Where the work lives
A common three-circle framework: find the work where what you love, what you're good at, and what the world needs all overlap. For me, that overlap is deep tech equity research.
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What pulls me in
Deep tech is the substrate of the next decade. Memory, AI infrastructure, quantum, the silicon and the networking that runs underneath all of it. The companies building this layer don't just sell products; they shape what's possible at the application layer above them.
Understanding how an HBM architecture choice today constrains an AI lab's training run in 2027, or how a quantum coherence time milestone pushes an entire category from research into commercialization, or how a hyperscaler's capex disclosure reshapes the demand backdrop for the entire memory cycle: that's the work I want to spend my time doing.
What I bring
Master of Financial Economics, University of Toronto
Bachelor of Economics (Honours), Finance major, Saint Mary's University
Before Harvard, five years split across three sides of how institutional capital actually moves. Ontario Teachers' Pension Plan ($270B AUM) on the direct private investments team: infrastructure and real estate underwriting, including a $1 billion capital commitment alongside AustralianSuper into the NIIF Master Fund. Scotiabank Investment Banking next: equity capital markets, corporate banking, and M&A coverage for fintech, insurance, industrials, and retail across EMEA and the US. Then Bentall Green Oak (Sunlife Global, $355B AUM) on the private equity data science team: $1.3 billion in commercial real estate and infrastructure adjacent commitments underwritten across data centers, self storage, and multifamily, with AI driven quantitative screens that lifted the hit rate of approved investments by 40 percent.
The Harvard Data Science and AI master's on top (3.9 GPA, Dean's List) is what makes me credible writing about deep tech specifically. I can read the 10‑K, build the model, and also defend a view on which technical claim in management commentary is signal versus marketing copy. The capstone (a machine learning framework for sovereign bond portfolio optimization, built for Citibank New York) scored 100 percent.
Financial Economics gives the valuation discipline (DCF, scenario weighting, sum of the parts, comparables) to model these businesses on their actual cash dynamics rather than narrative. Data Science and AI give the inside view on what is hard, what is a commodity, and what is genuinely defensible in the technology being valued. Both are needed to do this work honestly.
Where capital should flow
Capital should flow toward useful innovation, not hype. The public markets are full of stories. Most don't survive contact with cash flow. Deep tech is especially vulnerable: the science is hard to evaluate, the time horizons are long, and the gap between "demo" and "compounder" is enormous.
Sell side research has structural conflicts. Buy side research is locked behind paywalls most readers can't access. The result is a coverage gap where retail investors and serious enthusiasts choose between superficial coverage and silence. Rigorous, transparent, independent research (grounded in primary sources, free from banking relationship conflicts) is one small lever for connecting capital to substance instead of narrative.
This Library is the deliberate expression of that intersection. Each dashboard tries to answer one question: would I bet on this company, at this price, given what I know?
How it gets built