1. Leveraged Progressive and Multi-task learning for forecasting Expected time of arrivals (ETA), Dispatch and Route Management. Resulting in annual cost savings of $450,000 with an error rate of 5%.2. Designed LSTM model to classify driving behaviors as risky or safe, significantly enhancing trip safety and reliability. This model directly contributed to a usage-based driving quality assessments and improved decision-making for dispatchers.3. Implemented Redis Caching, slashing response times by 90% from 1.2 seconds to a mere 0.03 seconds, significantly enhancing user experience. Automated CI/CD pipelines using AWS CodePipeline and CodeBuild, enabling dynamic, automatic retraining of machine learning models to swiftly detect and adjust for model drift within 2 days, markedly down from 4 weeks, ensuring high model accuracy and reliability.4. Leading the migration from a legacy monolith backend service to an AWS Serverless backend, I transformed our ML infrastructure into over 20 scalable microservices sets. This initiative not only dramatically enhanced our data pipeline's scalability from handling up to 100K calls to 5 million API calls/day but also significantly improved security by developing robust authorization and authentication strategies for the APIs. I streamlined API endpoint integration and DNS management via Route 53, Gateway enhancing service reliability and achieving a seamless transition.

1. Trained large pretrained models on AWS EC2/Batch instances leveraging large unlabelled data and unlabelled data ( ̃100 million unique strings) to predict validity of string representations.2. Experimented with foundational models on limited fine tuning data for improving performance of graphical neural networks for generating text; resulting in a 15% improvement in accuracy.3. Design validation protocols for emerging models, using time-split and target-category split analysis to preemptively identify and mitigate risks of data leakage and model drift, ensuring model robustness and reliability.4. Designed end-to-end ML workflow using Airflow to manage model training on AWS EC2 instances, leveraging data from S3 buckets; successfully automated data extraction, preprocessing, & storage of trained models back into S3.

Developed an AI-driven drug design model, analogous to Named Entity Recognition, leveraging Monte Carlo Tree Search in a branching factor space of 2058.

Leveraged machine learning techniques to enhance the quality of building datasets, crucial for earthquake prediction models :-Data Imputation and Integrity: Implemented state-of-the-art machine learning algorithms to accurately impute missing data across extensive datasets, ensuring comprehensive data integrity and filling critical information gaps that were previously unaddressed.Machine Learning Validation Tools: Developed and deployed sophisticated machine learning validation tools to meticulously examine incoming datasets. This initiative was crucial in preventing data overlap with existing building records, thereby significantly enhancing the overall accuracy and reliability of our earthquake predictive modeling database.Data Verification Pipeline: Conceptualized and executed a robust data verification pipeline designed to reconcile inconsistencies between local source data (e.g., CoreLogic) and satellite building footprint information provided by leading tech giants Microsoft and Google.

Disaster Detection and Information Spread Monitoring: Utilized Python, alongside NLP libraries like NLTK and spaCy, to develop algorithms capable of identifying emerging disasters in real-time. Implemented machine learning models to track the spread of information during emergencies, evaluating public sentiment with over 90% accuracy. Geospatial Analysis for Crisis Management: Employed geospatial analysis tools and techniques, including GIS software and Python libraries like Geopandas, to map social media post locations and identify activity hotspots. Our approach significantly enhanced the organization's ability to understand and react to public reactions, leading to a 30% improvement in the efficiency of our emergency response initiatives.