MaryLena Bleile
MaryLena Bleile

MaryLena Bleile, Ph.D

Statistician

Control Theory, Causality, & Agentic AI (Reinforcement Learning)

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I'm a statistician who builds bridges between causal inference, control theory, and AI. I am the author of Optimal Control Using Causal Agents, forthcoming under contract with CRC Press; see the "Book" tab for more information.

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Previously I served as a biomarker study statistician at Sanofi, where I led a multidisciplinary methodological initiative for improving the efficiency of 'omics analysis. I have also had the pleasure of working in the Epi/Bio department at Memorial Sloan-Kettering Cancer Center as well as the AI and Automation lab at UT Southwestern Medical Center, where I performed my Ph.D research.

When I'm not writing or coding, you can usually find me on the mat doing Brazilian Jiu Jitsu, or lifting weights at the gym. You can also read my unserious math blog here.

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Recognition

  • Nominee, Person of the Year and Best New Book, Best of Causality 2025 community survey
  • Rising Star in AI: Young Role Model of the Year Finalist, North America Division, Women in AI (2025)
  • Biopharmaceutical Section Award, American Statistical Association
  • Organizer, Workshop on Causal Reinforcement Learning (2025), sponsored by CRC Press, Springer, and Morph
  • 15+ publications in Nature Communications, JNCI, Mathematical Control and Related Fields, and others
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Hire Me

I’m selectively open to translational roles in AI research, applied statistics, and quantitative methods. I’m particularly interested in roles where causal inference, reinforcement learning, and decision-making under uncertainty intersect.

What you get

I find structure and deep connections between concepts that other people miss. My career has been built on technical bridges: I unify concepts and fields that typically don’t talk to each other, from financial modelling to -omics pipelines and control systems. This has produced a forthcoming CRC Press monograph nominated as “Best Book in Causality of 2025”, 15+ publications including Nature Communications, FDA-cleared ML device work, and a multi-departmental methodological initiative at Sanofi that changed how teams approached high-dimensional biomarker analysis.

I’m a philosopher at heart and a writer by compulsion. My first published paper was in music theory, I’ve written at least a dozen magazine articles, and there was poetry in the appendix of my dissertation on Bayesian decision-making systems for radiotherapy optimization. I’m interested in many things, and my creative tendencies harmonize with my technical expertise. That combination is rare.

Where I do my best work

Big-picture roles. I thrive in projects where I can see the full architecture of a problem: how the research connects to the engineering connects to the decision. The more context I have, the more unexpected connections I’ll find for you.

Teams that sharpen each other. I want frequent code reviews, direct feedback, and collaborators who will unflinchingly (and kindly!) tell me when I’m wrong. I produce my best work when the people around me are rigorous and generous in equal measure.

Rooms full of weird thinkers. The colleagues I’ve learned the most from are the ones who take longer and arrive somewhere no one expected. I’m looking for teams that select for that kind of mind.

How to evaluate me

I love talking about real problems. Ask me to walk through a piece of my published work, pair-program on a challenge your team is actually facing, present my research, or design a solution to an open question in your domain. That’s where you’ll see what I can do.

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