Priya Donti

NeurIPS Conference 2025 Conference Paper

FSNet: Feasibility-Seeking Neural Network for Constrained Optimization with Guarantees

• Hoang Nguyen
• Priya Donti

Efficiently solving constrained optimization problems is crucial for numerous real-world applications, yet traditional solvers are often computationally prohibitive for real-time use. Machine learning-based approaches have emerged as a promising alternative to provide approximate solutions at faster speeds, but they struggle to strictly enforce constraints, leading to infeasible solutions in practice. To address this, we propose the Feasibility-Seeking Neural Network (FSNet), which integrates a feasibility-seeking step directly into its solution procedure to ensure constraint satisfaction. This feasibility-seeking step solves an unconstrained optimization problem that minimizes constraint violations in a differentiable manner, enabling end-to-end training and providing guarantees on feasibility and convergence. Our experiments across a range of different optimization problems, including both smooth/nonsmooth and convex/nonconvex problems, demonstrate that FSNet can provide feasible solutions with solution quality comparable to (or in some cases better than) traditional solvers, at significantly faster speeds.

NeurIPS Conference 2025 Conference Paper

PF∆: A Benchmark Dataset for Power Flow under Load, Generation, and Topology Variations

• Ana Rivera Him
• Anvita Bhagavathula
• Alvaro Carbonero
• Priya Donti

Power flow (PF) calculations are the backbone of real-time grid operations, across workflows such as contingency analysis (where repeated PF evaluations assess grid security under outages) and topology optimization (which involves PF-based searches over combinatorially large action spaces). Running these calculations at operational timescales or across large evaluation spaces remains a major computational bottleneck. Additionally, growing uncertainty in power system operations from the integration of renewables and climate-induced extreme weather also calls for tools that can accurately and efficiently simulate a wide range of scenarios and operating conditions. Machine learning methods offer a potential speedup over traditional solvers, but their performance has not been systematically assessed on benchmarks that capture real-world variability. This paper introduces PF∆, a benchmark dataset for power flow that captures diverse variations in load, generation, and topology. PF∆ contains 859, 800 solved power flow instances spanning six different bus system sizes, capturing three types of contingency scenarios (N, N –1, and N –2), and including close-to-infeasible cases near steady-state voltage stability limits. We evaluate traditional solvers and GNN-based methods, highlighting key areas where existing approaches struggle, and identifying open problems for future research. Our dataset is available at https: //huggingface. co/datasets/pfdelta/pfdelta/tree/main and our code with data generation scripts and model implementations is at https: //github. com/MOSSLab-MIT/pfdelta

NeurIPS Conference 2021 Conference Paper

Adversarially robust learning for security-constrained optimal power flow

• Priya Donti
• Aayushya Agarwal
• Neeraj Vijay Bedmutha
• Larry Pileggi
• J. Zico Kolter

In recent years, the ML community has seen surges of interest in both adversarially robust learning and implicit layers, but connections between these two areas have seldom been explored. In this work, we combine innovations from these areas to tackle the problem of N-k security-constrained optimal power flow (SCOPF). N-k SCOPF is a core problem for the operation of electrical grids, and aims to schedule power generation in a manner that is robust to potentially $k$ simultaneous equipment outages. Inspired by methods in adversarially robust training, we frame N-k SCOPF as a minimax optimization problem -- viewing power generation settings as adjustable parameters and equipment outages as (adversarial) attacks -- and solve this problem via gradient-based techniques. The loss function of this minimax problem involves resolving implicit equations representing grid physics and operational decisions, which we differentiate through via the implicit function theorem. We demonstrate the efficacy of our framework in solving N-3 SCOPF, which has traditionally been considered as prohibitively expensive to solve given that the problem size depends combinatorially on the number of potential outages.

NeurIPS Conference 2017 Conference Paper

Task-based End-to-end Model Learning in Stochastic Optimization

• Priya Donti
• Brandon Amos
• J. Zico Kolter

With the increasing popularity of machine learning techniques, it has become common to see prediction algorithms operating within some larger process. However, the criteria by which we train these algorithms often differ from the ultimate criteria on which we evaluate them. This paper proposes an end-to-end approach for learning probabilistic machine learning models in a manner that directly captures the ultimate task-based objective for which they will be used, within the context of stochastic programming. We present three experimental evaluations of the proposed approach: a classical inventory stock problem, a real-world electrical grid scheduling task, and a real-world energy storage arbitrage task. We show that the proposed approach can outperform both traditional modeling and purely black-box policy optimization approaches in these applications.

AAAI Conference 2015 Conference Paper

Predicting the Quality of User Experiences to Improve Productivity and Wellness

• Priya Donti
• Jacob Rosenbloom
• Alex Gruver
• James Jr Boerkoel