Hello!

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.

Writing

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

Talking

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!

You can see my back history of talks as well if you're thinking "wait, what conference was it where I saw her do that thing?"

I do a day job too

• 🤐: 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