Raghavendra Kharosekar

• Implemented a data pipeline to curate 25+ years of daily water data from multiple sources, generating structured datasets across 42+ hydrological parameters to train a CNN-based inverse model, improving prediction accuracy by 18% and reduced calibration time by 40%. • Orchestrated a data pipeline in Python to process 1.7M+ rows of time-series water-quality data across 8 watersheds, implementing scalable ingestion and data interpolation, to train a LSTM seq2seq model to predict 6 key parameters, boosting forecast accuracy by 15%. • Constructed a modular preprocessing framework in SQL and Python to process 600M+ time-series records, applying a self-developed hybrid imputation technique to augment missing data and deliver reliable trend analytics across 300+ parameters and 30+ monitoring stations, supporting environmental decision-making across Illinois.

• Automated a data pipeline to ingest and preprocess ECU telemetry data across 54 sensor features, applying feature selection and outlier detection, improving data readiness and pipeline efficiency by 60%. • Integrated a TensorFlow Feedforward Neural Network for two-wheeler drive range prediction with adaptive learning rates into the pipeline, reducing error by 40% and improving model performance. • Developed a regression pipeline with Scikit-learn, and employed feature engineering and ensemble methods to estimate battery power, increasing prediction accuracy by 30%.

- Spearheaded a team of 4 to develop efficient Python scripts for automation of hardware and software testing procedures, including Software power state testing, streamlining the process and ensuring consistency across multiple environments. - Automated 350 testing procedures, resulting in a 70% reduction in manual testing efforts and costs, improving operational efficiency. - Facilitated effective communication between the team and company officials, ensured timely project completion and precision in scripting using Agile practices, enhancing overall project success.

Coursework: Data Warehousing & Business Intelligence, Information Modeling, Data Science, Machine Learning Cloud, Data and Statistical Modelling, Information Consulting, Applied Business Research, Data Visualizations in Python

Majored in Electronics and Telecommunications Engineering along with an Honors in Artificial Intelligence and Machine Learning Coursework: Data Analytics, Data Science, Object Oriented Programming, Database Management Systems, Data Structures, Artificial Intelligence