About Dr. Zhang

Dr. Zhang leads the Biostatistics Core at the USF Health College of Nursing. Her research combines multi-omics and clinical information to study disease mechanisms and identify biomarkers, with interests spanning cancer, infectious disease and Alzheimer’s disease.

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Education

• PhD in Molecular Biology, University of Iowa

• MS in Statistics, University of Iowa

• MS in Molecular and Cellular Biology, University of Science and Technology of China

• BS in Biochemistry, Yantai University

Research Interests

• Biostatistics

• Bioinformatics

• Multi-omics

• Machine learning

Projects

How single-cell atlases define progenitor-exhausted T cells
Cancer immunology | Manuscript in preparation

Comparing published CD8+ T-cell atlases and how their definitions affect automated annotation, clonal analyses and clinical associations.

Cross-region gene-expression signatures in Alzheimer’s disease
Neurodegeneration | Published study

Combining brain transcriptomic datasets to identify gene-expression changes associated with Alzheimer’s disease across regions and cohorts.

Role: Conceptualization, methods and manuscript review

Funded Collaborations

Role of the non-canonical inflammasome in SARS-CoV-2-mediated pathology and coagulopathy
Infectious disease | Funded collaboration

Investigating inflammasome biology in SARS-CoV-2 pathology and coagulopathy.

Role: PI and Director, Biostatistics and Bioinformatics Core

Elucidation of human natural killer cell development
Cancer immunology | Funded collaboration

Studying NK-cell development in human tissues and its relationship to endometrial cancer.

Role: Co-investigator / Site PI

Targeting specific microRNA to alleviate Alzheimer’s disease pathobiology
Neurodegeneration | Funded collaboration

Examining microRNA regulation of autophagy and amyloid-beta clearance in Alzheimer’s disease.

Role: Co-investigator / Site PI

Targeting c-Myc stability in c-Myc-overexpressing large B-cell lymphoma
Cancer biology | Funded collaboration

Studying c-Myc regulation, tegavivint treatment and resistance mechanisms in large B-cell lymphoma.

Role: Co-investigator / Site PI

The role of the non-canonical inflammasome in innate immunity
Infectious disease | Funded collaboration

Investigating caspase-11-mediated clearance of Legionella infection.

Role: Co-investigator / Site PI

Host responses to the pore-forming toxin Listeriolysin O
Infectious disease | Funded collaboration

Studying host responses to Listeriolysin O and their relevance to therapies for toxin-mediated disease.

Role: Co-investigator / Site PI

Publications

Shah, K., Littlestone, E., Ahmmad, M., Wang, C., Xu, Y., & Zhang, X. (2026). A cross-region meta-analysis and machine learning identifies a 37-gene signature associated with Alzheimer’s disease. Biomedicines, 14(9), 2032.

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Kim, D., Allen, C. A., Chung, D., Meng, L., Zhang, X., Zhang, W., Ouyang, Y., Li, Z., & Hong, F. (2025). A novel TLR4 accessory molecule drives hepatic oncogenesis through tumor-associated macrophages. Cancer Letters, 217543.

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Brown-Burke, F., Saadey, R., Mao, H. C. C., Marra, P., Brooks, E., Wandtke, A., Hout, I., Leon, S., Sharma, A., Yasin, A., Cash, T., Ahmed, E. H., Baiocchi, E. J., Finoti, S. S., Zhang, X., Bhagwat, N., Vaddi, K., Scherle, P., Mozhenkova, A., … Shindiapina, P. (2025). PRMT5 inhibition reduces hyperinflammation in a murine model of secondary hemophagocytic lymphohistiocytosis (HLH). Blood Advances.

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Majumder, S., Mishra, S., Shinde, N., Cuitino, M. C., Bauer, M., Ahirwar, D., Basree, M. M., Bharti, V., Ormiston, K., Mawalkar, R., Alsammerai, S., Sarathy, G., Vilgelm, A. E., Zhang, X., Ganju, R. K., & Ramaswamy, B. (2025). Divergent paths of mammary gland involution: Unveiling the cellular dynamics in abruptly and gradually involuted mouse models. Breast Cancer Research, 27(1), 1.

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Zhang, X., Huang, Y., Yang, Y., Wang, Q. E., & Li, L. (2024). Advancements in prospective single-cell lineage barcoding and their applications in research. Genome Research.

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Singh, D., Qiu, Z., Jonathan, S. M., Fa, P., Thomas, H., Prasad, C. B., Cai, S., Wang, J. J., Yan, C., Zhang, X., Venere, M., Li, Z., Sizemore, S. T., Wang, Q. E., & Zhang, J. (2024). PP2A B55alpha inhibits epithelial-mesenchymal transition via regulation of Slug expression in non-small cell lung cancer. Cancer Letters, 598, 217110.

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Prislusky, M. I., Lam, J. G. T., Contreras, V. R., Ng, M., Chamberlain, M., Pathak-Sharma, S., Fields, M., Zhang, X., Amer, A. O., & Seveau, S. (2024). The septin cytoskeleton is required for plasma membrane repair. EMBO Reports, 25(9), 3870–3895.

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Kuang, Z., Miao, J., & Zhang, X. (2024). Serum albumin and derived neutrophil-to-lymphocyte ratio are potential predictive biomarkers for immune checkpoint inhibitors in small cell lung cancer. Frontiers in Immunology, 15, 1327449.

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Prasad, C. B., Oo, A., Liu, Y., Qiu, Z., Zhong, Y., Li, N., Singh, D., Xin, X., Cho, Y. J., Li, Z., Zhang, X., Yan, C., Zheng, Q., Wang, Q. E., Guo, D., Kim, B., & Zhang, J. (2024). The thioredoxin system determines CHK1 inhibitor sensitivity via redox-mediated regulation of ribonucleotide reductase activity. Nature Communications, 15(1), 4667.

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Denlinger, N., Song, N. J., Zhang, X., Jeon, H., Peterson, C., Wang, Y., Reynolds, K., Bolz, R. M., Miao, J., Song, C., Wu, D., Chan, W. K., Bezerra, E., Epperla, N., Voorhees, T. J., Brammer, J., Kittai, A. S., Bond, D. A., Sawalha, Y., … Yang, Y. (2024). Postinfusion PD-1+ CD8+ CAR T cells identify patients responsive to CD19 CAR T-cell therapy in non-Hodgkin lymphoma. Blood Advances, 8(12), 3140–3153.

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Liu, Z., Liao, Y., Hwang, C. L., Rethorst, C. D., & Zhang, X. (2024). Associations of online health information seeking with health behaviors of cancer survivors. Digital Health, 10, 20552076241238074.

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About Ethan

Ethan works on cancer immunotherapy, focusing on CD8+ T-cell exhaustion and progenitor-exhausted T cells. He uses single-cell and multi-omics analyses, AI-assisted workflows and research software to compare cell-state definitions and examine their implications for clinical analyses.

He develops reproducible analyses using Python, R, Bash and high-performance computing, with experience using Snakemake and Nextflow.

ORCID      View Résumé

Previous Experience

Before joining USF, Ethan held research appointments in the NIH Critical Care Medicine Department, where he studied fungal protein function and evolution using sequence analysis, Foldseek and AlphaFold. His earlier laboratory work at Rapid Genomics and the University of Florida included sequencing-library preparation, agricultural genotyping and mammalian cell culture for vaccine research.

Education

• MS in Bioinformatics, Northeastern University

• MS in Microbiology and Cell Science, University of Florida

• BS in Microbiology and Cell Science, University of Florida

Research Interests

• Cancer immunotherapy

• Single-cell and multi-omics analysis

• Infectious disease

• Structural bioinformatics

• Machine learning and AI

• Reproducible research software

Projects

How single-cell atlases define progenitor-exhausted T cells
Cancer immunology | Manuscript in preparation

Comparing published CD8+ T-cell atlases and how their definitions affect automated annotation, clonal analyses and clinical associations.

Cross-region gene-expression signatures in Alzheimer’s disease
Neurodegeneration | Published study

Combining brain transcriptomic datasets to identify gene-expression changes associated with Alzheimer’s disease across regions and cohorts.

Role: Manuscript review and editing

Publications

Shah, K., Littlestone, E., Ahmmad, M., Wang, C., Xu, Y., & Zhang, X. (2026). A cross-region meta-analysis and machine learning identifies a 37-gene signature associated with Alzheimer’s disease. Biomedicines, 14(9), 2032.

Read Publication

About Kashvi

Kashvi studies Alzheimer’s disease using transcriptomics, statistical genomics and machine learning.

Her first-author study combined five bulk RNA-sequencing datasets to identify a 37-gene signature associated with the disease across brain regions, examine its relationship with neuropathology and evaluate it in independent cohorts.

ORCID

Previous Experience

Kashvi’s broader experience includes cancer genomics, single-cell RNA sequencing, whole-exome and whole-genome sequencing and ChIP-seq. She develops reproducible analysis workflows with Python, R and Linux-based computing tools.

Education

• MS in Bioinformatics and Computational Biology, University of South Florida

• PharmD, Gujarat Technological University

Research Interests

• Alzheimer’s disease

• Transcriptomics

• Statistical genomics

• Meta-analysis

• Machine learning

• Clinical pharmacy

Projects

Cross-region gene-expression signatures in Alzheimer’s disease
Neurodegeneration | Published study

Combining brain transcriptomic datasets to identify gene-expression changes associated with Alzheimer’s disease across regions and cohorts.

Role: Conceptualization, methods and formal analysis; first author

Publications

Shah, K., Littlestone, E., Ahmmad, M., Wang, C., Xu, Y., & Zhang, X. (2026). A cross-region meta-analysis and machine learning identifies a 37-gene signature associated with Alzheimer’s disease. Biomedicines, 14(9), 2032.

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About Tanner

Tanner works on statistical and genomic analyses of non-small cell lung cancer. His work includes somatic mutation analysis to study clonal hematopoiesis and clinical data collection for immunotherapy biomarker and outcome studies.

He uses R, SAS and Python for data management, statistical analysis and visualization, drawing on his training in applied biostatistics and epidemiology. His broader research experience spans Alzheimer’s disease clinical trials, trauma research and infectious disease surveillance.

Previous Experience

Tanner’s previous research includes analyses of Alzheimer’s disease clinical trial availability, effect sizes reported in biomedical studies and statistical support for trauma research. He also gained experience in infectious disease surveillance through an epidemiology internship with the Florida Department of Health.

Education

• MPH in Applied Biostatistics, University of South Florida

• Graduate Certificate in Concepts and Tools of Epidemiology, University of South Florida

• BS in Public Health, University of South Florida

Research Interests

• Biostatistics

• Cancer genomics

• Immunotherapy outcomes

• Epidemiology

About Dr. Ahmmad

Ethan works on cancer immunotherapy, focusing on CD8+ T-cell exhaustion and progenitor-exhausted T cells. He uses single-cell and multi-omics analyses, AI-assisted workflows and research software to compare cell-state definitions and examine their implications for clinical analyses.

He develops reproducible analyses using Python, R, Bash and high-performance computing, with experience using Snakemake and Nextflow.

View Professional Profile

Education

• PhD in Biostatistics and Data Science, University of Mississippi, 2022

Research Interests

• Biostatistics

• Machine learning

• Cancer epidemiology

• Survival analysis

Projects

Cross-region gene-expression signatures in Alzheimer’s disease
Neurodegeneration | Published study

Combining brain transcriptomic datasets to identify gene-expression changes associated with Alzheimer’s disease across regions and cohorts.

Role: Manuscript review and editing

Publications

Shah, K., Littlestone, E., Ahmmad, M., Wang, C., Xu, Y., & Zhang, X. (2026). A cross-region meta-analysis and machine learning identifies a 37-gene signature associated with Alzheimer’s disease. Biomedicines, 14(9), 2032.

Read Publication