M.S. Financial Engineering @ Columbia University
Quantitative Research · Market Microstructure · Stochastic Systems · Machine Learning

I am a Master's student in Financial Engineering at Columbia University focused on systematic trading, market microstructure, stochastic control, optimization, and machine learning.

My work centers on building quantitative research systems for:

• 📈 Execution and market making
• 🧮 Stochastic modeling and control
• 🔍 Filtering and latent state estimation
• 🎲 Monte Carlo and sequential inference
• 🤖 Financial machine learning

Implementation and extension of the Avellaneda-Stoikov market making framework using real WRDS TAQ trade data.

• Event-driven replay simulation against historical trade prints
• Queue-position fill modeling under realistic execution assumptions
• Volatility-adaptive spread control using rolling realized volatility
• Empirical calibration of spread and fill-intensity parameters
• P&L, inventory, spread, and execution-quality analysis

Research framework for optimal execution in competitive markets with heterogeneous agents.

• Latent market regimes driven by Hidden Markov models
• Mean-field interactions through aggregate order flow
• Inventory-sensitive feedback controls
• Price impact dynamics and stochastic execution costs
• Simulation of finite-population effects and model misspecification

Monte Carlo simulation project focused on Bayesian filtering and latent state estimation.

• Kalman filtering for linear-Gaussian state space models
• Particle filtering for nonlinear/non-Gaussian systems
• Sequential importance resampling
• Latent state estimation and signal extraction
• Applications to dynamic hedge ratios and financial time series

Artificial Intelligence Researcher

Conducted machine learning research under Dr. Paulo Shakarian on hierarchical multi-label classification and robustness.

• Built hierarchical image classification datasets and preprocessing pipelines
• Benchmarked Vision Transformer and Inception architectures
• Integrated hierarchical logical constraints into deep learning workflows
• Co-authored a CIKM 2024 paper on error detection and constraint recovery

Error Detection and Constraint Recovery in Hierarchical Multi-Label Classification without Prior Knowledge
Joshua Shay Kricheli, Khoa Vo, Aniruddha Datta, Spencer Ozgur, Paulo Shakarian
CIKM 2024 Short Paper

• Building market microstructure and execution research systems
• Conducting optimization research at Columbia
• Developing filtering and Monte Carlo simulation projects
• Preparing for quant trading, strats, and systematic research roles