About

Tianjing Wang is a Postdoctoral Associate at the R.F. Smith School of Chemical and Biomolecular Engineering at Cornell University. The webpage does not provide specific details about her research focus, background, or key contributions. Therefore, no further biographical information is available from the provided content.

Research topics

• Computer Science
• Artificial Intelligence
• Optics
• Physics
• Machine Learning
• Materials science
• Acoustics
• Mathematics
• Optoelectronics
• Biology
• Engineering
• Electronic engineering

Selected publications

• Large-scale quantum reservoir computing using a Gaussian Boson Sampler

  npj Quantum Information · 2026-05-06

  articleOpen access

  Abstract A Gaussian boson sampler (GBS) is a special-purpose quantum computer that can be practically realized at a large scale in optics. Here we report on experiments in which we used a frequency-multiplexed GBS with > 400 modes as a quantum reservoir. We evaluated the accuracy of our GBS-based reservoir computer on a variety of benchmark tasks. We found that when the system was given access to the correlations between measured modes of the GBS, the achieved accuracies were the same or higher than when it was only given access to the mean photon number in each mode—and in several cases the advantage in accuracy from using the correlations was greater than 20 percentage points. This provides experimental evidence in support of theoretical predictions that access to correlations enhances the power of quantum reservoir computers. We also tested our reservoir computer when operating the reservoir with various sources of classical rather than quantum light and found that using squeezed light consistently resulted in the highest accuracies. Our work experimentally establishes that a GBS can be an effective quantum reservoir and provides a practical platform for experimentally exploring the role of quantumness and correlations in quantum machine learning at very large system sizes.

  PublisherOA PDFDOI

• Parallel Transport on Spectral Subbundles of the Similarity Group

  Mathematics · 2026-05-15

  articleOpen access1st authorCorresponding

  We construct a connection-theoretic framework for parallel transport of spectral components along parameter families of signals on the similarity group G˜=R×SO(2). Let {ft}t∈I be a signal family that evolves under a C1 group trajectory. The frequency support of the associated scale-rotation transforms produces three Hilbert subbundles over the parameter interval, and the trajectory velocity induces a covariant derivative on each subbundle. The standard spectral viewpoint treats transformation behavior at individual parameter values. Our formulation instead organizes the propagation of spectral components along the entire parameter path and provides closed-form transport operators together with error bounds on each subbundle. We derive three explicit parallel transport formulas. On the equivariant subbundle the transport is an exact isometric translation. On the coupled subbundle, the transport combines log-scale translation with a phase factor ein0Δθ. On the invariant subbundle, the transport is approximate, with the quantitative bound ∥ΠinvF−F∥≤ε|Δτ|∥F∥, where Πinv denotes the parallel transport operator on that subbundle. We introduce the notion of non-parallelism rate as a pointwise measure of deviation from parallel evolution, and we prove that cumulative deviation along the path is bounded by the path integral of this quantity. The bound separates into two parts. One part is controlled by trajectory estimation error and reflects geometric mismatch. The other part is controlled by intrinsic appearance variation and reflects non-geometric drift. We also show that regularity transfers from the signal family to the spectral sections, and we establish a discrete transport theorem whose finite-sum error bounds recover the continuous estimates in the small-step limit. The framework provides a quantitative geometric tool for multi-scale feature evolution under continuous scale-rotation transformations.

  PublisherDOI

• Federated dynamic graph neural network for cross-modal organizational real-time community detection

  Neurocomputing · 2025-08-19 · 1 citations

  article
  PublisherDOI

• Comparative Study on Monetary Policies Between Europe and America

  Advances in Economics Management and Political Sciences · 2025-12-10

  articleOpen access1st authorCorresponding

  Against the backdrop of increasingly interconnected global financial markets and increasingly diverse economic landscapes, a nuanced understanding of monetary policies in major economies has become critically important for policymakers, investors, and scholars, as it shapes decisions and insights across domestic and international arenas. This article conducts a comparative study of monetary policies in Europe and the United States. Through comparative analysis, it first explores the similarities and differences in the monetary policy goals of the two regions. Then, various monetary policy tools used by the central banks of European countries and the United States were analyzed, and a comparative study of these tools was conducted. Finally, it examines the implementation effect of monetary policies from the perspectives of economic growth and employment. This study concludes that while both regions share core monetary policy functions, they exhibit notable divergence in goals, tools, and effects due to contextual differences, offering valuable insights for cross-regional learning, tool innovation, and enhanced international policy coordination in an era of persistent global uncertainties.

  PublisherOA PDFDOI

• CAP-Net: A Unified Network for 6D Pose and Size Estimation of Categorical Articulated Parts from a Single RGB-D Image

  ArXiv.org · 2025-04-15

  preprintOpen access

  This paper tackles category-level pose estimation of articulated objects in robotic manipulation tasks and introduces a new benchmark dataset. While recent methods estimate part poses and sizes at the category level, they often rely on geometric cues and complex multi-stage pipelines that first segment parts from the point cloud, followed by Normalized Part Coordinate Space (NPCS) estimation for 6D poses. These approaches overlook dense semantic cues from RGB images, leading to suboptimal accuracy, particularly for objects with small parts. To address these limitations, we propose a single-stage Network, CAP-Net, for estimating the 6D poses and sizes of Categorical Articulated Parts. This method combines RGB-D features to generate instance segmentation and NPCS representations for each part in an end-to-end manner. CAP-Net uses a unified network to simultaneously predict point-wise class labels, centroid offsets, and NPCS maps. A clustering algorithm then groups points of the same predicted class based on their estimated centroid distances to isolate each part. Finally, the NPCS region of each part is aligned with the point cloud to recover its final pose and size. To bridge the sim-to-real domain gap, we introduce the RGBD-Art dataset, the largest RGB-D articulated dataset to date, featuring photorealistic RGB images and depth noise simulated from real sensors. Experimental evaluations on the RGBD-Art dataset demonstrate that our method significantly outperforms the state-of-the-art approach. Real-world deployments of our model in robotic tasks underscore its robustness and exceptional sim-to-real transfer capabilities, confirming its substantial practical utility. Our dataset, code and pre-trained models are available on the project page.

  PublisherOA PDFDOI

• DRO: A Python Library for Distributionally Robust Optimization in Machine Learning

  ArXiv.org · 2025-05-29

  preprintOpen access

  We introduce dro, an open-source Python library for distributionally robust optimization (DRO) for regression and classification problems. The library implements 14 DRO formulations and 9 backbone models, enabling 79 distinct DRO methods. Furthermore, dro is compatible with both scikit-learn and PyTorch. Through vectorization and optimization approximation techniques, dro reduces runtime by 10x to over 1000x compared to baseline implementations on large-scale datasets. Comprehensive documentation is available at https://python-dro.org.

  PublisherOA PDFDOI

• CAP-Net: A Unified Network for 6D Pose and Size Estimation of Categorical Articulated Parts from a Single RGB-D Image

  2025-06-10 · 1 citations

  article

  This paper tackles category-level pose estimation of articulated objects in robotic manipulation tasks and introduces a new benchmark dataset. While recent methods estimate part poses and sizes at the category level, they often rely on geometric cues and complex multi-stage pipelines that first segment parts from the point cloud, followed by Normalized Part Coordinate Space (NPCS) estimation for 6D poses. These approaches overlook dense semantic cues from RGB images, leading to suboptimal accuracy, particularly for objects with small parts. To address these limitations, we propose a single-stage Network, CAP-Net, for estimating the 6D poses and sizes of Categorical Articulated Parts. This method combines RGB-D features to generate instance segmentation and NPCS representations for each part in an end-to-end manner. CAP-Net uses a unified network to simultaneously predict point-wise class labels, centroid offsets, and NPCS maps. A clustering algorithm then groups points of the same predicted class based on their estimated centroid distances to isolate each part. Finally, the NPCS region of each part is aligned with the point cloud to recover its final pose and size. To bridge the sim-to-real domain gap, we introduce the RGBD-Art dataset, the largest RGB-D articulated dataset to date, featuring photorealistic RGB images and depth noise simulated from real sensors. Experimental evaluations on the RGBD-Art dataset demonstrate that our method significantly outperforms the state-of-the-art approach. Real-world deployments of our model in robotic tasks underscore its robustness and exceptional sim-to-real transfer capabilities, confirming its substantial practical utility. Our dataset, code and pre-trained models are available on the project page <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>.

  PublisherDOI

• Arbitrary control over multimode wave propagation for machine learning

  Nature Physics · 2025-12-08 · 3 citations

  articleOpen access

  Abstract Controlled multimode wave propagation can enable more space-efficient photonic processors than architectures based on discrete components connected by single-mode waveguides. Instead of defining discrete elements, one can sculpt the continuous substrate of a photonic processor to perform computations through multimode interference in two dimensions. Here we designed and demonstrated a device with a refractive index that can be rapidly reprogrammed across space, allowing arbitrary control of wave propagation. The device, a two-dimensional programmable waveguide, uses parallel electro-optic modulation of the refractive index of a slab waveguide with about 10 4 programmable spatial degrees of freedom. We implemented neural network inference on benchmark tasks with up to 49-dimensional vectors in a single pass, without digital pre-processing or post-processing. Theoretical and numerical analyses further indicated that two-dimensional programmable waveguides may offer not only a constant-factor reduction in device area but also a scaling benefit, with the area required growing as N 1.5 rather than N 2 .

  PublisherOA PDFDOI

• Thermodynamic formalism for non-uniform systems with controlled specification and entropy expansiveness

  ArXiv.org · 2025-04-17

  preprintOpen access1st authorCorresponding

  We study thermodynamic formalism of dynamical systems with non-uniform structure. Precisely, we obtain the uniqueness of equilibrium states for a family of non-uniformly expansive flows by generalizing Climenhaga-Thompson's orbit decomposition criteria. In particular, such family includes entropy expansive flows. Meanwhile, the essential part of the decomposition is allowed to satisfy an even weaker version of specification, namely controlled specification, thus also extends the corresponding results by Pavlov. Two applications of our abstract theorems are explored. Firstly, we introduce a notion of regularity condition called weak Walters condition, and study the uniqueness of measure of maximal entropy for a suspension flow with roof function satisfying such condition. Secondly, we investigate topologically transitive frame flows on rank one manifolds of nonpositive curvature, which is a group extension of nonuniformly hyperbolic flows. Under a bunched curvature condition and running a Gauss-Bonnet type of argument, we show the uniqueness of equilibrium states with respect to certain potentials.

  PublisherOA PDFDOI

• A spatial atlas of chemoradiation therapy in pancreatic cancer identifies cellular and microenvironmental determinants of persister populations

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-06-25 · 4 citations

  preprintOpen access

  The molecular pathways involved in the response to radiation therapy in pancreatic ductal adenocarcinoma (PDAC) remain poorly understood. We aimed to elucidate the adaptive mechanisms and cellular interactions within PDAC to radiation therapy (RT). We constructed a transcriptomic landscape of the cellular subtypes and spatially resolved neighborhoods from 50 patient samples, including 16 longitudinally matched single cell RNA sequencing and 34 spatial transcriptomics specimens. To resolve shortcomings of cell-type mixtures in spatial data, we developed a novel statistical method called SpaCCI (spatially aware analysis of cell-cell interactions) to profile cell-cell interactions and ligand-receptor enrichment. This revealed CXCL12/TGFβ-driven persister cell niches where activated fibroblasts reprogram tumor- associated macrophages and spatially exclude stress-response CD8 T cells after RT. Persister cancer cells displayed transcriptional evidence of recalcitrance to metal-induced cell death pathways of ferroptosis and cuproptosis which were recapitulated in preclinical models. Our study reveals the selective pressures experienced by PDAC following RT that may help provide insight for future multimodal therapeutic strategies.

  PublisherOA PDFDOI

Frequent coauthors

• Lin Chen

  Shanghai Innovative Research Center of Traditional Chinese Medicine

  69 shared

• Logan G. Wright

  63 shared

• Qingqing Sun

  51 shared

• Tatsuhiro Onodera

  46 shared

• Peter L. McMahon

  Cornell University

  41 shared

• Hao Zhu

  Fudan University

  37 shared

• Chris Xu

  35 shared

• David Wei Zhang

  Shanghai Innovative Research Center of Traditional Chinese Medicine

  33 shared

Labs

• Duffield EngineeringPI

Education

• PhD, Applied Physics

  Cornell University

  2018

Awards & honors

• SPROUT Awards
• EPICC Awards
• Research, Teaching, and Advising Awards
• Distinguished Alumni Award
• Cheng Distinguished Lecture Series