I completed my MTech from NIT Trichy in 2022 and have been working in industry since then. I now want to apply for PhD programs, both in India (IISc, IITs) and abroad (ETH, CMU, UW).
Does a two-year industry gap hurt PhD applications significantly? I have:
Would love to hear from people who have successfully navigated this situation.
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Great question.I went through something very similar in my second year.
The key insight for me was that LoRA is actually quite well-suited for NER tasks, especially in low-resource settings. I would recommend:
For Telugu-English code-mixed NER specifically, you might also look at MuRIL.it is pretrained on Indian language data and often outperforms XLM-R on Indic tasks even with less fine-tuning data.
Strongly agree with the point about choosing venues carefully. One paper in NeurIPS > five papers in obscure workshops.
For Zotero vs BibTeX management, I moved to Zotero + Better BibTeX 3 years ago and have not looked back.
My workflow:
.bib file that auto-updates whenever I add a paper.bib file via Dropbox syncOne tip: create a Zotero collection per paper/project. When you export the .bib, export only that collection to keep it clean.
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?
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.
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?
Overleaf + GitHub integration is underrated. You get version control and collaboration in one place.
As a senior faculty member (25 years in the system), I want to offer a different perspective. Yes, the pressures are real. But I have also seen the other side: students who produce excellent work, publish honestly, and build reputations over time. The system is imperfect but not entirely broken.
What has actually helped my students: choosing venues carefully (a few strong papers rather than many weak ones), developing genuine collaborations rather than transactional coauthorships.
Check whether your institution has a Springer/Elsevier waiver agreement.many NITs and IITs do.
Thank you! The reference to MuRIL is particularly useful.I had not considered it as an alternative to XLM-R for Indic languages.
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.
Seconding the recommendation for Zotero. Game changer for managing references across multiple projects.
CSIR-UGC NET preparation: past papers are the most important resource. Everything else is secondary.
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.
Really appreciate you taking the time to write this out in detail. This is going straight into my research notes.
+1 to everything said above. My experience was identical.
CSIR-UGC NET preparation: past papers are the most important resource. Everything else is secondary.
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.
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.
Highly recommend checking out the PMRF portal for the actual numbers.they update the stipend structure annually.