1. Time-Series Modeling of Order Flow Using Deep Learning:

Demonstrate how deep learning models, when applied to high-frequency, structured time-series data in kdb+/q, can enhance short-term price forecasting and execution decision-making.

2. Combining Domain Knowledge with Neural Architectures:

Discuss how deep learning complements traditional signal processing in kdb+/q, addressing interpretability, robustness, and latency trade-offs in real-world execution pipelines.

3. Real-Time Execution Analytics at Scale:

Present techniques for efficient venue benchmarking, slippage monitoring, and dynamic strategy refinement using kdb+/q, aligned with live market microstructure feedback.

These objectives aim to bridge cutting-edge AI applications with time-critical systems built on kdb+/q, offering practical value to quants, engineers, and traders alike.