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Graduate Research Assistant - Bergin Group

Duke University

• Analyzed data from remote sensors in Kenya, developed a data pipeline, and created calibration methods to ensure consistent sensor readings in the same environment
• Working towards investigating the impact of pollution on human health using Oura ring data, and analyzing factors such as SpO2, stress levels, and heart rate

Duke Innovation Co-Lab Student Developer

Duke University

• Leading the development of the Chatbot module for the extension project of Nurse Hackathon
• Working to integrate LLMs like LLama, Falcon, and Mistral into low-latency systems, aiming to enhance their effectiveness and optimize for resource-constrained applications.

Data Scientist

IBM

• Contributed to platform deployment and reporting for the Global Azure Team, utilizing Machine Learning and Azure Cloud expertise. Specialized in IBM's Content Intelligence platform for document intelligence
• Developed a Table detection Model(Computer Vision) and Similarity Search for Automatic Document Content Validation & processing ~100K Documents leading to 50% cost savings ($200-300 K per year) in the Manual Audit. Used Python, BERT, and Camelot for the same
• Developed a module for document meta-data extraction and hierarchy generation of connecting documents
• Enhanced IBM's Content Intelligence platform by developing document classification and custom key-value pair extraction modules, while optimizing end-to-end document processing
• Deployed end-to-end CI platform for clients on Azure Kubernetes Service using Terraform & Jenkins.

Data Scientist

Prescience Decision Solutions

• Contributed to the development of a PDF document search module for an intelligent search platform ,leveraging ElasticSearch and applying retrieval techniques such as TF-IDF and BM25
• Used Transformers like BERT, XLnet for passage rankings

Data Scientist Intern

IBM

• Made rule-based classification model, LSTM model, etc., for classification of different types of clauses
• Proposed and built two clause segregation approaches: Combined Random Forests with concrete features and other one utilized computer vision techniques.

Masters

Duke University

Artificial Intelligence

• Sourcing Data for Analytics, Modeling Process & Algorithms, Reinforcement Learning, Deep Learning Applications, Generative AI, Operationalizing AI (MLOps)

Bachelors

NIIT University

Computer Science with minors in Artificial Intelligence

• Machine Learning, Deep Learning, Artificial Intelligence, Computer Vision, Natural Language Processing, Operating Systems, Algorithms and Data Structures, C++, Python, Information Retrieval