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sparse-view clinical reconstruction: explicit vs implicit representations
Why sparse clinical reconstruction makes representation choice feel less like taste and more like a failure-mode decision.
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how teaching courses changed how i think about machine learning systems
Why teaching deep learning made me care more about clarity, failure modes, and the parts of ML people usually skip.
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what i look for in a strong medical ai research problem
Why the best medical AI problems usually have a real bottleneck, a clear failure mode, and a workflow that needs the output.
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the gap between benchmark performance and clinical usefulness
Why a high metric can still leave you with a model that is not ready for the workflow it claims to support.
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why i keep returning to geometry in biomedical ai
Why labels are useful, but shape, pose, deformation, and spatial structure keep feeling like the real problem.
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five open problems i care about in image-guided robotic intervention
Five technical problems that keep pulling me back toward surgical perception and geometry.
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a short guide to reading papers in surgical vision
How I try to read surgical vision papers without getting distracted by clean diagrams and impressive demos.
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what makes a research prototype different from a deployable clinical tool
Why a promising model still has a long way to go before it becomes something clinicians can rely on.
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reproducibility lessons from building multiple medical vision pipelines
Why repeated pipeline work teaches you to respect paths, splits, metadata, and boring logs.