The Ph.D. in Operations Research and Financial Engineering (ORFE) at Princeton University empowers students to develop sophisticated mathematical tools and models to solve complex decision-making problems. The following dissertation ideas span quantitative finance, logistics, optimization, and risk analytics.
Stochastic Control Models for Portfolio Optimization
Machine Learning in High-Frequency Trading Systems
Risk Measures and Capital Allocation Under Extreme Events
Optimal Execution Strategies in Illiquid Markets
Game Theory Applications in Algorithmic Bidding
Markov Decision Processes in Supply Chain Optimization
Systemic Risk Modeling in Interbank Lending Networks
Derivatives Pricing Using Jump-Diffusion Models
Financial Time Series Forecasting with Recurrent Neural Networks
Robust Optimization in Uncertain Economic Environments
Credit Scoring Models Using Ensemble Machine Learning
Numerical Methods for American Option Valuation
Sparse Optimization for Asset Allocation Problems
Blockchain-Based Smart Contracts in Risk Sharing
Network Flow Optimization in Disaster Response Logistics
Multivariate Risk Models for Insurance Portfolio Design
Mean Field Game Theory in Large Population Economics
Real-Time Optimization in Ride-Sharing and Mobility Services
Quantifying Information Leakage in Financial Networks
Dynamic Pricing Algorithms for E-Commerce Platforms
Collexa helps Princeton ORFE Ph.D. scholars with data modeling, optimization coding, financial simulations, algorithm development, and research paper structuring in operations and finance.
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