Partner at Lux Capital
Check size: No verified personal or standard Lux check-size range is published. Tess invests at both early and growth stages and participates in Lux company creation; founders should confirm round fit directly rather than infer an allocation from fund size or disclosed company rounds.
Tess is a technically trained generalist investing where AI, biology, aerospace, and the physical world intersect. She seeks engineers and scientists turning frontier research into companies, with particular interest in computational methods that make biology more engineerable, the data and model bottlenecks behind scientific discovery, and high-consequence systems such as biodefense.
Begin with the scientific bottleneck and why it has become solvable now. Explain the data—how it is generated, labeled, validated, and protected—then the model or engineering advantage, experimental loop, and path to a product. Quantify the technical milestone, capital required, real-world consequence, and what would falsify the thesis.
Engineers and scientists tackling problems at the edge of what is possible, research that can cross into a consequential product, new datasets or models that unlock scientific generalization, large patient or societal outcomes, and technically ambitious founders able to mobilize talent, capital, and creative force.
No explicit public pass list is available. Her discussions imply caution toward biology-AI claims without the right data, validation, or generalization; molecule-generation capability detached from disease understanding; scientific demos without a path to a company; and frontier narratives that cannot identify the current bottleneck. These are labeled inferences.
Identify whether progress is constrained by data generation, labels, model architecture, generalization, wet-lab throughput, regulation, manufacturing, or commercialization before evaluating the proposed breakthrough.
Trace how experimental and synthetic data train a model, how predictions are validated, and how each experiment improves the next prediction and the proprietary dataset.
Test whether frontier research can support a focused first product, a milestone-driven capital plan, defensible capabilities, and a team spanning science, engineering, and commercialization.
Pair technical difficulty with the magnitude of the patient, security, industrial, or scientific outcome, then ask whether the evidence and team justify that ambition.
“The future is already here... it's just not evenly distributed yet.”
— https://www.luxcapital.com/people/tess-van-stekelenburg
Bringing machine-learning researchers, software engineers, and computational biologists together can accelerate method development and generate new company formation in computational biology.
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