Teaching and mentorship
Most of what I teach is procedural: how to run an assay so the numbers mean something, how to tell a real signal from a pipetting error, and how to write down what you did so someone else can repeat it.
How I teach
Three things I try to get across, in laboratories and in one-to-one mentorship.
- A result you cannot reproduce is not a result
Working in high-throughput research makes this unavoidable: with ninety-six wells running at once, sloppy technique does not produce one bad number, it produces a bad dataset. I teach protocol discipline first, because everything downstream depends on it.
- Negative results are data
Students arrive expecting experiments to work and treat failures as wasted time. In screening work the conditions that fail define the boundary of the useful region, and they are worth recording with the same care as the ones that succeed.
- Start from the measurement
Before running anything, I ask what number will come out and what it would mean if it came out differently. It is a faster route to understanding an assay than working forward from the protocol.
Positions
Sept 2024 – present
Graduate assistant
Department of Biomedical Engineering, Rutgers University
- Supporting departmental operations and research initiatives.
- Assisting faculty with student mentorship.
- Collaborating on grant projects.
Sept 2023 – June 2024
Teaching assistant · Introduction to Biomedical Engineering
Rutgers School of Engineering · undergraduate course
- Led laboratory sessions and supported course instruction.
- Weekly office hours for students working through foundational material.
- Assessed assignments and provided written feedback.
Ongoing
Undergraduate research mentorship
Gormley Lab, Rutgers University
- Mentoring undergraduate researchers in polymer synthesis technique.
- Training on high-throughput screening workflows and liquid handling.
- Supervising data analysis for biomaterials experiments.
Prospective students
If you are a Rutgers undergraduate interested in polymer chemistry, laboratory automation or applying machine learning to experimental data, get in touch, and say what you have already tried, not just what you are interested in.