Blockchains have seen growing traction with cryptocurrencies reaching a market cap of over 1 trillion dollars, major institution investors taking interests, and global impacts on governments, businesses, and individuals.

Mobile dating applications such as Coffee Meets Bagel, Tantan, and Tinder, have become significant for young adults to meet new friends and discover romantic relationships. From a system designer's perspective, in order to achieve better user experience in these applications, we should take both the efficiency and fairness of a dating market into consideration, so as to increase the overall satisfaction for all users. Towards this goal, we investigate the nature of diminishing marginal returns for online dating markets (i.e., captured by the submodularity), and trade-off between the efficiency and fairness of the market with Nash social welfare. We further design effective online algorithms to the apps. We verify our models and algorithms through sound theoretical analysis and empirical studies by using real data and show that our algorithms can significantly improve the ecosystems of the online dating applications.

Taxi services and product delivery services are instrumental for our modern society. Thanks to the emergence of sharing economy, ride-sharing services such as Uber, Didi, Lyft and Google's Waze Rider are becoming more ubiquitous and grow into an integral part of our everyday lives. However, the efficiency of these services are severely limited by the sub-optimal and imbalanced matching between the supply and demand. We need a generalized framework and corresponding efficient algorithms to address the efficient matching, and hence optimize the performance of these markets. Existing studies for taxi and delivery services are only applicable in scenarios of the one-sided market. In contrast, this work investigates a highly generalized model for the taxi and delivery services in the market economy (abbreviated as"taxi and delivery market") that can be widely used in two-sided markets. Further, we present efficient online and offline algorithms for different applications. We verify our algorithm with theoretical analysis and trace-driven simulations under realistic settings.

Motivated by practical scenarios, we study congestion games with failures. We investigate two models. The first model is congestion games with both resource and agent failures, where each agent chooses the same number of resources with the minimum expected cost. We prove that the game is potential and hence admits at least one pure-strategy Nash equilibrium (pure-NE). We also show that the Price of Anarchy and the Price of Stability are bounded (equal to 1 in some cases). The second model is congestion games with only resource failures (CG-CRF), where resources are provided in packages, and their failures can be correlated with each other. Each agent can choose multiple packages for reliability’s sake and utilize the survived one having the minimum cost. CG-CRF is shown to be not potential. We prove that it admits at least one pure-NE by constructing one efficiently. Finally, we discuss various applications of these two games in the networking field. To the best of our knowledge, this is the first paper studying congestion games with the coexistence of resource and agent failures, and we give also the first proof of the existence of a pure-NE in congestion games with correlated package failures.