The SODALab consists of undergraduate and graduate students dedicated to developing algorithmic and economic tools to address uncertainty and strategic behaviors in systems modeling, optimization, learning, and decision-making. Some members focus on advancing the theory of “decision-making under uncertainty,” drawing on tools and insights from computer science, economics, statistics, and operations research. Others tackle societal challenges such as sustainability and fairness across various fields, including electrical grids, shared mobility, Internet advertising, cloud computing, and network optimization. For more details, explore some examples of our recent projects below.
Online Algorithms, Markets, and Platforms
Research in online algorithms focuses on a subset of optimization and decision-making problems where (i) data are incrementally revealed over time, and (ii) decisions need to be made sequentially (oftentimes also irrevocably). This differs from offline optimization and decision-making, where all the data is available upfront, and the algorithm can optimize the solution with complete knowledge of the future. Online algorithms have diverse applications, including Internet advertising, recommendation systems, financial trading, portfolio management, and supply chain management, etc.
Our research in this area focuses broadly on the theory of online algorithms and competitive analysis. We also explore their implications for market design in online platforms, including those for cloud computing, scheduling, routing, matching, and financial trading.
Recent work:
Fairness & Decision-Making under Uncertainty
Fairness is a difficult topic to talk about, as perspectives on fairness often vary across individuals and depend on the context. Nevertheless, addressing fairness in algorithmic decision-making has far-reaching implications, including promoting equitable access to limited public resources and reducing biases in critical domains such as healthcare, hiring, and education. Fairness can be conceptualized through various notions, including envy-freeness, proportionality, and maximin share, among others. Although these concepts have been extensively studied in offline settings, they present significant new challenges in dynamic and uncertain environments with sequential agent arrivals.
Our research in this area focuses on developing fair algorithms for decision-making under various forms of uncertainty. Specifically, our recent work has focused on group fairness and time fairness in online decision-making, aiming to design algorithms that treat sequentially arriving agents fairly across groups while ensuring high efficiency, consistency, and explainability over time.
Recent work:
Energy Sustainability and Markets
Market design plays a critical role in shaping the dynamics and outcomes of various industries, and the energy sector is no exception. As the global demand for reliable, affordable, and sustainable energy continues to rise, the need for effective market structures and mechanisms becomes more pressing. Market design in the context of energy involves creating frameworks and rules that facilitate efficient allocation, pricing, and trading of energy resources, while also promoting competition and environmental/social sustainability.
Our research in this area focuses on developing novel algorithms and market mechanisms for (i) electrical grid with distributed energy resources, (ii) the energy-mobility nexus (e.g., electric vehicle charging, shared mobility), and (iii) sustainable data centers.
Recent work:
We are grateful for the generous support from the following sponsors.
