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How Indian researchers can bridge the gap between academia and industry

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Starting 30 pts 0 followers
St Aloysius (Deemed to be University) · Posted
Biomedical Engineering Chemical Engineering

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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13 Replies

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Rohan Desai Distinguished 950 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 Rohan Desai
Om Prakash Growing 55 pts · Accepted answer

Really appreciate you taking the time to write this out in detail. This is going straight into my research notes.

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Gopal Naidu · Accepted answer

The ethics review situation in Indian universities is actually more complex than most people realize.

There is no uniform national framework equivalent to the US Common Rule. Each institution has its own Institutional Ethics Committee (IEC) or Institutional Review Board (IRB).

For anonymous survey research with no identifiable data and no vulnerable populations: most Indian IECs would classify this as exempt, but 'exempt' still needs a formal determination from the IEC.it is not self-certifying.

My practical advice: go to your institution's research ethics office now, describe what you did, and request an exempt determination in writing. Better to have documentation than to discover the issue during thesis review.

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Replying to Gopal Naidu
Karthik Rajan Growing 180 pts · Accepted answer

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.

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Ekta Choudhary Starting 45 pts · Accepted answer

I found that writing a paper abstract first (before the paper) and then reverse-engineering the paper from the abstract helped with clarity enormously.

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Zara Hussain Growing 55 pts · 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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Ganesh Menon Growing 140 pts · Accepted answer

I have a slightly different take from my experience in industry research. The reproducibility crisis is real but unevenly distributed. Fields with strong engineering culture (computational biology, ML with benchmarks) have actually improved significantly in the last 5 years. The bigger problem is in fields where data sharing is structurally difficult.clinical medicine, behavioral economics.

The ML community's move toward open code and reproducibility checklists has been genuinely effective.

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Replying to Ganesh Menon
Kiran Yadav Growing 130 pts · 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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Replying to Ganesh Menon
Gopal Naidu · 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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Harini Balakrishnan Active 215 pts · Accepted answer

For longitudinal data with missing values, mixed-effects models handle this more gracefully than imputation in most cases.

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Dinesh Kulkarni Distinguished 730 pts · Accepted answer

I have a slightly different take from my experience in industry research. The reproducibility crisis is real but unevenly distributed. Fields with strong engineering culture (computational biology, ML with benchmarks) have actually improved significantly in the last 5 years. The bigger problem is in fields where data sharing is structurally difficult.clinical medicine, behavioral economics.

The ML community's move toward open code and reproducibility checklists has been genuinely effective.

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Wajid Khan Growing 110 pts · Accepted answer

FDR (Benjamini-Hochberg) is widely accepted and preferable to Bonferroni when you have many tests and limited power. The key is to be explicit about which correction you used and why.

On replication: No independent cohort is a genuine limitation but not a dealbreaker for rare disease research, where cohort availability is a known constraint. Many high-quality papers acknowledge this and still get published in good journals. Frame it as future work.

Power analysis: Yes, report it. Showing you are aware of the limitation is better than not mentioning it.

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Hemant Patwa · Accepted answer

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.