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 the 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.

Read the Publication

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

P01AI175399 (MPI: Amer/Cormet-Boyaka/Li, OSU)              
04/10/2024 - 03/31/2029
NIAID
Title: Role of the non-canonical Inflammasome in SARS-CoV-2-mediated pathology and coagulopathy. Major Goals: This work is expected to broadly impact the development of vaccines and novel therapeutic strategies for a wide range of diseases in which pore-forming toxins are employed by pathogens.
Role: PI and director for the Biostatistics and Bioinformatics Core  

R01CA208353 (PI: Freud, OSU)                                                       
05/01/2023 - 04/30/2028
NIH/NCI
Title: Elucidation of Human Natural Killer Cell Development
Major Goals: Aim 1. To define NK cell and ILC1 developmental pathways in human tissues. Aim 2. To determine how NK cell development and function are shaped by human endometrial cancer.
Role: Co-I/Site PI 

R01AG082113 (PI: Amer, OSU)                                                         
04/01/2023-03/31/2028
NIH/NIAID
Title: Targeting specific MicroRNA to alleviate Alzheimer’s Disease pathobiology
Major Goals: To examine how specific small non-coding RNAs (miRNA) alter expression of key proteins that participate in autophagy process and we will test whether reducing the expression of a specific miRNA can correct autophagy, restore degradation of Aβ, and reduce AD pathobiology.
Role: Co-I/Site PI

R01CA266682 (PI: Alinari, OSU)                                                      
04/01/2022-03/31/2027
NIH/NCI
Title: Targeting c-Myc stability in c-Myc overexpressing large B-cell lymphoma.
Major Goals: To better understand the TBL1-modulated mechanism through which c-Myc turnover is regulated, to develop a novel therapeutic strategy (tegavivint) to treat this incurable disease, and to begin to characterize resistance mechanisms to tegavivint.
Role: Co-I/Site PI 

R01AI159452 (PI: Dr. Amer, OSU)                                                   
06/11/2021- 5/31/2026
NIH/NIAID
Title: The role of the non-canonical inflammasome in innate immunity 
Major Goals: This proposal will investigate the molecular mechanisms leading to caspase-11-mediated clearing of Legionella infection.
Role: Co-I/Site PI 

R01AI157205 (PI: Dr. Seveau, OSU)                                               
06/11/2021 - 05/31/2026
NIH/NIAID
Title: Host responses to the pore-forming toxin Listeriolysin O
Major Goals: This work is expected to broadly impact the development of vaccines and novel therapeutic strategies for a wide range of diseases in which pore-forming toxins are employed by pathogens.
Role: Co-I/Site PI 

*Corresponding or senior author 

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 Lett, 217543. PMID: 39929433. 

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 Adv. https://doi.org/10.1182/bloodadvances.2024013651. PMID: 39825858. 

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 Res, 27(1), 1. doi: 10.1186/s13058-024-01933-3. PMID: 39754221. 

*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 Res. https://doi.org/10.1101/gr.278944.124  

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 Lett, 598, 217110. https://doi.org/10.1016/j.canlet.2024.217110 

 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 Rep, 25(9), 3870-3895. https://doi.org/10.1038/s44319-024-00195-6  

#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. Front Immunol, 15, 1327449. https://doi.org/10.3389/fimmu.2024.1327449  

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. Nat Commun, 15(1), 4667. https://doi.org/10.1038/s41467-024-48076-9  

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 Adv, 8(12), 3140-3153. https://doi.org/10.1182/bloodadvances.2023012073  

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. Digit Health, 10, 20552076241238074. https://doi.org/10.1177/20552076241238074  

Xiaoli Zhang, PhD, MS
Professor
Director, Biostatistics Core
USF Health College of Nursing
12901 Bruce B. Downs Blvd., MDC 22 | Tampa, FL | 33612
O: 813-905-1067 
xiaoli18@usf.edu