about
My research studies the commercialization of scientific innovations and the diffusion of technologies into markets. Along with my research, I develop novel datasets and tools to help advance research on innovation.
Prior to academia, I founded a startup providing software and machine learning translation solutions for large firms. I also spent years in M&A, where I led numerous projects in technological sectors such as advanced water treatment technologies, biotech, energy generation, and materials science.
I will be on the job market during the 2025-2026 academic year.
published papers
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Measuring the Commercial Potential of Science
Strategic Management Journal, 2025, Articles in Advance. NBER Working Paper 32262
Abstract: We develop an ex-ante measure of commercial potential of science, an otherwise unobservable variable driving the performance of innovation-intensive firms. To do so, we rely on LLMs and neural networks to predict whether scientific articles will influence firms' use of science. Incorporating time-varying models and the quantification of uncertainty, the measure is validated through both traditional methods and out-of-sample exercises, leveraging a major university’s technology transfer data. To illustrate the methodological contributions of our measure, we apply it to examining the impact of university reputation and university privatization of science, finding that firms’ reliance on reputation may lead to foregone opportunities, and privatization (i.e., patenting) appears to increase firms’ use of the science of one university. We make our measure and method available to researchers.
working papers
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Science, Startups, and the Problem of Value Capture: Thin Markets, Weak Options
Abstract: Science-based innovations increasingly depend on startups to reach markets. Yet, in critical areas such as energy, industrials, and materials, entrepreneurial activity remains limited. A key challenge for these ventures is not only creating value but retaining it, especially at the point of acquisition, which represents the primary mechanism for realizing returns. This paper investigates whether limited value capture at exit constrains these ventures. First, I estimate value creation and capture in startup acquisitions by analyzing stock market returns of acquirers, using a parametric method to isolate acquisition-specific effects. I find that science-based startups create more value but capture only 46 cents per dollar of surplus generated, compared to 70 cents for non-science startups—a 34% penalty. Second, I examine a central mechanism: the market structure for startup acquisitions. I argue that science-based startups, due to the nature of their innovations, face thinner and more concentrated buyer markets, weakening bargaining power. To test this, I estimate the potential acquirer set for each startup. Science-based startups face 9% fewer potential acquirers, and their value capture is far more sensitive to market structure: those in less concentrated markets capture 26% more value than their peers in concentrated ones, compared to a difference of just 4% for non-science startups. The findings suggest that limited value capture may weaken incentives to develop science-based ventures, highlighting the role of competitive acquisition markets, markets for technology, and alternative commercialization pathways in supporting upstream innovation.
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Taste Before Production: The Role of Judgment in Entrepreneurial Idea Generation
Abstract: Crafting high-quality ideas is crucial for entrepreneurs to succeed, yet evidence about the factors that shape the idea-generation process is scarce. A long-standing question is whether differences across entrepreneurs in market judgment—the ability to evaluate business ideas—explain differences in ideas’ quality and composition. We conduct an experiment with an intervention that improves subjects’ ability to evaluate an idea’s market potential, finding that improved judgment leads subjects to generate ideas 15% higher in quality and more complete, with stronger effects among initially poorly-calibrated subjects. Our results support a potential mechanism: individuals with developed judgment mentally test more ideas and better filter them before committing to one. Simple training can improve judgment and idea quality, complementing ex-post, experimental methods by reducing the costs of testing ideas.
works in progress
- When Do Intermediaries Distort Scientific Diffusion? Evidence from Google Search