Many of my graduate students have papers in peer review. I share this with them to help them respond to the reviewers. I remember the challenge & pressure of writing my first papers. I sincerely hope that this helps. #AcademicChatter #academicWriting #mentorship #proflife

🚀 I'm teaching Artificial Neural Networks (ANN) from the recently updated chapter of my free, online e-book: "Applied Machine Learning in Python" 📷 I'm really excited about this update — I’ve focused on maximum accessibility by walking through everything step-by-step:

Today in my #MachineLearning course, we’re diving into artificial neural networks! 🧠 I’ve recently updated my course materials to cover all the nuts and bolts — from forward propagation through the network to backpropagating the error derivatives for updating weights and

Four of my PhD students joined in for a Saturday morning hike at River Place Trail, #Austin and then breakfast at my place. Awesome conversations and a challenging hike. Stoked! Thank you, Suin, Alexander, Dursun and Ahmed! Amazing #Longhorns!

Why did I become a professor? Why do I create so much accessible educational content? Why do I give everything away for free? Because I’m #FirstGen — and my entire path began with a single, unexpected moment at a gas station in Canada. I was a high school kid with no direction

Hybrid modeling—such as combining a smooth deterministic trend with a geostatistical residual—often confuses students. To demonstrate the benefits of explicitly modeling the trend before analyzing the residual, I built an interactive #Python @matplotlib dashboard. Students can

I recently taught tree-based gradient boosting in my #MachineLearning course, and I could tell some students did not fully understand how it works. So last night, I built an interactive #Python @matplotlib dashboard to bring it to life! You can train a gradient boosting model

Yes, I teach and research in #MachineLearning — but do you know my first love? 💚 #Geostatistics! I’ve always been fascinated by spatial phenomena — making maps, exploring patterns, and uncovering how relationships evolve across space. 🌍✨ To help my students experience the

I love teaching Monte Carlo simulation — such a powerful empirical method for exploring uncertainty distributions! To help my students see how they can apply it in their own careers, I built an interactive #Python dashboard using @matplotlib. It lets them simulate and visualize

Thank you, #Austin! I really appreciate the Veloway Cycling Track — it’s a spiritual place for me. A wonderful community gathers here to share their love of riding at this amazing facility. Rode 30 km this morning and feel completely refreshed! 🚴‍♂️✨

I asked @ChatGPTapp to take my tweet and write an poem. I think ChatGPT gets it! Ode to Distribution Transformation This morning, I find myself in quiet reflection— thinking of distribution transformations, those subtle alchemies of feature engineering. They are

This morning, I’ve been reflecting on distribution transformations—an essential aspect of feature engineering. These rank-preserving mappings allow us to extract more value from our data and enhance the performance of our predictive models. I like to think of a distribution

When I teach Bayesian probability, I hold up a coin and ask, "Is this a fair coin?" We break it down: Prior: What do we know about the coin before flipping? (Do you know me or the coin?) Likelihood: We flip the coin to gather data. Posterior: After flipping, we update our

Many graduate students eventually face a big question: academia or industry? I’m here to say, “I’ve lived both lives — and while they’re completely different, I’ve loved them both!” May I share a few candid observations that might help you as you make this important decision?

🚀 When I say I’m sharing my entire #DataAnalytics and #Geostatistics course with anyone eager to learn — I’m not joking! 😀📊 🎓 Here it is! Imagine, every lecture is paired with a free online e-book, plus hands-on, well-documented #Python workflows and interactive dashboards

Today’s focus in my #MachineLearning course: Bagging and Random Forests — powerful ensemble methods to reduce model variance! To illustrate the idea of bootstrapping and how it generates multiple data realizations for training multiple models, I built a simple Linear Regression

I’m deeply grateful for the opportunity to serve as a professor at The University of Texas at Austin. Every day, I’m inspired by our amazing students, dedicated staff, and innovative faculty. With this privilege comes a great responsibility — to educate and empower the next

On Monday’s lecture, I introduced Bayesian linear regression and explained MCMC using the Metropolis-Hastings algorithm to sample from the posterior… but I could tell it wasn’t fully landing with the students 😕. So yesterday, I built an interactive dashboard in #Python using

Monte Carlo Simulation (MCS) is one of the most important statistical developments of the 20th century. 1. MCS uses random sampling to solve problems that would otherwise be intractable, allowing us to model complex systems with uncertainty. 2. MCS is also powerful for