Research mentoring
Seven researchers cumulatively across research roles, with support in experimental planning, practical methods, troubleshooting, interpretation and scientific writing.
Research mentoring · Experimental practice
Mentoring and Research Training.
Across my research roles, I have mentored seven researchers in experimental planning, practical methods, troubleshooting, data interpretation and scientific writing.
My approach is to help people understand why an experiment is being done, choose informative controls and build confidence in interpreting the results.
01 · Research mentoring delivered
Seven researchers, cumulatively across research roles.
The count describes research mentoring over time. It does not mean seven simultaneous direct reports, formal degree supervision, or unverified student outcomes.
My mentoring is grounded in experimental molecular and cellular biology. I support the full path from a biological question to an interpretable result: defining the comparison, choosing controls, planning constructs or variants, working through practical methods, troubleshooting unexpected behaviour, and deciding what the data do and do not support.
The aim is not simply to hand over a protocol. I want a researcher to understand the logic behind the experiment, recognise the most informative control, keep a usable experimental record and become more confident deciding what should happen next.
- 01Understand why
Start with the biological question and the decision the experiment needs to inform.
- 02Choose informative controls
Make baseline, perturbation, rescue and alternative explanations explicit before interpretation.
- 03Build interpretive confidence
Separate observation from mechanism, document uncertainty and identify the next discriminating experiment.
Formal course teaching
This page does not present courses attended, planned laboratory teaching or research mentoring as a formal teaching appointment.
Professional development
Selected courses and workshops I attended are listed separately below and are not presented as courses I taught.
Learning resources
Future modules are clearly marked as proposed. No download is offered unless a real, reviewed public file exists.
02 · Documented mentoring examples
Bench support tied to real research settings.
These examples are anonymised and describe my own contribution and documented research process. They do not assign grades, degrees, publications or other outcomes to individual researchers.
Experimental planning & troubleshooting
Turn a biological question into a testable comparison.
- Research setting
- Max Planck Institute of Molecular Cell Biology and Genetics, Dresden
- Period
- July 2019 to February 2026
- Personal contribution
- Supported researchers in experimental planning, practical methods, troubleshooting and biological interpretation across collaborative projects, including work with construct and variant systems.
- How I guided the work
- Worked from question → controls → comparison → readout → troubleshooting decision, with an emphasis on records that another collaborator could understand and reuse.
- Documented outcome
- The research programme used controlled comparisons of protein localisation, dynamics and cellular phenotypes, with experimental records designed to remain useful across collaborating teams.
Quantitative interpretation
Move from an image or assay readout to a defensible conclusion.
- Research setting
- Max Planck Institute of Molecular Cell Biology and Genetics, Dresden
- Period
- July 2019 to February 2026
- Personal contribution
- Supported interpretation of molecular and cellular assays using quantitative imaging, phenotype definitions and complementary measurements, while troubleshooting alternative explanations.
- How I guided the work
- Separated what was directly observed from biological interpretation, asked which control could distinguish competing explanations, and used the result to define the next experiment.
- Documented outcome
- This evidence-first approach supported mechanistic TRIM-family studies and prospective cellular validation work while keeping the limits of each readout explicit.
Research example · PICNIC
Experimental validation and biological interpretation.
PICNIC is a computational prediction framework. My contribution included experimental design, prospective cellular validation and biological interpretation. I present it here as an example of the experimental reasoning behind my mentoring, not as evidence of a formal teaching appointment or as an individual mentee outcome.
03 · Professional development received
Training I attended, kept separate from training I delivered.
These courses and workshops strengthened my own experimental and analytical practice. They are not listed as courses I taught.
Antibody Engineering and Display Technologies
Cold Spring Harbor Laboratory · professional development attended.
Quantitative Bio-Image Analysis with Python
MPI-CBG · professional development attended.
Multiscale Imaging Workshop
Tübingen · professional development attended.
Data Science and AI
neue fische / SPICED Academy · June to October 2026. Completion remains in progress until confirmed.
04 · Proposed learning resources
Experimental modules I would build next.
These are proposed resources, not courses already delivered. No download buttons are shown because no reviewed public teaching files are attached to this page.
Choosing informative experimental controls
Baseline, perturbation, rescue, orthogonal controls and the alternative explanation each control is designed to test.
Construct and variant design
Turn a mechanism into a construct panel, choose informative variants and decide what comparison would falsify the working model.
Quantitative imaging
Separate image acquisition, segmentation, feature measurement and biological interpretation so uncertainty remains visible.
FRAP interpretation
Read recovery curves as evidence about molecular dynamics while keeping model assumptions, controls and alternative explanations explicit.
Protein-function assay reasoning
Connect biochemical and cellular readouts, use complementary measurements and decide when a result supports mechanism rather than correlation.
Scientific writing from evidence
Build a result paragraph from observation → comparison → boundary → interpretation → next test without overstating what the experiment established.