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To broaden efforts for improving diversity, equity, and inclusion (DEI) in biomedical engineering (BME) education—a key area of emphasis is the integration of inclusive teaching practices. While BME faculty generally support these efforts, translating support into action remains challenging. This project aimed to address this need through a 3-phase inclusive teaching training, consisting of graduate students, faculty, and engineering education consultants. In Phase I, graduate students and faculty participated in a 6-week learning community on inclusive teaching (Foundational Learning). In Phase II, graduate students were paired with faculty to modify or develop new inclusive teaching materials to be integrated into a BME course (Experiential Learning). Phase III was the implementation of these materials. To assess Phases I & II, graduate student participants reflected on their experiences on the project. To assess Phase III, surveys were administered to students in IT-BME-affiliated courses as well as those taking other BME-related courses. Phases I & II: graduate students responded positively to the opportunity to engage in this inclusive teaching experiential learning opportunity. Phase III: survey results indicated that the incorporation of inclusive teaching practices in BME courses enhanced the student learning experience. The IT-BME project supported graduate students and faculty in learning about, creating, and implementing inclusive teaching practices in a collaborative and supportive environment. This project will serve to both train the next class of instructors and use their study of inclusive teaching concepts to facilitate the creation of ideas and materials that will benefit the BME curriculum and students. 

We report the results of the COVID Moonshot, a fully open-science, crowdsourced, and structure-enabled drug discovery campaign targeting the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main protease. We discovered a noncovalent, nonpeptidic inhibitor scaffold with lead-like properties that is differentiated from current main protease inhibitors. Our approach leveraged crowdsourcing, machine learning, exascale molecular simulations, and high-throughput structural biology and chemistry. We generated a detailed map of the structural plasticity of the SARS-CoV-2 main protease, extensive structure-activity relationships for multiple chemotypes, and a wealth of biochemical activity data. All compound designs (>18,000 designs), crystallographic data (>490 ligand-bound x-ray structures), assay data (>10,000 measurements), and synthesized molecules (>2400 compounds) for this campaign were shared rapidly and openly, creating a rich, open, and intellectual property–free knowledge base for future anticoronavirus drug discovery.