{"id":36286,"date":"2021-11-22T13:53:31","date_gmt":"2021-11-22T18:53:31","guid":{"rendered":"https:\/\/engineering.jhu.edu\/chembe\/?page_id=36286"},"modified":"2023-05-30T12:25:52","modified_gmt":"2023-05-30T16:25:52","slug":"modeling-in-the-big-data-era","status":"publish","type":"page","link":"https:\/\/engineering.jhu.edu\/chembe\/research\/modeling-in-the-big-data-era\/","title":{"rendered":"Modeling in the Big Data Era"},"content":{"rendered":"

\"\"The Gray lab creates algorithms for protein engineering. Pictured: A deep learning model for antibody structure prediction from sequence. Credit: Jeff Ruffolo.<\/em><\/span><\/p>\n

Artificial intelligence (AI), machine learning (ML), modeling, and simulation have the potential to transform and accelerate the discovery, development and manufacturability of systems transcending length- and time-scales. The Department of Chemical and Biomolecular Engineering at has a remarkable breadth and depth of talent in these areas, with five faculty members deeply engaged in developing new algorithms to open new frontiers in chemical engineering.<\/p>\n

Our work spans the challenges of the 21st century: From climate change and sustainable energy to health and therapeutics, to flexible electronic materials and molecular design.<\/p>\n

We offer an educational program of courses tailored to Chemical and Biomolecular Engineering students, from ranging from foundational courses, such as Software Carpentry, to high-level courses, such as Modern Data Analysis and Machine Learning.<\/p>\n

Primary Faculty<\/strong><\/h4>\n

Michael Bevan<\/a>
\nBrandon Bukowski<\/a>
\nPaulette Clancy<\/a>
\nJeffrey Gray<\/a>
\nYannis Kevrekidis<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

The Gray lab creates algorithms for protein engineering. Pictured: A deep learning model for antibody structure prediction from sequence. Credit: Jeff Ruffolo. 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