College Park, Maryland, United States
As a Master’s student in Data Science at the University of Maryland, I thrive at the intersection of machine learning, analytics, and problem-solving. I’m passionate about transforming complex datasets into actionable insights that drive innovation and real-world impact. Currently, I am working as a Data Science Intern at Canaria Inc., where I build anomaly detection and data validation pipelines, design machine learning workflows for salary prediction and skill taxonomy, and integrate LLM APIs to accelerate content generation. Previously, I served as a Graduate Data Analyst at the UMD Counseling Center, where I applied clustering techniques to student engagement data, engineered SQL-based ETL workflows, and developed Tableau dashboards to improve reporting efficiency by 40%. My technical background spans Python, R, SQL, TensorFlow, PySpark, and Docker, with experience in predictive modeling, anomaly detection, cloud-based CI/CD, and business intelligence reporting. Beyond internships, I have applied my skills to impactful projects, including: Space Debris Risk Assessment – built ML pipelines to predict high-risk collisions, achieving 88% accuracy and reducing false positives by 22%. Farmland Protection – developed a YOLO-powered AI model to detect animal intrusions with 90% accuracy, awarded 2nd prize in a national hackathon and later published in a peer-reviewed journal. Academic Performance Prediction – deployed a forecasting model on AWS Elastic Beanstalk with automated CI/CD pipelines. I am deeply curious about how data-driven strategies and AI solutions can solve challenges across industries, from sustainability and space science to workforce analytics. I’m always open to connecting with professionals, researchers, and organizations working at the forefront of data science, AI, and analytics. Let’s connect and talk data!
I utilized advanced data science methodologies to optimize counseling services and enhance student engagement. • Conducted statistical hypothesis testing on behavioral data, collaborating with directors to implement data-driven policy decisions. • Applied NLP techniques to transform open-text survey data into actionable insights for service improvement. • Created automated Tableau and Power BI dashboards for tracking key performance indicators, streamlining leadership reviews.
• Migrated a legacy client database to a hybrid MongoDB/PostgreSQL architecture, enhancing query throughput by 40%. • Developed an XGBoost churn model and fine-tuned DistilBERT for automated support-ticket triage, reducing manual review time by 45%. • Architected a real-time Kafka streaming pipeline into a centralized AWS S3 data lake, supporting multiple client accounts.
• Conducted in-depth data analysis on Hybrid Electric Vehicles (HEV) to enhance predictive maintenance strategies. • Automated data processing and reporting workflows using Python and Excel to improve operational efficiency. • Integrated structured and unstructured datasets from multiple sources to identify key performance trends.