I will be at the INFORMS this week. I will be talking about a unifying perspective on Scientific Machine Learning for learning to optimize and learning to control, which is being materialized in the Neuromancer library: github.com/pnnl/neuroma... If you want to chat, send me a message :)

ROSEI is excited to unveil its first ever annual report, highlighting a year of rapid growth, groundbreaking research, and expanding impact! Explore the report and learn more about how the future of sustainable energy starts at JHU 🌎🔋💡 #HopkinsEnergy Report: energyinstitute.jhu.edu/annual-repor...

It was an absolute pleasure to give a talk at the Computing and Sustainability Seminar, hosted by MIT Laboratory for Information and Decision Systems (LIDS). You can watch the recording here: www.youtube.com/watch?v=W9Lo...

NeuroMANCER v1.5.6 is out! We have four new examples: 1, Learning neural differential algebraic equations via the operator splitting method 2, Learning mixed-integer neural policies via DPC 3, Grid-responsive DPC for building energy systems 4, DPC with prediction preview horizon

We’re excited to announce the 2nd Workshop on Physics-Informed Machine Learning at CDC 2025 in Rio de Janeiro on December 9th! 🔗 Workshop details: sites.google.com/view/2025cdc... 📝 Register here: cdc2025.ieeecss.org/registration (Early registration ends soon!)

Our paper on "Learning Neural Differential Algebraic Equations via Operator Splitting" was accepted to the IEEE CDC. If you don't have the time to read the full paper, check the paper summary with the Google Colab example at: drgona.github.io/NeuralDAEs/

NeuroMANCER v1.5.4 is out! github.com/pnnl/neuromancer 🆕 What's New in v1.5.4 💻 New Examples: Function Encoders (FE) is an algorithm for learning neural network-based basis functions. Two new examples include the use of FE for function approximation and FE-Neural ODEs.

🚀 Less than two weeks to go until the 2025 American Control Conference in Denver, Colorado! I’m excited for a packed schedule this year—with talks, a tutorial, a workshop, and most importantly, the chance to reconnect with friends and colleagues. 😊 sites.google.com/view/acc-phy...

The spring semester is over, and my new course, "Introduction to Machine Learning and Control for Building Energy Systems," @jhu.edu is now complete. The material, including lecture slides and code examples, is freely available on GitHub. github.com/drgona/ML_an...

Are you planning to attend the American Control Conference 2025 in Denver? Consider joining our Workshop on Physics-Informed Machine Learning in Control: An Introduction, Opportunities, and Challenges 📅 Date: July 7, 2025 📍 Denver, Colorado 🔗 workshop details sites.google.com/view/acc-phy...

I am seeking a postdoctoral researcher to work on Scientific Machine Learning for Real-Time Decision Making. 📧 Interested candidates should send their CVs to my email: [email protected] Please see the job description below for more information. www.linkedin.com/hiring/jobs/...

NeuroMANCER v1.5.3 is out! github.com/pnnl/neuroma... What is new? 🤖 NeuroMANCER-GPT Assistant 🐍 Python 3.11 Version Support 🏫 Building Control Comparison Example: Safe Reinforcement Learning vs Differential Predictive Control 💻 Improved Node Class

Our final #EWeek2025 post highlights Ján Drgoňa, who recently joined Hopkins as an associate professor in the Department of Civil and Systems Engineering! He was asked "How could your research make a future impact in the fight against climate change?" #HopkinsEnergy

Internship opportunity: Spatiotemporal Graph applications for Smart Buildings⚡⚡⚡ working closely with me, starting in early 2025. Considering applying or/and sharing this with your network please. Apply here: forms.gle/N3kwFxM3yEhS... LinkedIn ad here: www.linkedin.com/feed/update/...

New ROSEI researcher Q&A with @drgona.bsky.social just went live! He discussed how a project as an undergraduate shaped his passion for energy-efficient building controls, and why joining the Hopkins energy community was a "no-brainer." #HopkinsEnergy Story: energyinstitute.jhu.edu/rosei-resear...

In our latest NeuroMANCER release, we have two new examples demonstrating the use of Finite Basis Kolmogorov-Arnold Networks (FBKANs) for function approximation. 1D FBKAN example colab.research.google.com/github/pnnl/... #Neuromancer, #PNNL, #SciML, #KANs