Ni Dang

• Completed an intensive, full-time data science program focused on transitioning professionals into machine learning and applied data roles. • Gained practical experience in Python programming, software engineering, and applied mathematics through project-based learning and peer collaboration. • Developed and deployed a real-time pedestrian collision prediction system for autonomous driving as a portfolio project.

• Leading the development of end-to-end data pipelines, including data collection, preprocessing, and transformation, to support intelligent HR solutions. • Designing and implementing advanced machine learning algorithms, e.g., RAG, withafocusonmodelperformance,scalability,andbusinessimpact. • Deploying and integrating ML models into production environments, ensuring robustAPIdeliveryandreal-timeprocessingcapabilities.

• Managed and executed multiple interdisciplinary research projects in the fields of autonomous driving and predictive control, with published outcomes in peer- reviewed venues. • Designed and delivered tutorials, supervised over 15 student research projects (Bachelor & Master), and maintained the institute’s digital infrastructure, including web content systems.

• Dissertation: “Distributed Stochastic Model Predictive Control for a Microscopic Interactive Traffic Model” • Focus areas: model predictive control, inverse reinforcement learning, and multi-agent systems.

• Specialization in complex networks and graph theory. • Thesis: “Reconstruction of Networks from One-step Data by Matching Positions” • Awarded distinction: “Excellent Graduate Student”

• Thesis: “Community Detection in Complex Network Graphs”