Tariq Mohammad

Curriculum Vitae

Tariq Mohammad

ORCID: 0009-0009-1410-2482

Undergraduate researcher applying computational methods, machine learning, and statistical modeling to ecological questions in phenology, evolution, and species distribution. Seeking graduate opportunities in evolutionary genetics and zoology with emphasis on probabilistic and quantitative approaches.

Education

University of Arizona Expected May 2026

  • Bachelor of Science in Statistics and Data Science
  • Bachelor of Science in Mathematics, Applied Emphasis
  • Minor in Computer Science

Publications

  • Mohammad, T., Guralnick, R.P., Santiago-Blay, J.A., Crimmins, T.M. (in review). Hybrid machine learning approaches outperform mechanistic models of bloom timing in Eastern Redbud, Cercis canadensis.
  • Mohammad, T., Wiens, J. J., Jezkova, T. (in preparation). Do Presence-Only SDMs Overstate Extirpation?
  • Liang, Z., Mohammad, T., Rodstrom, O., Wang, J., Campbell, Z., Stephenson, B., Sulyok, C. (in preparation). Mathematically Modeling the Transmission Dynamics of Community-Associated Clostridioides difficile Infection.

Conferences

  • Liang, Z.; Mohammad, T. (presenting author); Rodstrom, O.; Wang, J.; Campbell, Z.; Stephenson, B.; Sulyok, C. (January 6, 2026) Mathematically Modeling the Transmission Dynamics of Community-Associated Clostridioides difficile Infection. Invited abstract, AMS Special Session Oral on Polymath Jr Student Research Session I, Joint Mathematics Meetings (JMM 2026), Washington D.C.
  • Rodstrom, O. (presenting author); Mohammad, T. (presenting author); Liang, Z.; Wang, J.; Sulyok, C.; Stephenson, B.; Campbell, Z. (January 6, 2026) Modeling Age-Structured Transmission of Community-Associated Clostridioides difficile Infection. Poster (AMS–PME Undergraduate Student Poster Session I), JMM 2026, Washington D.C.
  • Mohammad, T. (November 1, 2025) Hybrid Phenological Modeling: Combining Chill-Heat Dynamics with Machine Learning in Cercis canadensis. Poster, RISE 21st Annual Symposium, Tucson, AZ.
  • Mohammad, T. (October 23, 2025) A Hybrid Mechanistic–Machine Learning Model of Flowering Phenology in Eastern Redbud (Cercis canadensis). Oral, UR Inspiration / Inspiración Conference, Tucson, AZ.
  • Mohammad, T. (April 24, 2025) Competing for Spring: Random-Forest Modeling of Leaf-Out in Invasive Siberian Elm and Native Eastern Cottonwood. Poster, ALVSCE Research Showcase, Tucson, AZ.

Research Experience

Undergraduate Researcher (University of Arizona)

Aposematic Color Mapping in Oophaga pumilio via Computer Vision (RT-DETR + SAM-HQ) (Sep 2025 – Present)

  • Conducting a large-scale analysis of aposematic color variation focusing on the widespread polymorphism of O. pumilio, utilizing RT-DETR for detection, SAM-HQ for masking, and HSV/L*a*b* color metrics to quantify morph distributions from >7,000 curated iNaturalist images; refining precision via unsupervised clustering (k-means on ResNet embeddings, hierarchical methods).

Continental-Scale Species Distribution Modeling for North American Lizards (Spring 2025 – Present)

  • Developing SDMs for ~110 North American lizard species using GBIF and WorldClim BIO1–BIO19; presence-only MaxEnt pipeline (R/ENMeval, maxnet; terra, sf) with accessible area scaling, dynamic thinning (spThin), VIF screening, target-group background, 5-fold CV, and thresholded transfer tests to assess extirpation bias. Manuscript in preparation.

Hybrid Mechanistic–Machine Learning Phenology for Eastern Redbud (Fall 2024 – Present)

  • Built a 14-parameter chill–photoperiod–forcing core fit via Differential Evolution → Nelder–Mead; predicted flowering for Cercis canadensis, then corrected with LightGBM on PRISM 800 m daily climate. Site-blocked 5-fold CV reduced MAE from ~7.3 d to 6.1 d (~15%); performance held in herbarium/iNat backcasts. Under review at International Journal of Biometeorology.

Predator–Prey Cellular Automaton for Dynamical Systems Exploration (Mar 2025 – May 2025)

  • Implemented a deterministic 2-D toroidal CA (NumPy) with Moore-neighborhood diffusion: logistic prey growth; predators disperse toward prey-rich neighbors, consume via Holling-II, update by survival/reproduction; analyzed steady states, cycles, bifurcations.

Option Pricing Benchmarks: Black–Scholes, IV Surface, and ML Regressors (Jan 2025 – May 2025)

  • Built constant-σ and IV-surface Black–Scholes pricers (py_vollib) and ML baselines (RF, MLP) with features (moneyness, time-to-expiry, bid–ask spread). OOS: Black-Scholes R²≈0.49 (RMSE≈$18) → IV-surface R²≈0.91 (RMSE≈$7.6) → RF/MLP R²≈0.98/0.99 (RMSE≈$3.5/$2.3).

Polymath Jr. (Research Experience for Undergraduates)

Age-Structured ODE Model of Community Transmission (Antibiotics + Environment) (May 2025 – Present)

  • Contributing to a 15-ODE dynamic system for community-associated C. difficile transmission across infants, adults, elderly; includes antibiotic use, environmental reservoirs, person-to-person transmission; simulations with priors and uncertainty via sensitivity analysis.

Awards

  • Dean’s List (Full-Time), Spring 2025 (18 credits).
  • Dean’s List with Distinction (Part-Time), Summer 2025 (10 credits).

Skills

Programming Languages: Python, Java, R, Julia, Prolog, Haskell, Rust

Core Packages: NumPy/pandas/SciPy, scikit-learn/LightGBM, GLM/GLMM/GAM, ENMeval/maxnet, geopandas/rasterio/GDAL, OpenCV/YOLOv8/SAM, matplotlib/seaborn/ggplot2

Tools: LaTeX, MATLAB, QGIS/ArcGIS, Google Earth Engine, GDAL