Avishek Anand

Associate Prof. at the TU Delft

prof_pic.jpg

3.West.660, Building 28

TU Delft,

Delft, The Netherlands

Avishek Anand is an Associate Professor in the Web Information Systems (WIS) group within the Software Technology department at Delft University of Technology (TU Delft). His research centers on retrieval-augmented AI systems, especially on improving retrieval to make AI more reliable, transparent, explainable, and capable of scaling sustainably. More broadly, he develops intelligent and trustworthy machine learning methods that help people find, assess, and understand relevant information.

He earned his PhD in Computer Science from the Max Planck Institute for Informatics in Saarbrücken. Prior to joining TU Delft, he was an Assistant Professor in Information Retrieval at Leibniz University Hannover. His work has been supported by Amazon Research Awards, Schufa GmbH, the German Federal Ministry of Education and Research (BMBF), and the EU Horizon 2020 programme. He is also affiliated with the L3S Research Center and has been a visiting scholar at Amazon Search.

At TU Delft, he leads the Research, Engineering, and Infrastructure Team (REIT) within the Department of Software Technology. He also serves as General Chair of ECIR 2026, hosted in Delft this year.

news

Mar 29, 2026 🌍 ECIR 2026 is being held in Delft, The Netherlands! Excited to serve as General Chair for the 48th European Conference on Information Retrieval.
Jul 01, 2025 🏅 Thrilled to share that our paper “Correctness is not Faithfulness in Retrieval Augmented Generation” received an Honourable Mention Award at ICTIR 2025 in Padua 🇮🇹!
Apr 01, 2025 🏆 Best Paper Award in the IRforGood track at ECIR 2025 for our paper “FlashCheck: Exploration of Efficient Evidence Retrieval for Fast Fact-Checking”!

selected recent publications

  1. Test-time Corpus Feedback: From Retrieval to RAG
    Mandeep Rathee, Venktesh V, Sean MacAvaney , and 1 more author
    In Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2026
  2. Think Right, Not More: Test-Time Scaling for Numerical Claim Verification
    Primakov Chungkham, Venktesh V, Vinay Setty , and 1 more author
    In Findings of the Association for Computational Linguistics: EMNLP 2025, Suzhou, China, November 4-9, 2025
  3. A Study into Investigating Temporal Robustness of LLMs
    Jonas Wallat, Abdelrahman Abdallah, Adam Jatowt , and 1 more author
    In Findings of the Association for Computational Linguistics, ACL 2025, Vienna, Austria, July 27 - August 1, 2025
  4. Breaking the Lens of the Telescope: Online Relevance Estimation over Large Retrieval Sets
    Mandeep Rathee, Venktesh V, Sean MacAvaney , and 1 more author
    In Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2025, Padua, Italy, July 13-18, 2025
  5. Sample Efficient Demonstration Selection for In-Context Learning
    Kiran Purohit, Venktesh V, Sourangshu Bhattacharya , and 1 more author
    In Forty-second International Conference on Machine Learning, ICML 2025, Vancouver, BC, Canada, July 13-19, 2025
  6. Correctness is not Faithfulness in Retrieval Augmented Generation Attributions
    Jonas Wallat, Maria Heuss, Maarten Rijke , and 1 more author
    In Proceedings of the 2025 International ACM SIGIR Conference on Innovative Concepts and Theories in Information Retrieval, ICTIR 2025, Padua, Italy, 18 July 2025
  7. Local Feature Selection without Label or Feature Leakage for Interpretable Machine Learning Predictions
    Harrie Oosterhuis, Lijun Lyu, and Avishek Anand
    In Forty-first International Conference on Machine Learning, ICML 2024, Vienna, Austria, July 21-27, 2024
  8. Explainable Information Retrieval: A Survey
    Avishek Anand, Lijun Lyu, Maximilian Idahl , and 3 more authors
    arxiv, 2022