As an Independent Grant-funded AI/TensorFlow Developer, I specialize in advanced computational modeling of antibody CDRs, leveraging machine learning, NLP, and AWS cloud services to innovate in immunotherapy. Utilizing AWS SageMaker and Lambda, I develop robust and scalable models that incorporate transformers and attention mechanisms to enhance the analysis of antibody interactions. By applying NLP techniques and attention-based models, I have improved the identification of amino acids beyond known CDRs by 20%, significantly enhancing the precision and accuracy of antibody interaction models. My expertise in AWS, TensorFlow, NLP, and biotechnology positions me at the forefront of healthcare innovation and technological advancement, driving forward impactful research and medical breakthroughs.

As a TensorFlow Developer and Data Analyst, I specialize in end-to-end model development for RNA-seq datasets, using regression analysis and PCA clustering to uncover complex gene expression patterns. Leveraging TensorFlow, Dask, and AWS, I design scalable models that transform large-scale RNA sequences into actionable insights, advancing genomics research and personalized medicine. By optimizing complex datasets, I enhance data management and drive meaningful outcomes in research and healthcare.

As Principal Investigator and Database Engineer at VascuLogic, I led a team that enhanced oncological research through advanced data engineering and big data pipeline optimization. My role focused on automating the ingestion and processing of biological data to support studies on ligand-antibody interactions, such as Avastin-VEGF-2 binding strengths. Key achievements include:Automated Data Ingestion and Cleaning: Deployed Apache NiFi to automate and scale the ingestion and cleaning of large-scale biological datasets, greatly enhancing efficiency and data accuracy.Leadership in Targeted Research: Worked alongside clinicians to identify and focus on ten key oncological proteins, improving target identification rates by integrating clinical expertise into data-driven research.Big Data Pipeline Optimization: Leveraged PySpark and AWS Glue to optimize API call management, user authentication, and JSON data processing, significantly boosting system efficiency and security.Advanced Model Development: Utilized AWS technologies (Glue, S3, EC2) to manage and transform Parquet files, facilitating the development of sophisticated TensorFlow models and accelerating bioinformatics decision-making.By streamlining big data pipelines and fostering a collaborative environment that combined technical and clinical insights, I advanced our data processing capabilities and contributed to significant breakthroughs in cancer research.

At Livandala, Inc., I led computational biology projects, leveraging cloud computing and machine learning to enhance genetic database searches and data processing. Key achievements include:Cloud and Data Management: Optimized storage solutions with AWS, Google Cloud, Azure, and Snowflake, achieving an 80% increase in query efficiency and data retrieval speed through advanced SQL techniques.Data Visualization and Analysis: Improved target identification and data visualization with Python, Dask, Seaborn, and Matplotlib, resulting in a 60% increase in identification accuracy and eliminating visualization delays.Biomarker Identification: Utilized machine learning algorithms like Random Forest and SVM to enhance biomarker identification, boosting predictive accuracy by 30% and halving model training time.End-to-End AI Solutions: Developed comprehensive AI solutions from data ingestion to visualization, ensuring seamless integration across the drug design and analysis pipeline.Skills:Programming: Python, Shell Scripting, Unix/LinuxMachine Learning: TensorFlow, Deep Learning, Supervised/Unsupervised Learning, AI, Data Analysis, K-Nearest Neighbors (KNN)Data Management: PostgreSQL, Apache Spark, Snowflake, SQL, Data VisualizationCloud Computing: AWS, Google Cloud, AzureBioinformatics: Genomics, Proteomics

Created a computational model of nanoparticles with controlled release of bupivacaine to mitigate post-operative pain and swelling, a project that resulted in publication.Performed Computational Fluid Dynamics studies to determine the rate of nanoparticle release of various drugs that ranged in terms of hydrophobicity and nanoparticle pore-size

Performed Molecular Dynamics Simulations after docking a group of substrates with an enzyme active site using AMBER. Calculated thermodynamic parameters on XSEDE/NSF Supercomputer cluster.Carried out project management of several projects, including resource management and budget generation for reagents and equipment, as well as directing technical staff in meeting goals. Wrote an extensive review of the modern culture of protein-producing cells and subsequent manufacture of monoclonal antibodies utilizing several modern platforms.

I was hired as a Staff Scientist and Protein Engineer by a company focused on improving nanotechnology through the use of customized proteins and high throughput diagnostics. I was brought on board to research and identify new technologies as well as improve on existing ones. I managed the full cycle of projects, creating budgets for each project, in addition to performing computational modeling of protein and creating a library of these candidates from the results. My successes include:❖Introduced the CEO and CSO to new technologies through presentations, with the technologies consistently novel and representing significant opportunities for Zymera.❖Designed and formed new luciferase proteins for complexation to Quantum Dot technology. Saved the company $75,000 by using IP-free methods to create proteins.❖Invented simple conjugation methods to develop means of synthesizing protein ligations, leading to months of time savings and associated costs. Generated quick turnaround that was used for preliminary data in grants and promoted customer base research for targeted marketing.❖Conducted effective troubleshooting on experimental, ultra-sensitive, high-throughout assay for viral infection detection in VERO cells for vaccine production at Medimmune’s cGMP-compliant facility.❖Created new nanoparticle that could be used in vivo, completing project well ahead of schedule.