Data Science @ Brown Grants, 2020

Predicting the Course of Chemical Reactions with Deep Reinforcement Learning
Brenda Rubenstein, Cancan Huang, Leonard Sprague, Gopal Iyer (Chemistry)

Deep Learning Segmentation of Liver Structures, Tumors, and Effects of Treatment from Medical Images
Ben Kimia, Ben Hsieh (Engineering)

An Introductory Workshop on Machine Learning Methods for Undergraduate Students in MCM and STS
Theo Lepage-Richter (Modern Culture and Media)

Topological Data Analysis of Dynamic Tumor Architecture
Ian Wong, Dhananjay Bhaskar (Engineering), Lorin Crawford (Biostatistics)

Predictive models for microbial response to antibiotic nanoparticles: a hybrid approach of machine learning and mechanistic models
Zhijin Wu, Nicola Neretti (Biostatistics), with Jingyi Chen and Yong Wang (U of Arkansas)

SHARE: Secure Healthcare and Administrative Records Environment 
Seny Kamara (Computer Science), David Yokum, Kevin Wilson (The Policy Lab)

Big Virtual Reality in the Brown YURT for Data Visualization
David Laidlaw (Computer Science)

Machine Learning for Small Data -omic Problems: Identifying the Immunosuppressive Proteomic Signature for Adipose-Derived Stem Cells
Adrienne Parsons (Biotechnology Graduate Program), Lorin Crawford (Biostatistics), Eric Darling (Molecular Pharmacology, Physiology, and Biotechnology)