- Kevin P. Greenman, “Message-Passing Neural Networks for Molecular Property Prediction Using Chemprop”, nanoHUB Hands-on Data Science and Machine Learning Training Series (Virtual), April 2022. (nanoHUB | YouTube)
- Kevin P. Greenman, “Fast, Accurate, and Generalizable Prediction of Molecular Optical Properties from Multi-fidelity Data”, ARPA-E DIFFERENTIATE Meeting, Carnegie Mellon University (Virtual), March 2022.
- Charles McGill, Michael Forsuelo, and Kevin Greenman, “An Introduction to Chemprop”, Enko (Virtual), February 2022.
- Kevin P. Greenman, William H. Green, and Rafael Gómez-Bombarelli. “Multi-Fidelity Deep Learning and Active Learning for Molecular Optical Properties” International Symposium on Molecular Spectroscopy. Urbana, IL, June 2022 (Accepted).
- Kevin P. Greenman, William H. Green, and Rafael Gómez-Bombarelli. “Transfer Learning for Prediction of Absorption and Emission Spectra from Multi-fidelity Data”. American Institute of Chemical Engineers Annual Meeting. Boston, MA, November 2021.
- Kevin P. Greenman, Simon Axelrod, William H. Green, and Rafael Gómez-Bombarelli. “Predicting absorption spectra of molecular dyes using deep learning”. American Chemical Society Spring Meeting. Virtual, April 2021.
- Kevin Greenman, Logan Williams, and Emmanouil Kioupakis. “Lattice Constant and Band Gap Tuning in BInGaN Alloys for Next-Generation LEDs”. American Physical Society March Meeting. Boston, MA, March 2019.
- Kevin Greenman, “Computational Catalysis – Creating a User-Friendly Tool for Research and Education”. nanoHUB 3-minute Research Talk. West Lafayette, IN, August 2018. (nanoHUB)
- Kevin Greenman and Peilin Liao. “Computational Catalysis: Creating a User-Friendly Tool for Research and Education”. Purdue Summer Undergraduate Research Fellowship (SURF) Symposium. West Lafayette, IN, August 2018.