Curriculum Vitae

Xuezhi Xie

PhD student, Department of Computer Science, University of Toronto

Education

• Ph.D in Computer Science, University of Toronto, 2020 - 2025 (expected)
• M.S. in Computer Science, specialization in Artificial Intelligence, Western University, 2018 - 2020
• B.S. in Biology, Minor in Computer science, University of Waterloo, 2012 - 2016

Work experience

• Research assistant (machine learning) - University of Toronto, Toronto, On, Canada, 2020 - present
  • Developed a deep generative model for novel dextrorotary helical conformations to facilitate the peptide drug design.
  • Developed and implemented various MCMC searches to optimize the synthetic data with optimized pharmetutical properties. Published the work as first author in NeuIPS Workshop (MLSB) 2021.
  • Pre-trained a graph convolutional network to reconstruct masked amino acids in proteins
  • Supervisor: Professor Philip M. Kim
• AI developer & Research assistant - Kaizhong’s lab, Western University, London, On, Canada, Sep.2018 - Apr. 2020
  • Collected peptide data from online databases (IEDB, IPD-MHC). Preprocessed, and analyzed data by using PySpark
  • Implemented and compared decision tree- and neural network-based models for predicting peptide binding.
  • Designed and developed a novel CNN-LSTM model to solve a mhc-ligand binding classification task. Achieved state of the art performance (AUC : 92.3%).
  • Published the work as first author in IEEE-BIBM (2019) and IJDMB(2020).
  • Supervisor: Professor Kaizhong Zhang

Skills

• Languages: Java, Python, C, C#, C++, JavaScript
• Software: Eclipse, Jupyter, Visual Studio/Code
• Database: SQL, MongoDB
• Frameworks: Keras, Pytorch, Tensorflow, PySpark, Hadoop MapReduce
• System: Linux, Windows
• Version Control: Git/GitHub
• Machine learning: Proficient in deep Convolutional Neural Network, LSTM, logistic regression, support vector machine, decision tree, random forest, GBDT, naive bayes, k-means, PCA, collaborative filtering.
• Computer vision: Skilled in image processing (OpenCV), object detection, object segmentation, mapping and localization, and SLAM.
• Natural language processing: Skilled in text classification, sentiment analysis and speech recognition

Publications

jourals

• Xie et al. “CyclicBoltz1, fast and accurately predicting structures of cyclic peptides and complexes containing non-canonical amino acids using AlphaFold 3 Framework”Link for my paper, bioRxiv, 2025.

• Xie et al. “Antibody-SGM, a Score-Based Generative Model for Antibody Heavy-Chain Design”(link), Journal of Chemical Information and Modeling, 2024. Link for my paper

• Xie et al. “HelixDiff, a Score-Based Diffusion Model for Generating All-Atom α-Helical Structures”(link), ACS Central Science (IF 18.2), 2024. Link for my paper

• Xie et al. “HelixGAN a deep-learning methodology for conditional de novo design of α-helix structures”(link), Bioinformatics 2023.Link for my paper

• Xie et al. “MHCherryPan, a novel pan-specific model for binding affinity prediction of class I HLA-peptide” (link), Int. J. Data Mining and Bioinformatics, Vol. 24, No. 3, 2020. Link for my paper

Conferences & workshops

• Xie et al. “HelixFlow, SE(3)–equivariant Full-atom Design of Peptides With Flow-matching Models”, Machine Learning for Structural Biology (MLSB) Workshop at NeurIPS 2024 .Link for my paper Link for my talk.

• Xie et al. “HelixDiff: Conditional Full-atom Design of Peptides With Diffusion Models”(link), Machine Learning for Structural Biology (MLSB) Workshop at NeurIPS 2023 Link for my paper

• Xie et al. “Antibody-SGM: Antigen-Specific Joint Design of Antibody Sequence and Structure using Diffusion Models”, Computational Biology Workshop at ICML 2023 Link for my paper

• Xie et al. “HelixGAN: A bidirectional Generative Adversarial Network with search in latent space for generation under constraints”(link), Machine Learning for Structural Biology (MLSB) Workshop at NeurIPS 2021. Link for my paper Link for my talk

• Xie, et al. “MHCherryPan, a novel model to predict the binding affinity of pan-specific class I HLA-peptide” (link), IEEE International Conference on Bioinformatics and Biomedicine (IEEE - BIBM) 2019. Link for my paper