Call for Papers

The MMSPML conference series centers on mathematical modeling, signal processing, and machine learning, covering theoretical fundamentals, algorithmic innovations, and cross-disciplinary practical applications of intelligent systems. This conference intends to bring together researchers, scholars, and industry specialists engaged in fundamental and applied science, engineering, and technology fields to exchange their latest research outcomes.

We sincerely invite experts, scholars, and industry practitioners from universities and research institutions worldwide to join our academic exchanges.

Topics of interest include, but are not limited to:

Track 1: Mathematical Foundations for Signal Processing and Machine Learning	Track 2: Statistical Signal Processing and Adaptive Methods
Differential equation modeling of learning systems with stability analysis Stochastic processes, fractional-order models, and uncertainty quantification Detection and estimation theory in statistical signal processing Convex, non-convex, and game-theoretic optimization Bayesian inference and parameter estimation Inverse problems, regularization, and computational imaging methods Model validation, sensitivity analysis, and uncertainty propagation Data assimilation, model calibration, and dynamical system identification Mathematical foundations of graph signal processing and network data Manifold learning, information geometry, and geometric signal processing Random matrix theory for high-dimensional learning and generalization Mathematical principles of trustworthy and responsible AI	Statistical signal processing — detection, estimation, and classification Adaptive filtering, array processing, and beamforming Time-frequency analysis, wavelet theory, and multiresolution methods Compressed sensing, sparse representations, and dictionary learning Tensor decompositions and multilinear algebra for high-dimensional data Nonlinear and non-Gaussian signal processing Information-theoretic methods and optimal transport Low-rank modeling, matrix/tensor completion, and subspace methods Sensor array and multichannel signal processing Model-driven and data-driven representations in signal processing
Track 3: Machine Learning Algorithms and Theory	Track 4: Signal Processing-Driven Machine Learning
Supervised, semi-supervised, self-supervised, and unsupervised learning Transformers, state-space models, and diffusion models Reinforcement learning, decision intelligence, and multi-agent systems Graph neural networks and geometric deep learning Bayesian learning and probabilistic graphical models Ensemble learning and hybrid intelligent systems Causal inference, counterfactual reasoning, and explainable AI Federated learning, distributed optimization, and privacy preservation Model compression, knowledge distillation, and efficient inference Automated machine learning and neural architecture search Large language model architectures, training, and alignment Generative models — diffusion models, GANs, and variational methods Retrieval-augmented generation and multimodal foundation models AI safety, robustness, and adversarial learning	Model-based deep learning and physics-informed neural networks Differentiable signal processing and neural operators Algorithm unrolling and model-driven deep unfolding networks Signal models in self-supervised and unsupervised learning Signal priors for sequential data modeling (speech, audio, time series) Machine learning for radar, communications, and acoustic signal processing Multimodal signal fusion, joint learning, and cross-modal analysis Sparsity-promoting and low-rank methods in deep learning Harmonic analysis, wavelets, and structured representation learning Signal-model-driven algorithmic fairness and robustness Data-driven generative and inference methods
Track 5: Machine Learning for Signal Processing and Imaging Applications	Track 6: Signal Processing Systems and Hardware Implementation
Image and video analysis, enhancement, and understanding Radar, sonar, and communication signal processing Biomedical signal processing — EEG, ECG, fMRI, and neural decoding Computational imaging and multisource information fusion Speech processing, acoustic imaging, and audio analysis Optoelectronic signal detection and compensation Brain-computer interfaces and neural signal decoding 3D reconstruction, geometric processing, and photorealistic rendering Object detection, tracking, and recognition Immersive media, extended reality, and affective computing Signal processing and machine learning for communications and networks Digital twins and industrial simulation Machine learning for remote sensing, geophysical, and environmental signal processing	Design and implementation of signal processing systems AI accelerators and specialized architectures for signal processing Lightweight signal processing and machine learning for embedded systems Real-time signal processing on FPGA and ASIC platforms Hardware-software co-design for signal processing algorithms Low-power signal processing and edge AI Quantum signal processing and quantum-inspired algorithms Testing, verification, and reliability of signal processing systems Open-source signal processing toolchains and reproducible research