What Is DryLab?
DryLab pairs you with a PhD or MD mentor to run a unique research project in genomics, metabolomics, computational biology, or environmental health—entirely online. Build real skills and create publishable work without needing a physical lab.
Research Design
Data Analysis
Poster & Paper Writing
Capstone
Graduate With More Than Just Experience
1
Kickoff Call
Meet your mentor, align goals, choose your track.
2
Literature Review
Learn the field and shape your hypothesis with templates.
3
Data Analysis
Analyze real datasets (genomics, metabolomics, imaging, or environmental).
4
Publish & Present
Draft a manuscript and poster; rehearse your talk.
Mentor Spotlight
Meet Dr. Casey Bennett

PhD, Informatics & Computer Science (IU) • Chair, Health Informatics at DePaul • US-patented clinical AI researcher • Work featured by IBM’s Smarter Planet
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AI and robotics professor leading real healthcare innovation projects.
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20+ years experience in machine learning and digital health.
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Former senior data scientist at Cigna, CVS, and major hospitals.
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Trained students now working at Google, Samsung, Apple, Amazon.
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Builds real clinical AI tools used to improve patient care.
Mentor Spotlight
Meet Dr. Anthony Cirrincione

PhD, Cell and Molecular Biology (UM) • World-Class Expertise in Cancer, Genomics & Regeneration
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Award-winning cancer and genomics researcher with international presence.
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Skilled in both wet-lab and computational biology mentoring.
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Former clinical genomics scientist interpreting real patient DNA.
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10+ years experience across academia, biotech, and precision medicine.
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Supportive mentor who makes complex biology clear and confidence-building.
Mentor Spotlight
Meet Dr. Daniel Pierce (Dr. Dan)

PhD, Physical Chemistry (UMBC) • 9 first-author publications • Inventor of Fluorophore-Induced Plasmonic Current (FIPC)
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Mentors undergrads to publication-ready work (posters & manuscripts).
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Focus: environmental contaminants (PFAS, microplastics) & public health.
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Assay design (ELISA/qPCR), LC/GC-MS, fluorescence & optics; Python/R.
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Teaches a “full-stack” research mindset—from molecule to population.
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Provides strong letters of rec for students who complete capstones.

