About

Melika Baghi is a Ph.D. Candidate in Industrial and Systems Engineering at the Georgia Institute of Technology. She holds a Master of Science degree in Statistics from Georgia Tech, completed in 2026. Her academic training spans industrial engineering, aerospace engineering, and statistics, providing a strong interdisciplinary foundation in data science, optimization, and probabilistic modeling.

Melika's research focuses on machine learning for high-dimensional and multimodal systems, with an emphasis on reduced-order modeling, representation learning, and uncertainty quantification. Her work aims to develop interpretable and computationally efficient data-driven models for complex engineering and scientific applications, including aerospace systems, healthcare operations, and decision-support problems. Her recent papers cover adaptive acquisition of missing modalities with conformal guarantees and sequential decision-making under delayed feedback.

She earned dual bachelor's degrees in Aerospace Engineering and Industrial Engineering, followed by a Master of Science degree in Industrial Engineering with a focus on healthcare systems, from Amirkabir University of Technology. Her master's research produced a hospital simulation that reduced medical tourists' waiting time by 82 percent against an otherwise identical discrete-event-only model, work now under review at Health Care Management Science. Throughout her academic career she has received multiple competitive fellowships and honors, most recently the Most Innovative Approach Award in the NSF Future Manufacturing Data Challenge.

In addition to her research, Melika is actively involved in teaching and mentorship. She currently serves as Head Teaching Assistant for ISyE 6525: High-Dimensional Data Analytics, a graduate course of more than 150 students each semester, where she supports instruction in statistical learning, dimensionality reduction, and interpretable machine learning. She was named Outstanding Online TA in 2025.