About

Dr. Ching-Hsun Huang is a natural resource economist whose research integrates forest management, energy policy, and sustainable resource economics to advance resilient, low-carbon natural resource systems. Her work bridges ecological and economic analysis to quantify the environmental, health, and social co-benefits of sustainable forest and bioenergy strategies across the United States. Before joining Virginia Tech in 2020, Huang taught forest management and forest economics at Northern Arizona University, earning several teaching honors—including the College of Engineering, Forestry and Natural Sciences Distinguished Professor/Teacher of the Year Award—for outstanding instruction and student mentorship. As Department Head of Sustainable Biomaterials from 2020 to 2025, she led major curriculum reforms that redefined the department’s academic direction and launched the Sustainable Systems Science (S3) major—an interdisciplinary program connecting sustainability competencies with data-informed, real-world applications. Her research examines the economic optimization of forest management, valuation of ecosystem services, and the role of carbon trading and market-based incentives in advancing forest-based climate solutions. Her projects have spanned loblolly pine plantations in the U.S. Southeast and ponderosa pine forests in the Southwest, assessing how carbon sequestration, fuels-reduction treatments, and bioenergy utilization can jointly enhance ecological and economic outcomes. During her time in the Southwest, she collaborated with partners in the Navajo Nation to evaluate forest restoration and woody biomass utilization strategies that strengthened local economies and community resilience. Dr. Huang's recent analyses employ integrated air-quality and economic modeling tools to quantify the health co-benefits of biomass energy pathways, complementing broader research on the social and economic impacts of wildfire emissions. Her work in carbon sequestration and bioenergy has been supported by multiple agencies, including two projects funded by the U.S. Department of Energy, the USDA National Institute of Food and Agriculture, the U.S. Forest Service Rocky Mountain Research Station, and regional partners across the Southwest and Southeast. Huang’s teaching emphasizes integrative problem-solving, data-driven decision-making, and financial literacy as tools for sustainability leadership. She currently teaches SBIO 3004 – Sustainable Nature-Based Enterprises, a course that connects financial reasoning with environmental innovation and entrepreneurship, and looks forward to mentoring students in sustainability economics, the bioeconomy, and forest resource policy.

Research topics

• Computer Science
• Artificial Intelligence
• Machine Learning
• Algorithm
• Mathematics
• Materials science
• Mathematical optimization
• Telecommunications
• Engineering
• Mechanical engineering
• Physics
• Acoustics
• Optics

Selected publications

• RAG-Anything: All-in-One RAG Framework

  ArXiv.org · 2025-10-14

  preprintOpen accessSenior author

  Retrieval-Augmented Generation (RAG) has emerged as a fundamental paradigm for expanding Large Language Models beyond their static training limitations. However, a critical misalignment exists between current RAG capabilities and real-world information environments. Modern knowledge repositories are inherently multimodal, containing rich combinations of textual content, visual elements, structured tables, and mathematical expressions. Yet existing RAG frameworks are limited to textual content, creating fundamental gaps when processing multimodal documents. We present RAG-Anything, a unified framework that enables comprehensive knowledge retrieval across all modalities. Our approach reconceptualizes multimodal content as interconnected knowledge entities rather than isolated data types. The framework introduces dual-graph construction to capture both cross-modal relationships and textual semantics within a unified representation. We develop cross-modal hybrid retrieval that combines structural knowledge navigation with semantic matching. This enables effective reasoning over heterogeneous content where relevant evidence spans multiple modalities. RAG-Anything demonstrates superior performance on challenging multimodal benchmarks, achieving significant improvements over state-of-the-art methods. Performance gains become particularly pronounced on long documents where traditional approaches fail. Our framework establishes a new paradigm for multimodal knowledge access, eliminating the architectural fragmentation that constrains current systems. Our framework is open-sourced at: https://github.com/HKUDS/RAG-Anything.

  PublisherOA PDFDOI

• Joint Transmit-Receive Design for Airborne MIMO Radar Under Multiple Spatial-Frequency Constraints

  2025-05-19

  article

  This paper investigates the joint design problem of airborne multiple-input multiple-output (MIMO) radar transmit waveform and receive filter under multiple space-frequency spectral compatibility constraints. By maximizing the output signal-to-interference-plus-noise ratio (SINR), the joint design problem is established under constant modulus, similarity, and multiple spatial-frequency spectral constraints. Then, an iterative optimization algorithm is designed to solve the joint design problem. Specifically, the minorization-maximization and alternating direction method of multipliers (MM-ADMM) algorithm is designed to solve waveform optimization problems with non-convex fractional programming formulations. The simulation results show that the algorithm exhibits superiority in terms of spatial-frequency spectrum compatibility.

  PublisherDOI

• Efficient Multipath TOA Estimation in Large-Scale Scenarios: A Stepwise-Optimized SBL Approach with Coprime Sampling

  2025-05-23

  article

  High-precision time delay estimation algorithms are widely used in many applications. However, high-precision algorithms are characterized by high computational complexity. Data processing of large-scale multipath signals becomes a bottleneck. In this paper, we propose a Coprime Generalized Cross Correlation Sparse Bayesian Learning (Coprime GCC-SBL) algorithm to solve this problem. The algorithm is based on stepwise optimization and co-prime sampling techniques to reduce the signal dimension and solve the spatial dimension. Then, the sparse Bayesian learning method is used to accurately estimate the time of arrival (TOA) parameters of the multipath signal. Simulation results show that the proposed algorithm has more significant advantages in processing large-scale signals compared to the general SBL algorithm.

  PublisherDOI

• P108 Development of a novel algorithm to reliably identify patients with hereditary haemochromatosis in electronic patient records

  2024-10-01

  article

  <h3>Introduction</h3> Hereditary haemochromatosis (HH) is the commonest genetic condition among populations of Northern European ancestry. HH is characterised by excess iron deposition in various organs especially the liver. Despite its high prevalence, there is no specific International Classification of Diseases 10th edition (ICD-10) diagnoses code for HH making it challenging to identify these patients from electronic patient records (EPR). Patients with HH are usually assigned the ICD-10 code E83.1 ‘disorders of iron metabolism’. Many studies utilising large healthcare datasets have relied exclusively on the E83.1 code for identifying HH patients. However, patients with transfusion related iron overload from haematological conditions such as aplastic anaemia are also assigned the E83.1 code. HH is treated with venesection which has an OPCS Classification of Interventions and Procedures 4th edition (OPCS-4) code X36.2. We aimed to develop a novel algorithm to identify HH patients from EPR using ICD-10 and OPCS-4 codes. <h3>Methods</h3> All patients with an ICD-10 code of E83.1 within the Hospital Episodes Statistics Admitted Patient Care database from 1st April 2018 to 31st March 2023 in York and Scarborough Teaching Hospitals NHS Foundation Trust were included. Review of patients’ case notes was undertaken to assess their diagnosis, HH genotype, past medical history and previous medical procedures. A patient was considered to have HH if they were homozygous for the C282Y mutation. STATA MP 18 was used to create the algorithms using a combination of ICD-10 and OPCS-4 codes. <h3>Results</h3> A total of 9264 patient episodes were analysed corresponding to 787 unique patients identified with ICD-10 code E83.1. Only 479/787 (60.9%) of patients had a diagnosis of HH while 107 (13.6%) were C282Y/H63D compound heterozygotes and 42 (5.3%) had other HH genetic mutations. Of the remaining 159/787 (20.2%) patients without HH mutations, their diagnoses were 73 hyperferritinaemia, 49 incorrect coding, 17 transfusion related iron overload, 14 polycythaemia and 4 porphyria cutanea tarda (table 1). Among the patients with hyperferritinaemia, 24/73 (32.9%) had undergone venesection during the study period. Five algorithms were generated to improve the identification of patients with HH in EPR. The optimised version (algorithm 4) had a sensitivity of 85.8% and a positive predictive value (PPV) of 74.3% in detecting patients with HH. <h3>Conclusions</h3> Our innovative algorithm provides a more reliable method to identify HH patients from EPR compared to solely using the ICD-10 code E83.1. This is most valuable when carrying out population studies using large healthcare databases.

  PublisherDOI

• Maximin Design of Wideband Constant Modulus Waveform for Distributed Precision Jamming

  IEEE Transactions on Signal Processing · 2024-01-01 · 16 citations

  article

  Distributed precision jamming (DPJ) is an efficient way to control the combined power spectrum (CPS) of both target and friendly devices in electronic warfare. However, the existing methods neglect the design of worst-case CPS performance, and a great challenge is posed in determining an appropriate Pareto parameter to protect the friendly devices in practice. To address these issues, this paper investigates the maximin design of wideband constant modulus (CM) waveform for DPJ. Specifically, a novel optimization problem is established by maximizing the minimum CPS of the target equipment, and the maximum CPS of friendly devices is controlled under a given threshold in the constraint. The resultant problem is nonconvex and nonsmooth, together with CM and numerous quadratic constraints. Two algorithms, which can start from an infeasible initial point, are proposed to tackle the problem approximated by the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>p</i></sub> -norm. The first algorithm resorts to the alternating direction method of multipliers (ADMM) framework, and each subproblem is obtained with a closed-form solution. The second algorithm merges the penalty distance (PD) method and manifold optimization. The crux of this approach is to obtain the closed-form Euclidean PD term via the Karush-Kuhn-Tucker conditions and leverage the Riemannian conjugate gradient (RCG) framework, and we name this algorithm PD-RCG. Numerical examples demonstrate the effectiveness of our proposed algorithms. Comparatively speaking, the ADMM-based algorithm has better performance in protecting the friendly devices, while the PD-RCG algorithm performs better on the computational efficiency and the worst-case CPS of the target equipment.

  PublisherDOI

• Quantitative Characteristics of Gaseous Inorganic Compounds in Asphalt Fume with Combustion Condition

  SSRN Electronic Journal · 2024-01-01

  preprintOpen access
  PublisherDOI

• Dark‐Field Resonance Rayleigh Scattering Biosensor to Monitor Small Molecules and Determine the Secretory Ability of Single Neuron (Adv. Mater. Technol. 15/2024)

  Advanced Materials Technologies · 2024-08-01

  articleOpen access

  Resonance Rayleigh Scattering Accurate monitoring of neurotransmitters and precise characterization of the secretory ability of neurons are essential for effective treatment of neurological diseases. In article number 2301701, Mohamad Sawan and co-workers discover that the binding of small neurotransmitters to aptamers induces a conformational change that alters the effective radius of Au nanoclusters-aptamer complexes. This change can be detected in the resonance Rayleigh scattering intensity.

  PublisherOA PDFDOI

• Experimental Investigation of Permeability Evolution and Progressive Damage of Sandstone in the Complete Process of Direct Shear

  SSRN Electronic Journal · 2024-01-01

  preprintOpen access
  PublisherDOI

• Robust wideband waveform design with constant modulus and discrete phase constraints for distributed precision jamming

  Frontiers of Information Technology & Electronic Engineering · 2024-12-26 · 2 citations

  article

  Distributed precision jamming (DPJ) is a novel blanket jamming concept in electronic warfare, which delivers the jamming resource to the opponent equipment precisely and ensures that friendly devices are not affected. Robust jamming performance and low hardware burden on the jammers are crucial for practical DPJ implementation. To achieve these goals, we study the robust design of wideband constant modulus (CM) discrete phase waveform for DPJ, where the worst-case combined power spectrum (CPS) of both the opponent and friendly devices is considered in the objective function, and the CM discrete phase constraints are used to design the wideband waveform. Specifically, the resultant mathematical model is a large-scale minimax multi-objective optimization problem (MOP) with CM and discrete phase constraints. To tackle the challenging MOP, we transform it into a single-objective minimization problem using the Lp-norm and Pareto framework. For the approximation problem, we propose the Riemannian conjugate gradient for CM discrete phase constraints (RCG-CMDPC) algorithm with low computational complexity, which leverages the complex circle manifold and a projection method to satisfy the CM discrete phase constraints within the RCG framework. Numerical examples demonstrate the superior robust DPJ effectiveness and computational efficiency compared to other competing algorithms.

  PublisherDOI

• Dark‐Field Resonance Rayleigh Scattering Biosensor to Monitor Small Molecules and Determine the Secretory Ability of Single Neuron

  Advanced Materials Technologies · 2024-03-24

  articleOpen access

  Abstract Accurate monitoring of small molecules and precise characterization of the secretory ability of neurons are essential for effective diagnosis of neurological diseases. However, existing technologies for monitoring small molecules only provide approximate results. Plasmonic‐based biosensors are a potential solution in this biological application. Nevertheless, optical sensing nanoparticles require precise control of interparticle distance, particle shape and morphology, which can be challenging. This article discovers that the trapping of small molecules to aptamers induces a conformational change that alters the nanostructure of irregularly shaped Au nanoclusters (NCs) and aptamers due to the strong structure‐property relationships of Au NC‐aptamers. This change can be detected via resonance Rayleigh scattering (RRS) intensity in the dark‐field. This new technology overcomes the challenges of precise control of individual nanoparticles and the difficulty of small spectral peak shifts. The Au NC‐aptamers sensing device has a detection limit of 0.112 p m for dopamine, and this work detects 0.226 p m of dopamine surrounding a single epileptic neuron in polarization, which is more than those of healthy neurons. Overall, this easy‐to‐fabricate Au NC‐aptamers‐based technology has a high potential for monitoring small molecules and determining the secretory ability of single neurons, which is not possible using traditional technologies.

  PublisherOA PDFDOI

Frequent coauthors

• Qingsong Zhou

  The University of Tokyo

  8 shared

• Zhongping Yang

  National University of Defense Technology

  7 shared

• Yexin Zheng

  Beijing University of Technology

  6 shared

• Zhongrui Huang

  5 shared

• Yanping He

  5 shared

• Yi Su

  4 shared

• Zhihui Li

  National University of Defense Technology

  4 shared

• Meng Wu

  Zhengzhou University

  4 shared

Labs

• Sustainable BiomaterialsPI

Awards & honors

• College of Engineering, Forestry and Natural Sciences Distin…