Kevin Greenman @kevinpgreenman
🤖AI for Science 🌟☧ @CathInstTech Asst Prof 🎓@MITChemE Ph.D. '24 in @RGBLabMIT 💊💻 Ex: @MSFTResearch & @EliLillyandCo 🎓 @UMichChE '19 kevinpgreenman.com Cambridge, MA Joined May 2019-
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Looking forward to connecting with people at #ACSFall2025! I'll be giving a talk on Wednesday morning: "AI-driven chemistry research and undergraduate skill development with Chemprop" Session: Harnessing the Power of AI in Undergraduate Research and Training Day/Time: Wednesday…
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Kevin Greenman retweeted
Out now in @ChemicalScience! Give it a read if you're interested in molecular design, synthesizability, and/or organic electronics! Co-authors: James Damewood, @junonam_ , @kevinpgreenman , @Avni_Singhal , and @RGBLabMIT. pubs.rsc.org/en/Content/Art…
Out now in @ChemicalScience! Give it a read if you're interested in molecular design, synthesizability, and/or organic electronics! Co-authors: James Damewood, @junonam_ , @kevinpgreenman , @Avni_Singhal , and @RGBLabMIT. pubs.rsc.org/en/Content/Art…
Kevin Greenman retweeted
Benchmarking uncertainty quantification for protein engineering @PLOSCompBiol 1. Machine learning (ML) models have accelerated protein engineering, but estimating model uncertainty remains critical for optimizing biological properties and guiding experimental designs…
Kevin Greenman retweeted
We compared the calibration of various machine learning uncertainty estimation methods for protein engineering. No method excels across all scenarios, and uncertainty-based strategies for optimization often did not outperform methods without uncertainty.
Kevin Greenman retweeted
📢New preprint out! We constrain the molecular generation space to follow the "symmetry" of patented molecules that are likely to be synthesizable. Achieved with "symmetry-aware" fragment decomposition, and a constrained Monte Carlo Tree Search generator. arxiv.org/abs/2410.08833
Kevin Greenman retweeted
Zero-shot extrapolation for out-of-distribution (OOD) chemical property prediction is an important step towards high-performance materials discovery. Check out our spotlight at the #NeurIPS AI for Accelerated Materials Design Workshop! openreview.net/pdf?id=HkfnueE…
Kevin Greenman retweeted
We are delighted to host our first class of ACT-CMS (Accelerating Curricular Transformation in the Computational Molecular Sciences) Faculty Fellows for their workshop this week! They are developing learning activities to integrate programming & computation into their courses.😀
Kevin Greenman retweeted
What does the development of artificial intelligence say about the human soul? What leads scientific-minded people to convert to Christianity? The 2024 convention of the Society of Catholic Scientists will tackle both these questions and many more. catholicnewsagency.com/news/257864/on…
Kevin Greenman retweeted
🎉 We're thrilled to announce our 1st class of MolSSI Faculty Fellows, who will collaborate with us over the next 2 years to create cutting-edge curricular resources, integrating programming, data, & computational education into their courses. Welcome! 👏 molssi.org/fellowship/202…
Kevin Greenman retweeted
Congrats to Dr. Kevin Greenman on defending his Ph.D. thesis at MIT! His groundbreaking work in chemical engineering uses AI to optimize experiments, aiding novel molecule discovery for displays and biomedical imaging. He continues his research CatholicTech 🚀📚
Kevin Greenman retweeted
Meet Professor Kevin Greenman: Final-year Ph.D. candidate in chemical engineering and computation at MIT, NSF graduate research fellow. Watch the clip to learn more! #ChemicalEngineering #CatholicScientists #MIT #Innovation
Kevin Greenman retweeted
Chemprop: A Machine Learning Package for Chemical Property Prediction #DeepLearning pubs.acs.org/doi/10.1021/ac… @esther_heid @kevinpgreenman @YunsieC @FHVermeire @_chasmcgill #JCIM Vol64 Issue1 #ApplicationNote
Kevin Greenman retweeted
Excited to share that our Chemprop package paper is published on JCIM pubs.acs.org/doi/10.1021/ac… #machinelearning #ML
Kevin Greenman retweeted
Chemprop: A Machine Learning Package for Chemical Property Prediction #machinelearning #compchem pubs.acs.org/doi/abs/10.102…
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