
  • don't make English-speaking country a priority as such, rather let it be communication (this is true both for your lab environment and supervisor themselves); there is a lot of international labs everywhere. But being able to communicate is key.

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.

7

Fellow former PhD student here (I left after my Master's and took a job in the private sector).

You have to remember a few things:

  1. 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.

  2. 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."

  3. Go to your advisor and express your feelings of failure to him/her. They will generally sympathize. Go to another professor you like/have a good relationship with them, and talk to them about this feeling too. You will soon discover that every single PhD on earth went through this, and they will definitely make you feel better.

  4. 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.

  1. 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? Take your Master's and get a job in the private sector making $120k+ (I assume you're capable of coding since you're doing AI/machine learning).

  2. 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...so you can do it, and you are a graduate student.

5

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.

Things that happened to me include:

  • Getting results that are neither positive nor negative.
  • Spending a year studying a system just to realize that it is irrelevant to the issue I originally wanted to study and nothing interesting happens in it.
  • Reinventing a subfield and learning that it is, in fact, not new from paper's peer reviews.
  • Spending half a year working on a legacy code, only to realize that writing the same thing from scratch would take two months at most.

Things that happened to people around me include:

  • Studying the same obscure problem for three years only to get a single publication out (graduated, went to industry).
  • Trying to go after interdisciplinary research and being unable to meet requirements of either program as a result (graduated with MS).
  • Doing research that is so unconventional that it is very hard to publish and impossible to use for a job application, yet that has a Nobel prize-level potential (graduated, went to industry).

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.

To stay, you have to accept that you are navigating a stormy sea and take risks.

Some practical advice:

  • Do argue with your advisor when you disagree. Argue constructively, that is, with expressed intent of finding the truth. Stress-testing of all ideas is good and is usually appreciated. If you doubt something, say so. Your common goal is to figure out what is going on, so keeping doubts to yourself is not nice to anyone.
  • Change your opinions if the other side's arguments are solid and yours are not.
  • You will get into the situations when your and your advisor's professional interests are at odds. Negotiate a compromise.
  • If you can't communicate to your advisor constructively, consider switching research groups. But be careful, starting from scratch takes a lot of time.
  • Plan for failures. Make contingency plans upon contingency plans upon contingency plans. Keep in mind that failures may originate not only from within your system (non-publishable negative and inconclusive results), but also from outside (hardware failures, health problems, administrative issues). Keep in mind that you can't know or control everything and thus there are no guarantees. (N.Taleb wrote some good books on managing these kinds of situations.)
  • Use tests in software development. Use sanity checks and benchmarks in research.
  • Be very careful when using legacy code. If you can't get entirely comfortable with the codebase in a week it's usually better to rewrite the thing.
  • Avoid overworking yourself. This is a marathon, not a sprint. Yet, in this marathon it pays off to be prepared to take occasional intense sprints. Work a lot, but keep yourself in shape.
  • Allocate some time each week for exploring other people's work.
2

I think part of Ph.D. education is to become independent from your supervisor. Truth is, the moment you can convince your supervisor that you are right and he is not, you are ready to defend. The process of getting there can be frustrating.

You say you are 3 semesters or 1.5y in the studies. That is nothing. It is completely acceptable to feel lost at least 3/4 of the time. Towards the end, you will gain confidence and skill.

Develop your own mind, learn how do present and defend your ideas to your supervisor. Instead of asking "What should I do?", notify him "I am working on X because of y and z, I also contemplated a,b, and c, however they do not seem to give better results". Learn to become a collaborator to your supervisor and not a nuisance that needs constant guidance.

UPDATE:

Relax, very few people have actually failed their PhD defense. If there any probability of that, you will not be allowed to defend. Most go insane, give up in the process, or are pushed out for administrative reasons.

1

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.

I don't know what is the type of your supervisor whether he is a micro-manager, hands-on or off, I do think he gave you some liberty and that is an advantage to test, but you have to set a deadline to make things work out.

One of the strategies is attending workshops, conference, submit late results to a conference and that will build up the knowledge step by step. Also, it is a good habit to have in regular basis weekly meeting and make sure how you a good communication and sharing your ideas. Also, I think you have to interact more with senior researcher and other senior students, I think this would be more helpful to get feedback about your work, you can also interact with remote-researchers.

According to the language barrier, I don't think that being in an anglophone country will make the situation different, the language is not the problem at all, however, you have to train yourself to embrace your supervisor, try to make effort to have a common conversation, it is an art how to understand them even if they struggle to speak like you.

Lastly, maybe we don't have a complete picture of your situation and you are the only person who really foresees your situation, however, if you think of leaving your position, I would tell you this will not solve an issue, however, I am still little bit concerned with your supervisor is lacking a rudimentary knowledge in machine learning.

Give yourself a couple of months and start applying to other positions with a good professor who has proven knowledge in that topic, and starts observing whether there is an improvement in your research, in that case, it would be good, you are an independent researcher, if not you can consider the second option.

  • Thank you for the first paragraph. Also I want to point out, I do not discriminate based on language proficiency, I am rather patient and dont push people. But once there is a collective with their own language, it is very difficult to get in. We dont have any group meetings, so I feel like I do not have access to what is going on. I would like to know what options are there to get in touch with remote-researchers, other than conferences? – Martin G Feb 20 at 13:23
  • That is a good question, how to get in touch with remote-researchers? Read their paper, reproduce their codes on github, by the time and persistence, you can find an issue in their codes, or questions, and the always the easiest way is to get in contact with them via email, expect not all people would respond, but there are many people would really glad to discuss and help. Another effective point is scientific platforms where you can find your colleagues post questions and problems in machine learning, and you can watch and observe the expert in your topic answer. – user103209 Feb 20 at 13:49
  • Martin, I can understand your feelings, being lost, desparate and where are you going, this is a normal feelings, but the most important trait you have to learn is stamina and persistence. As long as you are in the place still welcoming you, you can prove yourself, read deeply, concentrated, try, code alot, test, I am sure you will do it. – user103209 Feb 20 at 13:51
1

I advise to look for some easier problems. Get a couple "datapoint" type papers done to give yourself some wins. Need to boost your confidence and you can learn from those also. Heck you can even end up stringing enough of them into a thesis.

1

It's a good idea not to trust your supervisor too much.

A critical approach to new ideas will help you get your head around the idea, and may uncover a quick way to avoid a lot of work. Most of the time you will need to do the grind, but if you can argue why it was needed to prove it wrong, you have still succeeded at research. Negative results aren't as publish-able but that's in part a failing of the system.

Good luck btw, I have been at least close to where you are, and learned the hard way.

1

I’ve spent more time in university than any human should (BSci MS PhD Mathematics, BA MA French Literature)

First: It is extraordinarily rare to find a topic on your first go unless you did it for your Master’s thesis. I went a long way into one area of math before switching and finding a truly I retesting dissertation topic.

Second: Every PhD candidate—all, I say—wants to put their notes and dissertation into the sink, in order to safely set them afire. It’s natural.

Third: I do not know what you want to do with your life, but the doctoral program and dissertation are a once-in-a-lifetime experience, which is both good and bad. I would easily go back and get another given the chance. Writing the dissertation was harrowing, and is unlike any research you’ll do afterward. It’s your first project, not your life. Many never do research again.

You are your own worst enemy at crisis points, since you don’t realize how common they are. Even throwing away a bunch of good (but invalidated) research. If you are in a life position to stick with it, pray do. It’s a unique experience that career and marriage and mortgage and children get in the way of later. Just be sure you like the subject you’re in, because a wonderful topic will send it’s way into your life and you’ll be happy again before you know it.

0

It is a common misconception that the guide knows nothing. That being said what you need to know is what your lab seniors have done.

What I mean is scrutinize your supervisor and his past students. What is his publication rate? What was theirs? What is the average number of papers your seniors produced? When did they publish? What were you expecting to even achieve? Did you have a research plan? A topic? Phds last upto 7 years.

3 semesters is nothing. You cannot have even given your comprehensive exam or state of the art seminar. It is way too early to even consider failure or success.

Also all ideas are not in literature at first. That's what makes them new. In any case, coursework is never an indication of research acumen.

This may seem harsh but you must first figure out the answers to the questions I asked before and then determine your course of action. It is very common for PhD students to only produce papers in their 4th and 5th year.

  • Thank you very much. I see I am still missing the larger picture. But interestingly, I see there are different modes of PhD. Mine program is 3 years + extension. We/I am supposed to produce 3 papers. Most/all other PhDs publish in a journal, that the supervisor is related to (he is editor of the letters). – Martin G Feb 20 at 13:19
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    Is this journal any good? Do the papers get cited by people outside of your research group? Does your research group publish in any other journals or conferences? Answers to all of these should be "yes" if this is exclusively where you publish. – penelope Feb 20 at 15:24
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    @MartinG well a three year program is for people with a masters degree so in which case you should focus exclusively on research instead of coursework.. When I said 4th and 5th I assumed a 5 year program, in your situation it would be 2nd or 3rd year. Also, how many students needed extensions? – HaoZeke Feb 20 at 15:29
0

First disclaimer: I have not advised many grad students. Secondly, this is not generalized, but merely a personal view to share a perspective.

I have done research in industry and teamed with academia. I consider it always my responsibility to be able to determine the reasonableness of what I am trying to show. If it is unreasonable, and I cannot see a viable path to the end, then I consider it very high risk. Since I don't like to waste my time, many high risk pursuits get trimmed.

Furthermore, I do not blindly follow what anyone asks me to do. It is evaluated, cross-checked, and tested for reasonableness. In the end, if I fail, it is not the supervisor's responsibility, it is mine. And if it was a hard problem (the kind I like) and it was high risk, I pursued it knowing that.

So from my perspective, each student is responsible for their path. If their supervisor/adviser is not reasonable, hated by the other faculty, or simply comes up with un-viable ideas, then it is time to go looking for a different gig. But look first at one's self to make certain that you are doing all you can and making reasonable decisions.

  • Thanks, I agree with that and I understand that I have failed/it is my responsibility, whatever the role of my supervisor was. – Martin G Feb 21 at 2:06
  • @mango, I do partially agree with you, of course the student has the responsibility, but not give them all responsibility. You are a PI, and it is expected that you expert in this research line, now we are taking about PhD, not masters, so as a student I am expecting at least to have some guidance and constructive feedback about my ideas and work. Why I am saying that this a critical? because in my experience in masters my PI, didnot have any kind of experience, which made me struggle and asking remote researcher, but I was missing how to do a sound research. – user103209 Feb 22 at 10:56
  • Later, I decided to select another mentor for PHD, he is expert in research line and he appointed to me to apoint out of his experience, and during discussion he misleaded me, and of course, I read alot I was telling him this a serious mistake in a polite way, and it could deteriorate our project. In the end, he didnot like my work and was furious, he told me that he doubt my work and you are independent researcher and he didnot like it and forced to leave after one year. – user103209 Feb 22 at 10:58
  • So, for the OP, just give yourself couple of months, if you see yourself in a deadblock and you cannot notice a progress or improvements, then you can be worried. Of course, we have different situation, PI's personalities. Likely, maybe your situation is going to get better. – user103209 Feb 22 at 11:00
  • Lastly, I am so sorry that you are crying sometimes alone, I really feel so much your pain and I wish sometimes someone could hug me and calm me down, but I am telling you, you have to be a persistent as much as you can and hope every thing would get better. – user103209 Feb 22 at 11:01
0

As others have suggested failing is normal in this area. I haven't done a Ph.D. but last year I completed my thesis work for M-Tech.

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 3-4 months I was able to submit my research work and it really felt so good.

So, I suggest you that instead of "Leaving Ph.D." give it another shot and look for work that you're interested in and it'll be plus if your mentor is able to help you if not then do it yourself look for similar courses online, Email people who are working on similar projects.

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