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How do you structure your research reading schedule? Tips and systems

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Growing 50 pts 0 followers
Alva's Institute of Engineering and Technology · Posted
Operations & Supply Chain

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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50 Replies

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Asha Pillai Distinguished 880 pts · Accepted answer

This is a known issue with XLM-R on code-mixed data. The MuRIL preprint has a section specifically comparing these.

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Replying to Asha Pillai
Dinesh Kulkarni Distinguished 730 pts · Accepted answer

Overleaf + GitHub integration is underrated. You get version control and collaboration in one place.

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Prakash Iyer Distinguished 640 pts · Accepted answer

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?

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Nandita Ghosh Distinguished 720 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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Bhavana Reddy Starting 30 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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Manish Tiwari Starting 10 pts · Accepted answer

This question comes up a lot. The answer really depends on your specific field and what your committee values.

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Geeta Rao · Accepted answer

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.

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Divya Krishnan Growing 85 pts · 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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Divya Krishnan Growing 85 pts · Accepted answer

Overleaf + GitHub integration is underrated. You get version control and collaboration in one place.

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Anita Rao · 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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Ganesh Menon Growing 140 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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Ishaan Verma Growing 60 pts · Accepted answer

The Fulbright SoP is very different from a typical PhD SoP. It needs to focus on cultural exchange and diplomacy, not just research.

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Karthik Rajan Growing 180 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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Gopal Naidu · Accepted answer

CSIR-UGC NET preparation: past papers are the most important resource. Everything else is secondary.

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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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Replying to Harini Balakrishnan
Anita Rao · 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.

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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.

0
Prakash Iyer Distinguished 640 pts · Accepted answer

The Fulbright SoP is very different from a typical PhD SoP. It needs to focus on cultural exchange and diplomacy, not just research.

0
Farhan Mirza · Accepted answer

Overleaf + GitHub integration is underrated. You get version control and collaboration in one place.

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Replying to Farhan Mirza
Indira Balan · 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 Farhan Mirza
Karthik Rajan Growing 180 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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Fatima Sheikh Growing 95 pts · Accepted answer

I am in a very similar situation. Would you be willing to share the outline of your PMRF proposal? Not the content.just the section headings and approximate word allocation.

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Disha Malhotra Starting 8 pts · Accepted answer

This question comes up a lot. The answer really depends on your specific field and what your committee values.

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Nandita Ghosh Distinguished 720 pts · Accepted answer

Strongly agree with the point about choosing venues carefully. One paper in NeurIPS > five papers in obscure workshops.

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Ashwin Murthy Distinguished 560 pts · Accepted answer

Seconding the recommendation for Zotero. Game changer for managing references across multiple projects.

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Admin NITK · Accepted answer

+1 to everything said above. My experience was identical.

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Replying to Admin NITK
Admin NITK · Accepted answer

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

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Replying to Admin NITK
Shiva Prasad Active 480 pts · Accepted answer

Thank you for the honest take. This is the kind of answer I was looking for.not the sanitized version.

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Replying to Admin NITK
Tara Singh Starting 5 pts · Accepted answer

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

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Disha Malhotra Starting 8 pts · Accepted answer

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

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Replying to Disha Malhotra
Deepa Krishnamurthy Distinguished 810 pts · Accepted answer

Thank you for the honest take. This is the kind of answer I was looking for.not the sanitized version.

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Replying to Disha Malhotra
Rohan Desai Distinguished 950 pts · Accepted answer

Check whether your institution has a Springer/Elsevier waiver agreement.many NITs and IITs do.

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Ashwin Murthy Distinguished 560 pts · Accepted answer

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?

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Xena D'Souza Starting 25 pts · Accepted answer

Strongly agree with the point about choosing venues carefully. One paper in NeurIPS > five papers in obscure workshops.

0
Pooja Nambiar Growing 115 pts · Accepted answer

This is a known issue with XLM-R on code-mixed data. The MuRIL preprint has a section specifically comparing these.

0
Deepa Krishnamurthy Distinguished 810 pts · Accepted answer

I am in a very similar situation. Would you be willing to share the outline of your PMRF proposal? Not the content.just the section headings and approximate word allocation.

0
Hemant Patwa · 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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Lakshmi Devi Growing 75 pts · 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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Royal Dsouza Starting 30 pts · 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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Amit Joshi Starting 15 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.

0
Chetan Jain Growing 100 pts · Accepted answer

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?

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Uday Bose Starting 5 pts · 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.

0
Admin NITK · 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.

0
Sowmya Narayanan Active 310 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 Sowmya Narayanan
Deepa Krishnamurthy Distinguished 810 pts · Accepted answer

+1 to everything said above. My experience was identical.

0
Replying to Sowmya Narayanan
Arjun Venkatesan Growing 195 pts · Accepted answer

Thank you for the honest take. This is the kind of answer I was looking for.not the sanitized version.

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Replying to Sowmya Narayanan
Harini Balakrishnan Active 215 pts · Accepted answer

The IEEE TPAMI turnaround in my case was 7 months for the first review. Plan accordingly.

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Lalitha Mohan Active 230 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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Wajid Khan Growing 110 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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Uday Bose Starting 5 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.