Last updated March 2026.

Current Research Systems Work

Building research infrastructure, analytical pipelines, and internal tools for a wealth management investment firm. The work centers on Python data pipelines and dashboard applications for portfolio analytics, factor scoring, and client reporting. Spending considerable time studying how large language models can augment analyst workflows, particularly in the areas of document processing, earnings analysis, and research summarization.

Active Research Questions

I am studying how AI adoption will manifest across all GICS sectors, and I continue to refine my criteria for identifying resilient business models, those positioned to be protected during periods of AI-driven disruption across the broader market.

I track emerging dynamics across high-growth technology names, as well as legacy businesses that have invested significant capital in products and services that are now, to varying degrees, AI-replaceable; understanding how that substitution risk is likely to affect their revenue and margin structures is a central area of focus.

I am also deeply engaged with the evolution of agentic systems and agent-driven workflows. I believe this is where the most significant operational value behind AI for institutional investors will be realized.

Reading

• Machine Learning for Algorithmic Trading by Stefan Jansen

Ongoing Research Infrastructure

I continue to develop my equity factor engine, a scoring system that combines proprietary metrics to compare and analyze the universe of public equities. The objective is to build a research environment that surfaces non-consensus signals through systematic factor construction.

I am exploring new ways to apply AI within investment management, spanning research automation, portfolio analytics, CRM integration, and client review generation, with the longer-term goal of constructing a comprehensive analytical platform for capital allocators.