Customer-obsessed science


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July 29, 2025New cost-to-serve-software metric that accounts for the full software development lifecycle helps determine which software development innovations provide quantifiable value.
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2024Understanding causal relationships among the variables of a system is paramount to explain and control its behavior. For many real-world systems, however, the true causal graph is not readily available and one must resort to predictions made by algorithms or domain experts. Therefore, metrics that quantitatively assess the goodness of a causal graph provide helpful checks before using it in downstream tasks
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PRX2024Cat qubits, a type of bosonic qubit encoded in a harmonic oscillator, can exhibit an exponential noise bias against bit-flip errors with increasing mean photon number. Here, we focus on cat qubits stabilized by two-photon dissipation, where pairs of photons are added and removed from a harmonic oscillator by an auxiliary, lossy buffer mode. This process requires a large loss rate and strong nonlinearities
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Fill-in-the-Middle (FIM) has become integral to code language models, enabling generation of missing code given both left and right contexts. However, the current FIM training paradigm, which reorders original training sequences and then performs regular next-token prediction (NTP), often leads to models struggling to generate content that aligns smoothly with the surrounding context. Crucially, while existing
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Journal of Physics Communications2024This study investigates the application of machine learning (ML) models for predicting transient responses in ball-impact elastodynamics simulations. We focus on the canonical problem of ball impact on laminated structures, which captures essential physics while maintaining computational tractability. Novel contributions include: (1) development of a temporal multi-resolution strategy for stable long-time
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Machine Learning for Health Symposium 20242024Generalist large language models (LLMs), not developed to do particular medical tasks, have achieved widespread use by the public. To avoid medical uses of these LLMs that have not been adequately tested and thus minimize any potential health risks, it is paramount that these models use adequate guardrails and safety measures. In this work, we propose a synthetic medical prompt generation method to evaluate
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