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Proficient in Python and C/C++, with strong experience in deep learning frameworks and machine learning libraries. Skilled in Natural Language Processing (NLP), large language models, and text representation techniques, including tokenization, embedding methods, and semantic analysis.
Experienced in working with data preprocessing, feature engineering, and model evaluation, along with handling large-scale structured and unstructured datasets. Familiar with computer vision fundamentals, image processing techniques, and multimodal data handling.
Strong understanding of machine learning concepts, optimization methods, and statistical analysis, with the ability to implement and fine-tune models efficiently. Comfortable using LaTeX and research tools for technical documentation and experimentation.
MIRA-KG: Developed a multimodal instruction-driven QA system integrating RAG with knowledge graphs and LLMs to enhance reasoning across text and visual data.
MuRAG-LLM: Designed a multimodal reasoning framework combining retrieval-augmented generation with large language models for improved contextual understanding.
Pythia-RAG: Built a multimodal knowledge graph-based RAG system to improve accuracy and explainability in question answering tasks.
Talk2Doc: Implemented a medical Q&A system leveraging weighted knowledge graphs and RAG for patient interaction and clinical decision support.
LLM-AMR: Developed a system integrating real-time conversations with electronic health records for intelligent medical advice and recommendations.
CIPM-R: Proposed a deep learning-based approach for medication recommendation using conditional intensity modeling.
Semantic Text Similarity: Designed a hybrid model using SBERT, Bi-LSTM, and attention mechanisms for improved semantic understanding.
Multimodal Sentiment Analysis: Built models combining multiple modalities to improve sentiment classification performance.
Bioinformatics Project: Applied deep learning for prediction of microRNA binding residues in proteins.
Low-resource NLP: Developed Roman Urdu and Pashto sentiment analysis systems for underrepresented languages.
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