I am a research scientist specializing in mathematical optimization. My expertise lies in first-order methods for convex optimization, which are widely used in various fields such as machine learning (ML), network control, and quantum computing. In the past, I have designed online algorithms that can effectively operate under noisy conditions that arise in practical systems. For example, in cloud computing, a job scheduler must allocate resources in a data center without prior knowledge of the job duration. Another example is when an ML algorithm needs to update the model parameters with only a partial view of the dataset. Currently, my research focuses on accelerating the training of Graph Neural Networks (GNNs) for digital healthcare and drug discovery applications.