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projects
CrisisCast
Developed a end-to-end real-time pipeline that ingests social signals from Reddit and Google, classifies posts using a fine-tuned LLM, and visualizes crises on a dynamic dashboard. Leveraged Kafka, PySpark, FastAPI, MongoDB, and Qdrant for scalable processing and semantic search.
Parameter-Efficient Transformer
Fine-tuned RoBERTa-base on AG News using LoRA to train just 0.4% of parameters, achieving 92.3% accuracy in 3 epochs. Reduced GPU memory by 50% and enabled scalable deployment under 5 min/epoch.
Time Banking
Built a Django-based service marketplace where users exchange time and quality as currency. Designed a responsive UI using HTML, CSS, and JavaScript for cross-platform use. Implemented RESTful APIs for backend logic and integrated CI/CD pipelines via GitHub Actions, achieving 85% test coverage. Followed Agile/Scrum practices to ensure reliable delivery and iterative development.
Efficient Deep Learning with Narrow ResNet-18
Built a compact and high-performing image classification model using a width-scaled ResNet-18 architecture on the CIFAR-10 dataset. Optimized for efficiency by reducing model size under 5M parameters and incorporating Cutout augmentation, label smoothing, and cosine annealing learning rate scheduling. Achieved 95.5% test accuracy in 250 epochs while significantly lowering memory and compute requirements, making it suitable for edge deployment.
Comparative Analysis of LLMs
Implemented BERT and GPT from scratch, fine-tuned on WikiText, SQuAD, and CNN/DailyMail. Achieved 1.67 (BERT) and 2.97 (GPT) loss, highlighting strengths in QA and summarization with modular inference pipeline.
publications
Precision Farming Methods Using Machine Learning
Published in ICESTM, 2024
Proposed a hybrid ML model for crop recommendation, improving prediction accuracy and yield optimization.
Recommended citation: Pawar, T. (2024). "Precision Farming Methods Using Machine Learning." ICESTM.
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talks
Talk 1 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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