46 citations · 414 across the 52 of their papers we have counts for
56 papers
Topological Data Analysis for High-Dimensional Dynamic Process Monitoring
Angan Mukherjee, Tyler A. Soderstrom, Michael J. Kurtz +1
Real-time process monitoring requires methods that extract actionable information from high-dimensional time-series data. In this work, we present a new approach for process monito…
Quantifying Space-Time Load Shifting Flexibility in Electricity Markets
Weiqi Zhang, Victor M. Zavala
The power grid is undergoing significant restructuring driven by the adoption of wind/solar power and the incorporation of new flexible technologies that can shift load in space an…
A Graph-Based Modeling Framework for Tracing Hydrological Pollutant Transport in Surface Waters
David L. Cole, Gerardo J. Ruiz-Mercado, Victor M. Zavala
Anthropogenic pollution of hydrological systems affects diverse communities and ecosystems around the world. Data analytics and modeling tools play a key role in fighting this chal…
Scalable Primal Decomposition Schemes for Large-Scale Infrastructure Networks
Alexander Engelmann, Sungho Shin, François Pacaud +1
The operation of large-scale infrastructure networks requires scalable optimization schemes. To guarantee safe system operation, a high degree of feasibility in a small number of i…
New Paradigms for Exploiting Parallel Experiments in Bayesian Optimization
Leonardo D. González, Victor M. Zavala
Bayesian optimization (BO) is one of the most effective methods for closed-loop experimental design and black-box optimization. However, a key limitation of BO is that it is an inh…
Exploiting GPU/SIMD Architectures for Solving Linear-Quadratic MPC Problems
David Cole, Sungho Shin, François Pacaud +2
We report numerical results on solving constrained linear-quadratic model predictive control (MPC) problems by exploiting graphics processing units (GPUs). The presented method red…
A Julia Framework for Graph-Structured Nonlinear Optimization
David L Cole, Sungho Shin, Victor Zavala
Graph theory provides a convenient framework for modeling and solving structured optimization problems. Under this framework, the modeler can arrange/assemble the components of an…
Pricing and Remunerating Electricity Storage Flexibility Using Virtual Links
Weiqi Zhang, Philip A. Tominac, Victor M. Zavala
Ambitious renewable portfolio standards motivate the incorporation of energy storage resources (ESR) as sources of flexibility. While the United States government aims to promote E…
Exploring the Impacts of Power Grid Signals on Data Center Operations using a Receding-Horizon Scheduling Model
Weiqi Zhang, Line A. Roald, Victor M. Zavala
Data centers (DCs) can help decarbonize the power grid by helping absorb renewable power (e.g., wind and solar) due to their ability to shift power loads across space and time. How…
Data Analysis using Riemannian Geometry and Applications to Chemical Engineering
Alexander Smith, Benjamin Laubach, Ivan Castillo +1
We explore the use of tools from Riemannian geometry for the analysis of symmetric positive definite matrices (SPD). An SPD matrix is a versatile data representation that is common…
SAFE-OCC: A Novelty Detection Framework for Convolutional Neural Network Sensors and its Application in Process Control
Joshua L. Pulsipher, Luke D. J. Coutinho, Tyler A. Soderstrom +1
We present a novelty detection framework for Convolutional Neural Network (CNN) sensors that we call Sensor-Activated Feature Extraction One-Class Classification (SAFE-OCC). We sho…
Random Field Optimization
Joshua L. Pulsipher, Benjamin R. Davidson, Victor M. Zavala
We present a new modeling paradigm for optimization that we call random field optimization. Random fields are a powerful modeling abstraction that aims to capture the behavior of r…
Solution of Large-Scale Supply Chain Models using Graph Sampling & Coarsening
Jiaze Ma, Victor M. Zavala
We present a graph sampling and coarsening scheme (gSC) for computing lower and upper bounds for large-scale supply chain models. An edge sampling scheme is used to build a low-com…
New Measures for Shaping Trajectories in Dynamic Optimization
Joshua L. Pulsipher, Benjamin R. Davidson, Victor M. Zavala
We propose a new class of measures for shaping time-dependent trajectories in dynamic optimization (DO). The proposed measures are analogous to risk measures used in stochastic opt…
A Unifying Modeling Abstraction for Infinite-Dimensional Optimization
Joshua L. Pulsipher, Weiqi Zhang, Tyler J. Hongisto +1
Infinite-dimensional optimization (InfiniteOpt) problems involve modeling components (variables, objectives, and constraints) that are functions defined over infinite-dimensional d…
Spatio-Temporal Economic Properties of Multi-Product Supply Chains
Philip A. Tominac, Weiqi Zhang, Victor M. Zavala
In this work, we analyze the spatio-temporal economic properties of multi-product supply chains. Specifically, we interpret the supply chain as a coordinated market in which stakeh…
Measuring and Optimizing System Reliability: A Stochastic Programming Approach
Joshua L. Pulsipher, Victor M. Zavala
We propose a computational framework to quantify (measure) and to optimize the reliability of complex systems. The approach uses a graph representation of the system that is subjec…
A Scalable Stochastic Programming Approach for the Design of Flexible Systems
Joshua L. Pulsipher, Victor M. Zavala
We study the problem of designing systems in order to minimize cost while meeting a given flexibility target. Flexibility is attained by enforcing a joint chance constraint, which…
A Computational Framework for Quantifying and Analyzing System Flexibility
Joshua L. Pulsipher, Daniel Rios, Victor M. Zavala
We present a computational framework for analyzing and quantifying system flexibility. Our framework incorporates new features that include: general uncertainty characterizations t…
A Mixed-Integer Conic Programming Formulation for Computing the Flexibility Index under Multivariate Gaussian Uncertainty
Joshua L. Pulsipher, Victor M. Zavala
We present a methodology for computing the flexibility index when uncertainty is characterized using multivariate Gaussian random variables. Our approach computes the flexibility i…
Remunerating Space-Time, Load-Shifting Flexibility from Data Centers in Electricity Markets
Weiqi Zhang, Victor M. Zavala
We study an electricity market clearing formulation that seeks to remunerate spatio-temporal, load-shifting flexibility provided by data centers (DaCes). Load-shifting flexibility…
On the Tightness of the Lagrangian Dual Bound for Alternating Current Optimal Power Flow
Weiqi Zhang, Kibaek Kim, Victor M. Zavala
We study tightness properties of a Lagrangian dual (LD) bound for the nonconvex alternating current optimal power flow (ACOPF) problem. We show an LD bound that can be computed in…
The Euler Characteristic: A General Topological Descriptor for Complex Data
Alexander Smith, Victor Zavala
Datasets are mathematical objects (e.g., point clouds, matrices, graphs, images, fields/functions) that have shape. This shape encodes important knowledge about the system under st…
Controllability and Observability Imply Exponential Decay of Sensitivity in Dynamic Optimization
Sungho Shin, Victor M. Zavala
We study a property of dynamic optimization (DO) problems (as those encountered in model predictive control and moving horizon estimation) that is known as exponential decay of sen…