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

Current Courses

DSC 232R: Big Data Analytics Using Spark

Master of Data Science Program, Halicioglu Data Science Institute, UC San Diego

An advanced graduate course focused on applying machine learning to massive datasets using Apache Spark and distributed computing.

Topics Covered:

  • Distributed computing fundamentals
  • Apache Spark architecture and optimization
  • PySpark programming for data processing
  • Supervised learning at scale (regression, classification)
  • Machine Learning pipelines with MLlib
  • Big data file formats (Parquet, ORC)
  • Performance tuning and optimization

Technologies: Apache Spark, PySpark, MLlib, Databricks, AWS EMR, Google Cloud Dataproc

CSE 150A: Introduction to Artificial Intelligence

Computer Science and Engineering Department, UC San Diego

Fundamental concepts in artificial intelligence including search algorithms, knowledge representation, probabilistic reasoning, and machine learning foundations.

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

Current Students

Graduate Students:

  • Mentoring MDS students on capstone projects involving genomics and ML
  • Advising on thesis topics in computational biology
  • Career guidance for data science positions

Undergraduate Researchers:

  • Supervising research projects in computer vision for agriculture
  • Teaching bioinformatics pipeline development
  • Preparing students for graduate school applications

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: esolares [at] ucsd [dot] edu