A human-in-the-loop Bayesian optimization framework extends Pareto Front Guided Sampling by incorporating probabilistic constraint satisfaction and input robustness as explicit objectives. It enables domain experts to iteratively refine selection criteria through interactive dashboard projections of trade-offs between performance, uncertainty, and feasibility in CHO cell culture optimization.