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My research is in the intersection of mathematical optimization, quantitative risk, decision making under uncertainty, statistical learning, and signal processing, while addressing relevant applications in resource allocation and management, autonomous systems, robustness, fairness and trustworthiness.
More specific topical areas which I am currently working on are:
- Risk-Aware/Distributionally Robust Optimization/Learning/Estimation/Control
- Policy Optimization/Learning for Resource Allocation in Wireless Autonomy
- Algorithmic Statistical Generalization Theory
I am broadly driven by three aspects of today’s technological landscape:
- The abundance of data (and the possible scarcity of good data),
- The complexity of modern systems, and
- The uncertainty in task execution and system operation.
Over the last three (3) decades (and even more so during the last decade), we have been witnessing an extraordinary advancement in the broad area of intelligent information systems, and this is observed in a variety of sectors; some relevant examples which I am drawing inspiration from are artificial intelligence and learning systems, wireless systems engineering and autonomy, and decision/control systems engineering. Despite the remarkable progress, however, the pervasive challenges of economically managing and intelligently processing large (even colossal) volumes of data, navigating intricate system models or structures (often a purely intractable task), and optimally mitigating endogenous (systemic) or exogenous operational uncertainties (inducing nontrivial operational risks) still loom large. These are some of the core challenges that I address in my work, mainly focusing on the development of new theory and the design of rigorously verifiable methods, while also providing a “path to computation”.