Pingchuan Ma

Orcid: 0009-0007-2380-3796

Affiliations:
  • Arizona State University, Tempe, AZ, USA


According to our database1, Pingchuan Ma authored at least 12 papers between 2024 and 2026.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book  In proceedings  Article  PhD thesis  Dataset  Other 

Links

Online presence:

On csauthors.net:

Bibliography

2026
On Causal and Anticausal LLM-based Data Synthesis.
Proceedings of the Nineteenth ACM International Conference on Web Search and Data Mining, 2026

CAMO: Causality-Guided Adversarial Multimodal DOmain Generalization for Crisis Classification.
Proceedings of the Advances in Knowledge Discovery and Data Mining, 2026

2025
Who's Your Judge? On the Detectability of LLM-Generated Judgments.
CoRR, September, 2025

Is Chain-of-Thought Reasoning of LLMs a Mirage? A Data Distribution Lens.
CoRR, August, 2025

Toward Intelligent Electronic-Photonic Design Automation for Large-Scale Photonic Integrated Circuits: from Device Inverse Design to Physical Layout Generation.
CoRR, July, 2025

SparseC-AFM: a deep learning method for fast and accurate characterization of MoS<sub>2</sub> with C-AFM.
CoRR, July, 2025

SP2RINT: Spatially-Decoupled Physics-Inspired Progressive Inverse Optimization for Scalable, PDE-Constrained Meta-Optical Neural Network Training.
CoRR, May, 2025

ChipMnd: LLMs for Agile Chip Design.
Proceedings of the 43rd IEEE VLSI Test Symposium, 2025

MAPS: Multi-Fidelity AI-Augmented Photonic Simulation and Inverse Design Infrastructure.
Proceedings of the Design, Automation & Test in Europe Conference, 2025

BOSON<sup>-1</sup>: Understanding and Enabling Physically-Robust Photonic Inverse Design with Adaptive Variation-Aware Subspace Optimization.
Proceedings of the Design, Automation & Test in Europe Conference, 2025

ADEPT-Z: Zero-Shot Automated Circuit Topology Search for Pareto-Optimal Photonic Tensor Cores.
Proceedings of the 30th Asia and South Pacific Design Automation Conference, 2025

2024
PIC2O-Sim: A Physics-Inspired Causality-Aware Dynamic Convolutional Neural Operator for Ultra-Fast Photonic Device FDTD Simulation.
CoRR, 2024


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