Nisansa Dilushan de Silva

I am a lecturer at the Department of Computer Science & Engineering, University of Moratuwa, Sri Lanka. I obtained my Ph.D (2020, advisor: Dejing Dou) and MS (2016) degrees in Computer and Information Science from University of Oregon, USA and my BSc (Hons) in Computer Science & Engineering (2011) degree from University of Moratuwa. I joined University of Moratuwa as a lecturer in 2011. From 2013 to 2014, I worked as a researcher at LIRNEasia. In 2018, I worked as a Givens Associate at Argonne National Laboratory, USA. I have over 80 peer reviewed publications in the field of computer science, mostly under the subfields of Natural Language Processing and Artificial Intelligence, earning more than 1400 citations. I am also an associate member of the IESL.

Professional Experience

  • Present 2020

    Senior Lecturer

    Department of Computer science & Engineering, University of Moratuwa,
    Sri Lanka

  • 2021 2020

    Research Fellow

    LIRNEasia,
    Sri Lanka

  • 2020 2014

    Graduate Research/Teaching Fellow

    University of Oregon, Department of Computer and Information Science,
    USA.

  • 2018 2018

    Givens Associate

    Argonne National Laboratory,
    USA.

  • 2020 2011

    Lecturer

    Department of Computer science & Engineering, University of Moratuwa,
    Sri Lanka

  • 2014 2013

    Researcher

    LIRNEasia,
    Sri Lanka

  • 2014 2013

    Visiting Lecturer

    Northshore College of Business and Technology,
    Sri Lanka

Education

  • Ph.D. 2020

    Ph.D. in Computer & Information Science

    University of Oregon, USA

  • MS 2016

    MS in Computer & Information Science

    University of Oregon, USA

  • BSc2011

    B.Sc Engineering (Hons)in Computer Science & Engineering

    University of Moratuwa, Sri Lanka

Featured Research

Hybrid Approach for Information Retrieval in Sri Lankan Legal Domain


N. Kasige, N. Kavinda, K. Kodithuwakku, and N. de Silva

Proceedings of the Engineering Research Unit Symposium, 2025, pp. 70-71,

Accessing a country’s legal domain is challenging, due to disparities in legal domain literacy among individuals and finding fragmented legal resources. This study addresses this predicament by developing an accessible information retrieval system tailored to users irrespective of their knowledge level in the legal domain. This solution employs a hybrid retrieval approach backed by keyword search retrieval with BM25 and semantic search retrieval with Legal-BERT, complemented by extractive summarization. This architecture enables users to identify the relevant documents based on their specific requirements. In addition to that, this research focusses on providing more context-aware responses, by Gemma-3 4B parameter model for domain-specific adaptation. Beyond the English language based implementation, this study aims to extend this framework’s capabilities to support legal queries in Sinhala language, which is a crucial yet unresolved problem in both legal and natural language processing domains. The proposed solution aims to bridge the gap between legal information accessibility and linguistic diversity, contributing toward a more inclusive and efficient legal information retrieval system.