Real-world deployment of deep learning violates the assumptions of i.i.d. training data and aggregate-metric evaluation: observations are dependent in time, space, and across users; decision latency is bounded by milliseconds; and the cost of a wrong prediction is rarely symmetric. My doctoral research argues that the structural priors hardest for a deep network to recover from data are precisely the ones that should be encoded into its architecture. The position can be stated in one line: what I cannot afford to learn, I encode.
The eleven papers in the dissertation span communication systems, physiological monitoring, physics-informed learning, clinical decision support, and large language models. Across them I make three claims. First, architectural priors generalize when they match the structure of the data: the dissertation traces a continuum from priors in the problem formulation, to priors in features and representations, to priors in the architecture, to priors that are the governing equation. Second, this claim is Pareto rather than monotone: aligned priors dominate where parameters are scarce and are dominated where they are not, a scope condition anchored at a clean empirical crossover in the language-model chapters. Third, aggregate metrics are the wrong audit: calibrated diagnostics (matched effective sample size, effective residual-stream depth, the shuffle gap, cost-conditional thresholds) repeatedly reorder conclusions drawn from AUC, perplexity, and fixed-length comparisons.
The seminar develops the first claim through three case studies, one at each depth of the continuum: AARL, a DRL scheduler for 5G millimeter-wave networks (prior in the problem formulation); Lab to Wrist, a neural-ODE and neural-Kalman framework that embeds cardiovascular physics architecturally for heart-rate and oxygen-consumption prediction on wearables (prior in the architecture); and a differentiable Randers-Finsler eikonal solver applied to cross-scene wildfire propagation (prior is the governing PDE itself)