teaching
Training the next generation of data scientists and computational biologists.
Teaching Philosophy
I believe that education should be accessible, engaging, and immediately applicable to real-world challenges. My teaching emphasizes:
- Hands-on learning: Working with real datasets and industry-standard tools
- Inclusive pedagogy: Reducing barriers to entry, especially for underrepresented groups
- Interdisciplinary connections: Bridging computer science, biology, and data science
Teaching Approach
Making Complex Topics Accessible
- Scaffolded Learning: Build from fundamentals to advanced concepts with clear learning objectives
- Real-World Relevance: Every concept connected to practical applications and current research
- Interactive Engagement: Live coding, collaborative problem-solving, debugging sessions
- Inclusive Practices: Diverse examples, multiple pathways to demonstrate mastery, flexible support
Mentorship
Research Mentees:
- Supervising research projects in computer vision for agriculture and aquaculture
- Teaching bioinformatics pipeline development
- Advising on thesis topics in computational biology
- Career guidance for data science and research positions
Alumni Outcomes
Former students have gone on to PhD programs at top institutions, data science positions at tech companies, medical school, postdoctoral fellowships, and faculty positions.
Contact
Interested in guest lectures, workshop facilitation, or curriculum consultation?
Email: info [at] esbailab [dot] org