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Qinru Shi

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4 papers

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AAAI Conference 2024 Conference Paper

Scaling Up Pareto Optimization for Tree Structures with Affine Transformations: Evaluating Hybrid Floating Solar-Hydropower Systems in the Amazon

Marc Grimson
Rafael Almeida
Qinru Shi
Yiwei Bai
Héctor Angarita
Felipe Siqueira Pacheco
Rafael Schmitt
Alexander Flecker

Sustainability challenges inherently involve the consideration of multiple competing objectives. The Pareto frontier – the set of all optimal solutions that cannot be improved with respect to one objective without negatively affecting another – is a crucial decision-making tool for navigating sustainability challenges as it highlights the inherent trade-offs among conflicting objectives. Our research is motivated by the strategic planning of hydropower in the Amazon basin, one of the earth’s largest and most biodiverse river systems, where the need to increase energy production coincides with the pressing requirement of minimizing detrimental environmental impacts. We investigate an innovative strategy that pairs hydropower with Floating Photovoltaic Solar Panels (FPV). We provide a new extended multi-tree network formulation, which enables the consideration of multiple dam configurations. To address the computational challenge of scaling up the Pareto optimization framework to tackle multiple objectives across the entire Amazon basin, we further enhance the state-of-the-art algorithm for Pareto frontiers in tree-structured networks with two improvements. We introduce affine transformations induced by the sub-frontiers to compute Pareto dominance and provide strategies for merging sub-trees, significantly increasing the pruning of dominated solutions. Our experiments demonstrate considerable speedups, in some cases by more than an order of magnitude, while maintaining optimality guarantees, thus allowing us to more effectively approximate the Pareto frontiers. Moreover, our findings suggest significant shifts towards higher energy values in the Pareto frontier when pairing hybrid hydropower with FPV solutions, potentially amplifying energy production while mitigating adverse impacts.

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AAAI Conference 2021 Conference Paper

Accelerating Ecological Sciences from Above: Spatial Contrastive Learning for Remote Sensing

Johan Bjorck
Brendan H. Rappazzo
Qinru Shi
Carrie Brown-Lima
Jennifer Dean
Angela Fuller
Carla Gomes

The rise of neural networks has opened the door for automatic analysis of remote sensing data. A challenge to using this machinery for computational sustainability is the necessity of massive labeled data sets, which can be cost-prohibitive for many non-profit organizations. The primary motivation for this work is one such problem; the efficient management of invasive species – invading flora and fauna that are estimated to cause damages in the billions of dollars annually. As an ongoing collaboration with the New York Natural Heritage Program, we consider the use of unsupervised deep learning techniques for dimensionality reduction of remote sensing images, which can reduce sample complexity for downstream tasks and decreases the need for large labeled data sets. We consider spatially augmenting contrastive learning by training neural networks to correctly classify two nearby patches of a landscape as such. We demonstrate that this approach improves upon previous methods and naive classification for a large-scale data set of remote sensing images derived from invasive species observations obtained over 30 years. Additionally, we simulate deployment in the field via active learning and evaluate this method on another important challenge in computational sustainability – landcover classification – and again find that it outperforms previous baselines.

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AAAI Conference 2021 Conference Paper

Learning Augmented Methods for Matching: Improving Invasive Species Management and Urban Mobility

Johan Bjorck
Qinru Shi
Carrie Brown-Lima
Jennifer Dean
Angela Fuller
Carla Gomes

With the success of machine learning, integrating learned models into real-world systems has become a critical challenge. Naively applying predictions to combinatorial optimization problems can incur high costs, which has motivated researchers to consider learning augmented algorithms that can make use of faulty or incomplete predictions. Inspired by two matching problems in computational sustainability where data are abundant, we consider the learning augmented min-cost matching problem where some nodes are revealed online while others are known a priori, e. g. , by being predicted by machine learning. We develop an algorithm that is able to make use of this extra information and provably improves upon pessimistic online algorithms. We evaluate our algorithm on two settings from computational sustainability – the coordination of opportunistic citizen scientists for invasive species management and the matching between taxis and riders under uncertain trip duration predictions. In both cases, we perform extensive experiments on real-world datasets and find that our method outperforms baselines, showing how learning augmented algorithms can reliably improve solutions for problems in computational sustainability.

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AAAI Conference 2018 Conference Paper

Efficiently Approximating the Pareto Frontier: Hydropower Dam Placement in the Amazon Basin

Xiaojian Wu
Jonathan Gomes-Selman
Qinru Shi
Yexiang Xue
Roosevelt Garcia-Villacorta
Elizabeth Anderson
Suresh Sethi
Scott Steinschneider

Real–world problems are often not fully characterized by a single optimal solution, as they frequently involve multiple competing objectives; it is therefore important to identify the so-called Pareto frontier, which captures solution trade-offs. We propose a fully polynomial-time approximation scheme based on Dynamic Programming (DP) for computing a polynomially succinct curve that approximates the Pareto frontier to within an arbitrarily small > 0 on treestructured networks. Given a set of objectives, our approximation scheme runs in time polynomial in the size of the instance and 1/. We also propose a Mixed Integer Programming (MIP) scheme to approximate the Pareto frontier. The DP and MIP Pareto frontier approaches have complementary strengths and are surprisingly effective. We provide empirical results showing that our methods outperform other approaches in efﬁciency and accuracy. Our work is motivated by a problem in computational sustainability concerning the proliferation of hydropower dams throughout the Amazon basin. Our goal is to support decision-makers in evaluating impacted ecosystem services on the full scale of the Amazon basin. Our work is general and can be applied to approximate the Pareto frontier of a variety of multiobjective problems on tree-structured networks.

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