Advancements in AI-Driven Experimental Design and Statistical Validity
This chapter explores the fusion of AI and statistical methods in biostatistics, focusing on its impact in fields like biology and drug discovery. It highlights advancements like target trial emulation and the challenges of predictive model accuracy, while emphasizing the need for next-generation biomarkers to validate drug effectiveness.
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Keynote by Manjari Narayan | The Effective Statistician Conference 2024
What You’ll Learn:
✔ Why the 21st century is truly the "century of biology" and what that means for statisticians
✔ The untapped opportunity for statisticians to innovate before clinical trials begin
✔ How AI-guided experiments are changing drug discovery—and the statistical challenges they bring
✔ Why experimental design and decision quality matter more than ever in biotech
✔ The critical need to rethink biomarker discovery through a counterfactual and regulatory lens
✔ How to increase the validity of translational models and bridge the in vitro-to-in vivo gap
✔ Reflections on career risk-taking, generalism, and increasing your “surface area of luck”
Why You Should Listen:
If you're curious about how artificial intelligence is transforming biotechnology—and what role statisticians can and should play—this keynote is for you. Manjari Narayan offers a rare perspective at the intersection of statistical thinking, AI, and early-stage drug development, showing how rigorous methodology can shape better decision-making in start-ups, research labs, and beyond. Whether you work in pharma, clinical research, or academic science, you'll gain a deeper understanding of how to improve experimental design, validate next-generation biomarkers, and contribute to high-impact innovations before clinical trials even begin. Beyond the science, Manjari also shares powerful insights on career growth, risk-taking, and how to increase your “surface area of luck” by stepping outside your comfort zone and pursuing ambitious problems.
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