Summary
Marko Sterbentz is a CS PhD candidate at Northwestern University with a decade of experience building systems that apply AI and large language models to automate data science and deliver contextualized insights from large relational databases. His research blends text-to-query models, planner/reasoner architectures, and representations of data-analytic knowledge to let users ask natural questions and receive grounded, actionable answers. He has practical research experience across national labs and industry, having implemented high-performance C++/Python components for scientific HPC, immersive visualization tools, and reinforcement learning methods for improving LLM reasoning during an IBM research internship. Based in San Francisco, he combines rigorous academic training with hands-on software engineering—shipping unit-tested, documented code and contributing to collaborative research projects. Beyond model design, he focuses on evaluation and steering of generative systems, aiming to make their outputs reliably useful for downstream analytic tasks.
10 years of coding experience
2 years of employment as a software developer
Master of Science - MS, Computer Science, Master of Science - MS, Computer Science at University of Southern California
Bachelor of Science - BS, Computer Science, Bachelor of Science - BS, Computer Science at Idaho State University
Doctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at Northwestern University
English, German