About me

From October 2025 to the present, I am a postdoctoral researcher at Johns Hopkins University in the group of Prof. Jan Drgona. From March 2023 to October 2025, I was a postdoctoral researcher at Massachusetts Institute of Technology in the group of Prof. Richard D. Braatz. Prior to that, I received my Ph.D. in March 2023 from IMT School for Advanced Studies Lucca, supervised by Prof. Alberto Bemporad.

My research focuses on the intersections between Modeling (high-fidelity), Control, Optimization, and Learning. Specifically, (1) I am an expert in Siemens gPROMS modeling platform (4.5 years of working experience) for process level modeling and Ansys Fluent for unit level modeling; (2) I have proposed several fast MPC algorithms as well as execution-time-certified MPC (an open challenge in the MPC community) algorithms, with work published in IEEE TAC; (3) I proposed a concept, which was missed in Analog Optimization: Arbitrarily Small Execution-Time Certificate; (4) I mainly work on Learning to Model (Koopman operator) and Learning to Optimize (trustworthy unsupervised neural solver.

Contact: wliang14@jh.edu

Fast MPC algorithms

[1] L. Wu and A. Bemporad, "A Simple and Fast Coordinate Descent Augmented-Lagrangian Solver for Model Predictive Control”, IEEE Transactions on Automatic Control, 2023, vol. 68, no. 11, pp. 6860-6866.

[2] L. Wu, et. al., “πMPC: A Parallel-in-horizon and Construction-free NMPC Solver”, under review by IEEE Transactions on Automatic Control. Available at https://arxiv.org/pdf/2601.14414

[3] L. Wu, et. al., “Koopman-BoxQP: solving large-scale NMPC problems at kHZ Rate”, accepted by L4DC 2026. Available at https://arxiv.org/pdf/2602.18331

Address the open challenge in MPC: execution time certificate

[4] L. Wu and R.D. Braatz, “A Direct Optimization Algorithm for Input-Constrained MPC”, IEEE Transactions on Automatic Control, 2025, vol. 70, no. 2, pp. 1366-1373.

[5] L. Wu, et. al., “EIQP: Execution-time-certified and Infeasibility-detecting QP Solver”, IEEE Transactions on Automatic Control, 2025.

[6] L. Wu, et. al., “A Time-certified Predictor-corrector IPM Algorithm for Box-QP”, IEEE Control Systems Letters, vol. 9, pp. 3059-3064, 2025.

[7] L. Wu and R.D. Braatz, “A Quadratic Programming Algorithm with 𝑂(𝑛^3) Time Complexity”, IEEE Transactions on Automatic Control, 2026.

Numerical Optimization & Analog Optimization

[8] L. Wu, et. al., “Arbitrarily Small Execution-Time Certificate: what was missed in Analog Optimization”, IEEE Transactions on Automatic Control, 2025.

[*] L. Wu, “XCo: A Factorization-free ADMM Solver for Extreme-scale Conic Optimization”, ongoing.

[*] L. Wu, “CCQP: An Efficient Complementarity Constrained Optimization Program Solver for Mixed-Integer MPC”, ongoing.

Learning to Model & Learning to Optimize

[9] L. Wu et al., “A Least-Squares Multi-Step Koopman Operator for Model Predictive Control”, submitted to Automatica. Available at https://arxiv.org/pdf/2601.11901

[*] L. Wu, “UN-MPC: Trustworthy and Differentiable Unsupervised Neural MPC”, ongoing.

[*] L. Wu, “Modern Second-order Optimizer for Large-scale Deep Neural Networks”, ongoing.

Multiphase CFD

[10] L. Wu, M. Gong, J. Wang (2018), "Development of a DEM–VOF model for the turbulent free-surface flows with particles and its application to stirred mixing system", Industrial & Engineering Chemistry Research, 57 (5), 1714-1725

Process modeling (Siemens gPROMS)

[11] L. Wu, "A rapid-prototype MPC tool based on gPROMS platform", Submitted to IFAC WC 2026.