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AI You Can Trust: From Deep Learning to Discovery with Prashnna Gyawali on Jan. 21
Join a focused session on reliable, trustworthy AI for science with Prashnna Gyawali. Jan. 21, 6 p.m., Clark Hall, Room 208; hosted by Chemistry and the local ACS section.
From Deep Learning to Scientific Discovery: Toward Reliable and Trustworthy AI
Researchers are invited to a focused session on dependable AI for science. The Department of Chemistry and the Northern West Virginia Local Section of the American Chemical Society will host this presentation at 6 p.m., Jan. 21, in Clark Hall, Room 208.
The presenter is Prashnna Gyawali, assistant professor in the Lane Department of Computer Science and Electrical Engineering.
Event details
- Title: From Deep Learning to Scientific Discovery: Toward Reliable and Trustworthy AI
- Host: Department of Chemistry and the Northern West Virginia Local Section of the American Chemical Society
- Presenter: Prashnna Gyawali, assistant professor, Lane Department of Computer Science and Electrical Engineering
- Time: 6 p.m., Jan. 21
- Location: Clark Hall, Room 208
Why this matters for your work
As models move closer to the bench, reliability and trust are non-negotiable. This session centers on how to build AI you can test, explain, and defend in peer review; researchers seeking hands-on workflows and reproducible practices may find the AI Learning Path for Data Scientists especially relevant.
Expect a practical lens on moving from neural nets to scientific results that hold up under replication.
Key concerns to bring to the discussion
- Data quality: controls, drift, and documentation
- Model trust: calibration, uncertainty estimates, and interpretability
- Reproducibility: versioning, pipelines, and audit trails
- Integration: pairing predictions with experimental design and validation
Make the most of the session
- Bring one active research question where AI could speed analysis or guide experiments.
- Outline how you would verify model outputs in your lab (ground truth, orthogonal methods, or blinded tests).
- List failure modes you worry about (overfitting, bias, sample leakage) and ask how to test for them.
Further reading and tools
- NIST AI Risk Management Framework for evaluating AI reliability and risk.
- AI courses by job to build the exact skills your role demands.
Put this on your calendar and plan to arrive a few minutes early. If you're working at the edge of chemistry, materials, biology, or engineering, this conversation will help you tighten your methods and get cleaner results from your models.