Connor Healy Email and Phone Number

As the leader of the Applied Mathematics team in the Research and Development department at Biofire Defense, I oversee a dedicated team of three professionals. Our primary focus is to provide robust software and analytics support for assay development, manufacturing, quality control, and post-market surveillance.In my role, I have conducted root cause analyses to identify and resolve product failures, and contributed to the design and initiation of new studies and product development. We adhere to standard software development procedures and utilize a variety of programming languages, including R, Python, C++, and MATLAB, to develop innovative solutions that save the company both time and money.Our team acts as an intermediary within the R&D department, offering analytics support across various teams. A significant achievement of ours is the successful setup and implementation of a Jira service desk, a first for Biofire Defense, which has streamlined the process of collecting, tracking, and managing requests made to our team.Despite the fast-paced and diverse nature of our work environment, we thrive on the challenges it presents. Looking ahead, my goal is to expand our team and extend our project management workflows to other teams and departments within Biofire Defense.

Conducted high dimensional analyses on complex datasets, employing advanced techniques such as preprocessing, clustering, dimensionality reduction, differential analysis, and visualization. These analyses provided insights into drug safety, efficacy, mechanisms of action, and therapeutic target identification.Led the creation, curation, and management of a relational database for clinical data, supporting an efficient machine learning-based biomarker discovery pipeline using PostgreSQL. The database contained information from 2000+ blood samples and 500+ patients.Analyzed biomarker data from diverse cancer patient cohorts, identifying predictive biological signatures for severe side effect incidence with up to 75% accuracy.Developed optimized code in R, automating data preparation and transformation steps, integrating 5 new statistical methods into the analytical pipeline. Resulted in expanded analysis capabilities, reduced errors, and improved efficiency by 15%.Implemented a robust 40-step QC pipeline across teams, ensuring data accuracy and consistency, and reducing re-analysis to zero in the past 6 months. Developed an algorithm improving batch-to-batch parity by up to 30%.Leveraged cloud computing technologies like AWS EC2 and S3 for scalable and cost-effective data processing, enhancing collaboration and computational capabilities.Built a Python tool for efficient pooling of clinical trial investigator information, resulting in a 10-fold increase in productivity and saving $15,000 for the marketing and sales team.Presented research findings at the 2022 SITC conference, generating significant interest and yielding 12 new leads for the sales and marketing team.Collaborated with global teams, fostering relationships with stakeholders and academic institutions, facilitating shared research and development goals through appropriate modeling and analysis approaches.

- Spatial Statistics: Developed and thoroughly tested a novel statistic to characterize spatial patterning of cells in tissue specimens.- Deep Learning and Computer Vision: Employed a neural network to identify cells in medical images. Developed software to automatically detect cells and other anatomical features in microscopy images. - Data Visualization: Devised several novel data visualization schemes including 3D animations of genetic machinery and graphics to show the average appearance of cells in the inner ear. - Biomodeling: Simulated stem cell and protein expression dynamics in the bone marrow using a combination of agent-based and deterministic models to assess factors influencing stem cell proliferation and differentiation.- Morphological Analysis: Devised a new metric for cell polarity that is comprehensive and flexible and enabled researchers to discover new biological phenomena in the inner ear.- Image Processing and Analysis: Enhanced images and devised segmentation schemes to improve throughput of medical specimen analysis.- Data Mining: Increased interpretability of a large dataset of cell morphologies using Principal Components Analysis. - Software Engineering: Developed and managed multiple repositories of code. Enhanced programming flexibility with Git.- Scientific Communication: Prepared clear and compelling research articles and scientific figures. Represented my lab and presented at conferences in industry, academia, and government. - Lab Management: Contributed to grant proposals, peer review requests, presentations, candidate training, and other lab group activities.

- Prepared detailed rubrics and keys for grading.- Met with students to develop strategies to lead them to academic success.- Planned and conducted lessons in fluid dynamics, molecular transport, and finite difference methods.- Designed, setup, and managed course laboratories.

Prepared and imaged brain tissue specimens for a research group that focuses on human behavioural and genetic disorders. Work included sectioning, staining, mounting, and flourescence microscopy as well as 3D solidworks design and 3D printing.

Prepared solution keys and graded and filed assignments for a course focused on electrical and biological signal analysis.

- Prepared and verified assignment keys and rubrics.- Scored student submissions for quality and accuracy.- Maintained detailed records to maintain course accreditation.