Neha Angadi
Graduate Assistant @The Data Science Institute at Columbia University
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WORK HISTORY
Graduate Assistant @The Data Science Institute at Columbia University
New York, NY, US
Organized the AI and Education Forum: Reimagining Teaching and Learning in the Age of AI- Co-Lead for the AI, Education, and the Future Town Hall series- Turning Data into Direction: Shaping Careers in Data Science- Data Science Day 2025: Data Science Institute flagship event- iCubed (Institute, Industry, Innovation) seminars: Recurring sessions with industry experts and working professionals in Data Science- Data Science Career Fair- Panel discussions with academic experts over the latest real-life growth in technology across diverse domains and the corresponding effects of the technological advancements- Computer Science Research Fair- AI & Society Catalyst Seminar Series- Columbia University AI Summit; DSI Program Partner for the workshop \"The Columbia Class of 2035: Will We Need To Reinvent Higher Education?\"- AI for Sciences & Engineering Workshop with the Computing Systems Research Center
EDUCATION
PES University
Bachelor of Technology - BTech, Computer Science
Columbia Engineering
Master of Science - MS, Data Science
Venkat International Public School
High School Diploma, Mathematics and Computer Science
ABOUT NEHA ANGADI
I recently graduated with a Master of Science in Data Science from Columbia University, building on my experience as a Data Engineer at Morgan Stanley. My background spans large-scale data engineering, distributed systems, and applied machine learning, including designing production-grade ETL pipelines, optimizing Kafka and Snowflake architectures, and developing reinforcement learning and graph neural network models for real-world autonomous systems.I am particularly interested in Data Engineering, Data Science, AI/ML, and quantitative roles where I can apply advanced modeling, statistical rigor, and scalable data systems to solve complex, high-impact problems.I operate at the intersection of engineering and analytics, building resilient data infrastructure, deploying ML systems, and translating complex data into strategic insight that drives measurable impact.
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