Humza Haider

Data-driven, entrepreneurial, and product focused employee. Experienced in taking products from ideas to performant, maintainable, and scalable software. Excited by all things software engineering with proficiencies in both Golang and Python.

• Using Survival Prediction Techniques to Learn Consumer-Specific Reservation Price Distributions: PLOS ONE (April 2021)
• Effective Ways to Build and Evaluate Individual Survival Distributions: Journal of Machine Learning Research (June 2020)
• The Pre-Registry Commercial Driver Medical Examination Screening Sensitivity and Certification Lengths for Two Safety-Related Medical Conditions: Journal of Occupational and Environmental Medicine (March 2020)
• Employer-mandated obstructive sleep apnea treatment and healthcare cost savings among truckers: Sleep (October 2019).
• Simultaneous Prediction Intervals for Patient-Specific Survival Curves: International Joint Conference on Artificial Intelligence (2019)
• Longitudinal Analysis of CAD/CAM Restoration Incorporation Rates into Navy Dentistry : Military Medicine (October 2018)
• Incorporating Allee effects into the potential biological removal level: Natural Resource Modeling (July 2017)

• Survival Evaluation, Developed a Python package (available on PyPi) for three different evaluation functions for survival analysis models. These functions were missing from other available packages and tie into my master's thesis on how to properly evaluate survival distributions.

• MTLR, Developed an R package (available on CRAN) for Multi-task Logistic Regression (MTLR). Uses of MTLR include building individual survival curves for estimating survival time, employee attrition, customer churn, and other areas based on time to event data. This package has over 20,000 downloads to date.