Eriberto Lopez

Pioneered the industry's first robotic cloud lab. Enables users to plan experiments, command-and-control robotic execution, securely capture proprietary data, and visualize real-time execution history from remote or on-site devices.

• Lead an internal team of 7 and third-party offshore development team as the tech lead for the workflows domain. Guide feature implementation to capture scientific intent and transform to machine-readable executable code.
• Boosted team velocity by 38% through enhancements in average story points per developer, coupled with an 83% sprint completion rate over the last year. Use Jira & Slack APIs to automate processes and improve accountability.
• Streamlined incident triaging processes, by improving team accountability for respective components. Resulted in a 113% increase in engineer volunteering rates.
• Spearheaded and incorporated data-driven initiatives into quarterly roadmaps, significantly reducing tech debt and improving the user experience. Achieved a 53% reduction in workflow builder front-end total load time.
• Optimized front-end React components by migrating to a MobX bi-directional stage-management approach.

Scaled Strateos' legacy scientific intent execution system from lambda functions to microservice architecture.

• Spearheaded the development of a workflow execution system, offering scientists a platform to execute complex CWL workflows on shared infrastructure while safeguarding proprietary data.
• Streamlined data ingestion pipelines for both web and robotic domains, reducing complexity and enhancing monitoring of raw data capture.
• Contributor in filed patent for 'Systems and Methods For Processing Experimental Workflow at a Remote Laboratory'

Developed and maintained features and scientific intent schemas that are device agnostic and reproducibly executed.

• Achieved 64x increase in cloud lab sample throughput by optimizing computer vision and robotic commands.
• Developed Python statistical software to automate cluster identification of multidimensional data, significantly reducing the hands-on quality control validation time for operational teams by 50%.
• Curator of open-source machine readable scientific intent schemas, autoprotocol.org & autoprotocol-py

Klavins Lab DARPA grant used to automate remote execution of the design, build, and test cycle for genetic systems.

• Enabled Synergistic Discovery and Design DARPA stakeholders to remotely design, build, and test genetic CRISPR systems.
• Developed a Python client to facilitate ETL processes, extracting and loading data from remote labs to Texas Advanced Computing Center's HPCs.
• Integrated Klavins Lab scientific devices with web application (Ruby on Rails, Angular) and AWS cloud resources.
• Co-authored algorithmic design of thousands of non-repetitive synthetic parts for genetic systems in microbes.

• Used graph theory and state by state path generation to generate thousands of non-repetitive genetic part designs.
• Characterized all promoters in a massively parallel RNASeq assay normalizing RNA fold expression by genomic copy number.

• Collaborated on development of web application which integrates experimental design, inventory management, protocol execution, and data capture.
• The containerized application allows scientists to scale up research, automate execution, and collaborate on biomedical and bioindustrial research.

• Specialization in Computing Systems
• Computer Networks, Intro to Information Security, Software Arch & Design
• Advanced Internet Computing, Robotic AI Techniques, Machine Learning for Trading