The SODALab consists of undergraduate and graduate students dedicated to developing mathematical and algorithmic tools to address uncertainty and strategic behaviors in systems modeling, optimization, learning, and decision-making. Some members focus on advancing the theoretical foundations of “decision-making under uncertainty,” drawing on tools and insights from optimization, economics, machine learning, and operations research. Others focus on societal challenges, such as energy sustainability and fairness in resource allocation, across various fields, including energy grids and markets, shared mobility, Internet advertising, cloud computing, and network optimization. For more information, see some of our current and past projects below.
Online Algorithms
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 online advertising, recommendation systems, financial trading, portfolio management, and supply chain management, etc.
Our research in this thrust has been focusing on the theory of online algorithms, primarily through an optimization lens such as the online primal-dual framework. We also explore its implications for various online decision problems in resource allocation, scheduling, routing, matching, time series search, financial trading, and beyond.
Sample publications:
Algorithmic Economics
Algorithmic economics lies at the intersection of computer science and economics, focusing on the design and analysis of algorithms for economic systems. This field addresses fundamental questions about how intelligent agents make decisions, interact, and allocate resources efficiently in settings such as markets, auctions, and networks. Algorithmic economics is inherently interdisciplinary, incorporating techniques from algorithms design and analysis, optimization, game theory, machine learning, and artificial intelligence to tackle real-world economic challenges, such as designing more efficient markets for large-scale cloud computing systems, optimizing resource allocation and pricing in communication networks, etc.
Our research in this area has been focusing on online markets and pricing, mechanism design & auctions, and resource allocation & fairness. We particularly emphasize exploring trade-offs between optimizing traditional objectives, such as revenue or welfare, and other key performance metrics, including robustness, fairness, risk, and simplicity.
Sample publications:
Energy, Markets, and Sustainability
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 increasingly evident. 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, innovation, and environmental sustainability.
Our research in this thrust has been focusing on developing novel markets and economic mechanisms for (i) electrical grid with distributed energy resources, (ii) electric vehicle charging, and (iii) the energy-mobility nexus (e.g., electric mobility-on-demand).
Sample publications:
We are grateful for the generous support from the following sponsors.
