Nuzla Ismail.

Computational Biology · Bioinformatics

Nuzla Ismail

PhD · Postdoctoral Fellow, Tulane University School of Medicine

Buffalo, NY, USA · open to faculty, scientist & industry roles

I build computational methods — co-expression networks, genome graphs, and machine learning — that turn high-dimensional genomic data into translational insight.

Download CV Email Google Scholar ORCID GitHub
gene co-expression network · modules

Research

What I work on

My work sits at the intersection of computational methods, big data, and translational biomedical science. I model gene regulation and disease dynamics from large genomic datasets — and I build the reproducible tooling that makes those analyses portable and repeatable. Lately my focus has been moving toward single-cell and spatial transcriptomics in mucosal immunology and infectious disease.

Gene co-expression networks · WGCNA, GTEx v10 Pangenome graphs · variant identification Single-cell & spatial · Seurat, Scanpy Machine learning · deep learning, XAI Mucosal immunology · COVID-19, RSV, IBD Reproducible workflows · containers, Nextflow Foundation models for genomics Regulatory genomics

Publications

Selected work

A selection across genomics, networks, and machine learning. The full record — including citation counts — lives on Google Scholar.

Peer-Reviewed Journal Articles

  1. Deep learning for regulatory genomics: a survey of models, challenges, and applications Ismail, F. N., Sengupta, A., & Amarasoma, S. — Bioinformatics Advances 6(1), vbaf271 · 2026 · doi
  2. AI-driven methane emission prediction in rice paddies: a machine learning and explainability framework Sengupta, A., Ismail, F. N., & Amarasoma, S. — Methane 4(4), 28 · 2025 · doi
  3. MARS: simplifying bioinformatics workflows through a containerized approach to tool integration and management Ismail, F. N., & Amarasoma, S. — Bioinformatics Advances 5(1), vbaf074 · 2025 · doi
  4. Atmospheric modeling for wildfire prediction Ismail, F. N., Woodford, B. J., & Licorish, S. A. — Atmosphere 16(4), 441 · 2025 · doi
  5. An assessment of existing wildfire danger indices in comparison to one-class machine learning models Ismail, F. N., Woodford, B. J., Licorish, S. A., & Miller, A. D. — Natural Hazards · 2024 · doi
  6. Pangenome graphs in infectious disease: a comprehensive genetic variation analysis of Neisseria meningitidis Yang, Z., Guarracino, A., Biggs, P. J., Ismail, F. N., et al. — Frontiers in Genetics 14, 1225248 · 2023 · doi
  7. One-class classification-based machine learning model for estimating the probability of wildfire risk Ismail, F. N., & Amarasoma, S. — Procedia Computer Science 222, 341–352 · 2023 · doi
  8. Recent trends and tools in pangenome graph analysis: a comprehensive review Ismail, F. N., & Amarasoma, S. — Pathogens (MDPI) in press

Selected Conference Proceedings

  1. An integrated genomics workflow tool: simulating reads, evaluating read alignments, and optimizing variant calling algorithms Ismail, F. N., & Amarasoma, S. — 12th ICBCB, 49–56 · 2024 · doi
  2. A comparison of one-class versus two-class machine learning models for wildfire prediction in California Ismail, F. N., Sengupta, A., Woodford, B. J., & Licorish, S. A. — AusDM, Springer, 239–253 · 2023 · doi
  3. Evaluating the boundaries of big data environments for machine learning Ismail, F. N., Woodford, B. J., & Licorish, S. A. — AI 2019: Advances in Artificial Intelligence, Springer, 253–264 · 2019 · doi

Under Review / In Preparation

  1. Generative AI for bioinformatics: foundations, applications, tools, and future directions Ismail, F. N., & Amarasoma, S. — Briefings in Bioinformatics under review
  2. Revolutionising bacterial genomics: graph-based strategies for improved variant identification Ismail, F. N., & Sengupta, A. — 12th Intl. Symp. on Applied Computing for Software and Smart Systems · arXiv:2505.07919
  3. Big data blueprint architecture for large organisations Ismail, F. N., Sengupta, A., & Amarasoma, S. — 24th Industrial Conf. on Data Mining · arXiv:2505.04717
  4. The power of one-class classification models in wildfire risk prediction Ismail, F. N., Woodford, B. J., & Licorish, S. A. — International Journal of Wildland Fire under review

Experience

Appointments

Dec 2025 – Present

Postdoctoral Fellow — Gastroenterology & Hepatology

Tulane University School of Medicine, New Orleans, LA (remote)
  • Investigate mucosal immunology in infectious and gastrointestinal disease (COVID-19, RSV, IBD, colorectal cancer).
  • Apply single-cell and spatial transcriptomic approaches to disease pathogenesis and therapeutic targets.
Jan 2025 – Nov 2025

Postdoctoral Research Fellow — Department of Mathematics

State University of New York at Buffalo, NY
  • Applied bioinformatics and computational biology to dissect complex genetic networks.
  • Built predictive models for disease dynamics using advanced statistical and ML methods.
Mar 2022 – Oct 2023

Postdoctoral Research Fellow — Department of Biochemistry

University of Otago, Dunedin, New Zealand
  • Developed and evaluated genome graph analytic tools; enhanced structural variant detection in admixed populations.
  • Led genetic variation analysis of Neisseria meningitidis; built genome graphs for the endangered kākāpō.

Teaching

Teaching & mentorship

Certified Carpentries Instructor (2023–present) across Data, Library & Software Carpentry — evidence-based instruction in data analysis, programming, and reproducible research. Founder and past president of the Auston Robotics Club, mentoring teams to the World Robot Olympiad.

Education

  • PhD, Information Science — University of Otago2022
  • BEng (Hons), Software Engineering — Staffordshire University2015
  • BSc, Applied Science — University of Sri Jayewardenepura2015

Recognition

Awards & grants

  • UUP Individual Development Award2025
  • NITMB Convergence Conference Travel Award2025
  • ACM Richard Tapia Diversity in Computing Award2017
  • University of Otago Doctoral Scholarship2017
  • Google Anita Borg Memorial Scholarship2014
  • CERN Port Humanitarian Hackathon Scholar2016

Toolkit

Languages
Python, R, Unix, SQL, Java, C/C++
Bioinformatics
Genome & pangenome graphs, WGCNA, GTEx workflows, variant calling, Seurat, Scanpy
ML / Data
Deep learning, XAI, ensembles, Docker, Singularity, Nextflow, HPC

Contact

Let's work together

I'm exploring faculty, scientist, and industry roles in computational biology, genomics, and single-cell / spatial transcriptomics. Reach out about collaborations, positions, or talks.

hello@nuzlaismail.net Google Scholar ORCID GitHub
© Nuzla Ismail, PhD · Buffalo, NY