I am Pengcheng Xu, a Master's Student at University of Illinois Urbana-Champaign.

MIRACLE: Multi-task learning based Interpretable Regulation of Autoimmune diseases through Common Latent Epigenetics

(Under Review) Pengcheng Xu*, Jinpu Cai*, Yulin Gao, Ziqi Rong, Hongyi Xin. 2023. Preprint Link: arxiv.org/abs/2306.13866

Multi-task learning based interpretable gene-level methylation estimations | Research Assistant Sep 2021 - Present Advisor: Hongyi Xin, Associate Professor of UM-SJTU Joint Institute, Shanghai Jiao Tong University

• Explored adaptable and interpretable neural network to find common genotype given 480k dimension sites, hundreds of sample.
• Designed an explainable site-gene-pathway ontology constraint to NN to discover new biomarkers by checking weights.
• Implemented a Variational Auto-Encoder to support gene-level embedding shared among datasets to obtain multi-task learning.
• Optimized a pretrain-finetune training scheme to increase accuracy by over 10%, wrote the paper under review in 2023.

Molecular de-novo design through Transformer-based Reinforcement Learning

(Under Review) TaoFeng*, Pengcheng Xu*, Tianfan Fu, Jimeng Sun. 2023. Preprint Link: http://arxiv.org/abs/2310.05365

Advisor: Tianfan Fu, Jimeng Sun

• Implemented a decision transformer architecture to improve the AUC for over fifteen molecular optimization tasks for 5% each on average.
• Applied Oracle-feedback reinforcement learning on the downstream tasks to reach higher performance than pretrained model.
• Carried out ablation study and investigation into loss curve and conditional probability over the next token as a function of previously chosen ones according to the model

Balancing Information Preservation and Computational Efficiency: L2 Normalization and Geodesic Distance in Manifold Learning

(Under Review) Ziqi Rong, Jinpu Cai, Jiahao Qiu, Pengcheng Xu, Lana Garmire, Qiuyu Lian, Hongyi Xin. 2023.

• The importance of distinguishable information in similarity measurement for unsupervised learning, manifold learning, and high-dimensional data visualization tasks.
• The limitations of conventional metrics like Euclidean distance after L1-normalization in handling high-dimensional data due to the "curse of dimensionality".
• The influence of normalization with different p-norms and the defect of Euclidean distance.
• The preservation of observation differences when normalizing data to a higher p-norm and using geodesic distance instead of Euclidean distance.
• The sufficiency of L2-normalization onto the hypersphere in preserving delicate differences in relatively high-dimensional data while maintaining computational efficiency.
• The presentation of HS-SNE, an augmentation to t-SNE based on a hypersphere representation system, which effectively addresses high-dimensional data visualization and similarity measurement intricacies.
• The better resolution of the hypersphere representation system in identifying subtle differences in high-dimensional data while balancing efficiency and computational feasibility.

Pipe-Déjàvu: Hardware-aware Latency Predictable, Differentiable Search for Faster Config and Convergence of Distributed ML Pipeline Parallelism

(To be submitted) Pengcheng Xu, Kaiyang Chen, Yuanrui Zhang, Indranil Gupta. 2023. Read the research report

Hardware-aware Latency Predictable, Differentiable Search for Faster Config and Convergence of Distributed ML Pipeline Parallelism Advisor: Indranil Gupta, Professor of CS UIUC | Advanced Distributed Systems| Researcher | Feb 2023 – May,2023

• Implemented a predictive model that considers communication cost, model computational cost, and hardware information to predict latency and resources of parallel configurations, saving time on pre-profiling before searching the parallel configuration.
• Proposed a differentiable parallel configuration search space inspired by DARTS, can potentially reach optimal configuration faster than the original dynamic programming.
• Employed parallel random initialization using sampling algorithms like Bayesian Optimization for faster train loss convergence

Vascular Intervention Training System Based on Electromagnetic Tracking Technology

Zhikai Yang, Pengcheng Xu, Dekun Yang, Yufeng Chen, Yancong Ma. ICVRV, 2020. ieeexplore.ieee.org/document/9479727

Advisor: Lixu Gu, Professor of Biomedical Engineering, Shanghai Jiao Tong University

• Developed the framework of an augmented reality surgery training assistant system for medical student and surgery.
• Predicted the operation trajectory using LSTM and used KD-Tree to calculate the distance for operation safety warning.
• Displayed vascular model in AR with OpenGL and designed the UI interface to support translation.
• Used the aruco library in OpenCV to coordinate positioning of the QR code.
• Published Vascular Intervention Training System Based on Electromagnetic Tracking Technology on ICVRV as second author.