Differentiable Optimization

Developed modular convex optimization layer that enables differentiation for any forward solver with seamless integration into neural networks and bi-level optimization tasks, addressing limitation in existing methods that rely on specific forward solvers.

AI-driven Protein Structure Reconstruction

Leading development of PyTorch-based open-source cryo-EM reconstruction software from scratch. Integrating the software to AI foundation models for downstream biology tasks, enabling enhanced protein structure prediction.

Statistical Model of Crystallographic Disorder

Developed statistical parametric model for analyzing positional and substitutional disorder in crystallography.

Coupling Structural and Functional Brain Connectome

Developed model for jointly analyzing brain structure connectome (DT-MRI) and the temporal dynamics of individual brain regions (fMRI)

Differentiation Through Black-Box Quadratic Programming Solvers

Connor W. Magoon*, Fengyu Yang*, Noam Aigerman, Shahar Z. Kovalsky (* equal contribution)

NeurIPS 2025

Differentiable optimization has attracted significant research interest, particularly for quadratic programming (QP). Existing approaches for differentiating the solution of a QP with respect to its defining parameters often rely on specific integrated solvers. This integration limits their applicability, including their use in neural network architectures and bi-level optimization tasks, restricting users to a narrow selection of solver choices. To address this limitation, we introduce dQP, a modular and solver-agnostic framework for plug-and-play differentiation of virtually any QP solver. A key insight we leverage to achieve modularity is that, once the active set of inequality constraints is known, both the solution and its derivative can be expressed using simplified linear systems that share the same matrix. This formulation fully decouples the computation of the QP solution from its differentiation. Building on this result, we provide a minimal-overhead, open-source implementation that seamlessly integrates with over 15 state-of-the-art solvers. Comprehensive benchmark experiments demonstrate dQP’s robustness and scalability, particularly highlighting its advantages in large-scale sparse problems.

[Code] [Paper]