Kian Maleki, PhD

Professional Summary

Physics PhD and machine learning practitioner focused on data science, MLOps, generative AI, RAG, and document intelligence. Experienced in Python-based modeling, end-to-end ML workflows, FastAPI services, reproducible pipelines, experiment tracking, drift monitoring, and high-dimensional scientific computing.

Technical Expertise

• Modeling: GenAI/RAG, experiment tracking, data versioning, drift monitoring, HPC, Monte Carlo simulations, Bayesian statistics.
• Engineering: Python, SQL, FastAPI, Pydantic, Docker, Linux/Bash, Git, GitHub Actions, Jenkins.
• Numeric and ML Stack: Pandas, NumPy, Polars, Numba, Scikit-Learn, Transformers, TensorFlow, PyTorch.
• MLOps: DVC, MLflow, Evidently, pytest, model registry, automated testing, performance gates, AWS S3/EC2.

Technical Experience

TripleTen | AI & Machine Learning Program 2026

• Completed supervised and unsupervised learning projects using Scikit-Learn for real-world ML tasks.
• Built deep learning systems for computer vision and NLP workflows.
• Completed 15+ end-to-end ML projects covering preprocessing, training, evaluation, and deployment.

Extern / Pfizer Context | AI-Powered Document Intelligence Externship 2026

• Built an AI-powered document intelligence pipeline for healthcare-document processing.
• Developed retrieval-augmented workflows to improve accuracy and reduce hallucinations in document-based question answering.
• Delivered a functional prototype demonstrating AI-driven automation for healthcare supply-chain operations.

Data-Driven Projects

RAG-Powered Knowledge Assistant 2026

• Built a local RAG system for document question answering using Transformers and PyTorch.
• Improved answer relevance with sliding-window chunking and overlap to preserve semantic continuity.
• Containerized the application with Docker for reproducible local execution.

Heart Disease Prediction: End-to-End MLOps Pipeline 2026

• Built a reproducible ML pipeline using DVC, MLflow, model registry, pytest, GitHub Actions, and Evidently.
• Enforced automated performance gates requiring accuracy above 0.80 before deployment.
• Implemented data and concept drift monitoring to support proactive retraining decisions.

SDF Document Information Extraction | Pfizer Externship Project 2026

• Extracted regulatory information from SDF documents using text parsing, regex, NLP, and Pandas.
• Converted semi-structured document content into structured datasets for downstream processing.

House Price Prediction API Service 2025

• Built a REST API for real-time house price prediction using FastAPI, Pydantic, Uvicorn, Scikit-Learn, and NumPy.
• Implemented schema validation and interactive API documentation with Swagger and ReDoc.

Professional Experience

Creighton University | Assistant Professor of Physics Aug 2025 – Present

• Teach undergraduate physics courses and design applied lab curricula.
• Develop interdisciplinary lab curriculum integrating AI tools and chatbots into scientific workflows.
• Supervise and mentor 1–5 teaching assistants.

University of Iowa | Graduate Research Assistant Aug 2018 – Aug 2025

• Optimized large-scale numerical simulations, reducing computational runtime from days to under one hour.
• Developed high-dimensional modeling frameworks for complex physical systems.
• Analyzed noisy experimental data using statistical inference and optimization techniques.

Creighton University | Graduate Research Assistant Aug 2015 – Aug 2018

• Developed Python and Fortran simulations, including N-body and molecular dynamics models.
• Engineered parallel computation pipelines using HPC resources.

Education

• University of Iowa — PhD in Physics, GPA 4.03
• Creighton University — MS in Physics, GPA 4.00
• TripleTen — AI & Machine Learning Program

Selected Publications

• Maleki, K. et al. “Crystal fields, exchange and dipolar interactions...” Physical Review B, 2025.
• Maleki, K. et al. “A General and Modular Approach to Solid-State Integration...” Nano Letters, 2025.