Yinan Bu

Yinan Bu

First year PhD student @ UC Berkeley, IEOR. B.S. in Statistics @ School of the Gifted Young, USTC.

Research

Research Projects

Efficient Synthetic Network Generation via Latent Embedding Reconstruction
Jul. 2025 – Dec. 2025
Advisors: Prof. Ji Zhu (Susan A. Murphy Collegiate Professor, Statistics, UMich) and Prof. Gongjun Xu (Professor, Statistics, UMich)

  • Developed a general, efficient framework for generating synthetic networks by combining latent space network models with a distribution-free generator over learned latent embeddings.
  • Built scalable pipelines for a diffusion-based latent embedding generator and a bootstrap-based latent embedding resampler, preserving key network characteristics while enabling efficient training with lower computational cost than many existing deep architectures(GitHub repository).
  • Conducted empirical studies on both simulated datasets and real-world datasets, showing that the proposed method efficiently generates networks that more faithfully preserve key characteristics than existing approaches.

Feature-Subspace Based Hyperdimensional Computing
Apr. 2024 – Apr. 2026
Advisor: Prof. Xueqin Wang (Chair Professor, Statistics and Finance, USTC)

  • Derived asymptotic information loss in vanilla Hyperdimensional Computing(HDC) and developed Hoeffding bounds for hypervector similarity and predictive accuracy.
  • Designed Feature-Subspace based Hyperdimensional Computing(FSHDC), a scalable model for fast classification and interpretation; applied to UK Biobank fMRI/MRI with +0.20 AUROC over vanilla HDC.
  • Integrated an attention mechanism into HDC training, improving accuracy by 30% on HAR vs. vanilla HDC and 15% vs. attention-only baseline.

Large Scale Optimization and GPU Acceleration
Jan. 2024 – Feb. 2025
Advisor: Prof. Xueqin Wang (Chair Professor, Statistics and Finance, USTC)

  • Worked on graph trend filtering (ℓ₁ minimization on graph differences) using ADMM; studied convergence vs. subproblem solvability trade-off.
  • Proposed Differential Operator Grouping–based ADMM(Doge-ADMM) with closed-form subproblems and parallel updates.
  • Built parallel implementations for first/second-order cases, achieving up to 30× speedup over existing methods (GitHub repo).

Academic Projects

Analysis of the Government Pension Fund of Norway (NBIM)
Jan. 2024 – Feb. 2025
Supervisor: Prof. Canhong Wen (Statistics and Finance, USTC)

  • Designed, implemented, and deployed an RShiny dashboard for the Norwegian Government Pension Fund Global (NBIM) with interactive Plotly charts and Leaflet world maps (Live demo, GitHub repo).
  • Conducted comprehensive analysis combining summaries, figures, and maps.

Uncertainty-Aware Time-Series Forecasting via Conformal Prediction
Dec. 2024 – Jan. 2025
Supervisor: Prof. Yu Chen (Statistics and Finance, USTC)

  • Reproduced the conformal prediction framework for probabilistic forecasting, building end-to-end calibration and evaluation pipelines.
  • Conducted experiments on AR/ARIMA, sales, air quality, and COVID-19 datasets, demonstrating robust uncertainty quantification with competitive interval widths and accuracy.