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Ruixuan Zhao

Publications and source records attributed to Ruixuan Zhao.

12 recordsLinked to original sources
arXiv2026

Causal Inference for All: Marginal Estimands for Outcomes Truncated by Death

In longitudinal studies, outcomes of interest are often truncated by death, meaning that they are only observed or well-defined conditional on intercurrent events such as survival. Existing strategies face a trade-off: causally interpretable estimands, such as survivor average causal effects, target a latent subgroup, whereas while-alive and composite summaries apply to the full population but are difficult to interpret as causal effects on the non-mortality outcome. We address these challenges by introducing methodology for a new set of estimands that (i) concern the entire population, (ii) remain causally interpretable, and (iii) leverage the longitudinal data commonly available in studies with outcomes truncated by death. The set of estimands includes single-world marginal separable effects that generalize conditional separable effects to full-population summaries. We develop identification and estimation results for these estimands and apply the methodology in a reanalysis of a prostate cancer trial, highlighting how different estimands can yield different treatment conclusions.

stat.ME↗
arXiv2026

Distributed State Estimation for Discrete-Time Systems With Unknown Inputs: An Optimization Approach

This paper proposes a novel Distributed Unknown Input Observer (DUIO) framework for state estimation in large-scale systems subject to local unknown inputs. We consider systems where outputs are measured by a network of spatially distributed sensors and inputs are introduced through multiple dispersed channels. In this framework, each local node utilizes only its local input and output measurements to estimate the maximal locally reconstructible state. Subsequently, nodes collaboratively reconstruct the whole system state via a distributed optimization algorithm that fuses these partial estimates. We provide a rigorous analysis showing that the estimation error is bounded, with the error bound explicitly dependent on the number of communication iterations per time step and strongly convexity constant determined by the system parameters. Furthermore, to counteract curvature anisotropy induced by poor conditioned system geometry, we embed a normalization step into the distributed optimization procedure. Simulation results demonstrate the effectiveness of the proposed framework and the performance improvements yielded by the normalization procedure.

eess.SY↗
arXiv2026

End-to-End Differentiable Predictive Control with Probabilistic Constraint-Satisfaction Guarantees for Building Demand Response

Building Demand Response (DR) benefits from Model Predictive Control (MPC) because thermal flexibility can be optimized over a future horizon while enforcing operational constraints, but the repeated online optimization required by MPC motivates Differentiable Predictive Control (DPC) as a computationally efficient explicit-policy alternative. Conventional DPC, however, separates model identification from policy optimization and remains sensitive to plant-model mismatch. This paper proposes an End-to-End DPC (E2E-DPC) framework that jointly optimizes the learned dynamics model and control policy using prediction, constraint, and economic objectives. Stage-wise conformal prediction (CP) calibrates recursive prediction errors to construct horizon-dependent tightened state constraints, while independent Hoeffding certification provides a high-confidence lower bound on plant-level satisfaction of the original state and input constraints under receding-horizon deployment. The framework is evaluated in a high-fidelity residential building simulation using EnergyPlus through the Energym interface and compared with online MPC and conventional DPC. Constraint tightening eliminates thermal violations for both DPC variants in the stress test and reduces violations by 99.4 percent for online MPC. Compared with conventional DPC, E2E-DPC reduces the mean constraint-tightening margin at the first prediction stage by 31.0 percent and increases the median certified plant-level lower bound from 0 to 0.814. The results further reveal a trade-off between stronger certification of plant-level constraint satisfaction and the ability to maintain nonempty tightened state sets as the prediction horizon increases, with stronger multi-step identification mitigating this trade-off.

eess.SY↗
arXiv2026

Distributed State Estimation for Discrete-time LTI Systems: the Design Trilemma and a Novel Framework

With the advancement of IoT technologies and the rapid expansion of cyber-physical systems, there is increasing interest in distributed state estimation, where multiple sensors collaboratively monitor large-scale dynamic systems. Compared with its continuous-time counterpart, a discrete-time distributed observer faces greater challenges, as it cannot exploit high-gain mechanisms or instantaneous communication. Existing approaches depend on three tightly coupled factors: (i) system observability, (ii) communication frequency and dimension of the exchanged information, and (iii) network connectivity. However, the interdependence among these factors remains underexplored. This paper identifies a fundamental trilemma among these factors and introduces a general design framework that balances them through an iterative semidefinite programming approach. As such, the proposed method mitigates the restrictive assumptions present in existing works. The effectiveness and generality of the proposed approach are demonstrated through a simulation example.

eess.SY↗
arXiv2026

Distributed Unknown Input Observer Design: A Geometric Approach

We present a geometric approach to designing distributed unknown input observers (DUIOs) for linear time-invariant systems, where measurements are distributed across nodes and each node is influenced by \emph{unknown inputs} through distinct channels. The proposed distributed estimation scheme consists of a network of observers, each tasked with reconstructing the entire system state despite having access only to local input-output signals that are individually insufficient for full state observation. Unlike existing methods that impose stringent rank conditions on the input and output matrices at each node, our approach leverages the $(C,A)$-invariant (conditioned invariant) subspace at each node from a geometric perspective. This enables the design of DUIOs in both continuous- and discrete-time settings under relaxed conditions, for which we establish sufficiency and necessity. The effectiveness of our methodology is demonstrated through extensive simulations, including a practical case study on a power grid system.

eess.SY↗
arXiv2025

High-speed optical microscopy for neural voltage imaging: Methods, trade-offs, and opportunities

High-speed optical imaging of dynamic neuronal activity is essential yet challenging in neuroscience. While calcium imaging has been firmly established as a workhorse technique for monitoring neuronal activity, its limited temporal resolution and indirect measurement restrict its ability to capture rapid inhibitory and excitatory events and subthreshold voltage oscillations. In contrast, voltage imaging directly measures membrane potential fluctuations, providing a comprehensive and precise representation of neuronal circuit dynamics. Recent advancements in voltage-sensitive dyes and, particularly, genetically encoded voltage indicators have significantly enhanced the feasibility of voltage imaging, prompting the development of advanced fluorescence microscopy methods optimized for high-speed acquisition. However, achieving millisecond-scale temporal resolution remains challenging due to inherent trade-offs among imaging speed, spatial resolution, and signal-to-noise ratio. Conventional raster-scanning approaches, including confocal microscopy, are fundamentally limited by their slow frame rates, precluding the capture of rapid neuronal events from multiple neurons simultaneously. Alternative techniques such as random-access scanning, spatiotemporal multiplexing, and computational optical imaging have successfully addressed these constraints, enabling kilohertz-level imaging of neuronal activity in both two-dimensional and three-dimensional contexts. This review summarizes recent progress in high-speed optical microscopy for voltage imaging and discusses its transformative potential for neuroscience research.

physics.optics↗
arXiv2025

A Review of Light-Field Imaging in Biomedical Sciences

Light-field imaging is an emerging paradigm in biomedical optics, offering the unique ability to capture volumetric information in a single snapshot by encoding both the spatial and angular components of light. Unlike conventional three-dimensional (3D) imaging modalities that rely on mechanical or optical scanning, light-field imaging enables high-speed volumetric acquisition, making it particularly well-suited for capturing rapid biological dynamics. This review outlines the theoretical foundations of light-field imaging and surveys its core implementations across microscopy, mesoscopy, and endoscopy. Special attention is given to the fundamental trade-offs between imaging speed, spatial resolution, and depth of field, as well as recent advances that address these limitations through compressive sensing, deep learning, and meta-optics. By positioning light-field imaging within the broader landscape of biomedical imaging technologies, we highlight its unique strengths, existing challenges, and future potential as a scalable and versatile tool for biological discovery and clinical applications.

physics.optics↗
arXiv2025

Distributed Unknown Input Observer Design with Relaxed Conditions: Theory and Application to Vehicle Platooning

Designing observers for linear systems with both known and unknown inputs is an important problem in several research contexts, for example, fault diagnosis and fault-tolerant control, and cyber-secure control systems, and presents significant challenges in distributed state estimation due to the limited sensing capabilities of individual nodes. Existing methods typically impose an individual input-to-output rank condition on each estimator node, which severely restricts applicability in practical applications. This paper presents a novel distributed unknown-input observer design scheme based on a geometric approach under much weaker assumptions than the ones available in the literature. By leveraging the properties of the $(C, A)$-invariant (conditioned invariant) subspace at each node, our methodology aims at reconstructing portions of the system state that remain unaffected by local unknown inputs, while integrating these estimates via a network-based information exchange. A case study on vehicle platoon control shows the effectiveness of the proposed approach.

eess.SY↗
arXiv2025

Bridging Centralized and Distributed Frameworks in Unknown Input Observer Design

State estimation for linear time-invariant systems with unknown inputs is a fundamental problem in various research domains. In this article, we establish conditions for the design of unknown input observers (UIOs) from a geometric approach perspective. Specifically, we derive a necessary and sufficient geometric condition for the existence of a centralized UIO. Compared to existing results, our condition offers a more general design framework, allowing designers the flexibility to estimate partial information of the system state. Furthermore, we extend the centralized UIO design to distributed settings. In contrast to existing distributed UIO approaches, which require each local node to satisfy the rank condition regarding the unknown input and output matrices, our method accommodates cases where a subset of nodes does not meet this requirement. This relaxation significantly broadens the range of practical applications. Simulation results are provided to demonstrate the effectiveness of the proposed design.

eess.SY↗
arXiv2024

Identifiability and Consistent Estimation for Gaussian Chain Graph Models

The chain graph model admits both undirected and directed edges in one graph, where symmetric conditional dependencies are encoded via undirected edges and asymmetric causal relations are encoded via directed edges. Though frequently encountered in practice, the chain graph model has been largely under investigated in literature, possibly due to the lack of identifiability conditions between undirected and directed edges. In this paper, we first establish a set of novel identifiability conditions for the Gaussian chain graph model, exploiting a low rank plus sparse decomposition of the precision matrix. Further, an efficient learning algorithm is built upon the identifiability conditions to fully recover the chain graph structure. Theoretical analysis on the proposed method is conducted, assuring its asymptotic consistency in recovering the exact chain graph structure. The advantage of the proposed method is also supported by numerical experiments on both simulated examples and a real application on the Standard & Poor 500 index data.

stat.ME↗
arXiv2021

Learning linear non-Gaussian directed acyclic graph with diverging number of nodes

Acyclic model, often depicted as a directed acyclic graph (DAG), has been widely employed to represent directional causal relations among collected nodes. In this article, we propose an efficient method to learn linear non-Gaussian DAG in high dimensional cases, where the noises can be of any continuous non-Gaussian distribution. This is in sharp contrast to most existing DAG learning methods assuming Gaussian noise with additional variance assumptions to attain exact DAG recovery. The proposed method leverages a novel concept of topological layer to facilitate the DAG learning. Particularly, we show that the topological layers can be exactly reconstructed in a bottom-up fashion, and the parent-child relations among nodes in each layer can also be consistently established. More importantly, the proposed method does not require the faithfulness or parental faithfulness assumption which has been widely assumed in the literature of DAG learning. Its advantage is also supported by the numerical comparison against some popular competitors in various simulated examples as well as a real application on the global spread of COVID-19.

stat.ML↗