Jr-Shin Li
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View article: Robust Quantum State Generation in Symmetric Spin Networks
Robust Quantum State Generation in Symmetric Spin Networks Open
In this work, we consider a parameterized Ising model with long-range symmetric pairwise interactions on a network of spin $\frac{1}{2}$ particles. The system is designed with symmetric dynamics, allowing for the reduction of the state spa…
View article: Uncertainty Quantification of Network Inference With Data Sufficiency
Uncertainty Quantification of Network Inference With Data Sufficiency Open
Network inference, which involves reconstructing the connectivity structure of a network from recorded data, is essential for broadening our understanding of physical, biological, and chemical systems. Although data-driven network inferenc…
View article: Distributional Control of Ensemble Systems
Distributional Control of Ensemble Systems Open
Ensemble control offers rich and diverse opportunities in mathematical systems theory. In this paper, we present a new paradigm of ensemble control, referred to as distributional control, for ensemble systems. We shift the focus from contr…
View article: Robust Quantum Control for Bragg Pulse Design in Atom Interferometry
Robust Quantum Control for Bragg Pulse Design in Atom Interferometry Open
We formulate a robust optimal control algorithm to synthesize minimum energy pulses that can transfer a cold atom system into various momentum states. The algorithm uses adaptive linearization of the evolution operator and sequential quadr…
View article: The Role of the Defense Production Act in Meeting United States Climate Commitments: A Quantitative Perspective
The Role of the Defense Production Act in Meeting United States Climate Commitments: A Quantitative Perspective Open
View article: The Functional Connectome Mediating Circadian Synchrony in the Suprachiasmatic Nucleus
The Functional Connectome Mediating Circadian Synchrony in the Suprachiasmatic Nucleus Open
Circadian rhythms in mammals arise from the spatiotemporal synchronization of ∼20,000 neuronal clocks in the Suprachiasmatic Nucleus (SCN). While anatomical, molecular, and genetic approaches have revealed diverse cell types and signaling …
View article: Hierarchical simplicial manifold learning
Hierarchical simplicial manifold learning Open
Learning global structures, i.e. topological properties, inherent in complex data is an essential yet challenging task that spans across various scientific and engineering disciplines. A fundamental approach is to extract local data repres…
View article: Reinforcement Learning for Infinite-Dimensional Systems
Reinforcement Learning for Infinite-Dimensional Systems Open
Interest in reinforcement learning (RL) for large-scale systems, comprising extensive populations of intelligent agents interacting with heterogeneous environments, has surged significantly across diverse scientific domains in recent years…
View article: A moment-based Kalman filtering approach for estimation in ensemble systems
A moment-based Kalman filtering approach for estimation in ensemble systems Open
A persistent challenge in tasks involving large-scale dynamical systems, such as state estimation and error reduction, revolves around processing the collected measurements. Frequently, these data suffer from the curse of dimensionality, l…
View article: Convergence of Iterative Quadratic Programming for Robust Fixed-Endpoint Transfer of Bilinear Systems
Convergence of Iterative Quadratic Programming for Robust Fixed-Endpoint Transfer of Bilinear Systems Open
We present a computational method for open-loop minimum-norm control synthesis for fixed-endpoint transfer of bilinear ensemble systems that are indexed by two continuously varying parameters. We suppose that one ensemble parameter scales …
View article: Legendre-Moment Transform for Linear Ensemble Control and Computation
Legendre-Moment Transform for Linear Ensemble Control and Computation Open
Ensemble systems, pervasive in diverse scientific and engineering domains, pose challenges to existing control methods due to their massive scale and underactuated nature. This paper presents a dynamic moment approach to addressing theoret…
View article: Feedback Control of Coupled Nonlinear Oscillators with Uncertain Parameters
Feedback Control of Coupled Nonlinear Oscillators with Uncertain Parameters Open
View article: An Iterative Approach to Data-Driven Inference for Decoding Oscillator Network Structures
An Iterative Approach to Data-Driven Inference for Decoding Oscillator Network Structures Open
In complex networks, interactions between multiple agents give rise to an array of intricate global dynamics, ranging from synchronization to cluster formations. Decoding the connectivity structure as well as the types of interactions from…
View article: Finding influential nodes in networks using pinning control: Centrality measures confirmed with electrochemical oscillators
Finding influential nodes in networks using pinning control: Centrality measures confirmed with electrochemical oscillators Open
The spatiotemporal organization of networks of dynamical units can break down resulting in diseases (e.g., in the brain) or large-scale malfunctions (e.g., power grid blackouts). Re-establishment of function then requires identification of…
View article: Optimal Phase-Selective Entrainment of Heterogeneous Oscillator Ensembles
Optimal Phase-Selective Entrainment of Heterogeneous Oscillator Ensembles Open
We develop a framework to design optimal entrainment signals that entrain an ensemble of heterogeneous nonlinear oscillators, described by phase models, at desired phases. We explicitly take into account heterogeneity in both oscillation f…
View article: Synchronization, clustering, and weak chimeras in a densely coupled transcription-based oscillator model for split circadian rhythms
Synchronization, clustering, and weak chimeras in a densely coupled transcription-based oscillator model for split circadian rhythms Open
The synchronization dynamics for the circadian gene expression in the suprachiasmatic nucleus is investigated using a transcriptional circadian clock gene oscillator model. With global coupling in constant dark (DD) conditions, the model e…
View article: A moment kernel machine for clinical data mining to inform medical decision making
A moment kernel machine for clinical data mining to inform medical decision making Open
Machine learning-aided medical decision making presents three major challenges: achieving model parsimony, ensuring credible predictions, and providing real-time recommendations with high computational efficiency. In this paper, we formula…
View article: Engineering spatiotemporal patterns: information encoding, processing, and controllability in oscillator ensembles
Engineering spatiotemporal patterns: information encoding, processing, and controllability in oscillator ensembles Open
The ability to finely manipulate spatiotemporal patterns displayed in neuronal populations is critical for understanding and influencing brain functions, sleep cycles, and neurological pathologies. However, such control tasks are challenge…
View article: Moment-Based Reinforcement Learning for Ensemble Control
Moment-Based Reinforcement Learning for Ensemble Control Open
Problems involving controlling the collective behavior of a population of structurally similar dynamical systems, the so-called ensemble control, arise in diverse emerging applications and pose a grand challenge in systems science and cont…
View article: Supplementary Data from Diffusion Histology Imaging Combining Diffusion Basis Spectrum Imaging (DBSI) and Machine Learning Improves Detection and Classification of Glioblastoma Pathology
Supplementary Data from Diffusion Histology Imaging Combining Diffusion Basis Spectrum Imaging (DBSI) and Machine Learning Improves Detection and Classification of Glioblastoma Pathology Open
Supplementary methods, figures and tables
View article: Data from Diffusion Histology Imaging Combining Diffusion Basis Spectrum Imaging (DBSI) and Machine Learning Improves Detection and Classification of Glioblastoma Pathology
Data from Diffusion Histology Imaging Combining Diffusion Basis Spectrum Imaging (DBSI) and Machine Learning Improves Detection and Classification of Glioblastoma Pathology Open
Purpose:Glioblastoma (GBM) is one of the deadliest cancers with no cure. While conventional MRI has been widely adopted to examine GBM clinically, accurate neuroimaging assessment of tumor histopathology for improved diagnosis, surgical pl…
View article: Data from Diffusion Histology Imaging Combining Diffusion Basis Spectrum Imaging (DBSI) and Machine Learning Improves Detection and Classification of Glioblastoma Pathology
Data from Diffusion Histology Imaging Combining Diffusion Basis Spectrum Imaging (DBSI) and Machine Learning Improves Detection and Classification of Glioblastoma Pathology Open
Purpose:Glioblastoma (GBM) is one of the deadliest cancers with no cure. While conventional MRI has been widely adopted to examine GBM clinically, accurate neuroimaging assessment of tumor histopathology for improved diagnosis, surgical pl…
View article: Supplementary Data from Diffusion Histology Imaging Combining Diffusion Basis Spectrum Imaging (DBSI) and Machine Learning Improves Detection and Classification of Glioblastoma Pathology
Supplementary Data from Diffusion Histology Imaging Combining Diffusion Basis Spectrum Imaging (DBSI) and Machine Learning Improves Detection and Classification of Glioblastoma Pathology Open
Supplementary methods, figures and tables
View article: Data-Driven Control of Neuronal Networks with Population-Level Measurement
Data-Driven Control of Neuronal Networks with Population-Level Measurement Open
Controlling complex networks of nonlinear neurons is an important problem pertinent to various applications in engineering and natural sciences. While in recent years the control of neural populations with comprehensive biophysical models …
View article: Data-Efficient Inference of Nonlinear Oscillator Networks
Data-Efficient Inference of Nonlinear Oscillator Networks Open
Decoding the connectivity structure of a network of nonlinear oscillators from measurement data is a difficult yet essential task for understanding and controlling network functionality. Several data-driven network inference algorithms hav…
View article: Koopman Bilinearization of Nonlinear Control Systems
Koopman Bilinearization of Nonlinear Control Systems Open
Koopman operators, since introduced by the French-born American mathematician Bernard Koopman in 1931, have been employed as a powerful tool for research in various scientific domains, such as ergodic theory, probability theory, geometry, …
View article: Controllability Canonical Forms of Linear Ensemble Systems
Controllability Canonical Forms of Linear Ensemble Systems Open
Ensemble control, an emerging research field focusing on the study of large populations of dynamical systems, has demonstrated great potential in numerous scientific and practical applications. Striking examples include pulse design for ex…
View article: Deep multi-modal learning for joint linear representation of nonlinear dynamical systems
Deep multi-modal learning for joint linear representation of nonlinear dynamical systems Open
View article: Bilinear Systems Induced by Proper Lie Group Actions
Bilinear Systems Induced by Proper Lie Group Actions Open
In the study of induced bilinear systems, the classical Lie algebra rank condition (LARC) is known to be impractical since it requires computing the rank everywhere. On the other hand, the transitive Lie algebra condition, while more commo…
View article: Interpretable Design of Reservoir Computing Networks Using Realization Theory
Interpretable Design of Reservoir Computing Networks Using Realization Theory Open
The reservoir computing networks (RCNs) have been successfully employed as a tool in learning and complex decision-making tasks. Despite their efficiency and low training cost, practical applications of RCNs rely heavily on empirical desig…