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            "innerText" : "I am a PhD candidate in Applied and Computational Mathematics at Institute for Computational and Mathematical Engineering, Stanford University. I am privileged to be supervised by Prof. Lexing Ying (Applied Mathematics) and Prof. Grant M. Rotskoff (Computational Chemistry).\n\nMy research interests lie in the intersection of machine learning, stochastic analysis, and numerical analysis. I am now working on the mathematical foundations and algorithmic design of flow and diffusion-based models, e.g., inference-time scaling of diffusion models (ICLR 2026), fast solver for discrete diffusion (NeurIPS 2025), and parallel diffusion sampling (NeurIPS 2024 Spotlight).\n\nDuring Winter 2026, I am a Student Researcher at ByteDance Seed. During Summer 2025, I was a Visiting Researcher at Flatiron Institute, Simons Foundation, hosted by Dr. Jiequn Han, working on the inference-time scaling of diffusion models. During Summer 2023 and 2024, I was an Applied Scientist Intern at Amazon Science, working on multi-objective LLM fine-tuning, and multi-objective optimization. I obtained my BS in Computational Mathematics from School of Mathematical Sciences, Peking University, supervised by Prof. Ruo Li."
         },
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            "innerText" : "For a complete list of my publications, please visit my publications page."
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            "innerText" : "Yinuo Ren, Wenhao Gao, Lexing Ying, Grant M. Rotskoff & Jiequn Han\n\nDriftLite: Lightweight Drift Control for Inference-Time Scaling of Diffusion Models\nInternational Conference on Learning Representations (ICLR), 2026  arXiv  Poster  Code  OpenReview"
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            "innerText" : "Workshop version @ Molecular Machine Learning Conference (MoML) 2025."
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            "innerText" : "Keywords: Diffusion Model, Inference-Time Scaling, Variance Reduction, Sequential Monte Carlo, Guidance"
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         {
            "innerText" : "We study inference-time scaling for diffusion models, where the goal is to adapt a pre-trained model to new target distributions without retraining. Existing guidance-based methods are simple but introduce bias, while particle-based corrections suffer from weight degeneracy and high computational cost. We introduce DriftLite, a lightweight, training-free particle-based approach that steers the inference dynamics on the fly with provably optimal stability control. DriftLite exploits a previously unexplored degree of freedom in the Fokker-Planck equation between the drift and particle potential, and yields two practical instantiations: Variance- and Energy-Controlling Guidance (VCG/ECG) for approximating the optimal drift with minimal overhead. Across Gaussian mixture models, particle systems, and large-scale protein-ligand co-folding problems, DriftLite consistently reduces variance and improves sample quality over pure guidance and sequential Monte Carlo baselines. These results highlight a principled, efficient route toward scalable inference-time adaptation of diffusion models."
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            "innerText" : "Yinuo Ren, Grant M. Rotskoff & Lexing Ying\n\nA Unified Approach to Analysis and Design of Denoising Markov Models\nUnder Review  arXiv"
         },
         {
            "innerText" : "Keywords: Generative Models, Markov Processes, Diffusion Models, Denoising Markov Models, Score-Matching, Lévy Processes"
         },
         {
            "innerText" : "Probabilistic generative models based on measure transport, such as diffusion and flow-based models, are often formulated in the language of Markovian stochastic dynamics, where the choice of the underlying process impacts both algorithmic design choices and theoretical analysis. In this paper, we aim to establish a rigorous mathematical foundation for denoising Markov models, a broad class of generative models that postulate a forward process transitioning from the target distribution to a simple, easy-to-sample distribution, alongside a backward process particularly constructed to enable efficient sampling in the reverse direction. Leveraging deep connections with nonequilibrium statistical mechanics and generalized Doob's \nℎ\n-transform, we propose a minimal set of assumptions that ensure: (1) explicit construction of the backward generator, (2) a unified variational objective directly minimizing the measure transport discrepancy, and (3) adaptations of the classical score-matching approach across diverse dynamics. Our framework unifies existing formulations of continuous and discrete diffusion models, identifies the most general form of denoising Markov models under certain regularity assumptions on forward generators, and provides a systematic recipe for designing denoising Markov models driven by arbitrary Lévy-type processes. We illustrate the versatility and practical effectiveness of our approach through novel denoising Markov models employing geometric Brownian motion and jump processes as forward dynamics, highlighting the framework's potential flexibility and capability in modeling complex distributions."
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            "innerText" : "Yinuo Ren*, Haoxuan Chen*, Yuchen Zhu*, Wei Guo*, Yongxin Chen, Grant M. Rotskoff, Molei Tao & Lexing Ying\n\nFast Solvers for Discrete Diffusion Models: Theory and Applications of High-Order Algorithms\nAdvances in Neural Information Processing Systems (NeurIPS), 2025  arXiv  Poster  Code"
         },
         {
            "innerText" : "Workshop version @ ICLR 2025, Frontiers in Probabilistic Inference: Learning meets Sampling."
         },
         {
            "innerText" : "Keywords: Discrete Diffusion Model, Fast Solver, High-Order Scheme, Runge-Kutta Method"
         },
         {
            "innerText" : "Discrete diffusion models have emerged as a powerful generative modeling framework for discrete data with successful applications spanning from text generation to image synthesis. However, their deployment faces challenges due to the high dimensionality of the state space, necessitating the development of efficient inference algorithms. Current inference approaches mainly fall into two categories: exact simulation and approximate methods such as \n𝜏\n-leaping. While exact methods suffer from unpredictable inference time and redundant function evaluations, \n𝜏\n-leaping is limited by its first-order accuracy. In this work, we advance the latter category by tailoring the first extension of high-order numerical inference schemes to discrete diffusion models, enabling larger step sizes while reducing error. We rigorously analyze the proposed schemes and establish the second-order accuracy of the \n𝜃\n-trapezoidal method in KL divergence. Empirical evaluations on GPT-2 level text and ImageNet-level image generation tasks demonstrate that our method achieves superior sample quality compared to existing approaches under equivalent computational constraints."
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            "innerText" : "Haoxuan Chen*, Yinuo Ren*, Lexing Ying & Grant M. Rotskoff\n\nAccelerating Diffusion Models with Parallel Sampling: Inference at Sub-Linear Time Complexity\nAdvances in Neural Information Processing Systems (NeurIPS), Spotlight (Top 2%), 2024  arXiv  Poster  Proceedings"
         },
         {
            "innerText" : "Keywords: Diffusion Model, Parallel Sampling, Stochastic Differential Equations, Probability Flow ODE"
         },
         {
            "innerText" : "Diffusion models have become a leading method for generative modeling of both image and scientific data. As these models are costly to train and evaluate, reducing the inference cost for diffusion models remains a major goal. Inspired by the recent empirical success in accelerating diffusion models via the parallel sampling technique, we propose to divide the sampling process into \n𝑂\n(\n1\n)\n blocks with parallelizable Picard iterations within each block. Rigorous theoretical analysis reveals that our algorithm achieves \n𝑂\n~\n(\npoly\nlog\n⁡\n𝑑\n)\n overall time complexity, marking the first implementation with provable sub-linear complexity w.r.t. the data dimension \n𝑑\n. Our analysis is based on a generalized version of Girsanov's theorem and is compatible with both the SDE and probability flow ODE implementations. Our results shed light on the potential of fast and efficient sampling of high-dimensional data on fast-evolving modern large-memory GPU clusters."
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      "https://yinuoren.github.io/thumbnails/fast_solver_thumbnail.png",
      "https://yinuoren.github.io/thumbnails/parallel_thumbnail.png",
      "https://yinuoren.github.io/img/favicon.png"
   ]
}