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The future of quantum computing research in India.opportunities and challenges

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Starting 5 pts 0 followers
Christ (Deemed to be University) · Posted
Economics Environmental Science Genomics & Bioinformatics

Background

I have been an Assistant Professor for three years now. Looking back, the most valuable thing in my career so far has been the research network I built.not my publications, not my grants, not my teaching record.

Here is what actually worked for building that network as a new faculty member:

  1. Email cold introductions after reading someone's paper.specific, about their work, not a generic 'let's collaborate' message. Response rate is surprisingly high (~40%) if the email shows you actually read the paper.

  2. Conference conversations.not the formal talks, but the coffee breaks and poster sessions. I have gotten more collaborators from standing next to someone at a coffee station than from any structured networking event.

  3. Reviewing papers.every paper you review is an invitation to learn what leading researchers in your area are working on before it is published.

  4. Social media presence.LinkedIn for professional connections, Twitter/X for research community discussions. One thread I posted on my research got picked up by a researcher at Cambridge who is now a collaborator.

What has worked for others?

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21 Replies

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Yogesh Pandey Growing 70 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 Yogesh Pandey
Ravi Patel Starting 25 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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Replying to Yogesh Pandey
Ganesh Menon Growing 140 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 Yogesh Pandey
Ashwin Murthy Distinguished 560 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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Disha Malhotra Starting 8 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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Replying to Disha Malhotra
Meena Sharma · 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 Disha Malhotra
Lalitha Mohan Active 230 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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Replying to Disha Malhotra
Ganesh Menon Growing 140 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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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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Lakshmi Devi Growing 75 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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Ashwin Murthy Distinguished 560 pts · Accepted answer

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

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Uday Bose Starting 5 pts · Accepted answer

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

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Royal Dsouza Starting 30 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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Replying to Royal Dsouza
Amit Joshi Starting 15 pts · 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 Royal Dsouza
Hema Suresh Starting 20 pts · 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 Royal Dsouza
Ravi Patel Starting 25 pts · Accepted answer

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

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Vikram Bhatia Active 420 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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Om Prakash Growing 55 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 Om Prakash
Quamar Ahmed Growing 65 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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Replying to Om Prakash
Amit Joshi Starting 15 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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Anita Rao · Accepted answer

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