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Failing PhD, how to go forward?
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.

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 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 ?

I just found it strange that you say you do exactly what he says which means "doing 12h a day machine learning", and then you say he doesn't know machine learning, so it means that he instructs you to use a method he doesn't understand himself?

@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.

@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)

In this case, I hope it should be reassuring that you aren't naive anymore. You seem to believe that it wasn't like "3 semesters or good work, no results". Instead, it is "3 semesters of doing irrelevant things, no results". So it means there is nothing inherently wrong with you, just start doing the right things, and you'll eventually have enough results for a PhD, probably.

I've been in a similar situation.  My advisor suggested a topic she new nothing about, and I wasted about a year on it (though in my case she didn't really give direction either).  For this and other reasons I left my Ph.D.  It was the right decision for me, but not saying it's the right decision for you.  I think the answers below are quite good, so I'd refer to them.  But if you decide in the end you need to leave, then just know that's an option.  But it may not be necessary.  Up to you.

I think it's important to specify how long Ph.D. students in your program typically take, and what timeframe your program is expecting. This varies from program to program and department to department. Not having anything at year 1.5 is very different in a program which typically takes 5-7 years, versus one where they try to rush you out the door at the end of year 2. (It's not uncommon to spin your wheels for the first N-1 or N-2 years, and only get "useful" results in the last year or two. [Not ideal, granted, but not uncommon.])

I think many have added their voice of encouragement.  I agree with them.  On the technical side, I would encourage you determine and analyze reasons 'why' an idea failed.  If you know why something doesn't work, apart from bugs in the code, then you can proceed to solve them; this is moving forward.  Your supervisor may like to try a 'wild' idea, and in machine learning, given its maturity, it is important to try an off beaten path.  If it works, then the reward is bigger.  Lastly, whether an idea works is not a yes/no answer.  It could be a 'yes' if you add x,y and z, but 'no' if it just x.

Voting to reopen, because even if the question depends strongly on individual factors, that's fine - it's entertaining (just look at the answers already written & the views), and that's why most of us are here, no?

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

look something else than a PhD? Honestly, many people waste their time therein for wrong reasons or talked into it by a professor

@MichaelSchmidt Very true! And after three semesters it should be clear to the OP whether they should quit. Perhaps write quit as your answer (with some elaboration on the rationale).

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

@MichaelSchmidt profs push/recommend/suggest PhD students to quit

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 ?

@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.

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...

@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.

To quote Edison: "I have not failed 700 times. I’ve succeeded in proving 700 ways how not to build a lightbulb."

@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 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 I should have clarified that the low hanging fruit consists of applications of those techniques. For example, we have only really begun to understand the myriad of uses for GANs. Applications like creating usable datasets with rare diseases without issues regarding sparsity is an extremely useful application of a GAN.

@JWH2006 Ah! I've heard similar from a couple professors I know that teach fuzzy logic.

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.

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:

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."
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.
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? 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).
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.

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.

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.

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.

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.

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.

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.

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).

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.

@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?

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.


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What is the proper response when a professor teaches a side topic related to the course but the information is wrong?
I've been a software developer for about 10 years or so and am familiar with various security related concepts (SQL injection, server-client validations, man-in-the-middle attacks, etc.)
I'm upgrading from a college diploma and found myself sitting in an "Enterprise Application Development" class learning about SQL.
The core content is very basic and normally doesn't cover security, but the teacher decides to talk about it. He advised students to use mixed cased table names as "security" so that it is harder to guess. This is known as "Security Through Obscurity" and is one of the least effective form of security (it is treated as insecure). Security is talked about throughout the class and they're all forms of the same thing and extremely dangerous since it is completely ineffective.
As a professional that knows better, if he was an employee I would've had to sit him down and have a thorough talk, and probably advise some security training.
What can do I do as a student? Since the other students are probably clueless
and security is not even one of the program's topic, there is no foreseeable chance for correct learning and they will probably end up doing this in the future, possibly jeopardizing their own career, the company, and all the data they work with.

Does the professor seem generally reasonable?

If the prof is reasonable, then you could take him to the side after lecture and suggest to him better ways towards security and why this method does not work. But be very aware of not using a patronising attitude ("I would've sit him down and have a thorough talk ..."). Most profs I know are happy to learn, but they have considerable experience in their main field of expertise and, while they will be happy to improve their knowledge, they won't be reacting well to be reprimanded like recalcitrant beginners.

sounds like an answer @CaptainEmacs

@CaptainEmacs Sorry, that line was suppose to mean that the degree of misinformation is very severe. That line was suppose to indicate my response if I was a manager of an employee. I didn't imply I can do this to a professor, so hence the question.

@Nelson Understood. I think Patricia's answer below is probably a good version of what I would try to say, too.

If the teacher is not a reasonable person, and some are not, you should keep your head down and hope that your fellow students will get some real security training, at least on the job, before making decisions in this area.
If the teacher is reasonable, select a very small number of references on the topic, preferably including peer-reviewed papers. Look for an opportunity to talk with the teacher privately, definitely not in class and preferably without any of your fellow students nearby.
Explain that you have had to learn about some security issues because of your jobs over the last 10 years, and that you have more up-to-date information than is being presented in class. (Yes, I know something of the history of security-through-obscurity, but we are being tactful here). Offer your references. How you play it from there on will have to depend on how receptive the teacher is, and how helpful you are willing to be.

I don't know enough of the details to actually judge, but there is a benign explanation. You say the the course overall is quite elementary. You also say that it is focused elsewhere than security and that this is a side topic. Let me start with that.
There is a Pedagogical Pattern called Spiral in which it is suggested that advanced topics can be mentioned in early courses just to put out a few terms and to put the ideas into the heads of students so that when they see the topic later, in a more complete way, they have already seen a few basic ideas. The later course then builds on the earlier base. So, learning is a spiral with each loop being deeper than the earlier one.
Unless the instructor is presenting obscurity as an important security technique, perhaps they are just introducing the idea that security, itself, is an important consideration (to be explored later). After all, if they give the complete course in security within the current course it can't meet its objectives. It would be bad, of course, if they suggest that obscurity is sufficient.
Look at the overall picture before you make a judgement. You may not need to challenge the professor, but it might be worth a visit to the office to see what their goals are in opening the door to security in this way. In general, mentioning advanced things early to set the stage for later development is a good thing that gives students a bit of perspective on the whole field.

Related to this is that instructors teaching a first programming course might have students implement something simple like the Caesar Cypher, and mention the idea of encryption, but not suggesting that this is a good one.

Although this is a possible explanation, there are much better choices than security-through-obscurity that could be used to introduce the idea of security in an elementary SQL course.

@PatriciaShanahan, yes, of course.

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Can A Lazy Person Complete a Statistics PhD? (and other questions)
I'm about to graduate with my BS and begin a PhD program in statistics. However, I have some doubts about my ability to succeed just because everyone says that a PhD is a grueling, long journey and that work ethic is more important than just being smart. I think my motives for doing the degree are fine -- I don't intend on trying to join academia and I want to do the degree because I find the field powerful and because I want access to the research roles in industry that are generally more interesting. I'm young, have no debt, and my program is fully-funded so I think that helps the opportunity cost aspect, although there is always what me and my friends joke about as the "opportunity cost of not becoming a software engineer."
However, throughout college and my education thus far, I feel like I have been extremely lazy. One of the reasons why I enjoyed studying math and statistics on a structural level in terms of college major was that the courses generally had two or three grades; midterm, final, and sometimes graded homework. So pretty much since my grade depended only on those things I would just study like one day in advance of each exam and forget about the course entirely in between. I don't think I've studied for a single exam more than a day in advance in my entire four years. Also, unless a class had mandatory attendance (nearly all of the math/stats classes did not) I would ditch it. I would rather just read out of the textbook myself and I can't listen to people teach math to me because the speed of my thoughts does not match the speed of how people talk. Overall, I think I have a realistic view of my abilities though. I'm not a genius or anything, just lazy, so I calculate exactly how lazy I can be and then I be that lazy. Of course, for something like qualifying exams I recognize I would not be able to do that and would take more time.
I also enjoy smoking weed, and I am a daily smoker. I feel like I have a different view than other people in this field traditionally: I'd rather just chill, hang out with my friends, and enjoy life. However, this is not to say that I have no interest in statistics. I have an enormous appreciation for the elegance of statistical theory and I've done a lot of interdisciplinary research using applications of statistics. I love doing research thus far and always felt motivated to work on projects etc. and in fact we have a paper accepted at a conference and my PI has found funding to pay me this summer to hopefully push out one or two more papers. I go out of my way to participate in research and for the last year I've been working in two different groups at once. I just really am quite lazy with school and I see the point of it but it is simply easier and more efficient for me to read out of the textbook, especially since this field is a mathematical one.
Based on my context, I had two questions:
(1) What happens to lazy statistics PhD students like me?
(2) So far in my undergraduate curriculum we have only been learning theories and concepts that have already been developed, and not developing anything new. Probability theory classes and real analysis were probably the most difficult parts, but it was not doing anything novel. The rest of the stats electives like sampling/experimental design/regression methods at the level of the courses taught were pretty much just trivial plug-and-chug type material. Since PhD students would be creating novel ideas in statistics (maybe like new methods is what stands out to me), how do they make this jump from "plug-and-chug" to doing this type of original work? How well-defined are research problems in statistics (in the sense that there are straightforward paths to approach well-known problems vs. the necessity of having some Einstein idea and needing that fundamental creativity to even generate a research question)? Of course this depends on subfield, but I am asking more generally.
Thank you!

Sounds like you've never been challenged.

If you are not absolutely certain you should get a PhD, don't.  "Can" is the wrong question.  There are many things you can do that you shouldn't.

"I don't intend on trying to join academia" -- then why not go straight into industry? Or could the academic credentials of a 1-2 year MS program be enough (based on coursework similar to the courses you've succeeded in so far), vs the very real (no joke!) opportunity cost of a 5+ year PhD (where long-term daily persistence is needed and "study like one day in advance of each exam" does not really apply to the kind of work involved)? It would help if you can clarify why you would need a PhD specifically. I did plenty of interesting gov't research with only a MS in Statistics.

"What happens to lazy statistics PhD students like me?"  A more relevant question would be what happens drug addicts like you.  You probably don't think of yourself that way, but the description you give sounds like an addict to me.  Frankly I would not accept you to a PhD if I knew you daily used drugs - there's a good chance you'd drop out, wasting an opportunity others would give their eye-teeth for.  Drug use would also block you from many career paths even with a PhD, so I'd strongly advise you to break from drug use.  Now is the time to kick something out of your life that can ruin it.

@StephenG-HelpUkraine I don't think we can be sure OP is unable to function without smoking. Besides, a lot of academics would, at least initially, produce less if forced to quit caffeine. Also, it may not even be illegal where OP is from.

Shouldn't this be "What percentage of lazy statistics students pass their PhD?"

Have you ever been assessed for ADHD? What you describe as "being lazy" sounds suspiciously similar to me.

Lazy people can complete PhD in any part of math or statistics. I know two lazy people who later became Fields medallists. In fact laziness is often the driving force for research in math. I invent new tools because I am lazy to use the old ones.

If you have at least some of the following talents: (1.) finding fashionable subject to write about. (2.) Networking (3.) Getting other people to do work for you  (4.) Taking credit for work other people have done (5.) talking confidently about what you have no grasp of - then, yes, you can definitely complete a Ph.D. in statistics and much of anything else.

Could it just be that your preferred learning style is not lecture based? Some people learn better from books and this is not laziness. However, you need to love your subject to enable you complete a PhD successfully. If there's any doubt, explore other options-a good masters degree could land you industry roles in stats. Smoking weed is bad for your mental health long term-get help if needed.

You say you’re lazy, but this is such a long question!

Not a joke: What if you did a statistical study about procrastination in itself?

@markvs: I invent new tools because I am lazy to use the old ones → this is an interesting comment. I have the same, sometimes I do not want to look at what I built because I "forgot" to comment it enough, and rewrite it in another language. (taking the opportunity to learn something). I always thought "what's wrong with me?". Now I know I am just lazy :)

@WoJ: Right. That is what I meant, in particular. When I get a problem which . requires computation, my first inclanation is to write a code and run it for a month. But writing a good code takes a lot of efforts, so at the end I figure out something else.

While I would certainly counsel against some aspects of how you are undertaking your educational journey (especially the daily weed smoking), I don't see any inherent reason that you cannot continue to succeed educationally with your present approach.  If you have sufficient intelligence/aptitude for this field to obtain a funded PhD admission while being "lazy" and indulging in habitual marijuana use, then I see no reasons that the step up to a PhD program would necessarily require substantially more time devoted to study.  Admission to a funded PhD place already usually requires a high-level honours degree in the same or a related field, which provides substantial evidence of your ability to be educationally successful  while exercising your current habits.
In respect to being "lazy", what you describe might just mean that you find the material you are learning manageable with only a small amount of study (it is evidently relatively easy for you) and you have other interests you would prefer to pursue in your other time.  When I was a student I also spent very little time on study relative to other pursuits, and I ended up with a PhD in statistics.  If it is a field you find interesting (tick) and you have the intelligence/aptitude for it (tick) and you have a history of academic success in previous degree programs (tick) then you meet all the major indicators for continuing success.  Subject to some checks on your previous work and education history, I would be happy to supervise a student like yourself, so long as your interest in the subject manifests in curiosity, perseverance and grit in solving problems in the field.  Research problems range in the level of originality they require, though some insight and creativity is a usual prerequisite to do interesting work.  It is also necessary to be interested enough in the field that you are willing to read widely and play with problems, without expecting new research to come out of everything you investigate.  If your "laziness" were to crowd that out, it would be a problem, but I see no indication from your post that this would necessarily occur.
Don't kill the goose laying the golden eggs: Finally, I would be remiss if I did not point out that much of your present success probably hinges on high intelligence and aptitude for technical analysis, which you are presently destroying with habitual pot-smoking.  Regular long-term marijuana use is notorious for its adverse effects on cognitive ability, including adverse effects on the ability to plan, organise, solve problems, make decisions, remember, and control emotions.^  This is not something that older adults just made up because we are old fuddy-duddies --- it is a frequently replicated finding of medical studies, and something that most older people have directly observed amongst at least some of the promising younger people they grew up with.  I strongly recommend you try to supplant this habit with a coffee addiction, or some other type of recreational activity that will not damage your highly-functioning brain.

^ Much of the literature on this topic involves statistical inference based on uncontrolled observation, rather than randomised controlled-trials, so causal inference comes with some caveats.  Also, there is substantial variation in sample sizes (with some studies using small samples) and there is variation in the findings for different elements of cognitive decline.  Nevertheless, the statistical associations derived from these cases tend to be unidirectional (showing types of cognitive decline from marijuana use) and remain after removal of relevant confounding factors, which is consistent with a causal effect.  There are also some mixed results about whether this decline is reversible with a long period of abstinence.  For a sample of the literature on this matter, see e.g., Block and Ghoneim (1993), Pope Jr and Yurgelun-Todd (1996), c.f., Hart et al (2001), Hart et al (2010), Crean, Crane and Mason (2012), Lisdahl and Price (2012), Winward et al (2014), Auer, Vittinghoff and Yaffe (2016) and Dahlgren et al (2016).

I second the warning about weed. Long ago, I knew a doctoral student (in math) who frequently smoked weed. He was convinced that it made him more creative, but actually it just made him more confused.

@AndreasBlass, reminds me of an English teacher I had in high school. He told us once about how he used to use drugs in his younger days and would write poetry while high, thinking it was the most insightful, creative, mind-blowing stuff anyone had ever written. Then he would go back and read it after sobering up and realize it was all crap.

@SethR I saw the same claims and result with a Youtuber who started smoking weed.

I appreciate the research and don't reject your analysis, but it is worth mentioning that some of your sources are a little old. Neuroscience has changed a lot over the past 20 years, especially with regards to controlled substance effects. Still +1 though

Fair enough --- I don't keep up with the field enough to be a specialist, so happy to hear other views of the matter.  In any case, it is notable that most of the evidentiary material in the papers is longitudinal/non-longitudinal data and corresponding statistical inference, which is robust to changes in neuroscientific theories.

There is a danger for you, but it isn't necessarily a blocking one.
The first question you want to ask yourself is how excited and driven you are to study and work in your field. The second question is what if it becomes necessary, quite suddenly perhaps, to not be lazy anymore and work hard. Will the excitement still be enough to enable you to power through. If so, then you can teach yourself the work skills needed to succeed.
Some people come to their "natural" level of ability fairly early. Up to then, learning was easy and the material seemed easy to grasp and master. But after that point it takes them work to learn. Lots of repetition, note taking, review, etc. Some people come later to that point. I learned fairly early on that I had to work to learn math. But I was so driven to it that I had no options but to power through. My sister hit her mark much later, but it actually stopped her progress, though she had other interests by then.
It is probably unlikely that you will complete a doctorate before you find the limit and will most likely need to get rapidly and seriously un-lazy. But knowing that such an event horizon might exist for you can have you prepared. Having good people around you in a doctoral program can help also, since you have a source to get your questions asked.
Let me give you two data points. The first is the movie Good Will Hunting in which Matt Damon plays a young person who finds very advanced math almost trivial. But he doesn't really have the heart for what it takes and doesn't develop his skill.
The other point is the book The Art of Changing the Brain by James Zull. Among other things, it describes how we learn, by rewiring the brain itself physically. This is normally accomplished slowly through repetition, rather than be seeing something once (which only affects short term memory). Deeper learning requires building structures of neurons (etc) that hold long term memory - deep learning.
So, being lazy but still successful may work, but probably not forever. Again, the deeper question is how driven you are.

I'm not attempting to give an exhaustive answer and for sure there are some valuable points made in other answers.
I'd like to add the following things. The question "Can a lazy person complete a Statistics PhD?" seems to imply that laziness is some kind of unchangeable characteristic of a person. It isn't. In fact you are responsible for what you do, and what you don't do. There isn't any general answer to this question because there isn't a well defined group of "lazy persons". The very simple answer is you've got to do enough and you've got to find out how much "enough" is for the specific topic that you have, in the specific place, with the specific examiners. If that means (which it doesn't necessarily do) that you've got to get out of your lazy ways, so be it. That's for you to find out and for you to decide. Any general statement on whether lazy people generally can do it is meaningless for you as a person.
Question (2) is a good one though. In fact, in many undergraduate or MSc programmes there are projects in which the students go beyond what you call "plug-and-chug" and I suspect that if your programme is 100% of this kind, it is not very good. In fact my experience suggests that how students do in a research project (which of course at undergraduate level is somewhat easier and clearer defined than at PhD level) is a much better predictor for PhD success than how they do in courses of the type you mention. If you don't do anything beyond that and have no clue whether you're good at exploring things yourself in a more autonomous way, thesis writing and the like, you are indeed not in a very good position to know whether that's for you. You may try to find out whether there's an opportunity for a research internship somewhere. If you can choose to do a project in your undergraduate/MSc programme, by all means do that if you want to find out more. (Obviously this may conflict with the "laziness" you ascribe to yourself, but no reasonable answer here should take anything away from the fact that this is not a given characteristic but your own responsibility.)
Furthermore, PhD projects come in fairly different shapes. Particularly you may have very different levels of supervision. There are supervisors who largely leave their students on their own and even think it's the student's responsibility to define their own topic, and then there are supervisors who give a student a fairly well defined topic and make much more effort to push the student in what they think is the "right direction". There are pros and cons of both approaches, but in any case it will very likely be very very different from how statistics apparently has felt to you up to now. As somebody who has supervised a good number of PhD students (in Statistics!), I know that there are methods to put many different kinds of students on a track to ultimately achieve their PhD, however the process involves in many cases phases of uncertainty and confusion on the side of the student, and for sure at the end they will have done something based on their own initiative and judgement, and will have done an amount of work in that often is more than they would have expected in advance.
People who fail or give up do exist, so don't take anything for granted. I rather recommend the attitude that it is up to your responsibility, your work, and your ability to adapt whether you will succeed.

Yes, I agree. People who are lazy in their undergraduate time often become hardworking later, when they need to.

I found this question interesting; so much so that I actually created a StackExchange account so that I could write an answer. Normally I find it more efficient to simply use the information from other people's questions, and this could be a bit like the laziness you mention :)
As somebody who holds a PhD in a related field and shares certain characteristics that you describe I think my experience could be useful to you.
But first to give some background: In my first try at University (at about your age) I started off pretty much like you describe... do fun things all semester and smoke weed and such, then catch up before the exam... no worries... but didn't get great marks, failed some subjects due to required work not handed in during the semester... then tried to repeat the same trick at 2nd year level and found I was simply too far behind to be able to catch up quickly :( I ended up at 3rd year level with a hodge podge of subjects from different levels and eventually dropped out to become a software developer, so I guess I didn't pay that opportunity cost :)
When I eventually went back to University as a mature age student I was determined to attend all the classes and hand in all required work and did so, but I still got a bit of a rude shock when my "revision" for Mathematics A involved mostly reading through the lecture notes, and I found in the exam that without having practiced all the techniques I could not do it... still passed of course, but after that my revision consisted of mainly doing all the practice problems and practice exams I had available. And this enabled me to get good marks from then on.
Based on my experience, I'm honestly surprised that you're at the point of finishing your statistics degree and can still get by with just brushing up before the exam. I don't consider myself genius material (my talent is mainly in writing code) but I am certainly interested in mathematics and motivated and reasonably good at it. Either you're much more talented in mathematics or the material isn't like I'm used to (I did more pure maths).
I also have something in common with you where I have an enormous appreciation of the elegance and so on, and I got the PhD for similar reasons to what you describe -- I was never that interested in an academic career, although I've flirted with it at different times and have worked for the University in various capacities for short stints before returning to industry... I guess the other main reason I got the PhD was simply to prove to myself and others that I could do it. However, I should say that the opportunity cost of the degree was extremely high (took me 6 years and if you include the undergraduate degree that I did as a mature age student, I was out of the workforce for 10 whole years -- it's an enormous loss in economic terms even though my PhD was also fully funded).
Now with that background out of the way I shall talk a bit about the process of doing the PhD. Basically, you need to put all romantic notions aside and figure out how to work the system to your advantage. The entire goal of your sciencey PhD is to get about 4 high quality papers published and each of these is a chapter for your thesis. You'll have another 1-2 chapters of warm up and another chapter of cool down material (future directions etc).
So if you do not want to be there for 6 years as I was, you need to forget about contributing any really novel ideas and do something kind of incremental... as my first project, that eventually (after many twists and turns) became my first chapter... my supervisor suggested that I simply recreate someone's experiment so as to verify their findings, and report anything else interesting. This is a good approach to start with. And really the rest of your work has to follow the same pattern: Take something that already works and make it slightly better. Keep in mind you can do something really original AFTER your graduate, unless like me you ended up in a postdoc position where the supervisor didn't like new ideas :( I suppose my point is that research really isn't glamorous :( It's mainly about justifying to the sponsor you did something :( And getting it past the censorsXXXreviewers.
So, the question is really whether you can knuckle down, play the system and get the letters after your name. And to be honest from reading what you've written I am not 100% sure that you are ready. I think you're doing a lot better than I was in my first try (before I crashed and burned), but you might just crash and burn later than me. Or possibly you really are a genius and can continue your current way of working into your PhD. You can decide.
Now I also have to make the obligatory noises about the marijuana. Although in this case I'm not actually just repeating the research but I'm speaking from personal experience, as one who smoked weed every day from about age 18 to 30... I can definitely attest that it hurts motivation and although I'm not sure if my brain has been permanently damaged by the marijuana (my whole body has been permanently damaged by being alive for 46 years so it is really hard to judge)... I do feel that my brain took a lengthy period to rebuild itself and reach a more peak condition, and I never want to go back to the weed.
I used to have lengthy sessions where I would smoke weed and write computer code, and although it felt at the time like the weed was absolutely essential to the process and that the code was highly creative, looking back I think I was probably fooling myself to some extent. The words "pipe dream" exist to describe this situation literally -- I would come up with extremely complex algorithms and very detailed approaches while smoking many joints or pipes and staring into space building a complex mental model of what I was planning to do... but how much of the mental model actually transferred to code is a question. I think as a programmer I'm much more productive now than I was then, of course I'm more experienced but I'm also weed-free, something I didn't think was possible then.
So my suggestion would be to give up the weed and then re-evaluate. Giving up the weed was a process that took me many years but each time I quit for longer. So you have to be really persistent. I also suggest that many times you think you need a joint your body may actually just be wanting the nicotine that you smoke WITH the weed. I found the only effective way for me to quit was to stop cigarettes, weed AND alcohol (since I'd often end up smoking again after a big drinking session wound down and inhibitions were lowered)... the alcohol can be added back later but only when the quitting has really stuck.
Feel free to PM me if you want to bounce anything off me on the topic. Also, you might find this book interesting:
https://www.amazon.com/Curse-High-Iq-Aaron-Clarey/dp/1522813756

This answer is more anecdotical than the other ones, but might still be useful.
I'm a very lazy person and I completed my PhD in theoretical physics/computation without any issues. My supervisor was a non-lazy person and so were the other students, but they all seemed to think my work was good (and I've published some peer-reviewed articles if that's worth anything). I'm a very quick learner and efficient worker, so I rarely had to put in more than 10 hours a week. So it's definitely possible to do a PhD and not "hit your limit", depending on how instinctive the field you study is for your brain.
However, I had a hard time finding a good job in academia (maybe if I'd worked more and published more it would have been different, but most of my smart, hard-working friends who completed their PhD's have encountered the same problem...), so I went back to school and did a medical degree, also without needing to study or even attend classes very much. At this point, I'm working in hospitals and I find I'm sometimes at a disadvantage compared to people who are used to hard work and have acquired more endurance for it... But, like everything else, it improves with practice ;)
I can only chip in with the others and recommend being careful with marijuana. I'm working in a psychiatric hospital these days and I've seen plenty of people with drug-induced (or exacerbated) psychosis, many of which don't recover fully.

@AppliedAcademic Don't know what to tell you. My lab is one of the top ones in it's domain, supervisor world renowned. I fulfilled all requirements, published, aced my pre-doctoral exam and thesis defense. You would have failed me just because I didn't put in lots of hours?

I have always been hardworking (probably not that bright), even with simple things. I did plenty of exercises on a given topic to never be surprised by a test. Ended up with a PhD and left academia.
My son is completely different: he is brilliant and does everything with ease - but just enough to get top marks.
When I look at his way of working, it seems that he has unlimited capacities, but just stops when he has what he needs (again, top marks). When something is harder, he does more work. When something is really hard, he does a lot of work.
This is to say that you may have never faced something that really pushed you into (and outside) your boundaries, where you had to do a herculean effort to understand what you were doing.
When you get to this, pay close attention at your reaction. Is it "fuck, this is too complicated, I give up", or "fuck, I will not rest until I understand this shit" (the wording is what I was thinking, verbatim but in French).
Your answer will show whether you have the capacities to do a PhD.

I am currently a PhD student at a top Uni (global top 10) and I can tell you firsthand, right now after several years in academia and industry, there is no place for laziness or drug-taking.  If you are lazy, then consider other options in fields better suited to your personality.  Having aptitude and laziness is one thing, but a PhD is a job, like any other, which requires a routine and daily persistence and task workflow.  Even if you manage a PhD, which is a longshot in my opinion, don't mind the bluntness,  your employer will sack you for laziness anyway.
You should not jump into the frying pan, consider a Masters, see how you get on, and then revisit this, if you still want to read a PhD.  Try and see if you if can manage a place at a top Uni (global top 10) to ascertain your apitude.
I have known a few very smart people who got kicked out to their PhD program an dUni altogether in their first year for lack of preparation and laziness.
Harsh words, but it's the truth.

In my experience, laziness is much less of a barrier to completing a PhD than surviving or doing well in industry. I also did my PhD at a global top 10 university and there was a small number of exceptionally talented students that could get away with working much less than I did. Furthermore, virtually no one failed to complete their PhD - and the few that did all made that choice themselves (it was not imposed by the university). I have no argument about the pot smoking though - I have personally witnessed numerous tragedies over the years (including two close friends).

Even so, no one will ever tell you their PhD was a walk in the park.  For all it is sweat blood and tears .  In fact if you're not feeling the heat, then your PhD is not worthy of it.  Every PhD is different, but that doesn't mean you appear to be doing more than your other  "exceptionally talented students"  who might be doing longer-hour experiments, because that is the nature of their work.  Do you know what they do and how they work exactly?  Do you sit with them in their homes and offices and see how they work?  No you perhaps don't.  To undertake a PhD one has to be 100% dedicated.

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What will happen if the panelist in my thesis defense does not like my thesis in College level? Will I fail?
I am currently graduating student and having a thesis project to pass the course and requirement for graduation. Is it possible to fail the my program if the panelist in my defense does not like my thesis? Does it affect my possibility to pass the course and graduate?

Say something about numbers. Are there several "panelists"? Are you asking about the opinion of one of many or something else? Is your thesis advisor a member in any sense?

Panelists don't fail students and prevent them from graduating on a whim. What does your thesis project advisor say about your work?

It wouldn't be an exam if there were no possibility of failure. So the answer is "yes, it is possible to fail". The consequences of failing would depend on your program. If passing the thesis project is a requirement for graduation, then odds are you will not be able to graduate, but you'll get another chance to pass the thesis project next semester / next year.

Put is a thesis defense always an exam? (Or is it a ritual?)

As @allure already says, it wouldn't be an exam if there wasn't the possibility for failure. But it's worth remembering that (i) it isn't about "liking a thesis", but assessing *whether a candidate has the qualifications required to pass a certain level, and (ii) the people on your panel are professionals with many years of experience in evaluating theses. As a consequence, panelists will be able to make a difference between "I don't like this" and "I don't think this candidate is qualified". Of course, in the latter case the candidate should not pass, but it isn't because the panelist doesn't like you or your thesis.


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Does switching majors look bad for graduate admissions?
Here's my situation: I've want to get into a master's or PhD level program in biomedical engineering. However, the college I go to does not offer a biomedical engineering degree. So, out of the options I was given and at a advice of my advisor, I decided to major in chemical engineering degree.
I pursued research with a professor in the chemical engineering department and was preparing to take the GRE. Once, I got to my actual Chemical engineering courses in the second semester of my sophomore year. I really ended up disliking my courses and not doing well in them (I got mostly Bs and two Cs and it's my fault). I went to my academic advisor for advice on the situation and I was told that the average GPA for the chemical engineering class was a 3.2, and that it's really hard for people to make a high enough GPA in this major to get into graduate school and the my grades are what most people make in the classes.
I don't know if this is the standard for engineering or something but it feels really discouraging to me cause it feels like I can't fix my grades and grind through to get a good enough GPA to just get into graduate school. I like my research, but I'm unable to handle the rigor and intensity of my chemical engineering engineering classes despite reaching out to my professors and my university's tutoring center. I feel like me not liking the major is also a major factor in why I am unable to pass my classes. I will be a junior this year.
Should I just stick it out with this major that I don't like at this point and work harder to keep up my grades since I will be doing BME master's anyway? I really want to get into a good masters program for biomedical engineering, but I really dislike my current major. Would changing majors hurt my graduate school admission chances?

This is a cliche, but it can be really different country by country how negatively changing major is viewed. That being said bad grades are viewed negatively everywhere, so maybe changing your major is a much safer choice than you realize.

TLDR: You should switch majors
Engineering (of any kind) is hard. If you are excited about it and love it, then putting in the work is possible.  But if you hate the classes, you are going to find it very hard to be motivated enough to do the work needed to get As.
And you do want As for grad school. One bad semester won't kill your grad school chances.  Four more bad semesters in a row will.
Either Electrical or Mechanical are both good starting points for BME (just as good as if not better than Chemical).  Pick whichever you suits you best and switch.

Changing majors is actually more or less a good thing when it comes to graduate.  It's actually more common than you think, with some people changing major in the middle of their undergrad career, between undergraduate and graduate, or even maybe during graduate studies itself.  It's a sign that you know what you like and you don't like, which is a good thing when you eventually apply for graduate school.  And if you know what you want to study for graduate school, you might as well get a head start while you still can, and it will also be a good thing to explain if graduate schools question your lower grades in ChemE, especially if you experience more success as a BME major.
The only issues that this will present here, is how much of the undergraduate curriculum for BME you would be able to cover, as you noted you would be a junior when transferring in.  Is there any of the chemical engineering coursework that would transfer to satisfy requirements for the BME major?  Since you have two years left, assuming you want to graduate within the four years, would you be able to satisfy all the BME degree requirements, as well as gain research experience or an interest into a specific subfield (through electives) by the time you graduate?  You may find yourself needing to take a extra semester or two in order to finish the requirements of your new major.
TLDR: If you feel you would be happier in the new major, and you can do so with more success, I would go for it and change to the BME major.  Hope this helps.

Changing majors isn't likely to be a factor in itself. When creating a CV or resumé, you can choose what to put in and omit. Ultimately you will want to shine a spotlight on a particular qualification as the proof that you meet the criteria for the particular job (or course in this case).  To this end the particular major you have pursued, followed by the grade you achieve, are the most important factors. If you change major you can simply leave out the previous course your pursued.
In my experience a 1.1 or 2.1 or even 2.2 degree in a relevant subject is plenty qualification for a masters (perhaps not a 2.2 for research masters). That's what... about > 3.08 GPA. I'm not privy to your specific circumstances, either in terms of the university you are going to, or the particular course you are attending, but demanding an exceptionally high GPA to qualify for a masters seems unusual to me (unless there's some sort of scholarship element)
The issues with changing course stem from monetary concerns and those relating to motivation. Going back to scratch in terms of most of your degree is a big ask, and you would be wise to work out in advance if it's something you will be prepared for, both mentally and financially. A degree with an average GPA is infinitely better than a hypothetical degree with an excellent GPA that never gets finished. On the flip side if you don't think it's likely that you will be able to finish the current major you are doing (either because you find it too difficult or just not enjoyable enough) then you have nothing to lose by switching.


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Will having multiple published work in well-established journals increase my chances for acceptance to a master's program?
I have published two research works in Q1 and Q2 journals, working as a research assistant (co-author). I am wondering whether the published work increases my chances of acceptance for a master's program, if all other factors are good but not exceptional. The two works improved the state-of-the-art in a particular subfield and my involvement in both works was very much in every aspect of the research, from idea development, modeling to writing.
Note: my field is electrical engineering
Edit: To add more substance, would having such publications and research experience make me an equivalent candidate to, say,  someone applying with ~4.0 GPA but no research experience?  Which usually aims high when applying (for reference, my GPA is 3.7)

Of course, publication matters in academic and research admissions. However, recommendations plays a great role in the process as @user37208 said.

Can you give a little more detail about what country/region you are planning on applying in?  In, for example, the US, I would suggest considering also applying to PhD programs directly if you are committed to continuing research.  Your gpa, publications, and (presumably) a recommendation letter from your lab head has the potential to make a strong PhD application as well as MS application.

To answer user58322's question, I'm indeed applying for the US

Usually the 'fact' of participating in research experience as UG is the important piece because it teaches them many things about how research works. If I saw a UG on several credible papers compared to another UG on one credible paper, I would usually just assume that he was lucky to be in a productive group  with a supportive mentor). It would be the recommendation letters that make the difference if those 'multiple' papers were truly meaningful difference. Even then, I'd be reading between the lines of whether the research advisor was just blowing steam.

Yes! It shows you have experience with (and are good at) writing and research. It would be even better if you could get a letter of recommendation from someone you worked with on these research works.

This seems correct but kind of trivial. Also you don't address the OPs further questions about how this weighs vs. GPA.

Yes, it would. The whole point of the admissions process is trying to determine whether or not you have the markers of someone who will be able to do graduate research - having already done research that's resulted in publications is a very strong indicator.
Supplementing that with a strong recommendation letter from those you worked with will help solidify that benefit.
As to the question of GPA, it's really impossible to say for two reasons: There isn't a formula, where "One Paper = 0.15 GPA points" or anything like that. There's not necessarily a score at all. Additionally how each individual program weights the components of an application will vary - for example, my program treated the GPA as something of a "You must be this tall to ride..." criteria, while some others will take it very heavily into account.

An excellent record (GPA 4.0) is not an evidence for good performance for a master's degree or beyond (PhD). Different qualities are required and assessed. For instance:  autonomy ("every aspect of the research"), endurance (to deliver usable results, and during the publication process), cooperative skills (RA, co-author), innovation ("idea development") and additionally this double success in publishing (potentially in English), are very important factors.
I do not believe there is a strict equivalence: you simply  cannot count your papers as 2x0.15. For myself, I would spend more time considering your application than the other (I am in Electrical Engineering too), and I do generally advise my colleagues to do the same.
For some persons in charge, the number of papers would suffice. For others, they will read the papers, and judge how good they are; Q1 or Q2 rankings  could be biased depending in the subfield.

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Send link to a document/draft instead of attaching it to mail
When sending a mail to a professor/ academic, is it OK to send link of a document/draft/preprint instead of attaching it to the mail? What is the convention?
The reason is, if you attach it,  it takes some space in the mailbox of recipient. Thus, the recipient might delete it to save space, after some time.

This is no different from sending an email to any other person on the planet. #ProfessorsArePeopleToo.

I'm voting to close this question as off-topic because the question is not about academia: sending an email to an academic is no different from sending one to anybody else.

I think you should specify whether the document is expected by the recipient or not. (For what it's worth, I will neither follow a link nor open an attachment in an unsolicited mail from an unfamiliar sender; otherwise, I prefer links to attachments above (say) 1Mb -- even if space and bandwidth is cheap nowadays, it starts to add up once you get into triple figure emails per week (or day!), and IT starts getting huffy once your IMAP account passes several Gb on their servers. Just make sure the link doesn't rot quickly, to give your recipient time to look at it if they're busy at the moment.)

No.
You want to make sure it is easy for the recipient to read your mail, no extra clicking and waiting. In any case, cold sent mail asking to review your work will be silently ignored. If it contains  external link or not, it will pretty soon be cleaned up (or hang around forever, not looked at again).

I might send you a review request :) , you academics are horrible!

@Jim, just overworked and forgetful.

I understand "overworked and forgetful", but  still you(plural) should have some space for people like "us".

@Jim, most of the time we get crank mail, or "offers of brilliant ideas, just need to be worked out" (the "work out" part is 99% of the job, and I have enough ideas of my own, thank you), or stuff that lies well out of our area of expertise. One fast (inaccurate, sure; but it weeds out 95% of nonsense) filter is just leave stuff from unknown senders/uninteresting subjects "for later". And we all know "later" means "somewhen after retirement, if at all".

Ooening an attachment usually requires at least one extra click as well, unless it's displayed inline such as an image or plain text.

In my experience, the key concern about attachments is not the amount of space they take on the recipient's computer (most people have big hard drives these days), but whether they will cause your message to fail to be delivered.  There are two issues that I frequently encounter in which attachments cause problems with email:

Most mail servers have an upper limit on the attachment size they will allow.  At the moment, this is often seems to be no more than 10MB, but I've encountered as low as 2MB.
Aggressive protective software sometimes gets upset about attachments, especially known malware vectors like zip files.

If you trigger a filter like this, your email will disappear into a black hole and, depending on the configuration of the system that ate your email, you may not even know that this has happened. Thus, for any attachment above about 1MB or of an unusual type, I recommend sending a link instead.
Note, however, that links can also be viewed as suspicious, by either software or people, since there's a lot of nasty phishing schemes out there.  Thus, I recommend not using link shorteners and if possible hosting an academic institutional server or on a well-known and semi-trusted service like DropBox.

Thanks for your response,  BTW, I did not mean Hard drive space, I meant Mail server. Anyway  sending link appears to be okay.

By sending a link for a document, if the document is editable (such as google drive), you could edit the file after sending it.  This way revisions could be made and would be seen without sending out a new file.
To counter this though, if the link is deleted, the file is lost.  In an attachment, as long as you have email access you would still have the file.
Overall most files are not too large so that storage space does not matter.  Only videos and large data sets would be hard to store, but the email program would not be able to send it in the first place as file size limits are normally about 15MB.

I agree with many of the other answers (large attachments can be a problem, while linked documents can change or disappear and can look like phishing), but my main concern is surveillance.  When someone sends me an attachment, they won't know whether I looked at it.  By contrast, when someone sends me a link I typically assume they are deliberately monitoring whether I follow it and, if so, when and perhaps from where.  It feels like being stalked.
This doesn't particularly bother me in some cases, such as referee requests.  However, it bothers me a lot if the email comes across as unconventional.  Then I worry that I don't want to encourage or provoke the sender.  (Over the years, I've received a few disturbing or abusive emails, for example from someone upset that I wouldn't endorse his proof of Fermat's Last Theorem.)  It sometimes feels better to let the sender wonder whether the email was caught by a spam filter, rather than knowing I accessed the file.

If your document is light enough (below 5 Mbytes, to be safe), I suggest you to send it attached. Very often, you can often shrink the document, using for instance pdf options (low resolution images). It is important to make sure that the document is easy to open, so a pdf can be a good option; be careful with, for instance, .doc or .odt versions.
But you can easily provide the attachement, and an additional link in the mail, for instance to a full resolution or color version that could be bigger.

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Papers on ArXiv as main references
I am going to prepare a math research paper and one of my main and necessary references is an article on ArXiv. I do not know whether I continue to prepare my article or not. Since I have doubt with ArXivian papers and I think it is not good choice arxiv papers as a main part of a research work.

Tagged "mathematics" because both the question and the accepted answer are specific to that field.

In my experience, it is common and acceptable for papers in mathematics to rely on arxiv preprints.  While there may be something in the idea that if you have a choice, it is better to rely on peer-reviewed publication, if the choice is to use an arxiv preprint or delay the publication of your paper, almost certainly you should use the preprint.
To go a little deeper: the imprimatur carried by a peer reviewed publication is partially real but mostly cultural.  Most veteran mathematicians can point to any number of published papers with (sometimes serious) mistakes and also can point to papers of theirs that were accepted by reputable (or strong, or top) journals with so little commentary that they cannot have any confidence that the referee checked the results for correctness in any serious sense.  (Note to academics in other fields: I said "referee," not "referees."  In pure mathematics the most common number of referees for a paper is one.)
Whenever your work uses others' results, you have to perform some process of evaluating those results.  Ideally that would include a careful checking of the proof, but to be honest that is not always practical or possible.  (Sometimes you need a result whose proof uses ideas and techniques that would take you months or years of work to understand.)  To be honest, the evaluation process is often cultural as well as intellectual, and this is why it can indeed be a little better to rely on a published work: whether someone read the proof carefully or not, at least it is likely that expert eyes evaluated the work for plausibility.
There are ways to evaluate an arxiv preprint for plausibility without reading it line by line:

Look up the authors.  Have they published similar results before?  Have their prior results been used by others?
Does the paper look professional?  Is it clearly written?
Is it reasonable that one could attain the given results with the given techniques?
Do the results used appear to be a major breakthrough?  In that case, you should probably talk to others about the preprint quickly: if it's correct, then likely many other mathematicians are making use of it, possibly the same way you are, and by waiting for publication you could be losing everything.  But also it's more likely to to be wrong.  (Sorry to say: all other things being equal, the better result is more likely to be wrong.)  You don't want to waste your time building on a foundation that evaporates later.

Finally, yes it is possible that if you submit your paper for publication, the referee could say "Well, I don't know about this arxiv preprint, and I don't care to referee it too.  You'll have to wait until the preprint is published."  Well, referees say the darnedest things.  It is still better to have tried.

I'm glad I haven't (yet) suffered from the latter case, as many of my most recent papers have relied on an arxiv paper the author of which has been extremely lax in submitting.  It's several years old now and to my knowledge it's never even been submitted anywhere.  I have heard similar stories from others, where important and oft-referenced papers have somehow gone unpublished for no apparent reason.

@zibadawatimmy Yeah, these things happen. But the fact that you've based several papers on it suggests that you're very familiar with what it says and have read it very carefully. In real terms, that's probably better than peer review, which is supposed to be careful but is rarely as careful as the reading done by somebody who wants to extend the work.

@zibadawatimmy There are a few well-known preprints that have been extremely influential and are frequently cited but for various reasons have never been submitted for publication anywhere.

Is there a case to be made that if some elements of the reference are both essential and are to be taken on trust, then that is called out. Even titling the paper "Implications of ..."?

Famous example of work that was posted on arXiv only: Grigori Perelman's proof of Thurston's geometrization conjecture.


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What does “senior graduate student” mean in this context?
I am an Indian student enrolled in an integrated master’s-of-science program and currently in my fifth (final) year of the program. So should I be called a senior graduate student or senior undergraduate student?
[Edit]: I read this https://www.numfocus.org/programs/john-hunter-technology-fellowship/ on the first line of second paragraph.
I quote that here:

The program consists of a 6 month project-based training program for postdoctoral scientists or senior graduate students.

Also the eligibility is:

Eligibility: Eligible applicants are postdoctoral scientists or senior PhD students, or have equivalent experience in physics, mathematics, engineering, statistics, or a related science.

Am I eligible? I am an Applied Mathematics student (maths major).

Welcome to Academia SE. Can you please [edit] your question to elaborate why you want to know this?

Hi thanks. I have updated my post, have a look at the "Edit" section.

OK, I edited the question, because there is still no "senior graduate student" as a feature of the US system. The description has nothing to do with the US.

When talking about undergraduate studies, "senior" has a specific meaning in the US system, i.e. a student in their last year of a bachelor's degree.  But there isn't a specific meaning in the context of graduate studies; here "senior" just has the generic meaning of "more advanced, more experienced".  Of course, this is relative.
In the context you mention, referring to "postdoctoral scientists or senior PhD students", it seems clear to me that they mean a PhD student who is, at least, in the second half of their studies.  US PhD programs usually involve 2-3 years of advanced coursework, followed by dissertation research.  This program seems to be intended for students in the research stage, and it will likely expect that the students have a base of advanced knowledge in the relevant subfield.
From your description, I don't think you are eligible for this program.

As much I am sad to hear that 'I am probably not eligible for the program', I am also happy that I got help from this stackexchange community. Thank you.

I would add that PhD students at the end of their studies seems to be the minimum requirement here.  A postdoctoral student is on who has finished their PhD and is still doing similar types of research beyond the scope of a degree granting program.

There's no such thing as a senior graduate student, and strictly speaking, you're not an undergraduate senior. You're either a Master's student or in the final year of a BS/MS (or BA/MA, etc.), depending on how specific you want to be.
Edit: Given the subsequent clarification, I agree with Nate Eldredge. Since the description refers to postdocs as well, "senior" means "advanced," so within a year or two of completing a PhD.

Hi, I have updated my post with [Edit] section, may be from where I read the statement it may become a little clear as to what is expected.

In the USA, "senior graduate student" is not a formal title.  It refers to the graduate student who has been in the research group the longest.
Someone in your situation would be referred to as a "masters student."

I've also seen "senior graduate student" used informally to mean dissertation-level student, but I believe the OP is referring to a generally recognized level in the program.

With the clarification, I don't think this is the intended meaning in this context.


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Making conferences accessible for people with anxiety
I'm looking for some information about how academic conferences can or already do accommodate the needs of people with anxiety. For example, do you know of any conferences which have special measures in place to encourage people who have anxiety to attend (e.g. related to travelling to a conference, attending conferences, presenting at conferences)?
Or perhaps, are there any ways that universities themselves make conference attendance more accessible for people with anxiety?

There are many types of anxiety. What do you have in mind? I also fail to see how a conference could help someone with anxiety related to traveling to a conference...

I changed the "health-issues" tag to "disability" since this is an accommodation issue.

Some good ideas in this link.

@ff524. why not build a proper answer around that link?

Not an answer, but you might appreciate this article by someone with a lot of conference-related anxiety.

I agree with @AzorAhai that this question needs to be more specific in order to be appropriate for SE.

Thanks @ff524 - I hadn't seen that link. Some good starting points.

@CPDDDM what features do you think could make a conference more attractive to researchers with anxiety? The conferences I attended so far were very friendly, peaceful and exciting places with a chance of finding a quiet corner anytime. I find the smaller ones more convenient though.

@BalazsToth well for example I often struggle to sit through talks due to anxiety relating to fear of embarrassment (fear of having a panic attack and having to leave a room filled with people who will think I'm rude or stupid for leaving halfway through a talk!) I therefore miss most of the talk while concentrating on not panicking. A solution could be to screen the talk in a separate, smaller room where people feel free to move around.But I'm interested in hearing other examples that have or would help others. But I recognise this may be a problem for a very small % of conference goers

@CPDDDM: When someone leaves a room during a talk, my thought isn't that they're rude or stupid, but that they probably have to go to the restroom.  (If I notice at all.)

Since the interpretation of the question changed I'll add an answer for the new one, leaving the old one in place in case it helps an individual.
Some conferences have two simple features that might be adapted to your needs. The first is a "newcomers/first-timers session" early on the first day of the conference in which people will learn about what to expect in various kinds of sessions. This can, perhaps, be used to put people at ease with only minor changes.
The second thing is that many conferences also have student volunteers who help out with various things but are also allowed to attend any sessions without fee. Some student presenters are also volunteers, lowering the cost of attendance. But one of the uses of the volunteers is to be an assistant to any presenter who requests it, handling unforeseen issues so that the presenter can concentrate on what they want to say. This might be reassuring to some people that "bad things" are less likely to happen.
An additional advantage for the volunteer is that they, themselves, become more familiar and hopefully comfortable with what to expect at a professional conference  so that later they don't have first-timer anxiety themselves.

There are a lot of sources for anxiety, some are merely not being used to it, and therefore not being comfortable. But there are a couple of conditions, such as asociality and autism.
To a certain extent these can be "overcome" in practice, merely through dedication and practice. Extreme introverts can, for example, seem to be extroverts when necessary just by role playing.
I don't think you are going to find a lot of accommodation, maybe none at all. But you can and should consult a professional for help in overcoming any phobias you may have. Such phobias can limit your professional life.
However, note that in many fields (math and computer science, for example), many of the people that you might meet at such conferences, including the main speakers and field leaders, will probably be just like you but have worked to present a face to the world that is more open than they naturally feel. You aren't alone.
One of my colleagues, when asked, admitted that I'd "changed my personality" from the time we first met. I once suffered an academic setback simply for fear of speaking up when it was necessary to do so.

I think the question is asked from the point of view of a conference organizer (what accommodations can I make?) not from the point of view of an attendee (what accommodations can I expect?)

@ff524, you may be right. Perhaps the OP will clear it up.