Advances in machine learning over the past decade, particularly hardware developments enabling the effective implementation of deep neural networks, have driven recent state-of-the-art results on benchmark tasks in computer vision and speech recognition. Combined with the availability of increasingly large datasets, these technical advances have facilitated the successful application of deep neural networks to diverse applications, including identifying exotic particles in high-energy physics experiments, predicting the chemical properties of small molecules, and predicting protein structure from amino acid sequence. My current work focuses on understanding deep neural networks, and on applying machine learning techniques to tasks in the natural sciences.



ClusterCAD: a computational platform for type I modular polyketide synthase design.
C.H. Eng, T.W.H. Backman, C.B. Bailey, C. Magnan, H.G. Martin, L. Katz, P. Baldi, and J.D. Keasling.
Nucleic Acids Research, 2017.
[article] [abstract]
Jet substructure classification in high-energy physics with deep neural networks.
P. Baldi, K. Bauer, C. Eng, P. Sadowski, D. Whiteson.
Physical Review D, 2016.
[article] [arxiv] [abstract]
Alteration of Polyketide Stereochemistry from anti to syn by a Ketoreductase Domain Exchange in a Type I Modular Polyketide Synthase Subunit.
C.H. Eng, S. Yuzawa, G. Wang, E.E.K. Baidoo, L. Katz, and J.D. Keasling.
Biochemistry, 2016.
[article] [abstract]
Enzyme analysis of the polyketide synthase leads to the discovery of a novel analog of the antibiotic α-lipomycin.
S. Yuzawa, C.H. Eng, L. Katz, and J.D. Keasling.
Journal of Antibiotics, 2014.
Broad Substrate Specificity of the Loading Didomain of the Lipomycin Polyketide Synthase.
S. Yuzawa, C.H. Eng, L. Katz, and J.D. Keasling.
Biochemistry, 2013.
[article] [abstract]