Publications

Applications of artificial intelligence for wildlife protection have focused on learning models of poacher behavior based on historical patterns. However, poachers' behaviors are described not only by their historical preferences, but also their reaction to ranger patrols. Past work applying machine learning and game theory to combat poaching have hypothesized that ranger patrols deter poachers, but have been unable to find evidence to identify how or even if deterrence occurs. Here for the first time, we demonstrate a measurable deterrence effect on real-world poaching data. We show that increased patrols in one region deter poaching in the next timestep, but poachers then move to neighboring regions. Our findings offer guidance on how adversaries should be modeled in realistic game-theoretic settings.

The COVID-19 pandemic has created a public health crisis. Because SARS-CoV-2 can spread from individuals with pre-symptomatic, symptomatic, and asymptomatic infections, the re-opening of societies and the control of virus spread will be facilitated by robust surveillance, for which virus testing will often be central. After infection, individuals undergo a period of incubation during which viral titers are usually too low to detect, followed by an exponential growth of virus, leading to a peak viral load and infectiousness, and ending with declining viral levels and clearance. Given the pattern of viral load kinetics, we model surveillance effectiveness considering test sensitivities, frequency, and sample-to-answer reporting time. These results demonstrate that effective surveillance, including time to first detection and outbreak control, depends largely on frequency of testing and the speed of reporting, and is only marginally improved by high test sensitivity. We therefore conclude that surveillance should prioritize accessibility, frequency, and sample-to-answer time; analytical limits of detection should be secondary.

Several social interventions (e.g., suicide and HIV prevention) leverage social network information to maximize outreach. Algorithmic influence maximization techniques have been proposed to aid with the choice of “influencers” (often referred to as “peer leaders”) in such interventions. Traditional algorithms for influence maximization have not been designed with social interventions in mind. As a result, they may disproportionately exclude minority communities from the benefits of the intervention. This has motivated research on fair influence maximization. Existing techniques require committing to a single domain-specific fairness measure. This makes it hard for a decision maker to meaningfully compare these notions and their resulting trade-offs across different applications. We address these shortcomings by extending the principles of cardinal welfare to the influence maximization setting, which is underlain by complex connections between members of different communities. We generalize the theory regarding these principles and show under what circumstances these principles can be satisfied by a welfare function. We then propose a family of welfare functions that are governed by a single inequity aversion parameter which allows a decision maker to study task-dependent trade-offs between fairness and total influence and effectively trade off quantities like influence gap by varying this parameter. We use these welfare functions as a fairness notion to rule out undesirable allocations. We show that the resulting optimization problem is monotone and submodular and can be solved with optimality guarantees. Finally, we carry out a detailed experimental analysis on synthetic and real social networks and should that high welfare can be achieved without sacrificing the total influence significantly. Interestingly we can show there exists welfare functions that empirically satisfy all of the principles.

Illegal wildlife poaching threatens ecosystems and drives endangered species toward extinction. However, efforts for wildlife protection are constrained by the limited resources of law enforcement agencies. To help combat poaching, the Protection Assistant for Wildlife Security (PAWS) is a machine learning pipeline that has been developed as a data-driven approach to identify areas at high risk of poaching throughout protected areas and compute optimal patrol routes. In this paper, we take an end-to-end approach to the data-to-deployment pipeline for anti-poaching. In doing so, we address challenges including extreme class imbalance (up to 1:200), bias, and uncertainty in wildlife poaching data to enhance PAWS, and we apply our methodology to three national parks with diverse characteristics. (i) We use Gaussian processes to quantify predictive uncertainty, which we exploit to improve robustness of our prescribed patrols and increase detection of snares by an average of 30%. We evaluate our approach on real-world historical poaching data from Murchison Falls and Queen Elizabeth National Parks in Uganda and, for the first time, Srepok Wildlife Sanctuary in Cambodia. (ii) We present the results of large-scale field tests conducted in Murchison Falls and Srepok Wildlife Sanctuary which confirm that the predictive power of PAWS extends promisingly to multiple parks. This paper is part of an effort to expand PAWS to 800 parks around the world through integration with SMART conservation software. 

Background: On March 24, India ordered a 3-week nationwide lockdown in an effort to control the spread of COVID-19. While the lockdown has been effective, our model suggests that completely ending the lockdown after three weeks could have considerable adverse public health ramifications. We extend our individual-level model for COVID-19 transmission [1] to study the disease dynamics in India at the state level for Maharashtra and Uttar Pradesh to estimate the effect of further lockdown policies in each region. Specifically, we test policies which alternate between total lockdown and simple physical distancing to find "middle ground" policies that can provide social and economic relief as well as salutary population-level health effects.

Methods: We use an agent-based SEIR model that uses population-specific age distribution, household structure, contact patterns, and comorbidity rates to perform tailored simulations for each region. The model is first calibrated to each region using publicly available COVID-19 death data, then implemented to simulate a range of policies. We also compute the basic reproduction number R0 and case documentation rate for both regions.

Results: After the initial lockdown, our simulations demonstrate that even policies that enforce strict physical distancing while returning to normal activity could lead to widespread outbreaks in both states. However, "middle ground" policies that alternate weekly between total lockdown and physical distancing may lead to much lower rates of infection while simultaneously permitting some return to normalcy.

Network security games (NSGs) are widely used in security related domain to model the interaction between the attacker and the defender. However, due to the complex graph structure of the entire network, finding a Nash equilibrium even when the attacker is fully rational is not well-studied yet. There is no efficient algorithms known with valid guarantees. We identify two major issues of NSGs: i) non-linearity ii) correlation between edges. NSGs with non-linear objective function are usually hard to optimize, while correlated edges might create exponentially many strategies and impact the scalability. In this paper, we analyze the distortion of linear and non-linear formulations of NSGs with fully rational attacker. We provide theoretical bounds on these different formulations, which can quantify the approximation ratio between linear and non-linear assumption. This result can help us understand how much loss will the linearization incur in exchange for the scalability.

As machine learning models are increasingly being deployed in critical domains such as criminal justice and healthcare, there has been a growing interest in developing algorithms that are interpretable and fair. While there has been a lot of research on each of these topics in isolation, there has been little work on their intersection. In this paper, we present an empirical study for understanding the relationship between model interpretability and fairness. To this end, we propose a novel evaluation framework and outline appropriate evaluation metrics to determine this relationship across various classes of models in both synthetic and real world datasets.

Machine learning components commonly appear in larger decision-making pipelines; however, the model training process typically focuses only on a loss that measures average accuracy between predicted values and ground truth values. Decision-focused learning explicitly integrates the downstream decision problem when training the predictive model, in order to optimize the quality of decisions induced by the predictions. It has been successfully applied to several limited combinatorial problem classes, such as those that can be expressed as linear programs (LP), and submodular optimization. However, these previous applications have uniformly focused on problems with simple constraints. Here, we enable decision-focused learning for the broad class of problems that can be encoded as a mixed integer linear program (MIP), hence supporting arbitrary linear constraints over discrete and continuous variables. We show how to differentiate through a MIP by employing a cutting planes solution approach, an algorithm that iteratively tightens the continuous relaxation by adding constraints removing fractional solutions. We evaluate our new end-to-end approach on several real world domains and show that it outperforms the standard two phase approaches that treat prediction and optimization separately, as well as a baseline approach of simply applying decision-focused learning to the LP relaxation of the MIP. Lastly, we demonstrate generalization performance in several transfer learning tasks.