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Thank you for this detailed answer! A quick follow-up: when you mention cosine learning rate with warm-up, are you warming up the LoRA parameters specifically or the entire model including the frozen backbone?
Highly recommend checking out the PMRF portal for the actual numbers.they update the stipend structure annually.
The IEEE TPAMI turnaround in my case was 7 months for the first review. Plan accordingly.
If you are not already using a reading group format with your students, I would strongly recommend it. Forces everyone to articulate what they read.
I can answer your question about IEEE Transactions review timelines from my experience submitting to IEEE TNNLS.
For Q1 IEEE journals, expect:
As for code and data, most IEEE journals now request code upon acceptance, not submission. Regarding your 2.3% improvement.that is borderline. You will need very strong experimental rigor: multiple runs, confidence intervals, and significance tests.
This is a known issue with XLM-R on code-mixed data. The MuRIL preprint has a section specifically comparing these.
I found that writing a paper abstract first (before the paper) and then reverse-engineering the paper from the abstract helped with clarity enormously.
I'd push back slightly on the idea that industry collaboration is universally beneficial for academic research. The incentive misalignment is real.
Industry partners want deliverables on a schedule. Academic research is often exploratory and unpredictable. When the two clash, it is usually the researcher who has to compromise.either by rushing results, constraining publication, or steering away from results that are inconvenient for the sponsor.
This doesn't mean industry collaboration is bad. But the terms matter enormously. IP rights, publication rights, and the ability to pursue negative results should be negotiated upfront and protected in writing.
Highly recommend checking out the PMRF portal for the actual numbers.they update the stipend structure annually.
This is exactly what I needed to hear. One clarifying question: you mentioned FedProx handles non-IID data better.does that mean I should switch to FedProx by default, or only if I observe poor convergence with FedAvg first?
This is exactly what I needed. Thank you so much.I have been going back and forth on this for weeks and this clears it up completely.
The short answer: it depends heavily on the journal and field, but here is a general framework.
If your null result is well-powered and the hypothesis was reasonable: Absolutely publish it. Null results in well-designed studies are as valuable as positive results. Journals like PLOS ONE explicitly welcome them.
If your study is underpowered: Be transparent about it. Report effect sizes and confidence intervals, not just p-values. A small, underpowered study with honest reporting is still a contribution.
What kills papers is when limitations are obvious but the authors have not addressed them.
[Moderator] Pinning this thread as it contains highly useful information for new PhD students.
This is really helpful context. One more question: for the ethics committee approval, does the institution where the data is collected or the institution where the analysis is performed need to be the primary ethics approver?
Just to make sure I understand the Zotero workflow correctly.is the .bib file synced to Overleaf automatically every time you add a new paper, or do you need to trigger an export manually?
If you are not already using a reading group format with your students, I would strongly recommend it. Forces everyone to articulate what they read.
Respectfully, I think this framing misses the systemic dimension. Individual researchers cannot solve a collective action problem. If you submit to open-access journals and your colleague submits to Nature, your colleague gets the promotion. Until evaluation criteria change at the institutional level.which requires policy intervention.individual choices have minimal impact.
The Fulbright SoP is very different from a typical PhD SoP. It needs to focus on cultural exchange and diplomacy, not just research.
Just to make sure I understand the Zotero workflow correctly.is the .bib file synced to Overleaf automatically every time you add a new paper, or do you need to trigger an export manually?
For longitudinal data with missing values, mixed-effects models handle this more gracefully than imputation in most cases.
This is exactly what I needed to hear. One clarifying question: you mentioned FedProx handles non-IID data better.does that mean I should switch to FedProx by default, or only if I observe poor convergence with FedAvg first?
This is really helpful context. One more question: for the ethics committee approval, does the institution where the data is collected or the institution where the analysis is performed need to be the primary ethics approver?
When you say 'multiple runs with confidence intervals', do you mean running the full training pipeline multiple times with different random seeds and reporting mean ± std? Or something more statistically rigorous?
The short answer: it depends heavily on the journal and field, but here is a general framework.
If your null result is well-powered and the hypothesis was reasonable: Absolutely publish it. Null results in well-designed studies are as valuable as positive results. Journals like PLOS ONE explicitly welcome them.
If your study is underpowered: Be transparent about it. Report effect sizes and confidence intervals, not just p-values. A small, underpowered study with honest reporting is still a contribution.
What kills papers is when limitations are obvious but the authors have not addressed them.
I have successfully received PMRF (lateral entry, second year). Happy to share what worked in my proposal.
On the research proposal:
On citations: Yes, cite your MTech thesis. Framing your PhD as an extension of prior work you did shows continuity of thought.
Overleaf + GitHub integration is underrated. You get version control and collaboration in one place.
I think you are conflating two separate problems here. The publish-or-perish culture creates pressure, yes.but the solution is not to criticize researchers who are navigating an unfair system. The real problem is with evaluation committees who treat publication count as a proxy for research quality. Fix the evaluation, and the incentives change.
Also worth noting: open review has been implemented in some fields and has its own problems. It can disadvantage early-career researchers who fear retaliation from senior colleagues they have reviewed critically.
Highly recommend checking out the PMRF portal for the actual numbers.they update the stipend structure annually.
The IEEE TPAMI turnaround in my case was 7 months for the first review. Plan accordingly.
Thank you for the honest take. This is the kind of answer I was looking for.not the sanitized version.
Open access is the right direction but the APCs are prohibitively expensive for many Indian researchers without institutional funding.
The IEEE TPAMI turnaround in my case was 7 months for the first review. Plan accordingly.
The IEEE TPAMI turnaround in my case was 7 months for the first review. Plan accordingly.
This is exactly what I needed to hear. One clarifying question: you mentioned FedProx handles non-IID data better.does that mean I should switch to FedProx by default, or only if I observe poor convergence with FedAvg first?
Seconding the recommendation for Zotero. Game changer for managing references across multiple projects.
Highly recommend checking out the PMRF portal for the actual numbers.they update the stipend structure annually.
Strongly agree with the point about choosing venues carefully. One paper in NeurIPS > five papers in obscure workshops.
Seconding the recommendation for Zotero. Game changer for managing references across multiple projects.
This is exactly what I needed. Thank you so much.I have been going back and forth on this for weeks and this clears it up completely.
For longitudinal data with missing values, mixed-effects models handle this more gracefully than imputation in most cases.
For longitudinal data with missing values, mixed-effects models handle this more gracefully than imputation in most cases.
For longitudinal data with missing values, mixed-effects models handle this more gracefully than imputation in most cases.