Served in a multifaceted capacity as a manager, developer, designer and data scientist. Invented, designed and developed an AI based platform for structural modeling and predicting the activity of novel therapeutic designs. Used this system to build the leading therapeutic designs for the company. Designed and managed experiments that tested the novel therapeutic designs. Managed a team of computational scientists, bioinformaticians and IT consultants, overseeing many of the company’s research, software development and IT needs.• Developed and implemented algorithms to leverage informatics (SCAP 2.2) leading to new molecular designs improving binding to the target ligand with 10-fold greater activity in preliminary testing. • Drafted research-based documents for the C-suite used in communication of our current platform and future direction with the board of directors and investors.• Seamlessly managed a team of SRE’s and IT specialists improving and stabilizing Mantra Bio IT infrastructure, leading to less down time for the company.• Successfully managed a team of bioinformaticians and biostatisticians that executed data analyses, statistics and RNASeq analysis on numerous studies that resulted in impactful insights into the in vivo models and identification of biomarkers for evaluation of our therapeutic and potential client populations. • Served as Cloud Manager, Scrum Team leader reducing system costs and project completion times for the team.

Development of an AI approach to protein structure function modeling. Inventing and incorporating novel algorithms and informatics to reduce search space. In charge of overseeing Mass Spec analyses. Act as scrum master the data science and engineering team. Manage the bioinformaticians and biostatisticians and oversee the data science related projects for the company.• Invented, planned and developed an AI based protein structure to function pipeline (SCAP) that I used to design the company’s lead candidate therapeutics. • Optimized the SCAP (2.0) pipeline to assess and filter the design space from ~12 billion designs to 20 viable candidates within a 20-day timeframe. Resulted structures with greater than 3-fold increase in in vitro activity.• Researched and proposed to C-suite a new therapeutic program leading to preliminary testing of the concept and potential execution if funded.• Designed, managed and analyzed Mass Spec and RNAseq project in support of the lead therapeutics• Developed and ran summer internship program exploring the latest in molecular docking innovations.

A public research university, I teach Ph.D. level classes in relational database design, bioinformatics, and machine learning. Currently we’re exploring Covid-19 mRNA molecular structure and propagation through a combination machine learning and bioinformatics tools for linkage in potential exogenistic molecular catalysis for genetic predisposed diseases.Research is done in a Linux environment using OMP, Unifold, BLASTn, SeqNFind, and Kbase. Model mRNA production using Knime, Random Forest, Pyspark, and classification trees. Use Python for data cleansing and pipeline development and R for graphics and statistical analysis. Database design coursework taught in PostgreSQL and SQL Lite.Accomplishments include:-Workflow Optimization- I developed and patented a pipetting tool to help make lab scientists more efficient.-System Development- I created a topological mapping system to allow multi-dimensional data to fit into standard tools including Random Forests and SVM. -Pipeline Processing- I conceptualized a pipeline for processing genome data to be taught to students after identifying gap in analyzation of NGS data.

In this role, I managed the computational lab for Dr. Jennifer Weller’s research while serving as DataFATE lead and project manager.From a technical perspective I worked with Python, R, WEKA, PyQT, SQL, and processed data using kNN, neural nets, SVM, rulefit, and random forests.My accomplishments included:-Database System Build- I used mereological/ ontological based relational model to allow construction of strongly typed but No Schema required scientific data tracking within a standard SQL environment, allowing data from different sources to be stored in compact manner. System used to carry out bioinformatic analyses.-Pipeline Creation- We needed a system to examine data to provide external collaborator to Howard University on analysis of genomic ties with colorectal cancer on men of African descent. I also conceptualized a pipeline While here I won the UNCC Inaugural Research Award for a poster: DataFATE: A prototypic SIMS” for Ontology construction and Data Mining.

Accelerated Technologies Laboratories is a leader in laboratory information management systems which enable labs in a variety of industries to automate their operation and improve their efficiency.I was hired to oversee the creation of a high through-put GPGPU accelerated algorithms for parsing bioinformatics data to provide faster sequence comparison across multiple genomic targets. I developed the bioinformatics database in SQL for use in MySQL, PostgreSQL, Oracle, or SQL server, prototyped the system in Python with system analysis and reporting in R, and served as IT Manager of Linux-based GPGPU clusters (Red Hat, Ubuntu) and workstations.My accomplishments included:-Invention- This was a new bioinformatics tools suite and we built it using state of the art GPGPU technology.-Data Processing Improvement- We shrunk the processing time by 46 hours on a single workstation and reduced overhead without degradation of information.-Project Management- We delivered the project 2.5 years ahead of schedule.

A public research university established in 1957, I was the principal investigator and developer of a CRISP-DM pipeline used to reclassify and align the Cambridge and NIST crystal structure databases. My research was presented at the American Chemistry Society National Convention and published: D. Andrew Carr, et al. “Machine learning approach for structure-based zeolite classification” Microporous and Mesoporous Materials, July 2008.I also served as a guest lecturer in machine learning covering: Classification and Regression with specific focuses on Neural Nets, SVM, Classification Trees. My accomplishments included:-Computational System Creation- I developed this to help classify three-dimensional crystal structures that was used by NIST and Cambridge to help consolidate and the Zeolite x-ray crystallography database.-Machine Learning- I built a method for comparison and classification of known crystal structures enabling removal of duplicate entries and reclassification of structures that were either novel or entered incorrectly.-Tool Design- I designed a computational tool to enable codified method for alignment of crystal structures.

I worked various computational labs supporting Big Data research on Unix and Linux machines. Primary programmer and systems developer on various projects including: -Vaisman Lab- The application of Delaunay tessellation to known 3D protein structures, the study involved compilation of a large dataset (SQL) and using machine learning, “rule fitting” and random forests; programming was done in Python, PostgreSQL and R. -Weller Lab- Design and implementation of a scientific database system underpinned by experimenting with specific ontologies utilizing ‘ProbeFATE’ database (NSF funded grant, PI Weller); functioned as the IT manager for the Linux servers and played an interictal part in the study as the primary developer working in SQL, Python, PyQT, and R.-Visualization software development- worked to extend capabilities of a GIS based visualization system GLISTEN; additional development of GLISTEN entailed visualization of multi-dimensional data utilizing Java, OpenGL, Python, C for development.-Kinser Lab- Production of binary and fuzzy-logic based extensions of self-solving neural nets coded in Python; directly supported the project team by acting as the IT manager of the Linux environments which were coded in Python.-Wallin Lab- Developed and coded software for rendering 3D visualizations of galaxy collisions in the “holodeck” virtual reality lab utilizing Fortran and Java.