Diya Goswami

AI/ML & Data Science Specialist

Agartala, IN.

About

Highly motivated B.Tech student specializing in Health Informatics with a robust foundation in AI/ML, data science, and advanced analytics. Demonstrated ability to design and implement innovative, data-driven solutions, evidenced by impactful projects in retail feedback analysis, cardiac risk prediction (achieving 85.85% accuracy), and intelligent skin detection. Eager to apply strong problem-solving skills and technical expertise to contribute to cutting-edge advancements in a challenging and dynamic environment.

Education

Vellore Institute of Technology Bhopal

Bhopal, Madhya Pradesh, India
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B. Tech Computer Science and Engineering with specialization in Health Informatics Grade: 8.98 CGPA

Auxilium Girls' School

Agartala, Tripura, India
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Higher Secondary Science Grade: 95.2 Percentage

Auxilium Girls' School

Agartala, Tripura, India
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High School General Studies Grade: 96.2 Percentage

Awards

Smart India Hackathon 2024 Finalist

Government of India

Achieved finalist status in the Smart India Hackathon 2024, demonstrating advanced problem-solving and technical skills in a competitive national environment.

Health Hackathon JHU & VITB Finalist

Johns Hopkins University & Vellore Institute of Technology Bhopal

Secured finalist position in the Health Hackathon jointly organized by Johns Hopkins University and VIT Bhopal, applying innovative solutions to critical healthcare challenges.

Publications

Research on ML in Cardiac Disease Prediction

ICDCC 2024

Authored, presented, and published research work on Machine Learning for cardiac disease prediction at the International Conference on Data Communication and Computing (ICDCC 2024), contributing to advancements in health informatics.

Certificates

IBM Blockchain Fundamentals and Developers

IBM

FacePrep Mastering Data Structures and Algorithms

FacePrep

Google Data Analytics

Google

Skills

Programming Languages

Java, C++, Python, SQL.

Machine Learning & AI

Machine Learning, Deep Learning, Generative AI algorithms, TensorFlow, PyTorch, Scikit-learn, Keras, LLMs, LangChain, LangGraph, ChromaDB/PineconeDB.

Data Analysis & Visualization

Pandas, NumPy, Matplotlib, Tableau.

Development Tools & Methodologies

Open-Source Contribution, GitHub.

Projects

EchoRetail: Retail Feedback Captured and Analyzed by AI

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Summary

An AI-driven retail analytics system designed to generate synthetic transaction datasets using Generative Adversarial Networks (GANs) and simulate realistic customer behavior for comprehensive market insights.

AI-Augmented Cardiac Risk Prediction using Synthetic Data Generation Techniques

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Summary

Designed and implemented a synthetic data pipeline utilizing advanced techniques like CTGAN, VAE, and Table Diffusion to address dataset imbalance and enhance heart disease prediction.

SkinSight: Intelligent Skin Type Detection System

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Summary

Developed a real-time intelligent skin type detection system leveraging CNN, ResNet-50, and Haar Cascade algorithms on a Raspberry Pi 5 platform for personalized dermatology applications.