Machine Learning & Computational Biology Leader, Biotech

rivehill@gmail.com · github.com/Yue-Jiang · Google Scholar · LinkedIn

I've built and led ML/comp-bio teams that deploy models into therapeutic design pipelines and deliver analyses that inform manufacturing and clinical-trial decisions — across gene therapy (RNA editing, AAV capsid design) and CAR T cell therapy, from pre-clinical through early clinical stages — while staying hands-on in the science.

Experience

Senior Principal Data Scientist → Director, Head of Machine Learning 2021 – present
Shape Therapeutics Seattle, WA (remote from Los Angeles, CA)
Data Scientist → Associate Director 2016 – 2021
Juno Therapeutics / Celgene / Bristol-Myers Squibb Seattle, WA
Decision Support Engineering Analyst Intern 2015
Google [X] / Verily Life Sciences Mountain View, CA

Education

Ph.D., University Program of Genetics and Genomics 2009 – 2015
Duke UniversityDurham, NC
M.S., Statistical Science (concurrent) 2013 – 2015
Duke UniversityDurham, NC
B.S., School of Life Sciences 2004 – 2008
Peking UniversityBeijing, China

Skills

Languages & toolsR, Python, PyTorch, Nextflow, AI coding agents (Claude, Codex), AWS (former Certified Solutions Architect – Associate)
DomainsDeep learning, generative models, statistical modeling, bioinformatics pipelines, nucleic-acid & protein sequence design, CMC/translational/clinical data analysis

Patents

Inventor on 15+ US patents and applications across RNA-editing guide design, AAV capsid engineering, and CAR-T cell therapy (4 issued; Shape Therapeutics, Juno/Celgene/BMS).

Selected Publications

Rett syndrome lifespan extension in mice via AI-guided ADAR editing Preprint, 2026

In-mouse efficacy and safety of the DeepREAD-designed MECP2 guide RNAs; restored MeCP2 and extended lifespan in a Rett-syndrome model.

Savva YA, Booth BJ, Shumaker L, Fasnacht R, Burleigh SM, Jiang Y, Cao Y, Johnson B, Bagepalli LR, Golic F, Enger N, Feiring R, Sadowski A, Rich S, Lakshmanan A, Milani N, Chadwick EM, Hauskins C, Works MG, Huss DJ, Briggs AW, VanSchoiack AA. doi.org/10.64898/2026.06.23.734060

Helix: a structure-aware deep learning model for accurate prediction of A-to-I RNA editing by endogenous ADARs Preprint, 2025

Transformer-based model for predicting RNA-editing outcomes; paired with DeepREAD's generative model via noisy-student distillation into DeepHelix, a unified predict-and-generate workflow for zero-shot guide-RNA design.

Cao Y, Bagepalli LR, Savva YA, Collins SM, Adams K, Letizia AJ, Boysen J, Abiar A, Edwards SR, Bussema L, Burleigh SM, Shumaker L, Hause RJ, Booth BJ*, Jiang Y*. *corresponding authors. doi.org/10.64898/2025.12.18.695251

An engineered U7 small nuclear RNA scaffold greatly increases ADAR-mediated programmable RNA base editing Nature Communications, 2025

Engineered snRNA expression scaffold that boosts editing efficiency; contributed bioinformatics analysis.

Byrne SM, Burleigh SM, Fragoza R, Jiang Y, Savva YA, Pabon R, Kania E, Rainaldi J, Portell A, Mali P, Briggs AW. doi.org/10.1038/s41467-025-60155-z

Generative machine learning of ADAR substrates for precise and efficient RNA editing Preprint, 2024

Bit-diffusion generative model (DeepREAD) for de novo guide-RNA design in RNA editing.

Jiang Y*, Bagepalli LR*, Banjanin BS, Savva YA, Cao Y, Guo L, Briggs AW, Booth B, Hause RJ. *equal contributions. doi.org/10.1101/2024.09.27.613923

High-Throughput Single-Cell Sequencing with Linear Amplification Molecular Cell, 2019

Yin Y, Jiang Y, Lam KG, Berletch JB, Disteche CM, Noble WS, Steemers FJ, Camerini-Otero RD, Adey AC, Shendure JA. doi.org/10.1016/j.molcel.2019.08.002

Anti-B-cell maturation antigen chimeric antigen receptor T cell function against multiple myeloma is enhanced in the presence of lenalidomide Molecular Cancer Therapeutics, 2019

Works M, Soni N, Hauskins C, Sierra C, Baturevych A, Jones JC, Curtis W, Carlson P, Johnstone TG, Kugler D, Hause RJ, Jiang Y, Wimberly L, Clouser CR, Jessup HK, Sather B, Salmon RA, Ports MO. doi.org/10.1158/1535-7163.MCT-18-1146

Molecular profiling of activated olfactory neurons identifies odorant receptors for odors in vivo Nature Neuroscience, 2015

Jiang Y, Gong N, Hu X, Ni J, Pasi R, Matsunami H. doi.org/10.1038/nn.4104

Invited Talks

Computational RNA Design & Delivery Summit, Boston, 2024.