First order stochastic convex optimization is an extremely well-studied area with a rich history of over a century of optimization research. Active learning is a relatively newer discipline that grew independently of the former, gaining popularity in the learning community over the last few decades due to its promising improvements over passive learning. Over the last year, we have uncovered concrete theoretical and algorithmic connections between these two fields, due to their inherently sequential nature and decision-making based on feedback of earlier choices, that have yielded new methods and proofs techniques in both fields. In this note, we lay down the foundations of these connections and summarize our recent advances.