December 2024: New paper on fault isolation in high-end industrial printers with experimental results
>> Fault Isolation for the Ink Deposition Process in
High-End Industrial Printers
December 2024: New paper to appear in NeurIPS 2024
>> Scalable Kernel Inverse Optimization, [Code]
December 2024: Invited lecture at PhD Winter School on Advanced Stochastic Optimization at NTNU, Norway
November 2024: Updated paper to appear in Transactions on Automatic Control
>> Distributionally Robust Model Predictive Control: Closed-loop Guarantees and Scalable Algorithms, [Code]
November 2024: Invited seminar at Workshop on Insurance and Financial Mathematics, Leibnizhaus Hannover, Germany
September 2024: New paper on real-time experiments of robust fault estimation in self-driving cars
>> Robust Fault Estimation with Structured Uncertainty: Algorithms and Experimental Validation
>> Experiment Video
September 2024: Updated paper to appear in Mathematical Programming
>> Nonlinear Distributionally Robust Optimization, [Code]
August 2024: Updated paper to appear in Operations Research
>> Learning in Inverse Optimization: Incenter Cost, Augmented Suboptimality Loss, and Algorithms, [Code]
August 2024: New paper on a variant of Q-learning with optimal sample complexity
>> Variance-Reduced Cascade Q-learning: Algorithms and Sample Complexity, [arXiv]
August 2024: Updated paper on closed loop performance and efficient algorithms for distributionally robust MPC
>> Distributionally Robust Model Predictive Control: Closed-loop Guarantees and Scalable Algorithms, [Code]
May 2024: New paper on a new adaptive stepsize for first-order convex optimization
>> Adaptive Accelerated Composite Minimization, [Code]
May 2024: Invited seminar at Risk Measures and Uncertainty in Insurance, Leibnizhaus Hannover, Germany
May 2024: New paper on a new convex loss for offline RL via inverse optimization
>> Offline Reinforcement Learning via Inverse Optimization, [Code]
April 2024: Invited seminar at ETH AI Center, Zurich, Switzerland
February 2024: Updated paper on learning drivers’ behavior in the Amazon Challenge!
>> Inverse Optimization for Routing Problems, [arXiv], [Code]
January 2024: Updated paper on new loss function for inverse optimization with a robustness interpretation and efficient algorithms
>> Learning in Inverse Optimization: Incenter Cost, Augmented Suboptimality Loss, and Algorithms, [arXiv], [Code]
November 2023: New paper on a unified framework and algorithms for control and optimization (encompassing both dynamic programming as model-based control and reinforcement learning as model-free control)
>> From Optimization to Control: Quasi Policy Iteration, [arXiv]
October 2023: New paper on fault estimation with prior frequency content information
>> Robust Multivariate Detection and Estimation with Fault Frequency Content Information, [arXiv]
September 2023: New paper on distributionally robust MPC and when it outperforms nominal/robust counterparts
>> Distributionally Robust Model Predictive Control: Closed-loop Guarantees and Scalable Algorithms, [arXiv]
July 2023: New paper on learning drivers’ behavior in the Amazon Challenge!
>> Inverse Optimization for Routing Problems, [arXiv], [Code]
June 2023: New paper on saddle points of distributionally robust nonlinear risk measures
>> Nonlinear Distributionally Robust Optimization, [arXiv]
May 2023: New paper on inverse optimization with a focus on new notion of robustness and algorithms
>> Learning in Inverse Optimization: Incenter Cost, Augmented Suboptimality Loss, and Algorithms, [arXiv], [Code]
May 2023: New paper on nonlinear fault estimators for nonlinear systems
>> Robust Fault Estimators for Nonlinear Systems: An Ultra-Local Model Design, [arXiv]
April 2023: New paper on the application of dynamic fault diagnosis in microgrid systems
>> Real-Time Ground Fault Detection for Inverter-Based Microgrid Systems, [arXiv]
December 2022: New paper on a fast first-order algorithm for a class of non-smooth quadratic programs
>> Fast Algorithm for Constrained Linear Inverse Problems, [arXiv], [Code]
September 2022: I am thrilled to be selected as the best lecturer of the Systems and Control program in 2021-2022.
May 2022: New paper on online optimization exploiting gradient predictions and dynamic environments
>> Adaptive Online Optimization with Predictions: Static and Dynamic Environments, [arXiv]
February 2022: I am honored to receive the European Control Award.
January 2022: New paper a Bayesian approach for active fault detection in large-scale dynamical systems
>> A Bayesian Approach for Active Fault Isolation with an Application to Leakage Localization in Water Distribution Networks
November 2021: New paper on real-time estimation of multiplicative fault signals for LPV systems
>> Real-time Fault Estimation for a Class of Discrete-Time Linear Parameter-Varying Systems, [arXiv]
October 2021: New paper on mode detection in switched linear systems with noisy measurements
>> Multimode Diagnosis for Switched Affine Systems with noisy Measurement, [arXiv]
September 2021: I am thrilled to be selected as the best lecturer of the Systems and Control program in 2020-2021.
September 2021: Our paper on fast dynamic programming appears in NeurIPS 2021.
>> Fast Approximate Dynamic Programming for Infinite-Horizon Continuous-State Markov Decision Processes, [arXiv]
>> The d-CDP MATLAB package: [Code]
May 2021: New paper on scalable algorithms for sparse quadratic programs to appear in ICML 2021.
>> Principal Component Hierarchy for Sparse Quadratic Programs, [arXiv]
April 2021: We will present two works in ACC 2021
February 2021: MOSEK dedicates a jupyter notebook for the Wasserstein DRO problems using their Fusion API for
Python.
>> Link, video
February 2021: New paper on fast dynamic programming for the infinite horizon optimal control
>> Fast Approximate Dynamic Programming for Infinite-Horizon Continuous-State Markov Decision Processes, [arXiv]
>> The d-CDP MATLAB package: [Code]
February 2021: New paper on how to simplify complex controllers through the lens of learning
>> Learning for Control: An Inverse Optimization Approach, [Link], [Code]
January 2021: New paper on generalized gauge functions as a nonconvex learning machine
>> The Nonconvex Geometry of Linear Inverse Problems, [arXiv]
December 2020: Our Delft AI Lab “Sustainable Energy Systems: System-Theoretic AI for Health Monitoring and Control of Dynamic Energy Systems” has been granted.
November 2020: New paper on nonlinear fault estimation in dynamical systems: a system-theoretic approach to regression
>> Multiple Faults Estimation in Dynamical Systems: Tractable Design and Performance Bounds, [arXiv]
November 2020: Received the INFORMS Frederick W. Lanchester Prize for the best contribution to operations research and management science in the past five years.
October 2020: Our paper appears as a full paper in the Transactions on Automatic Control (TAC)
>> Learning Robust Controllers for Linear Quadratic Systems with Multiplicative Noise via Policy Gradient, [arXiv]
September 2020: Awarded the ERC Starting Grant entitled with “Control without Trust: A Distributionally Robust Approach”.
August 2020: New paper: From fast Fourier transform to fast dynamic programming; a step toward quantum dynamic programming?!
>> Fast Approximate Dynamic Programming for Input-Affine Dynamics, [arXiv]
>> The d-CDP MATLAB package: [Code]
June 2020: Appointed as an Associate Editor of Open Journal of Mathematical Optimization
April 2020: New paper on fault detection by leveraging high fidelity simulators
>> Data-Assisted Model-Based Anomaly Detection for High-Fidelity Simulators of Power Systems, [arXiv]
March 2020: New paper on robust dynamic control and its event-triggering implementation
>> Robust Dynamic Controllers for Output Regulation: Optimization-Based Synthesis and Event-Triggered Implementation, [arXiv]
February 2020: Two PhD positions in anomaly detection and data-driven control
>> Please see Vacancies page for further information
January 2020: Our cohesion proposal “Artificial Intelligence for Sustainable Real-Time Transportation Systems” with the department of Maritime and Transport Technology got awarded.
January 2020: Updated paper accepted with minor revisions in the IEEE Transactions on Automatic Control (TAC)
>> Macroscopic Noisy Bounded Confidence Models with Distributed Radical Opinions, [arXiv]
December 2019: Tutorial paper on distributionally robust optimization and its application in Machine Learning
>> Wasserstein Distributionally Robust Optimization: Theory and Applications in Machine Learning, [Link]
December 2019: Updated paper accepted with minor revisions in Management Science (MS)
>> From Data to Decisions: Distributionally Robust Optimization is Optimal, [arXiv]
November 2019: Appointed as an Associate Editor of Operations Research
November 2019: We received the NWO Perspectief grant “Integration of Data-drIven and model-based enGIneering in fuTure industriAL Technology With value chaIn optimizatioN (DIGITAL TWIN) ”, [video]!
November 2019: New paper on an estimation problem through a distributioanlly robust perspective
>> Bridging Bayesian and Minimax Mean Square Error Estimation via Wasserstein Distributionally Robust Optimization, [arXiv]
September 2019: New paper on policy gradient of LQR systems with multiplicative noise
>> Learning Robust Controllers for Linear Quadratic Systems with Multiplicative Noise via Policy Gradient, [arXiv]
September 2019, Our paper A Decentralized Event-based Mechanism for Robust Model Predictive Control to appear in the IEEE Transactions on Automatic Control (TAC) as a full paper, [arXiv]
September 2019, Our paper From Static to Dynamic Anomaly Detection with Application to Power System Cyber Security to appear in the IEEE Transactions on Power Systems [arXiv]
September 2019: Our paper Generalized Maximum Entropy Estimation to appear in Journal of Machine Learning Research (JMLR), [arXiv]
August 2019: New paper on recent developments in distributionally robust optimization with applications in Machine Learning
>> Wasserstein Distributionally Robust Optimization: Theory and Applications in Machine Learning, [arXiv]
August 2019: We will present three works in CDC 2019:
July 2019: Semi-plenary talk “Robust and Distributionally Robust Optimization”
>> International Conference on Stochastic Programming (ICSP), Trondheim, Norway
June 2019: Our paper Regularization via Mass Transportation to appear in Journal of Machine Learning Research (JMLR), [Link], [arXiv], [Code]
May 2019: Open two-years postdoc position in Mathematical Optimization
>> Please see Vacancies page for further information
May 2019: New paper on opinion dynamics with stochastic bounded confidence model under radical agents
>> Macroscopic Noisy Bounded Confidence Models with Distributed Radical Opinions, [arXiv]
May 2019: New paper on dynamic detection of multivariate attacks on sensor measurements
>> From Static to Dynamic Anomaly Detection with Application to Power System Cyber Security, [arXiv]
March 2019: Our paper Distributionally Robust Inverse Covariance Estimation: The Wasserstein Shrinkage Estimator to appear in Operations Research
February 2019: New paper on continuous-time dynamics of accelerated methods with restarting schemes
February 2019: New paper on Ultra Wideband pulse design using Sturm–Liouville boundary value problem
>> UWB Orthogonal Pulse Design using Sturm-Liouville Boundary Value Problem, to appear in Signal Processing
February 2019: Our cohesion proposal “Artificial Intelligent-based Optimization of Lightweight Fracture-resistant Components” with the department of Precision and Microsystems Engineering got awarded.
January 2019: Our paper Data-driven Distributionally Robust Optimization Using the Wasserstein Metric: Performance Guarantees and Tractable Reformulations is the 2018 top most-read article in Mathematical Programming
January 2019: Invited seminar at “Models and Algorithms for Sequential Decision Problems under Uncertainty”
>> Banff International Research Station for Mathematical, Innovation and Discovery (BIRD), Alberta, Canada
December 2018: New paper on a decentralized even-triggering mechanism for robust model predictive control
>> A Decentralized Event-based Mechanism for Robust Model Predictive Control, [arXiv]
December 2018: Our paper Distributionally Robust Inverse Covariance Estimation: The Wasserstein Shrinkage Estimator is the winner of the George Nicholson Outstanding Student Paper.
December 2018: New paper on distributional robust filtering
>> Wasserstein Distributionally Robust Kalman Filtering, Conference on Neural Information Processing Systems (NIPS), selected for spotlight presentation (top 3.5%), 2018, [arXiv], [Code]
November 2019: Invited seminar at “Innovations in Predictive Control ”
>> IPC 2018, Indian Institute of Technology Bombay, India
August 2018: New paper on automated sentiment classification using a novel source domain selection scheme
>> Distance Based Source Domain Selection for Sentiment Classification, [arXiv]
May 2018: New paper on finite approximation of infinite dimensional linear programs
>> From Infinite to Finite Programs: Explicit Error Bounds with an Application to Approximate Dynamic Programming, [arXiv],
to appear in SIAM Journal on Optimization (SIOPT)
May 2018: New paper on a class of nonlinear shrinkage estimators and a tailored efficient numerical algorithm
>> Distributionally Robust Inverse Covariance Estimation: The Wasserstein Shrinkage Estimator, [arXiv], [Code]
March 2018: Invited seminar at “Distributionally Robust Optimization ”
>> Banff International Research Station for Mathematical, Innovation and Discovery (BIRD), Alberta, Canada
October 2017: New paper on regularizing learning problems through an optimal transport lens
>> Regularization via Mass Transportation, [arXiv], [Code]
August 2017: New paper on estimating the probability distribution maximizing the entropy
>> Generalized Maximum Entropy Estimation, [arXiv]
May 2017: Invited seminar at “Optimal Transport meets Probability, Statistics and Machine Learning ”
>> Banff International Research Station for Mathematical, Innovation and Discovery (BIRD), Oaxaca, Mexico
April 2017: New paper on a class of distributionally robust programs as an optimal decision-making mechanism
>> From Data to Decisions: Distributionally Robust Optimization is Optimal, [arXiv]
March 2017: New paper on linear programming approach to optimal control problems under weaker required assumptions
>> On Infinite Linear Programming Approach to Deterministic Infinite Horizon Discounted Optimal Control Problems, [arXiv]