At SODALab, we study algorithms and decision-making under uncertainty, with a particular interest in sequential decision systems involving interacting people, institutions, and computational agents. A central theme of our research is understanding how algorithmic performance depends on the structure of the system, the uncertainty in its environment, and the information available for decision-making. These include objectives, constraints, and strategic interactions; adversarial, stochastic, temporal, and distributional structure; and predictions, offline data, and feedback acquired through interaction.
On the applied side, our work is driven primarily by sequential resource allocation problems in large-scale systems such as online advertising, cloud computing, electrical grids, and AI infrastructure. Across these settings, we seek to understand which forms of structure, information, and incentives fundamentally change what algorithms can achieve. Explore some of our recent projects below.
Online Algorithms
Research in online algorithms studies decision-making problems in which information arrives over time and decisions must often be made irrevocably before the future is fully known. Such problems arise throughout modern decision systems, including online platforms, cloud computing, electrical grids, and large-scale computing infrastructure.
Our research asks how the capabilities and limitations of online algorithms change when classical assumptions are relaxed or enriched by additional structure. We study forms of decision flexibility such as bounded adaptivity, reusability, and cancellation; objectives involving risk and structured constraints; and additional information in the form of predictions, historical data, or feedback acquired through repeated operation. A central goal is to understand which features fundamentally change what can be achieved online, and to design algorithms that exploit them while retaining rigorous guarantees.
Recent Highlights
Individuals vs Groups in Sequential Allocation
Whenever a scarce resource must be allocated to requests arriving over time, decisions about whom to serve shape both the total benefit created and its distribution across groups. These groups may have different needs, priorities, and opportunities, making fairness and efficiency central concerns in settings ranging from healthcare and education to digital platforms and computing infrastructure. Balancing their interests is especially challenging when future requests are unknown and early commitments can limit the opportunities available to groups that arrive later.
Our research studies sequential allocation across groups with potentially competing objectives, and more broadly, sequential resource allocation with multiple objectives. We ask how different notions of welfare, fairness, and service can be balanced over time, and how the resulting trade-offs depend on arrival structure, available information, and the ability to adapt or revise earlier decisions.
Recent Highlights
Offline Data and Sequential Interaction
Modern decision systems often begin with historical data but must ultimately operate through sequential interaction with an uncertain environment. Offline data can improve initial decisions and reduce costly exploration, but it may be incomplete, biased, or collected under different policies. This raises a fundamental question: how should prior data and new information acquired through interaction be combined?
Our research studies the foundations of this offline-to-online transition, drawing on online learning, reinforcement learning, and statistical decision theory. We ask when historical data can be trusted, when new exploration is necessary, and how the quality and coverage of prior data shape what can be learned safely and efficiently during deployment.
Recent Highlights
Algorithms and Incentives in Infrastructure Systems
Modern infrastructure systems increasingly rely on algorithmic decisions to coordinate many interacting users, devices, and organizations under uncertainty. These systems must balance local incentives with broader objectives such as efficiency, reliability, and sustainability, often while operating in real time and under physical constraints. Such challenges arise across power systems, electric mobility, data centers, and other large-scale computing and energy infrastructures.
Our research studies how algorithms, incentives, and system constraints interact in these settings. We draw on optimization, game theory, and machine learning to design robust and adaptive mechanisms for resource allocation, coordination, and control, and to understand how individual behavior and institutional rules shape system-level outcomes.
