I'm a machine learning engineer in Chicago, and you may know me from my public presentations about data science, machine learning, and data engineering! I love my work and I especially love sharing it with others.
I write a monthly column on Towards Data Science about AI, machine learning, and society. Please subscribe via RSS to get it emailed to you!
Here's some highlights I recommend to get started:
- Machine Learning’s Public Perception Problem
- What Does It Mean When Machine Learning Makes a Mistake?
- How Human Labor Enables Machine Learning
- How Do We Know if AI Is Smoke and Mirrors?
- Seeing Our Reflection in LLMs
- Economics of Generative AI
- Evaluating LLMs for Inference, or Lessons from Teaching for Machine Learning
I also give talks all over the place, and I usually make the decks available here on github. Here's a few!
- 🏢 Just Do Something With AI: Bridging the Business Communication Gap for ML Practitioners
- 💼 Decoding Data Science Roles: A guide to the different jobs data scientists (really) do
- 🌐 Setting Healthy Boundaries: Generating Geofences at Scale with Machine Learning
- 📚: Deploying ML Models with Flask or Voila
- 🔧: Tutorial Workshop on Functions in R
- 🎨: New Python Data Visualization Tools: Beyond Matplotlib and Seaborn
- 🚄: Scaling Up Deep Learning with GPU Cluster Computing
- 🌳: Branching Out into Isolation Forests: Tree Based Anomaly Detection
- ✊: Data science can be better, sociology can help!
- 🤐: I am a staff machine learning engineer at DataGrail.
- I used to work at: project44, Saturn Cloud, Journera, Uptake, the University of Chicago, and DePaul University