• Developed a Bayesian framework for estimating 3D urban wind velocities using UAV motion data, enabling real-time estimation for urban air mobility systems.• Developed a mixed-integer model for risk-averse drone delivery with Markov chain for wind prediction and solved by a 2-phase heuristic for 100x speedup and <5% failure rate in large-scale urban cases, enhancing safety and efficiency in operations.• Led a cross-university team to use Bayesian bandit and RL framework to optimize drone dispatch and balance exploitation and exploration in uncertain urban environments, achieving provable faster convergence and lower operational costs.• Deployed a LabVIEW-based real-time vibration monitoring system at The Metropolitan Museum of Arts, New York for continuous data acquisition and event safety.

•Built and maintained the database of 1000+ real estate indicators pulled from the Wind dataset by MATLAB•Evaluated the investment potential of commercial real estates in Shenzhen from macroeconomics data, industrial data, and case study, formatting investment advice with 30,000 words research report•Analyzed the real estate investment trust (REITs) development in Singapore, Hong Kong, and the United States from the Wind and Bloomberg data to predict the trend of incoming REITs products in China