Debasis Ganguly

@debforit

Lecturer/Asst. Professor at the School of Computing, University of Glasgow (@UofGlasgow/@GlasgowCS/@IDAglasgow/@ir_glasgow)

Glasgow, Scotland

gdebasis.github.io

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Debasis Ganguly
@debforit
Sep 17
Why should a simple factoid question and a complex multi-hop question receive identical treatment? We introduce DRAG, which dynamically chooses the retriever and the generator yielding better answers and/or lower latency. arxiv.org/abs/2609.17709 @PayelSantra17 @sbhatia_
Debasis Ganguly
@debforit
Aug 21
🧵 Excited to share that our paper "Modeling Ranking Properties with In-Context Learning" has been accepted at #EMNLP2026! 🎉 Joint work with @nilanjansb, @MrParryParry, and Pabitra Mitra. Preprint:
arxiv.org
Modeling Ranking Properties with In-Context Learning
While standard IR models are primarily designed to optimize relevance, real-world search often needs to balance additional objectives such as diversity and fairness. These objectives depend on...
Debasis Ganguly
@debforit
Aug 10
🧵 Exciting news from @ir_glasgow: 3 papers from our group have been accepted at #CIKM2026! 🎉 Two long papers and one short paper covering agentic RAG, efficient reranking, and retrieval robustness. Huge congratulations to the students and collaborators involved! 👏 🧵👇
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Debasis Ganguly
@debforit
Jun 16
To live up to my ISI CS postgraduate roots, I finally wrote a theoretical IR paper 🙂🧮 to estimate how risky is stochastic reranking before we actually apply it? 🎲 Paper: arxiv.org/abs/2606.16970 (to appear in #ictir2026) Code: github.com/gdebasis/srea
arxiv.org
A Theoretical Framework for Risk Analysis of Stochastic Rankers
Different from deterministic rankers that seek to maximize relevance at top ranks, stochastic ranking policies instead estimate distributions over permutations, from which rankings are sampled,...
Debasis Ganguly
@debforit
May 31
A single-authored paper always feels special, perhaps even more so these days, when most of my research time is devoted to working with students. Delighted to share that my paper, “A Theoretical Framework for Risk Analysis of Stochastic Rankers,” has been accepted at #ictir2026.