🧑‍🎓 Biography

Dr. Mieradilijiang Maimaiti is currently an Associate Professor at the School of Computer Science and Technology, Xinjiang University (XJU). Before joining XJU, he served as a Senior Algorithm Engineer in Natural Language Processing (NLP) at the Language Technology Lab, Alibaba DAMO Academy. He received his Ph.D. from the Department of Computer Science at Tsinghua University (THU), where he was advised by Prof. Maosong Sun and co-advised by Prof. Yang Liu in the THUNLP Lab. His research interests include natural language processing, machine translation, semantic representation learning, and large language models. Since 2016, he has been conducting research on machine translation, with a particular focus on neural machine translation (NMT) for low-resource and morphologically rich languages, including minority languages. His work has also explored domain adaptation, transfer learning, and data augmentation for low-resource machine translation, as well as morphological analysis for agglutinative and inflectional languages.

Before joining THUNLP, he obtained both his bachelor’s and master’s degrees from the School of Information Science and Engineering at Xinjiang University (XJU), majoring in Computer Science and Software Engineering, respectively. During his master’s studies, he conducted research on computational morphology, speech recognition, speech synthesis, and morphological analysis under the supervision of academician Prof. Wushouer Silamu. Before pursuing his Ph.D., he also gained extensive experience in software development for desktop, web, and mobile platforms.

Currently, his research focuses on multilingual and multimodal large language models for low-resource languages, with particular interests in text generation, reasoning, retrieval-augmented generation (RAG), tool learning, semantic retrieval, and multimodal learning.

Nothing is impossible!

🔎 Research Interests

Mieradilijiang has a broad research interest in language technology:

• Natural Language Processing
  • Machine Translation
    • Low-Resource Languages Scenario
    • Data Augmentation
    • Transfer Learning
    • Domain Adaptation
    • Prior-Knowledge
  • Morphological Analysis & Word Segmentation
  • Semantic Representation: Sentence Retrieval / Knowledge Retrieval
  • Sentiment Classification: Few-Shot Learning & Zero-Shot Learning
  • Multilingual Processing
• Large Language Models (LLMs)
  • Pre-training for Low-Resource Languages
  • Instruction Tuning / Supervised Fine-tuning (SFT)
  • Post-training and Alignment
  • Retrieval-Augmented Generation (RAG)
• Multimodal and Low-Resource NLP
  • Multimodal Fusion
  • Multimodal Data Construction
• Evaluation
  • Benchmarking for Low-Resource Language Models
• Information Retrieval
  • Cross-Language Information Retrieval
• Machine Learning for NLP
• NLP Applications

🗞️ Latest News

• 06/04/2026 Interspeech2026 One regular paper and one tutorial paper have been accepted by Interspeech2026.
• 05/19/2026 Academic Seminar We held an academic seminar between XJU & NUU.
• 03/30/2026 ESWA2026 One paper has been accepted by ESWA2026.
• 03/21/2026 ICNLP2026 I was Invited as a speaker and session chair at ICNLP2026.
• 03/20/2026 IJCNN2026 Four papers have been accepted by IJCNN2026.

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