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Best practices for onboarding new students into your research group (64)

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Growing 110 pts 0 followers
Bearys Institute of Technology · Posted
Public Health & Epidemiology Human Resource Management Higher Education Policy

My Setup

I run a machine learning lab with 8 PhD students. Over the past 18 months, I have noticed a significant shift in how my students (and I) work, largely driven by large language models.

Things that have genuinely improved:

  • Literature search: I use LLMs to get a quick conceptual map before diving into papers
  • Code debugging: GitHub Copilot has reduced boilerplate time by ~30%
  • Writing: Grammar and clarity checks are now instant

Things I am cautious about:

  • Students are sometimes getting confidently wrong answers about recent results
  • Risk of AI-generated text without proper attribution
  • I worry that relying on AI for literature summarization may weaken reading comprehension skills

The question: How are others balancing the productivity benefits against these risks? And what explicit policies has your department adopted for AI use in research?

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19 Replies

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Farhan Mirza · Accepted answer

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.

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Replying to Farhan Mirza
Meghana Rao Growing 170 pts · Accepted answer

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.

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Replying to Farhan Mirza
Ganesh Menon Growing 140 pts · Accepted answer

Marking this as the accepted answer. Incredibly helpful and specific. Really appreciate you taking the time.

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Lakshmi Devi Growing 75 pts · Accepted answer

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?

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Priya Nair · Accepted answer

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.

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Replying to Priya Nair
Indira Balan · Accepted answer

This is very reassuring. I was worried the null result would kill the paper but your framing around transparency and effect sizes makes a lot of sense.

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Replying to Priya Nair
Pooja Nambiar Growing 115 pts · Accepted answer

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?

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Replying to Priya Nair
Rajesh Kumar · Accepted answer

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?

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Arjun Venkatesan Growing 195 pts · Accepted answer

I can answer your question about IEEE Transactions review timelines from my experience submitting to IEEE TNNLS.

For Q1 IEEE journals, expect:

  • Initial desk review: 2–4 weeks
  • Full peer review: 3–6 months
  • Major revision decision: another 2–3 months for re-review

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.

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Replying to Arjun Venkatesan
Suresh Iyer · Accepted answer

Could you clarify what you mean by results-blind review? I have heard of it but never understood how it works in practice. Wouldn't reviewers need to see results to evaluate whether the methodology is sound?

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Replying to Arjun Venkatesan
Eshan Patil Growing 70 pts · Accepted answer

Open access is the right direction but the APCs are prohibitively expensive for many Indian researchers without institutional funding.

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Replying to Arjun Venkatesan
test tester Growing 165 pts · Accepted answer

Highly recommend checking out the PMRF portal for the actual numbers.they update the stipend structure annually.

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Lakshmi Devi Growing 75 pts · Accepted answer

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.

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Anita Rao · Accepted answer

I have successfully received PMRF (lateral entry, second year). Happy to share what worked in my proposal.

On the research proposal:

  • 60–70% technical approach, 30% context and impact
  • Preliminary results absolutely help. Coursework projects are fine if genuinely relevant
  • The societal impact section should be substantive.PMRF is a national initiative; reviewers want to see why this matters

On citations: Yes, cite your MTech thesis. Framing your PhD as an extension of prior work you did shows continuity of thought.

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Replying to Anita Rao
Karthik Rajan Growing 180 pts · Accepted answer

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?

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Replying to Anita Rao
Tara Singh Starting 5 pts · Accepted answer

Open access is the right direction but the APCs are prohibitively expensive for many Indian researchers without institutional funding.

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Ashwin Murthy Distinguished 560 pts · Accepted answer

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:

  1. Use LoRA with r=8 or r=16.don't go higher for 8K samples
  2. Apply LoRA to attention layers only, not the feed-forward layers
  3. Use a cosine learning rate schedule with warm-up (10% of steps)

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.

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Replying to Ashwin Murthy
Hema Suresh Starting 20 pts · Accepted answer

Could you clarify what you mean by results-blind review? I have heard of it but never understood how it works in practice. Wouldn't reviewers need to see results to evaluate whether the methodology is sound?

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Replying to Ashwin Murthy
Quamar Ahmed Growing 65 pts · Accepted answer

Much appreciated. I will restructure my proposal along these lines. The point about societal impact being substantive is something I would have gotten wrong.