Caleb Class is an Assistant Professor and accomplished data scientist with 16 years of experience applying computational chemical engineering and bioinformatics to real-world problems. He earned a PhD from MIT where he built predictive kinetic models and experimental workflows, then transitioned to translational research at MD Anderson developing NGS-focused pipelines, interactive visualization tools, and the iDINGO R package for differential network inference. Now in academia at Butler University, he blends teaching with collaborative translational projects that bridge clinicians, biologists, and statisticians. Caleb’s work spans from reaction mechanism generation to multi-omics integration, and he brings a practical mindset for turning complex data into intuitive tools. Outside the lab he enjoys coding, puzzling, and running—though he’s adamantly not a fan of beets.
16 years of coding experience
7 years of employment as a software developer
Doctor of Philosophy (PhD), Chemical Engineering, Doctor of Philosophy (PhD), Chemical Engineering at Massachusetts Institute of Technology
Bachelor of Science (BS), Chemical Engineering, Bachelor of Science (BS), Chemical Engineering at Purdue University
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