Welcome to my blog.

I am a Machine Learning Engineer and Applied AI Researcher specializing in Generative AI, Large Language Models (LLMs), and production-grade AI systems. I focus on building scalable and robust AI solutions using domain-adaptive fine-tuning, LoRA/PEFT methods, and reinforcement learning, with hands-on experience in fast-paced startup environments.

I have end-to-end experience across the ML lifecycle, including large-scale data preprocessing, feature engineering, model training, evaluation frameworks, inference optimization, and performance benchmarking. I am proficient in Python and deep learning frameworks such as PyTorch, TensorFlow, and HuggingFace Transformers, with practical experience fine-tuning transformer models like BERT for domain-specific applications.

My work bridges research and engineering, translating state-of-the-art ML techniques into production systems through systematic experimentation, error analysis, and scalable ML pipelines on GCP and AWS.

I am a first author of two peer-reviewed research papers published in Springer Nature and PMLR proceedings, indexed in DBLP, ACM, and Google Scholar.

Education : Master of Science – Computing & Information Systems (focus on ML/NLP) Athabasca University, Canada Thesis - A Parameter-Efficient Framework for Word Sense Disambiguation through Low-Rank and Decomposed Adaptation

Grade: GPA - 4.0/4.0

1. Recipient of Outstanding Distinction Award 🏅 for Graduate Students

2. Recipient of Graduate Research Fellowship Award 🏅

Publications (First author)

1. Vijayalakshmi Manikandan, Dunwei Wen, M. Ali Akber Dewan (2026). “GlossAdapter: Enhancing word sense disambiguation via LoRA adapters.” Proceedings of the Canadian Conference on Artificial Intelligence. Link - CANAI Link to paper

2. Vijayalakshmi Manikandan, Dunwei Wen, M. Ali Akber Dewan (2026). “Parameter-Efficient Multi-Task Learning for Biomedical Abbreviation Disambiguation” .
   Link: https://aiih.cc/
   Got accepted at the International Conference on AI in Healthcare. AIiH 2026 (Following the conference on August 28, 2026, the proceedings will be officially published in Springer Nature.)

On my blog, you’ll find a collection of articles exploring the fascinating realms of AI and ML. My mission is simple: to share my ML journey, from experiments and coding adventures to valuable insights and learnings. Join me as we dive deep into the world of machine learning together!