My research paper, “Unlocking Transparency: The Role of Non ‘Black Box’ Machine Learning Models in Medicine,” has been published in the November Issue 56 (Part 2) of Curieux Academic Journal, exploring interpretable AI models like SVMs in medicine. Go check it out! pic.twitter.com/VPFH1IovMd

— Jack Spitzer (@JackDSpitzer) October 20, 2025

I’ll be at the American Chemical Society Western Regional Meeting on 10/26! I’m excited to see all the fascinating research being presented, and I’ll be presenting my research, “Exploring Acetaldehyde Photodissociation Pathways Using Interpretable AI Models.” Come by and say hi! pic.twitter.com/YDhU9xsCgc

— Jack Spitzer (@JackDSpitzer) October 20, 2025

Created acetaldehyde molecule using TURBOMOLE to build the geometry, then ran a 10-step simulation with FROG, which I visualized in VMD. This molecule will be important for the continual learning model we're developing with FROG and should help enhance our simulation accuracy. pic.twitter.com/UctfyliUOM

— Jack Spitzer (@JackDSpitzer) July 10, 2025

Cool simulation I created and ran recently! Used TURBOMOLE to generate optimized molecular geometries, then used FROG software to prepare a short molecular dynamics simulation (about 10 steps). Finally, visualized the trajectory of the HCl molecule in VMD! pic.twitter.com/A7WiGXS2ma

— Jack Spitzer (@JackDSpitzer) July 10, 2025

Cool simulation that I ran a few days ago: The simulation is 10 steps and represents a ubiquitin solvated in water. Ubiquitin is protein primarily used for ubiquitination: acting as as molecular tag, marking proteins for degradation by the proteasome - also used for DNA repair. pic.twitter.com/SF0jkg2689

— Jack Spitzer (@JackDSpitzer) June 13, 2025