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Siwei Xu

Curriculum Vitae

Education
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Ph.D. in Computer Science, University of California, Irvine
📍Irvine, CA
Jun. 2021 - Mar. 2026
Computational Genomics · Interpretable AI · Multi-omic Integration
  • Dissertation: From Modality Translation to Regulatory Reasoning: Interpretable AI for Single-Cell Multi-Omic Systems
  • Advisor: Prof. Jing Zhang
M.S. in Computational Biology, Carnegie Mellon University
📍Pittsburgh, PA
Aug. 2018 - May. 2020
  • Cumulative GPA: 3.85
B.S. in Computer Science with a Specialization of Bioinformatics, University of California, San Diego
📍La Jolla, CA
Sep. 2015 - Jun. 2018
  • Cumulative GPA: 3.84 / Major GPA: 3.91
  • Advisor: Awards: Provost Honor, Cum Laude

Research
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🏛️Zhang Lab, University of California, Irvine
📍Computational Genomics · Single-Cell Multi-Omics
🗓️Jan. 2025 – Present
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Aligned spatial transcriptomics, epigenomics, and histology via multimodal representation learning for in situ states.
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Designed robust cell-type mapping under sparsity and partial observability across tissues and protocols.
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Trained cross-modality recovery models to denoise and impute gene expression at higher resolution.
🗓️Jan. 2022 – Mar. 2026
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Modeled opioid–HIV response programs from consortium-scale single-cell profiles in neural and immune cells.
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Built reproducible QC, batch correction, and harmonization pipelines for cross-site integration.
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Standardized data assets: curated matrices, metadata schemas, and visualization-ready outputs for collaborators.
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Coordinated multi-institution analyses and experimental follow-ups to validate computational hypotheses.
🗓️Jan. 2024 – Jun. 2025
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Integrated snRNA-seq, snATAC-seq, and spatial Xenium across >2M nuclei in cohorts.
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Built cross-cohort harmonization to mitigate protocol, site, and donor-level confounding effects.
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Reconstructed cell-type-specific regulatory networks linking stress pathology to gene regulation and chromatin.
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Delivered analysis-ready atlases, annotations, and figures for consortium publication and reuse.
🗓️Jun. 2023 – Oct. 2024
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Developed scACT to translate between paired scRNA-seq and scATAC-seq with high fidelity.
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Designed modality-bridging embeddings robust to sparsity, library-size shifts, and batch effects.
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Benchmarked against state-of-the-art translators across datasets, cell types, and perturbations.
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Enabled imputation of missing modalities to support downstream regulatory inference and integration.
🗓️Sep. 2022 – Jun. 2024
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Integrated multi-cohort brain single-cell epigenomic datasets for atlas-scale regulatory analysis.
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Built reproducible pipelines for QC, harmonization, and confound-aware cross-study integration.
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Learned robust chromatin representations to stabilize cell-state inference under sparsity and batch shifts.
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Produced cell-type annotations and regulatory programs to support consortium figures and publications.
🗓️Jun. 2023 – Feb. 2024
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Curated genomic and clinical datasets from public repositories; harmonized formats and metadata.
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Developed ML curriculum and delivered hands-on workshops for biomedical researchers.
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Coordinated with partner institutions to deploy AI workflows for translational health studies.
🗓️Jun. 2021 – Jul. 2022
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Introduced Translator, a transfer-learning framework pretraining scATAC-seq models on high-quality references.
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Learned reusable chromatin embeddings that improved clustering and cell-type annotation under extreme sparsity.
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Performed domain adaptation to heterogeneous protocols, reducing batch effects and sequencing-depth shifts.
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Benchmarked across cohorts and tissues; delivered stable peak–gene links and regulatory program discovery
🏛️Carnegie Mellon University
📍Biomedical Machine Learning · Multimodal Modeling
🗓️Jul. 2020 – Jun. 2021
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Supervisor: Prof. Ziv Bar-Joseph; Collaborator: Astarte Medical
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Built ML models predicting neonatal growth trajectories from clinical and microbiome time series.
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Modeled missing and irregular EHR records with Hidden Markov Models and imputation.
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Suggested feeding interventions via model-based counterfactual analysis and cohort stratification
🗓️Nov. 2018 – May. 2020
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Supervisor: Prof. Jian Ma
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Linked histology images and transcriptomes via graph neural networks for joint tissue profiling.
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Learned shared multimodal embeddings using autoencoders for alignment and cross-modality retrieval.
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Enabled spatially aware interpretation via feature attribution over learned tissue representations.
🏛️University of California, San Diego
📍Biomedical Databases
🗓️Jun. 2016 – Jun. 2018
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Supervisors: Prof. Xiaoqian Jiang, Prof. Shuang Wang
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Mapped clinical variables into OMOP CDM schemas to improve interoperability across databases.
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Built visualization tools for cohort exploration, missingness auditing, and temporal trend analysis.
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Contributed to OHDSI efforts on scalable, global health data standardization and reuse.

Work Experience
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🏛️University of California, Irvine
🗓️Sep. 2021 - Mar. 2026
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Developed interpretable ML for single-cell multi-omics: translation, integration, and regulatory reasoning.
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Built transfer-learning and generative models for modality prediction under sparsity and batch shift.
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Collaborated in PsychENCODE, SCORCH, and PTSD/MDD consortia to deliver pipelines and atlases.
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Co-authored peer-reviewed publications; released reproducible code, datasets, and documentation for reuse.
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Mentored junior researchers on model design, evaluation, and scientific communication.
🗓️Sep. 2021 - Mar. 2025
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Head TA for ICS 6D Discrete Mathematics and TA for CS 141 Programming Languages in multiple offerings (247+ students).
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Designed problem sets, quizzes, and exams; aligned objectives, rubrics, and learning outcomes.
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Built Gradescope autograding and Q&A workflows to scale feedback for large enrollments.
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Led sections, review sessions, and office-hour systems across in-person and hybrid delivery.
🏛️Carnegie Mellon University
🗓️Jul. 2020 - Jun. 2021
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Built predictive models for neonatal growth using longitudinal clinical and microbiome time series.
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Engineered cohort-aware feature pipelines with missingness modeling and temporal alignment of visits.
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Applied probabilistic sequence models to handle irregular sampling and sparse EHR data.
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Evaluated generalization via site-aware splits, calibration, and uncertainty-aware model selection.
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Produced reproducible training/inference workflows and analysis reports for clinical collaborators.
🗓️Jan. 2020 - May. 2020
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Led recitations and office hours; supported the rapid transition to remote/hybrid instruction.
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Developed programming assignments, solutions, and rubrics aligned with course learning objectives.
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Built autograding and feedback workflows to scale timely, consistent grading for large enrollments.
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Provided targeted support on debugging, scientific computing, and quantitative modeling fundamentals
🏛️University of California, San Diego
🗓️Mar. 2016 - Mar. 2018
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Led small-group and one-to-one tutoring in programming, data structures, and data-science fundamentals.
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Coordinated weekly with instructors to design sections, refine assignments, and target common misconceptions.
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Built fair, rubric-driven assessments; improved clarity of exam questions and scaled feedback with Gradescope.
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Provided inclusive, scaffolded support via flexible office hours and high-touch debugging guidance.

Skills
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  • Programming: Python, R, Java, C/C++, SQL, Ocaml, Haskell, Prolog, Matlab, WebDev
  • AI/Machine Learning Tools: PyTorch, Tensorflow, Conda, Huggingface
  • Bioinformatics Tools: Seurat, ArchR, Signac, Pegasus, Squidpy, Cellranger, Samtools, Bowtie, David
  • Teaching: Tools Canvas, BlackBoard, Gradescope, Zybooks
  • Languages: Chinese (Mandarin), English, Japanese

Services
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Journal Reviewer
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  • BioData Mining
  • BMC Bioinformatics
  • BMC Genomics
  • Discover Computing
  • Discover Genetics and Evolution
  • npj Artificial Intelligence
  • Scientific Reports
  • The Journal of Supercomputing
  • Translational Psychiatry

Conference Reviewer
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  • 2024 IEEE International Conference on Bioinformatics and Biomedicine
  • 2024-2025 Conference on Information and Knowledge Management
  • 2024-2026 International Conference on Intelligent Systems for Molecular Biology
  • 2026 ACM SIGKDD Conference on Knowledge Discovery and Data Mining

Program Committee Member
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  • 2024-2025 International Conference on Artificial Intelligence for Medicine, Health, and Care

Awards
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  • Best Student Paper Award, 2024 Asian Conference on Machine Learning
  • Recipient, NSF Student Travel Award

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