CV

Education

• PhD candidate, Chemical Engineering, Yale University, 2022–present
• BS, Chemical Engineering, University of Tehran

Research

Graduate Researcher, Computational Soft Matter Group — Yale University

• Study homogeneous and heterogeneous ice nucleation using molecular dynamics and rare-event sampling.
• Investigate the interactions between antifreeze proteins and ice-water interfaces.
• Apply high-throughput analysis and machine-learning methods to improve reaction coordinates for molecular simulation.

Selected leadership and service

• STEM Career Fellow, Yale Office of Career Strategy
• Advanced Graduate Leadership Program, Yale Engineering
• Graduate Professional Experience Fellow, Yale Office of International Students & Scholars
• Alumni Chair, Graduate Society of Women Engineers (GradSWE), Yale University

Methods and tools

• Molecular dynamics and rare-event simulation
• Statistical thermodynamics and soft-matter modeling
• High-throughput data analysis and machine learning
• Scientific computing with Python

Skills

• Programming: Python, C++, MATLAB, Linux, Bash
• Scientific Computing: GROMACS, LAMMPS, PLUMED, VMD, PyMOL, Molecular Dynamics, Replica Exchange MD, Umbrella Sampling, Monte Carlo
• Data & ML: AlphaFold, scikit-learn, PyTorch, data pipelines, SHAP, feature engineering, model evaluation
• Infrastructure & Workflow: HPC, SLURM, job arrays, reproducible analysis pipelines, workflow automation, file-schema standardization, performance profiling

Publications

The impact of hydration shell inclusion and chain exclusion in the efficacy of reaction coordinates for homogeneous and heterogeneous ice nucleation

K. Sinaeian and A. Haji-Akbari (2025). "The impact of hydration shell inclusion and chain exclusion in the efficacy of reaction coordinates for homogeneous and heterogeneous ice nucleation." The Journal of Chemical Physics 162, 164102.