Machine Learning

Context At Bayer, I interned with the Solutions team under Engineering and Data Science, working on machine learning, data engineering, and operational analytics for seed processing and field equipment operations. What I Built I built XGBoost and LightGBM regression models to predict machine-level seed processing times from historical throughput and runtime data, achieving RMSE = 35. These models supported capacity planning and maintenance scheduling for upcoming planting cycles. I also engineered ETL pipelines over 10GB+ of agronomic and equipment data from BigQuery and SQL Server, consolidating multi-year data from multiple sources and reducing retrieval latency by 40%. ...

Context For my UIUC research project, I worked on SHERPA, a semi-structured RAG framework designed to improve retrieval and reasoning by combining unstructured biomedical text with structured knowledge graphs. Traditional RAG systems rely heavily on vector search over unstructured documents. Our goal was to explore whether adding structured memory through entity-relation extraction and knowledge graph construction could improve retrieval fidelity, reasoning, and answer quality for biomedical question answering. What I Built My contribution focused on two components: query classification and NER / relation extraction for triplet generation. ...

Context During my undergraduate research at SASTRA University, I worked on computer vision and multimodal AI problems across two research directions: semantic segmentation of aerial imagery and dense video captioning for assistive vision. The common thread across both projects was visual understanding: how can deep learning systems interpret complex visual inputs and convert them into useful structured outputs, whether pixel-level land-cover labels or natural-language descriptions of video content? What I Built For my B.Tech thesis, I worked on semantic segmentation of high-resolution aerial imagery using the ISPRS Vaihingen and Potsdam benchmark datasets. The goal was to classify every pixel in urban aerial images into land-cover categories such as buildings, impervious surfaces, vegetation, trees, cars, and clutter. ...