In the relentless pursuit of alpha, speed and sophistication are everything — and AI is the new battleground. This panel brings together leaders at the frontier of quant strategy to unpack how machine learning, advanced data pipelines, and next-gen infrastructure are transforming signal discovery and execution. From deep learning breakthroughs to obscure alternative data feeds, we’ll expose what’s working, what’s hype, and what it really takes to stay ahead in the most competitive race in finance.

Proposed panel discussion questions:

1.    How are quant teams engineering proprietary edge in a world where everyone has the same models and compute power?

2.    What alternative data sets and AI architectures are actually delivering differentiated alpha — and which are dead ends?

3.    How do you balance speed, scale, and complexity when deploying ML models across global markets?

4.    Is the future of quant strategy defined by human insight augmented by machines, or will autonomous AI eventually outpace us all?

Large Language Models and NLP’s have moved from labs to trading desks—shaping the way we trade:

1.    Can you give real-world applications of LLMs in quant finance—from sentiment analysis to automated document parsing?

2.    How are you using NLP to accelerate research, generate alpha, and improve operational efficiency?

3.    How have you grasped the strengths, limitations, and responsible deployment of LLMs in production environments?

·      What are the most impactful advancements in multi-factor risk modelling, and how are they reshaping portfolio construction in high-volatility environments?

·      In what ways are AI, machine learning and quantum computing techniques enhancing or challenging traditional portfolio construction?

·      How can quants effectively integrate signal uncertainty and regime shifts into dynamic risk models and portfolio rebalancing strategies?