Gaurav Chakravorty explains how recommender systems can be utilized for investment management and details how AI and deep learning are used in trading today.
Gaurav begins by diving into chief investment offices, which are growing their in-house machine learning teams to fine-tune their allocation, using both traditional and alternative strategies. Gaurav shares a novel approach to deciding asset and strategy allocations, inspired by research in recommender systems.
Gaurav then explores the application of deep learning in trading, discussing useful techniques for AI-driven asset managers as well as the blind alleys they’ve gone down. With these cases as context, Gaurav addresses some of the technical and operational aspects of AI, such as key bottlenecks in training and inference, the software frameworks and hardware platforms that are most useful for those workloads, deployments, the scaling challenges, and the key drivers of the cost.
Gaurav Chakravorty is the cofounder and head of strategy development at qplum, a digital wealth management firm that specializes in family offices and HNWI. Qplum also works with endowment funds and pension funds to provide outsourced asset management services that integrate with the company’s in-house technology. Gaurav has been one of the early pioneers in machine learning-based high-frequency trading. He built the most profitable algo-trading group at Tower Research and was the youngest partner in the firm. Gaurav has been a guest speaker on a few popular podcasts.
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