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
• Evaluation of LLMs and AI agents

Latest Papers

The five most recent. All publications →

1. A Product-Grassmann Tucker Surrogate for Parametric Field Prediction

   Melika Baghi, Xiao Liu, Kamran Paynabar

   Submitted to the Journal of Computational Physics · arXiv:2610.34559

   Paper

2. Pooling and Drift in Delayed Bandits

   Melika Baghi

   Under review · arXiv:2609.01761

   Paper Code

3. Conditional Validity for Adaptive Modality Acquisition: When the Policy Chooses Its Own Calibration Group

   Melika Baghi

   Under review · arXiv:2608.15520

   Paper Code

4. Managing Shared Hospital Capacity for Medical Tourism: Patient Behavior and Hybrid Simulation

   Melika Baghi, Hadi Mosadegh

   Under review, Health Care Management Science · arXiv:2605.19139

   Paper Code DOI

5. Constrained Extreme Gradient Boosting for Parametric Proper Orthogonal Decomposition Subspace Prediction

   Melika Baghi, Xiao Liu, Kamran Paynabar

   Under review · arXiv:2605.04130

   Paper Code

Experience

• 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.

• 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.

• 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 channel choice, physician switching, treatment abandonment and deterioration. A 256-run fractional factorial design found bed capacity the most consistent driver of waiting, and against an otherwise identical discrete-event-only model the hybrid model's mean inpatient admission wait was 42% lower (14.8 against 25.8 days). Quantified patient flow, abandonment, emergency escalation and resource utilisation.

• 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.

• 2019 – 2020

  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

• 2023 – 2027 (expected)

  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

• 2023 – 2026

  M.S., Statistics, Georgia Institute of Technology

• 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

• 2018 – 2021

  B.S., Industrial Engineering, Amirkabir University of Technology

  GPA 3.91/4.0

• 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 TensorFlow Hugging Face Transformers LLM evaluation Retrieval-augmented generation

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 Gurobi Pyomo

Tools & computing

Git Docker Linux AWS Azure Cloud computing GPU / HPC SLURM Airflow MLflow Power BI Tableau LaTeX

Research methods

Conformal prediction Bandits Tensor methods Reduced-order modeling CFD FEA LLM-based information extraction