This month we are featuring our knowledgeable instructor, Logan Thomas.
1) What is your name and where are you currently located?
My name is Logan Thomas, and I’m currently based in Austin, Texas.
2) How did you end up in engineering education?
I came into engineering education through a natural progression from industry into teaching. I started my career applying machine learning and data science in domains ranging from digital media to mechanical engineering to biotech. Along the way, I discovered how much I enjoy breaking down complex concepts and helping others level up their skills. This led me to teaching roles at Enthought and mentoring opportunities through conferences like SciPy, where I’ve served as the Tutorials Chair. I love helping others build confidence in technical topics.
3) How do you stay current with the latest advancements in engineering technology and industry practices?
I stay up-to-date through a combination of hands-on work, professional communities, and continuous learning. I regularly contribute to and attend conferences like SciPy, stay active on GitHub, and follow key publications and blogs in data science, machine learning, and software engineering. I also enjoy experimenting with new tools and libraries in side projects and applying them in my role as a Data Science Software Engineer at Fullstory.
4) Can you describe your teaching philosophy and how it aligns with Diller Digital’s mission and values?
My teaching philosophy is rooted in curiosity, empathy, and empowerment. I believe the best learning happens when students feel safe to ask questions, explore, and make mistakes. I aim to connect abstract concepts to real-world problems and encourage students to become confident problem-solvers. I bring not just technical depth but a coaching mindset that helps learners develop independence.
5) What engineering software and tools do you have experience with, and how do you incorporate them into your teaching?
I have extensive experience with Python, TensorFlow, PyTorch, PySpark, and data science libraries like numpy, pandas, scikit-learn, and matplotlib. I’ve also worked with engineering tools like MATLAB, Abaqus, and simulation platforms during my earlier mechanical engineering roles. In teaching, I use these tools to build hands-on labs and project workflows that mirror industry applications—for example, guiding students through feature engineering in Python or designing reproducible machine learning pipelines.
6) How do you balance theoretical knowledge with practical, hands-on learning in your classes?
I try to lead with intuition, then reinforce with both theory and practice. I introduce concepts through stories or visuals, connect them to math and science fundamentals, and then move into code or simulation exercises. I often use real-world datasets and scenarios to bridge the gap between textbook theory and professional problem-solving.
7) Can you discuss your experience with project-based learning and how you guide students through the data analysis workflow?
Project-based learning is at the core of how I teach. I’ve led corporate hackathons, taught project-based machine learning courses, and mentored students through the entire data science lifecycle—from framing the problem and wrangling data to building models and interpreting results. I emphasize documentation, version control, and modular design to instill good engineering habits while keeping things collaborative and fun.
8) What strategies do you use to assess student understanding and provide constructive feedback on their work?
I focus heavily on active engagement and asking probing questions to gauge where students are in their understanding. During live coding sessions, I pause frequently to ask why a certain approach might work or what might happen if we changed a parameter—this helps surface both strengths and misconceptions in real time. I also use “coding karaoke,” where students follow along and fill in missing pieces of code to reinforce concepts and promote deeper learning. These interactive techniques give me a window into their thought process, which is often more insightful than a finished project. When giving feedback, I keep it specific, kind, and actionable—usually highlighting what they did well and nudging them to reflect on one or two key areas to improve. I also encourage self-assessment and goal-setting to build metacognitive skills and confidence over time.
9) What strategies do you use to communicate complex engineering concepts to students with varying levels of understanding?
I rely on analogies, visual aids, interactive demos, and scaffolding. I check in often, ask open-ended questions, and adjust based on the group’s energy and comprehension. I also try to normalize “not knowing”—creating an environment where curiosity is more important than correctness. Teaching, for me, is more about coaching than lecturing.
10) What is your favorite way to spend a Saturday? Favorite meal?
My favorite Saturday is one where I get some good coffee, play outside with my wife and two boys, and maybe sneak in a run or catch a baseball game. In the evening, nothing beats a homemade meal—especially if I can grill it in the backyard with friends and family around.
Logan and family in Austin
Thanks for your answers to these questions, Logan so we can get to know you better as one of our respected instructors.