PhD Researcher in Computational Oncology

Developed and implemented machine learning models to drive innovation in cancer research, creating robust algorithms for data-driven insights in oncology.

• Specialties: Machine learning models, GNNs, VAEs, embeddings, predictive analytics in biomedical applications.
• Client Relevance: Expertise in creating machine learning models to uncover novel insights from complex datasets—ideal for clients aiming to advance data-driven solutions in precision medicine, healthcare, and beyond.

Consultant and Mentor

Provide tailored mentorship in Angular, Python, Machine Learning, and Data Science to clients and students of diverse backgrounds.

• Client Relevance: Customized, one-on-one guidance to boost technical skills and confidence, helping clients meet specific project needs and learning goals.

CEO & Founder

Co-founded a tech company providing machine learning solutions for musicians and producers. Led product development and managed cloud infrastructure.

• Specialties: Full-stack app development, Azure infrastructure management, batch processing solutions.
• Client Relevance: Experience in scalable app and ML service delivery, aligning technical solutions with creative industry needs.

CEO & Founder

Launched a consulting firm specializing in data science and IT solutions, offering clients data-driven strategies and training services.

• Specialties: Strategic consulting, data science, custom training sessions.
• Client Relevance: Proven success in executing marketing strategies and delivering tailored training for improved client operations.

Oregon Health & Science University

As a PhD candidate, I developed advanced computational models to enhance cancer research, focusing on methods that deliver actionable insights from complex biological data. Leveraging Variational Autoencoders (VAE), Autoencoders (AE), and Graph Neural Networks (GNN), I created data embeddings to optimize analysis and interpretation, significantly improving prediction accuracy and data processing efficiency.

• Key Achievement: Led and collaborated on multiple projects utilizing ML models to uncover novel insights in cancer biology.
• Impact: Innovations from this research could drive advancements in vector databases and protein imputation, with impactful applications in cancer treatment and patient survival.

University of Applied Sciences Karlsruhe

Thesis: Developed a geolocation framework to streamline customer acquisition processes for businesses.

• Final Grade: 2.1 (Equivalent to Excellent)

Computational Pipeline to Identify Gene Signatures Defining Cancer Subtypes

Developed a novel computational pipeline for identifying gene signatures in cancer subtypes, demonstrating the power of data-driven approaches in oncology research.

Imputing Single-Cell Protein Abundance in Multiplex Tissue Imaging

Innovative approach to impute protein abundance in tissue imaging, advancing the analysis of complex biological systems and enhancing data interpretation in biomedical research.

Tumor model to tumor treatment: Applying deep learning approaches to map multimodal data from cancer model systems to patients

Novel VAE-based strategy for correcting platform effects in cancer drug response data, enhancing cross-model prediction accuracy and advancing the translatability of proteogenomic studies for improved insights into cancer biology and patient treatment strategies.