Babak Aslani - Healthcare Analytics

Scalable Optimization Frameworks for Large-Scale Decision Analytics in Healthcare

Provide mathematically rigorous frameworks for solving high-dimensional treatment planning and clinical decision problems with millions of variables.
Improves computational efficiency and precision with faster convergence and higher accuracy in optimization-based healthcare analytics compared to state-of-the-art solvers.
Combining first-order and second-order optimization paradigms to enable warm-started interior-point methods that are both scalable and robust for clinical applications.
Integrates advanced optimization techniques with predictive and prescriptive analytics to support evidence-based medical decision-making.
Establishes a scalable optimization foundation applicable to diverse areas such as personalized medicine, resource allocation, and medical operations management.

Software Highlight: warmip — An open-source Python package for warm-started interior-point quadratic programming.

Funding Agency: Department of Medical Physics at MSKCC.

Role: Postdoc Research Fellow

Highlights

Warm-IP: A novel hybrid optimization framework combining Path-Following ADMM with a primal-dual Interior-Point Method (IPM).
Addresses key IPM limitations — improves warm-start capability and early-iteration efficiency.
Path-Following ADMM efficiently generates warm-starts along the central path, enabling fast IPM convergence.
Demonstrated superior performance on real-world and synthetic quadratic programming (QP) benchmarks.
Achieved 11× speedup and 53% less computation time compared to MOSEK, the fastest commercial solver.
Scales to large problems with up to 2 million variables and constraints.
Offers a scalable and accurate framework for optimization in healthcare, control, and operations research.

Publications

• Aslani, B., Tefagh, M., Jhanwar, G., Zarehpisheh, M., (2025). Warm-IP: A Path-Following ADMM Warm-start for Interior-Point Methods. (Submitted to INFORMS Journal on Computing).