Melika Baghi
Ph.D. Candidate, Industrial & Systems Engineering
Data Science · Machine Learning · Statistical Modeling
H. Milton Stewart School of Industrial & Systems Engineering, Georgia Institute of Technology
I develop machine learning and statistical methods for high-dimensional, structured, and incomplete data, spanning applied ML, uncertainty quantification, scientific computing, and healthcare analytics. I care about models that are not only accurate but reliable enough to support real decisions.
I am a Ph.D. Candidate in Industrial & Systems Engineering at Georgia Tech, advised by Professor Kamran Paynabar and Professor Xiao Liu. I hold an M.S. in Statistics from Georgia Tech and an M.S. in Industrial Engineering with a focus on Healthcare Systems from Amirkabir University of Technology.
Open to Data Science, Machine Learning, Applied Scientist and Research Engineering internships across technology, healthcare and other data-intensive applications. Get in touch.
Research Interests
- High-dimensional statistical learning
- Uncertainty quantification and conformal prediction
- Multimodal learning and missing data
- Sequential decision making and bandits
- Reduced-order and surrogate modeling
- Tensor and geometry-aware learning
- Scientific machine learning
Latest Papers
The four most recent. All publications →
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Pooling and Drift in Delayed Bandits
Melika Baghi
ML×OR 2026 Workshop (non-archival) · arXiv:2609.01761
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Conditional Validity for Adaptive Modality Acquisition: When the Policy Chooses Its Own Calibration Group
Melika Baghi
Under review · arXiv:2608.15520
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Reducing Waiting Time for Medical Tourists Through Hybrid Agent-Based and Discrete-Event Simulation: A Hospital Case Study
Melika Baghi, Hadi Mosadegh
Under review, Health Care Management Science · arXiv:2605.19139
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Constrained Extreme Gradient Boosting for Adapting Reduced-Order Models
Melika Baghi, Xiao Liu, Kamran Paynabar
Under review · arXiv:2605.04130
Experience
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2023 – Present
Graduate Research Assistant, Georgia Institute of Technology
H. Milton Stewart School of Industrial & Systems Engineering · Advisors: Kamran Paynabar, Xiao Liu
Develop machine learning and statistical methods for high-dimensional data: gradient boosting, deep learning, uncertainty quantification, multimodal learning and surrogate modeling. Design and evaluate end-to-end ML pipelines in Python, PyTorch and scikit-learn across healthcare, scientific computing and simulation datasets, and carry projects from problem formulation through experimentation, statistical validation, reproducible code and publication.
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2022 – 2023
Course Instructor, Artificial Intelligence Laboratory
Royan Institute, Tehran
Designed and delivered a project-based AI curriculum covering rule-based expert systems, hybrid architectures, and applied machine learning in clinical healthcare contexts. Supervised student projects from problem formulation through evaluation.
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2021 – 2023
Graduate Research Assistant, Amirkabir University of Technology
Healthcare Systems Engineering, Tehran · Advisor: Hadi Mosadegh
Built a hybrid agent-based and discrete-event simulation in AnyLogic of a hospital serving medical tourists and local patients, modelling patient behaviour, resource sharing, admissions and capacity constraints. Designed and evaluated a compatible-section bed-sharing policy: medical tourists' hospital-queue wait fell 82% and the inpatient admission queue 43% against a discrete-event-only baseline. Quantified patient flow, abandonment, emergency escalation and resource utilisation.
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2018 – 2020
Undergraduate Research Assistant, Amirkabir University of Technology
Machine Learning & Simulation, Tehran · Advisor: Mehdi Sabzeh Parvar
Built a physics-based simulator in MATLAB/Simulink from system velocity equations and operational constraints, generated training and test data across operating scenarios, and implemented machine learning models in Python, including a three-layer neural network, to predict system behaviour and expand the feasible operating range under hybrid inputs.
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2018 – 2019
Research Intern, R&D Division
Arvand Pishro (Automotive R&D), Safadasht, Iran
Designed and validated a closed-loop fuel tank cooling and monitoring system integrating sensor feedback and automated temperature control.
Education
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2023 – Present
Ph.D., Industrial & Systems Engineering (System Informatics & Control), Georgia Institute of Technology
Advisors: Kamran Paynabar, Xiao Liu · Dissertation: Physics-Based Machine Learning for Fast Prediction of Engineering Simulations
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2023 – 2026
M.S., Statistics, Georgia Institute of Technology
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2021 – 2023
M.S., Industrial Engineering (Healthcare Systems), Amirkabir University of Technology
GPA 4.0/4.0 · Advisor: Hadi Mosadegh · Thesis on scheduling and patient flow for medical tourists · Direct entry as an elite student, ranked 3rd of 120 applicants
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2018 – 2021
B.S., Industrial Engineering, Amirkabir University of Technology
GPA 3.91/4.0
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2016 – 2020
B.S., Aerospace Engineering, Amirkabir University of Technology
GPA 3.79/4.0 · Selected as an elite student and approved to pursue both degrees concurrently
Technical Skills
- Programming & data
- Python SQL R PySpark NumPy pandas SciPy MATLAB
- Machine learning
- scikit-learn XGBoost PyTorch Deep learning Representation learning Multimodal learning Diffusion models
- Statistics & modeling
- Statistical modeling Nonparametric statistics Gaussian processes Design of experiments Uncertainty quantification Calibration
- Optimization & simulation
- Mixed-integer programming AnyLogic (ABS + DES) Arena Simulink Monte Carlo GAMS
- Tools & computing
- Git Linux AWS Cloud computing GPU / HPC SLURM LaTeX
- Research methods
- Conformal prediction Bandits Tensor methods Reduced-order modeling CFD FEA LLM-based information extraction