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Hi there! šŸ‘‹

Icons borrowed from David Stutz

I am Sonish Sivarajkumar, a PhD Candidate in Intelligent Systems Program at the School of Computing and Information, University of Pittsburgh. My research interests lie in Clinical Natural Language Processing, Information Retrieval, Information Extraction, Few/Zero-shot Learning, and Clinical Large Language Models (cLLMs). I am also interested in Foundational AI models, Biomedical Informatics,and Electronic Health Records (EHR)

  • šŸ’» Check out my website
  • šŸ”­ I am currently working at Pitt NLP and AI Lab (PittNAIL)
  • šŸŒ± I work on foundational AI models and language models for patient representation
  • šŸ’¬ Ask me about Clinical NLP, Information Retrieval, Information Extraction, Large Language Models, and Foundational healthcare AI models .
  • šŸ“« You can reach me at [email protected] or [email protected].
  • šŸ‘Ø Pronouns: he/him/his
  • šŸ˜Ž Fun fact: Iā€™m an electrical engineer turned clinical NLP researcher. I switched from electrical transformers to natural language transformers.

Sonish Sivarajkumar's Projects

awesome-quantum-machine-learning icon awesome-quantum-machine-learning

Here you can get all the Quantum Machine learning Basics, Algorithms ,Study Materials ,Projects and the descriptions of the projects around the web

comp3225-nlp icon comp3225-nlp

Natural Language Processing coursework consisting of regex tasks and named entity recognition (NER) using conditional random fields (CRF)

deep-learning-drizzle icon deep-learning-drizzle

Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!!

districtcollector icon districtcollector

District Collectorate management system. Java Web App for application of different certificates

human-activity-recognition-using-machine-learning icon human-activity-recognition-using-machine-learning

Artificial Neural Networks (ANN), k-Nearest Neighbors, Random Forest classifier and Support Vector Machines (SVM) were trained over a HAR dataset on Python and the accuracies achieved by each of the algorithms were compared with each other.

latex-templates icon latex-templates

Website for personal collection and previewing of LaTeX templates. Presented with Python/Jinja2.

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