Ziming Huang

Research Project Management | Immunology | Single-cell Sequencing and Analysis | Myocardial Infarction • Managed research projects, leading teams in large-scale single-cell sequencing (120,000+nuclei) and comprehensive transcriptomic data analysis to dissect cardiac cellular heterogeneity and uncover novel therapeutic target • Integrated clinical and real-world datasets including SNP genotyping, and electronic medical records, discovering key genes and pathways in cardiomyopathies.

Chemical Text Mining | QSAR | Early Drug Discovery • Fine-tuning large language models for chemical text mining. Resolved API integration codes for models like GPT-3.5-turbo in chemical text-mining tasks, and designed batch data transfer strategies to support large-scale chemical literature processing. • Conducted structure-based virtual screening to identify potential inhibitors targeting the interaction between SARS-CoV-2 spike protein

Computer Vision | AI Model Training | Generative Models • Built a solution, boosted model robustness by 40%, enabled the accurate digital conversion of 1.6 million legacy chemical scans into machine-readable InChI format. • Engineered a ResNet-Transformer for image captioning to generate over 50 diverse chemical fingerprint candidates.

Teamwork | Genomics | SQL • Developed bit-parallel regex pipeline for genomes of 5 Takifugu species, which data size over 30 GB. • Annotated over 60 percent non-conserved gene fragments in the chilo suppressalis (silkworm) genome, overcoming homology gaps via de novo functional inference (undergraduate research).

Teamwork | Academic Writing | Mathematical Modeling · National Second Prize among Graduates • My roles included lead model developer and scientific report writer. • Developed a robust statistical model to identify disease-associated SNPs from genome-wide data, which reveal the relationship between SNP and phenotypes

• MUDS program: structural data science and biomedical training in Helmholtz information and data science academy. • Research area: cardiovascular diseases, single-cell/nucleus data analysis, causal interference.

• Transcript: 83.5/100 • Research area: computer-aided drug design, immune checkpoint inhibitor, drug virtual screening, multi-omics data integration.

• GPA: 3.63/4 • Core course: Calculus, Linear Algebra, Probability Theory and Mathematical Statistics, Discrete Mathematics, Data Structure, Molecular Biology, Biochemistry, Immunology, Biostatistics, Comprehensive Practice of Bioinformatics Software, Cheminformatics, Introduction to Rational Drug Design, Data Mining

• GPA: 3.63/4 • Core course: Calculus, Linear Algebra, Probability Theory and Mathematical Statistics, Discrete Mathematics, Data Structure, Molecular Biology, Biochemistry, Immunology, Biostatistics, Comprehensive Practice of Bioinformatics Software, Cheminformatics, Introduction to Rational Drug Design, Data Mining