Yubraj Sigdel

I research explainable AI and AI for low-resource languages, with a focus on Nepali NLP, OCR for handwritten scripts, and open-source tooling — alongside production AI engineering at Veel Inc.

Research statement

What I work on, and why

My research centers on making AI systems trustworthy and usable in settings where trust, language, or data are scarce. I'm particularly drawn to AI content moderation — building systems that flag misaligned content reliably while remaining auditable — and to explainable AI more broadly, since a moderation, medical, or safety-critical decision is only as useful as the reason behind it.

A second thread is NLP and multimodal LLMs for low-resource languages, particularly Nepali: OCR and handwritten-script digitization, semantic search, and Romanized-to-Devanagari transliteration are all instances of a broader question — how do we bring the same quality of language technology to languages with a fraction of the training data and tooling that high-resource languages have?

I'm also increasingly interested in AI for medical/healthcare applications, where the explainability and low-resource-data constraints I already work with intersect directly with real consequences for patients and clinicians. I'm open to research collaborations and conversations across any of these areas.

Thesis

Network Intrusion Detection System

  1. Network Intrusion Detection System Kathmandu Engineering College, 2023.

    The rapid advances in the internet and communication fields have resulted in a huge increase in the network size and the corresponding data. With the advancement of internet over the years, the number of attacks over the internet have been increasing. An intrusion detection system (IDS) is one such tool that prevents the network from possible intrusions by inspecting the network traffic, to ensure its confidentiality, integrity, and availability. Machine Learning algorithms are applied in IDS in order to identify and classify security threats. This report explores the comparative study of various machine learning algorithms such as K-nearest neighbor, random forest, Adaboost based decision tree, and support vector machine for various applications such as to provide security to organizations as well as help organizations identify their bugs and error-prone areas in the network. The above-mentioned algorithms are used to detect and classify the attack type. The NSL-KDD dataset, taken from Kaggle, is used to train and test the system model.

    Keywords: intrusion, confidentiality, Adaboost, NSL-KDD.

    Code

Publications

Peer-reviewed & preprints

  1. Yubraj Sigdel. College Chatbot Using RASA. KEC Journal of Science and Engineering, 2024. DOI Code

Talks & presentations

Conferences & workshops

  1. [[ Talk title ]] [[ Venue ]], [[ Date ]]. Slides Video

Education

Degrees & coursework

  1. Bachelor's in Computer Engineering Kathmandu Engineering College, 2018–2023.

    82.41%
    • Minor Project: a computer vision system for face detection and recognition applied to criminal identification.
    • Major project: a Network Intrusion Detection System benchmarking multiple ML algorithms.

    Coursework Artificial Intelligence (AI), Mathematics (I, II, III), Applied Mathematics, Probability & Statistics, Numerical Methods, Discrete Structure, Big Data Technologies, Data Mining, Database Management System (DBMS), Digital Signal Analysis & Processing, Operating Systems (OS), Data Structures & Algorithms (DSA), Theory of Computation (TOC), Object-Oriented Programming (OOP)

  2. High School Gorkha United Public School, 2016–2018.

Also

Research skills & methods

  • Research areas AI Content Moderation, Explainable AI (XAI), NLP, Multimodal LLMs, Low-Resource Language Modeling, AI for Medical/Healthcare, OCR & Handwritten-Script Digitization, Semantic Search, Transliteration
  • Methods & models ViT, NepBERT, mT5, YOLOv8, RoBERTa, Sentence Transformers, Fine-Tuning
  • Tooling PyTorch, Hugging Face, Pandas, NumPy