NYC AI & Quantitative Research

This is a community for researchers, engineers, data scientists, and students exploring the intersection of artificial intelligence, machine learning, and quantitative finance.

Quantitative research is being transformed by modern AI — reinforcement learning, large language models, deep learning for time series, and more. The academic and technical challenges at this intersection are some of the hardest and most interesting problems in applied machine learning. This meetup is a space to study them together.

This is not an investment club. We do not provide financial advice, recommend trades, or promise financial outcomes of any kind. This is a technology and research community focused on learning, sharing knowledge, and exploring ideas.

What we explore:

• Reinforcement learning research — PPO, TD3, SAC, DDPG, and how RL agents learn sequential decision-making in stochastic environments
• LLMs and NLP for financial text — applying language models to earnings calls, SEC filings, news, and unstructured data as a research problem
• Deep learning for time series — transformers, CNN-BiLSTM, temporal fusion architectures, and neural forecasting methods
• Signal processing and feature engineering — extracting structure from noisy, non-stationary data
• Technical indicator analysis — Ichimoku, Bollinger Bands, RSI, and the quantitative study of traditional charting methods
• Statistical methods — factor modeling, cointegration, regime detection, and classical quantitative techniques
• Portfolio theory and optimization — mean-variance, risk parity, Black-Litterman, and modern approaches to portfolio construction as a research topic
• Market microstructure — order book modeling, price formation, and the academic study of how markets work
• Backtesting methodology — walk-forward analysis, cross-validation for time series, data leakage, lookahead bias, and the science of not fooling yourself
• Alternative data research — satellite imagery, sentiment analysis, web data, and novel data sources as research subjects
• Infrastructure and engineering — data pipelines, real-time systems, Python, PyTorch, Spark, and the engineering behind quantitative research
• Simulation and synthetic data — market simulation, multi-agent modeling, and generating synthetic financial environments for research
• Academic papers and research reviews — reading and discussing the latest from journals, arXiv, and conference proceedings
• Responsible AI in finance — fairness, explainability, model governance, and ethical considerations in applying AI to financial systems
• Career development — navigating careers in quantitative research, data science, and financial technology

What to expect:

Paper discussions, technical deep dives, coding workshops, research presentations, and open conversations about methodology and best practices. We focus on the science, the engineering, and the ideas — not on financial outcomes.

Community standards:

• No financial advice, trade recommendations, or investment solicitation
• No claims of financial performance or guarantees of any kind
• No signal selling, course selling, or paid promotions
• Respect the research. Respect the math. Respect each other.

This is a space for learning and intellectual curiosity. Come explore with us.