As Apertura’s first computational hire, I played a pivotal role in the company’s spin-out from the Deverman Lab at the Broad Institute, establishing foundational capabilities in data science, software engineering, and IT. I led the tech transfer, fostered strategic early partnerships, and developed robust workflows for next-generation sequencing (NGS) data analysis, directly identifying lead capsid variants and supporting regulatory proposals for clinical trials. My mathematical, data, and computational fluency, combined with intellectual curiosity, leadership, and cross-functional collaboration skills, has enabled me to thrive in fast-paced environments and effectively tackle complex scientific challenges while communicating insights across diverse teams.3 Key Experiences and Contributions at Apertura:1. Computational Team Leadership: As Apertura’s first computational hire, I spearheaded the development of the company’s computational and data storage infrastructure, facilitating the tech transfer from the Deverman Lab and integrating essential IT services. I was involved in early computational and data-related parter strategy and hiring, especially for the computational and capsid engineering teams.2. Data Analysis and Decision Support: I led the analysis of next-generation sequencing (NGS) data, identifying CNS capsid variants critical for follow-up studies and supporting regulatory proposals for clinical trials, including evidence supporting FDA interactions.3. Cross-Functional Leadership: I demonstrated strong leadership in cross-departmental collaborations, establishing data models and workflows across teams, and communicating findings effectively to drive interdisciplinary projects and inform strategic decision-making for both engineering and identifying lead capsids, and internal process development.

Developed machine learning models and algorithms for protein engineering, generating novel AAV capsid variants optimized for blood-brain barrier (BBB) crossing. This involved processing and analyzing biological data, creating automated pipelines, and improving scoring and prioritization methods to identify high-performance capsids with enhanced in vivo success compared to prior approaches.Much of this work culminated in the following paper (linked in media):- Huang Q, Chen AT, Chan KY, Sorensen H, Barry AJ, Azari B, Zheng Q, Beddow T, Zhao B, Tobey IG, Moncada-Reid C, Eid FE, Walkey CJ, Ljungberg MC, Lagor WR, Heaney JD, Chan YA, Deverman BE. "Targeting AAV vectors to the central nervous system by engineering capsid-receptor interactions that enable crossing of the blood-brain barrier." PLoS Biol. 2023 Jul 19;21(7):e3002112. doi: 10.1371/journal.pbio.3002112. PMID: 37467291; PMCID: PMC10355383.