Curriculum Vitae

Keguang Cheng · kc6376@nyu.edu

Education

New York University

M.S., Biology (General Biology) · New York, NY, USA · 2025–present · Expected May 2027
GPA: 3.889/4.0

Selected coursework: Machine Learning in Medicine and Biology; Biological Databases & Datamining; Applied Genomics; Statistics in Biology; Programming for Biologists.

Beijing Forestry University

B.S., Biological Sciences (National Science Base) · Beijing, China · July 2024
GPA: 3.75/4.0

Additional training — Fudan University

Graduate International Summer School of Life Sciences · Shanghai, China

• Evolution and Biological Big Data, August 2025
• Genetics and Developmental Biology, July 2025

Selected courses, skills, and academic transcripts →

Publications

Manuscripts in preparation

• Beyond Canonical NLR Classes: Phylogenetic Diversity and Chromosomal Organization in Gymnosperms — First author; research manuscript; in preparation; working title.
• From Evolution to Design: Diversity, Constraints, and Engineering of Plant NLR Immune Receptors — First author; review manuscript; in preparation.

Research experience

Organ identity and development in Podostemum ceratophyllum

Research project, New York Botanical Garden · May 2026–present
Advisor: Cecilia Zumajo

• Investigating the developmental basis of unusual organ morphology through comparative analysis of root, leaf, and whole-plant RNA-seq data.
• Developed the literature review and computational analysis plan; used Trinity-based de novo assembly and transcriptome quality assessment to establish a reference for developmental gene-expression analysis.
• Screened developmental regulators using reciprocal BLAST to guide planned phylogenetic validation and root–leaf expression comparisons aimed at resolving organ homology.

NLR gene-family evolution in gymnosperms

Independent researcher, Beijing Forestry University · March 2023–present
Advisor: Pingli Liu

• Classified NLRs across 29 gymnosperm genomes by domain architecture; reconstructed phylogenies under alternative classifications, addressing long-branch attraction and incorporating angiosperm and algal sequences.
• Resolved a large, strongly supported lineage outside the canonical TNL, RNL, and CNL classes, positioned between TNL and non-TNL clades in phylogenetic analyses.
• Mapped NLR loci and gene clusters; observed concentration on a single chromosome in multiple gymnosperm species and tested chromosome-level enrichment.

Population genomics of Tetracentron sinense

Team member, Beijing Forestry University · February 2022–March 2025
Advisor: Pingli Liu

• Analyzed population structure from genome-wide resequencing data using PCA and ADMIXTURE to characterize genetic differentiation and ancestry patterns.
• Estimated nucleotide diversity (π), Watterson’s θ, and population differentiation (FST) to compare genetic variation within and among populations and inform conservation-genomic interpretation.
• Created, revised, and assembled all figures for the research manuscript in R and Adobe Illustrator, integrating analysis outputs into publication-ready visualizations.

RNA-seq analysis of NLR expression in Pinus tabuliformis

Course project, Applied Genomics, New York University · Spring 2026
Instructor: Manpreet Katari

• Reanalyzed a public 12-sample time-course RNA-seq dataset from pine wood nematode-infected Pinus tabuliformis using a curated catalogue of 661 NLR genes, Salmon/tximport, and DESeq2 likelihood-ratio testing.
• Examined class-specific temporal expression with Mfuzz and WGCNA while accounting for module-size effects; used exploratory co-expression and promoter-motif analyses to prioritize candidate NLRs and transcription-factor families.
• Documented the workflow in R Markdown and produced figures, tables, and an interactive Shiny dashboard.

Functional analysis of very small introns in plants

Team member, Beijing Forestry University · December 2022–June 2023
Advisor: Hongbo Gao

• Reviewed intron splicing and evolution; performed primer design, RT-PCR, and gel electrophoresis to investigate minimum intron length and splicing accuracy.
• Used Python for data processing and figure preparation.

Cold-stress physiology in Daurian ground squirrel

Team lead, Beijing Forestry University · September 2021–May 2022
Advisor: Qiang Weng

• Led a four-member team studying cold-induced mitochondrial adaptation.
• Designed assays for mitochondrial abundance and oxidative-stress markers, coordinated troubleshooting, and presented results in a team report and presentation.

Field experience

• Comprehensive Field Internship in Biology, July 2023: primary-forest surveys, plant and insect data collection, and 31 fungal specimens; led a ten-member team comparing slope aspects with Shannon–Wiener and Simpson diversity indices, Excel, and R.
• Jiufeng Forest Reserve Medicinal Plant Survey, August 2021: transect surveys and ten focal medicinal species; contributed to a team dataset of 547 vascular plant species (352 genera, 112 families) and the Medicinal Plant Catalogue of Jiufeng.
• Field Internship in Zoology, May 2021: identified 126 insect species; photographed 39 bird species; designed a salinity-stress experiment on frog larvae, estimated LC50, and analyzed survival.
• Field Internship in Botany, April 2021: surveys in suburban forests and the Beijing Botanical Garden; prepared 132 plant specimens and a plant identification guide.

Skills

• HPC & Linux: Linux/Bash and Slurm job arrays for running genomic workflows on HPC. Reproducible analysis with Git, Jupyter, R Markdown, and Shiny.
• Programming & data visualization: Python (NumPy, pandas, Biopython) and R (tidyverse, ggplot2) for data processing and visualization; Adobe Illustrator for figure preparation. SQL with MySQL and SQLite.
• Genomics: RNA-seq quality control and differential expression; de novo transcriptome assembly and evaluation; gene-family analysis, phylogenetics, population genomics, and VCF processing.
• Statistical training: Hypothesis testing; linear and logistic regression; ANOVA; PCA; bootstrap and permutation tests; false discovery rate control.
• Machine learning: Scikit-learn, classification, and cross-validation.
• Wet-lab methods: RT-PCR, gel electrophoresis, fluorescence staining, and Western blot.
• Field methods: Ecological and medicinal-plant surveys; botanical and zoological specimen collection and identification; field study design and team coordination.

Honors

• BJFU Academic Excellence Scholarship, 2022 and 2023.
• BJFU Outstanding Student Scholarship, 2021 and 2023.
• Second Prize, 9th Beijing University Student Biology Knowledge Competition, 2023.
• Third Prize, Beijing University Student Biology Experimental Design Competition, 2022 and 2023.