Below is a short description of some research directions I'm currently pursuing, along with some of my representative papers on these topics. I no longer keep track of all publications on my website, but I have found that DBLP catches most of them.
Algorithmic Mechanism Design: When real people interact with algorithms, they impose additional constraints beyond the algorithm's speed, storage, and correctness. For instance, real people have their own incentives that may misalign with the designer's goal -- for this reason, they may choose not to follow a protocol as asked of them. Real people also behave more predictably in simple and transparent interactions, so this motivates formally considering the simplicity of algorithms deployed in these settings when measuring their quality. Below is a non-exhaustive list of domains where this kind of reasoning is necessary.
Auction Design: Every auction designer has an objective -- perhaps to make as much revenue as possible. But participants in an auction have their own objectives -- to get the goods they like the most and to pay as little as possible for receiving them. It turns out to be much more difficult (both conceptually and computationally) to design auctions that properly account for these incentives than to just solve the underlying algorithmic problems. Much of my work in this area aims to understand the structure of optimal auctions, and when the simple auctions we see every day are approximately optimal.
Simple and Approximately Optimal Auctions: [BILW 14, RW 15, CDW 16, EFFTW 17]. Simple Auctions with Additional Bidders (Competition Complexity / Resource Augmentation): [EFFTW 17, BW 19, DRXW 24]. Nearly-Simple Nearly-Optimal Auctions (Approximation Schemes): [KMSSW 19]. Credible Mechanisms via Commitment Schemes: [FW 20, EFW 22]. Smoothed Analysis of Simple Auctions: [PSW 19, PSW 22]. Black-box Reductions from Mechanism to Algorithm Design: [CDW 12, CDW 12b, CDW 13, CDW 13b, DW 15, DDW 15, CW 20]. Mechanism Design for Learning Agents: [BMSW 18, CWWZ 23, GKWTVRW 24]. Sample Complexity of Multi-Item Auctions: [GW 18]. Interdimensional Mechanism Design: [DW 17, SSW 18, DGSSW 20]. Complexity of Optimal Auctions: [BCKW 10, WZ 22]. Symmetries in Optimal Auctions: [DW 12, EW 22]. Applications: [HWZJC 17, CFFHW 18, EGW 24].
Combinatorial Auctions: Consider a set of players who wish to allocate a set of items amongst themselves in a way that maximies the welfare. That is, each player has a valuation function over subsets of items, and they wish to jointly maximize the sum of players' values for the set she receives. More specifically, they would like to do so with as little communication as possible. Such problems are fairly well-understood in the absence of incentives, but what if players selfishly want to maximize their own value and may manipulate a proposed communication protocol?
Truthful Mechanisms vs. Protocols: [BMW 18, AKSW 20, RRTWZ 21, QW 24, RTWZ 24]. Protocols and Lower Bounds: [EFNTW 19, BW 23]. Alternate Solution Concepts: [CTW 20]. Price of Anarchy of Simple Auctions: [DMSW 15, BMW 16].
Blockchain Protocols: Blockchain protocols specify desired behavior for network participants, and offer rewards. However, reward schemes in practice rarely incentivize the prescribed desired behavior. It is important to understand participants' incentives in existing systems, and also to design reward schemes that correctly incentivize intended behavior.
Profitably Manipulating Consensus Protocols: [CKWN 16, BNPW 19, FW 21, FHWY 22, FGHHWY 24, AW 24]. Undetectable Profitable Manipulations: [BW 24, CLWZ 24]. Transaction Fee Mechanism Design: [GTW 24]. Incentivizing Decentralization: [AW 22].
Algorithms Under Uncertainty: Often, we must make irrevocable decisions today with limited information about how things will play out tomorrow. My own work has focused largely on online selection problems, where you are trying to select the best element(s) of a set. The catch is that elements are revealed to you one at a time, and you must immediately and irrevocably decide whether to accept or reject with uncertainty about whether better elements might arrive in the future.
Introductory Material: [YouTube Video 1, YouTube Video 2]. Prophet Inequalities: [KW 12, CCFPW 22, SVW 23, DW 24]. Sample-Based Prophet Inequalities: [AKW 14, RWW 20]. Secretary Problems: [BBSW 21]. Behavioral Models for Prophet Inequalities: [CGW 23]. Graph Algorithms with Cut Queries: [RSW 18, GPRW 20, PRW 24]. Parallel Sorting with Noisy Comparisons: [BMW 16].
Economics and Computation, Broadly: I'm also generally interested in the field of Economics and Computation broadly, including in projects that don't neatly fit into one of the above directions.
Incentive Compatible Tournament Design: [SSW 17, SWZZ 20, DW 21, DW 22, DFRSW 22]. Scoring Rules: [NNW 21]. Information Design: [DNPW 19] Strategic Learning: [BMSW 19, BSW 21] Information Aggregation in Social Networks: [FILW 14, BIMW 20] Deep Learning for Optimal Auctions: [RJW 21, RJBW 21].