Our other ICML work focuses on transfer learning for large foundation models. It can selectively transfer useful features and integrates complementary information from multiple large foundation models. #transferlearning #ICML2024 paper: arxiv.org/abs/2406.07337

We are looking for a PhD student for an Applied Scientist Intern position this fall at AWS, based in Santa Clara, to conduct research on model inference and serving. If you are interested in this position, please send your resume to [email protected]. Thank you!

Call for Papers! ROEGEN@RECSYS'24 📍 Bari, Italy We are now inviting paper submissions. Would you like to share your groundbreaking work on generative AI for RecSys with a wide audience and engage with leading researchers in the field? Submit your paper to us by August 30!

Thrilled to share that CaMML received the #ACL2024 Area Chair Award! 🎉 This marks my 4th award in 5 years, following Best Paper Award runner-up at WSDM 2020, Outstanding Paper Award at ICLR 2021, Best of the Best conference Paper Award at GMC 2023. Grateful for the recognition!

⚠️Today is deadline day for many of the excellent workshops (INRA, LargeRecSys, ROEGEN, RobustRecSys , CONSEQUENCES, RecSoGood, IntRS, AltRecSys, Introspectives, KARS, HealthRecSys) at #RecSys2024. Still unsure which one you will submit to? Find them 👉 recsys.acm.org/recsys24/works…

Do not miss #ROEGEN if you are interested in topics around #GenealrtiveAI and #LLMs for/in #RecommenderSystems. We have had noteworthy number of submissions, the workshop still welcomes new submissions! (Full, short, ideas, surveys etc.) ACM RecSys

Happy to be included in the list of the world's top 2% most cited scientists for the third consecutive year (affiliation and country are outdated). elsevier.digitalcommonsdata.com/datasets/btchx…

The notifications for #ROEGEN are out. Check out the accepted papers list!

Don’t miss these cutting-edge talks at #ROEGEN of RecSys@2024 (Oct 8) featuring top researchers from industry and academia. Topics include alignment, scaling laws, evaluation, responsible AI, hybrid methods, etc. for #RecSys in the age of #GenAI. roegen-recsys2024.github.io.

I'm happy to share that we had a paper accepted at NeurIPS! Keep revising and resubmitting your work—you never know what surprises await! #NeurIPS2024

Excited to share that our paper *PipeRAG* has been accepted to KDD 2025! Congratulations to all the authors! Wenqi Jiang Boran Han, Jie Wang, Bernie Wang , Tim Kraska arxiv link: arxiv.org/abs/2403.05676 code: github.com/amazon-science…

If you happen to be at NeurIPS Conference, join us, the fantastic five (Nick Erickson @ ICML Tony Boran Han Shuai Zhang Abdul Fatir) will present "AutoGluon 1.2: Advancing AutoML with Foundational Models and LLM Agents." Room: West Ballroom C of the Vancouver Convention Center

🚨 Call for Contributions! 🚨 #CallForPapers Our #ACM_TORS special issue on Generative AI for Recommender Systems (#GenAI #RecSys) is now open for submissions! 📅 Deadline: July 1, 2025 Be part of shaping the future of #GenAI and #Recsys. Details: dl.acm.org/pb-assets/stat…

🚀 We are coming back to SIGIR 2025 ! Delighted to share that our workshop *GENNEXT: The Next Generation of IR and Recommender Systems with #LANGUAGE #Agents, Generative Models, and Conversational AI* has been accepted to #SIGIR2025. 📍GENNEXT: July 13-18,2025 (Padua, Italy)

I'm excited to share that I've been promoted to Senior Applied Scientist at Amazon Web Services (AWS)!

🚀 Excited to announce #GENNEXT at #SIGIR2025 — the first workshop dedicated to next-gen IR & recommender systems with #agents, LLMs, and conversational AI! 📄 Papers will be indexed in CEUR 🗓️ Submission deadline: April 23, 2025 sigirgennext.github.io/GENNEXT-SIGIR-…

We should do the thinking, not LLMs.

Can your RAG pipeline trust the documents it retrieves? We propose a method - CrEst - to estimate document credibility—without human labels—and offer a recipe to integrate it into open-source or private models. Check it out: arxiv.org/pdf/2506.14912

I’m attending #KDD2025 this week in Toronto! 🇨🇦 We will present PipeRAG, rethinking RAG for systems efficiency—achieving 2.6× faster latency with: 🔁 Pipeline parallelism 📦 Stale-query prefetching 📈 Query-aware performance modeling 📄 Paper: arxiv.org/abs/2403.05676 #RAG #LLM