Predictive modeling in high-stakes domains often suffers from limited observed features due to ethical and practical constraints. To address this challenge, we propose a novel approach that formulates latent feature mining as a text-to-text propositional logic reasoning task, facilitating domain knowledge integration and improving the interpretability of latent features. We design FLAME, a domain knowledge-augmented reasoning framework for latent feature mining, offering an efficient training paradigm to strengthen the domain-specific reasoning capabilities of large language models (LLMs) for latent feature extraction. The goal of our framework is to augment observed features with inferred latent features, enhancing the performance of predictive models in downstream machine learning tasks. We validate our approach through two case studies: (1) the criminal justice system, where data collection is ethically challenging and inherently limited, and (2) the healthcare domain, where patient privacy concerns and the complexity of medical data restrict comprehensive feature collection. Experimental results demonstrate that the inferred latent features significantly enhance the performance of downstream classifiers by over 10%.

Incarceration-diversion programs have proven effective in reducing recidivism. Accurate prediction of the number of individuals with different characteristics in the program and their program outcomes based on given eligibility criteria is crucial for successful implementation, because this prediction serves as the foundation for determining the appropriate program size and the consequent staffing requirements. However, this task poses challenges due to the complexities arising from varied outcomes and lengths-of-stay for the diverse individuals in incarceration-diversion programs. In collaboration with an Illinois government agency, we develop a framework to address these issues. Our framework combines ML and queueing model simulation, providing accurate predictions for the program census and interpretable insights into program dynamics and the impact of different decisions in counterfactual scenarios. Additionally, we deploy a user-friendly web app beta-version that allows program managers to visualize census data by counties and race groups. We showcase two decision support use cases: Changing program admission criteria and launching similar programs in new counties.

This paper studies when a market-making firm should place orders to maximize their expected net profit, while also constraining risk, assuming orders are maintained on an electronic limit order book (LOB). To do this, we use a model-free and off-policy method, Q-learning, coupled with state aggregation, to develop a proposed trading strategy that can be implemented using a simple lookup table. Our main training dataset is derived from event-by-event data recording the state of the LOB. Our proposed trading strategy has passed both in-sample and out-of-sample testing in the backtester of the market-making firm with whom we are collaborating, and it also outperforms other benchmark strategies. As a result, the firm desires to put the strategy into production.

Traditionally, research focusing on the design of routing and staffing policies for service systems has modeled servers as having fixed (possibly heterogeneous) service rates. However, service systems are generally staffed by people. Furthermore, people respond to workload incentives; that is, how hard a person works can depend both on how much work there is, and how the work is divided between the people responsible for it. In a service system, the routing and staffing policies control such workload incentives; and so the rate servers work will be impacted by these policies. This observation has consequences when modeling service system performance, and our objective in this paper is to investigate those consequences.