Exploring Network Applications: Dan Mark Orapa and Shawn Pacura Lead ModApp's Second Seminar of the Semester

For our second Journal Club session of the semester this past April, the ModApp group gathered at MB 126 for another highly engaging double-feature. This time, we were thrilled to feature two of our graduate students, Dan Mark Orapa and Shawn Pacura, who presented their work on the diverse applications of graph and network models.

Dan Mark Orapa kicked off the session by presenting his research titled, “Structure-based message-passing graph neural network models to predict protein-protein interactions.”  The presentation captivated the room, prompting a thoughtful discussion on the future of machine learning in biological datasets.

Following Dan Mark, Shawn Pacura took the floor to present his study on “Modeling Tuberculosis dynamics using exponential random graphs and temporal centrality-driven interventions.” The timely topic sparked a lively Q&A session right after the presentation, with attendees eager to discuss the real-world policy implications of his models.

It was another fantastic afternoon of shared knowledge, showcasing the power of mathematical modeling across completely different scales—from the microscopic level of proteins to population-level disease dynamics.

Join the ModApp Journal Club! We meet every other week throughout the semester, and everyone is welcome to join the discussion. You can check out our schedule of upcoming talks and browse our past meetings here: ModApp Journal Club