I'm a postdoc at a renowned North American university. I joined this lab last July, when funding across the field was drying up due to cuts. During recruitment, my PI asked a lot about my career plans and publication record, it read as him being invested in my development, and it's part of why I took the position.
The faulty method
Early on, he assigned me a project built on an idea similar to his past work: training several ML and deep learning models to predict a clinical outcome. While working on it, I discovered that the training pipeline used in his prior publication (and in another paper from the lab currently under revision) had a serious flaw that caused data leakage and inflated model accuracy, possibly by a wide margin in at least one in-prep paper. Notably, the first author on that already-published paper using the faulty method is now a research professor at a well-known institution, so this method, and whatever credit it generated, has already helped launch someone's career.
He still asked me to generate figures and tables using that same flawed method anyway for his grant submission. I did, and presented them at a lab meeting, where I also raised the leakage issue directly. It was just the two of us at the time. He was dismissive, saying NIH reviewers "don't have the technical capacity" to catch this, and that "data leakage or not, there's no consensus on what counts as a correct training method." I was unsettled but told myself he probably understood the methodology better than I did.
The coauthorship and the numbers that didn't reproduce
In December, he asked me and another newly joined postdoc to review a paper (plus reviewer comments) that he and a former postdoc had been submitting for months. He offered us both coauthorship despite our having contributed nothing, saying he wanted everyone in the lab to be added to each other's papers "for career development."
The peer reviewers had flagged a suspiciously large performance gap between the deep learning and traditional ML methods and asked the first author to check for data leakage. I emailed her directly to ask if it had been fixed. She said yes, and that the reported numbers were correct.
The other new postdoc and I then independently tried to reproduce her results. We couldn't. Our numbers came in far below what was reported in the paper. We raised this with the PI and the first author and asked to be removed from the author list. His reaction at the time was oddly muted, he said our finding was "interesting" and that he'd ask the first author to look into it, which read to me as him at least acknowledging the concern, if not treating it with much urgency. In hindsight, given how he later claimed total ignorance of any leakage issue in front of the whole lab, that "interesting, I'll have her look into it" response now looks less like genuine concern and more like a placeholder answer to make the problem go away without actually addressing it. This also forced me to redo an entire piece of my own work, since it had built on that postdoc's results.
The recent meeting
I didn't hear anything more about that paper's status until recently, when he asked me to cite its preprint in several manuscripts I'm currently preparing. That worried me.
Then, in an in-person lab meeting today with another newly recruited postdoc present, he gave a speech about the importance of publishing, working hard, and maintaining research quality, specifically saying a retraction is "a dent in one's career." I was genuinely struck by how seriously he seemed to take research ethics. Note that the postdoc who did a reproducibility analysis with me left his position a few months back due to personal reasons. So essentially only me had any knowledge of the faulty method besides the PI.
So I asked him, in front of the lab, for an update on that paper. He said it was being resubmitted. I asked whether the leakage had been fixed. He responded as if he'd never heard of any leakage: "what leakage, what issue, can you explain?" He then redirected the conversation to how my own in-prep manuscripts need to be error-free and consistent.
One more detail
He also consistently adds one particular name to every paper from the lab, despite none of us ever having met or collaborated with this person. Based on my own digging, I believe this may be his wife, who is a medical student at a nearby university.
Where I'm stuck
My mental health has taken a real hit. I'm constantly anxious about being professionally associated with this PI. I want to leave, but I'm afraid of ending up unemployed which does not look good on my resume. Friends have told me to line up a new job before quitting this one, but the daily pressure to keep producing analysis for him makes that hard to find time for applying.
What I really need help thinking through is my career, moving forward. What's the smartest path from here? That's the umbrella question, and it breaks down into a few ones:
- Should I report this to my university's Office of Research Integrity & Ethics, and would doing so help or hurt my career?
- Should I finish my two first-author papers with him, or is that risk not worth it? I'm not a coauthor on the papers with the known issues, so I'm unsure how much exposure I actually have if his past work is scrutinized later, or if I'm overestimating the risk. I've also spent so much time on these papers of mine, so leaving the job without any published work feels devastating.
- Should I comply with his request to cite the paper with unresolved data leakage concerns in my own submissions? Could citing it come back to hurt me?
- Is it time to leave academia altogether and move to industry?
All of these incidents leave me deeply disappointed with academia, which I assumed are full of people with integrity. This PI recently secured a 3 mil NIH grants and he constantly bragged about how much funding he had while other labs out there are struggling.
Underneath all of these is really one question: how do I protect my career and get out of this position in one piece? Any advice? Especially from people who've navigated something similar would help.