About

Ning Hao is an Associate Professor in the Department of Mathematics at The University of Arizona and a member of the Graduate Faculty. He is also affiliated with the Statistics-GIDP program. His contact information includes an email at nhao@arizona.edu and a phone number at 520-621-2416. Hao's academic role involves teaching and research within the department, contributing to the university's mathematics and statistics programs. His work supports the department's broader mission of advancing mathematical sciences through education, research, and community engagement.

Research topics

• Computer science
• Artificial intelligence
• Mathematics
• Materials science
• Algorithm

Selected publications

• Efficient modification of microstructure and magnetic properties of Nb-doped (Nd, Ce)-Fe-B magnets with high-Ce content

  Journal of Alloys and Compounds · 2026-04-13

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• Towards robust visual odometry in dynamic environments: A hybrid approach with confidence-guided masking

  Engineering Applications of Artificial Intelligence · 2026-03-28

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  PublisherDOI

• Optimal Design of Hard Rock Underground Gas Storage Caverns and Sensitivity Analysis of Storage Pressure

  Journal of Physics Conference Series · 2026-03-01

  articleOpen access

  Abstract As the penetration of intermittent renewable energy sources increases, compressed air energy storage (CAES) in subsurface caverns has gained attention for its scalability, cost-effectiveness, and long operational lifespan. In regions lacking suitable salt formations, hard rock caverns provide a viable alternative due to their widespread availability and geological stability. This study develops and evaluates two representative cavern configurations—tunnel-type and tank-type—using finite element models based on typical geological profiles. A four-stage simulation framework was employed to assess the mechanical response of the surrounding rock and lining structures under varying internal gas pressures ranging from 10 MPa to 20 MPa. Sensitivity analyses revealed a linear relationship between internal pressure and deformation, with maximum displacements remaining within acceptable millimeter-scale limits. No plastic deformation or structural failure was observed in either the surrounding rock mass or the reinforced concrete lining, indicating sufficient mechanical resilience. Additionally, a limit equilibrium model based on the Mohr-Coulomb strength criterion was developed to evaluate the uplift stability of overlying rock layers. Simulation results for cavern diameters of 12 m and 15 m at a burial depth of 120 m confirm structural safety under a maximum operating pressure of 13.3 MPa. The findings support the suitability of smaller-diameter (φ12 m) tunnel-type caverns for high-pressure CAES applications in hard rock formations, offering an optimal balance between stability, constructability, and storage capacity. The proposed analysis framework provides a robust methodological reference for future underground gas storage design in complex geological settings.

  PublisherDOI

• Community Detection with Heterogeneous Block Covariance Model

  Journal of Computational and Graphical Statistics · 2025-05-19

  articleSenior author
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• Time-Frequency Domain-Based No-Reference Algorithm for Image Blurriness Evaluation

  Lecture notes in computer science · 2025-10-31

  book-chapter
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• A reference-guided iterative approach to polish the nanopore sequencing basecalling for therapeutic RNA quality control

  Communications Biology · 2025-10-01

  articleOpen access

  Nucleotide modifications deviate nanopore sequencing readouts, therefore generating artifacts during the basecalling of sequence backbones. Here, we present a reference-guided, iterative approach to polish modification-disturbed basecalling results. We show that such an approach is uniquely suitable for training biomolecule-specific high-accuracy basecallers, by improving the basecalling of both artificially-synthesized and real-world molecules. With demonstrated efficacy and reliability, we exploit the approach to precisely basecall therapeutic RNAs consisting of artificial or natural modifications. We first analyzed vaccine mRNAs, which are artificially modified to promote stability and reduce immunogenicity. Specifically, we quantified the sequence purity and integrity, the two most important quality metrics to be controlled during mRNA vaccine production. We also analyzed BioRNAs, which are human tRNA-based carriers for therapeutic RNA interference (RNAi) agents. Specifically, we examined modification hotspots, which are naturally incorporated in vivo during BioRNA production and essential for therapeutic efficacy. Our analysis expands the scope of therapeutic RNA quality control, from the conventional sequence-level to the current modification status-level.

  PublisherOA PDFDOI

• A Note on the Identifiability of the Degree‐Corrected Stochastic Block Model

  Stat · 2025-05-19

  articleOpen accessSenior authorCorresponding

  ABSTRACT In this short note, we address the identifiability issues inherent in the degree‐corrected stochastic block model (DCSBM). We provide a rigorous proof demonstrating that the parameters of the DCSBM are identifiable up to a scaling factor and a permutation of the community labels, under a mild condition.

  PublisherOA PDFDOI

• Approximate Convex Decomposition-based Whole-Body Trajectory Optimization for Robots in Dense Environments

  2025-10-19

  articleSenior author

  Whole-body planning is critical for enabling robots to navigate effectively in complex and dense environments. Traditional obstacle-based planning methods methods often restrict the representation of both robots and obstacles to simple convex polyhedra. This limitation may fail to adequately address intricate geometries of real-world obstacles involved in constructing compact convex polyhedral envelopes around more intricate obstacle shapes found in such environments. In this paper, we propose an approximate convex decomposition (ACD) based method to generate convex polyhedral maps that effectively represent the non-convex shapes of robots as assemblies of multiple convex objects. Furthermore, we propose a differentiable convex polyhedron collision evaluation method to facilitate collision detection. Extensive experiments demonstrate that our method not only enhances the accuracy of collision detection in cluttered environments but also expands the potential applications of robotics in complex scenarios.

  PublisherDOI

• Training data diversity enhances the basecalling of novel RNA modification-induced nanopore sequencing readouts

  Nature Communications · 2025-01-15 · 15 citations

  articleOpen access

  Accurately basecalling sequence backbones in the presence of nucleotide modifications remains a substantial challenge in nanopore sequencing bioinformatics. It has been extensively demonstrated that state-of-the-art basecallers are less compatible with modification-induced sequencing signals. A precise basecalling, on the other hand, serves as the prerequisite for virtually all the downstream analyses. Here, we report that basecallers exposed to diverse training modifications gain the generalizability to analyze novel modifications. With synthesized oligos as the model system, we precisely basecall various out-of-sample RNA modifications. From the representation learning perspective, we attribute this generalizability to basecaller representation space expanded by diverse training modifications. Taken together, we conclude increasing the training data diversity as a paradigm for building modification-tolerant nanopore sequencing basecallers. Accurately basecalling sequence backbones in the presence of nucleotide modifications remains a significant challenge in nanopore sequencing bioinformatics. Here, authors report that basecallers exposed to diverse training modifications gain the generalisability to analyse novel modifications.

  PublisherOA PDFDOI

• Ultra-high performance concrete and high-strength steel: compressive behavior of concrete-encased cellular steel columns with spiral confinement

  Construction and Building Materials · 2025-11-09 · 3 citations

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Frequent coauthors

• Amirreza Oghbaee

  Northeastern University

  40 shared

• Nate Derbinsky

  Northeastern University

  32 shared

• Mohammad Rostami

  Rowan University

  32 shared

• Fenghua He

  Harbin Institute of Technology

  30 shared

• Yue Niu

  Australian National University

  12 shared

• Hao Helen Zhang

  University of Arizona

  12 shared

• Yong Yang

  10 shared

• Chungeng Tian

  Harbin Institute of Technology

  9 shared