Education

  • Masters of Science in Computer Science
    A focus of evolving AI Q-Learning swarm systems with an application to chaos theory.
    John Hopkins University
  • Bachelors of Science in Physics and Applied Mathematics
    A focus of chaos theory and Monte Carlo simulations for astronomical applications.
    Arizona State University

Experiences

  • Senior Machine Learning Engineer
    Working on the Azure cloud platform, focusing on AI, LLMs and machine learning services.
    Microsoft
  • Senior Data Scientist
    Worked on various data science projects, focusing on machine learning and AI applications at scale.
    Percient Inc
  • Data Scientist
    Worked on various healthcare-related data science projects, focusing on predictive modeling and patient outcome analysis.
    Mayo Clinic

Last publications

  • Self Correcting Complex Systems of Multi Agent environments.

    Authors: Eduardo Carrasco Jr, Alan Turing

    Science translational medicine • 2024

    In the past few years AI Agents with self correction has been taking off. In this paper our goal is to explore the implications of self-correcting mechanisms in complex multi-agent systems.

  • Sex differences in GBM revealed by analysis of patient imaging, transcriptome, and survival data

    Authors: Eduardo Carrasco Jr, W Yang, NM Warrington, SJ Taylor, P Whitmire

    Science translational medicine • 2016

    Sex differences in the incidence and outcome of human disease are broadly recognized but, in most cases, not sufficiently understood to enable sex-specific approaches to treatment. Glioblastoma (GBM), the most common malignant brain tumor, provides a case in point. Despite well-established differences in incidence and emerging indications of differences in outcome, there are few insights that distinguish male and female GBM at the molecular level or allow specific targeting of these biological differences. Here, using a quantitative imaging–based measure of response, we found that standard therapy is more effective in female compared with male patients with GBM. We then applied a computational algorithm to linked GBM transcriptome and outcome data and identified sex-specific molecular subtypes of GBM in which cell cycle and integrin signaling are the critical determinants of survival for male and female patients, respectively. The clinical relevance of cell cycle and integrin signaling pathway signatures was further established through correlations between gene expression and in vitro chemotherapy sensitivity in a panel of male and female patient-derived GBM cell lines. Together, these results suggest that greater precision in GBM molecular subtyping can be achieved through sex-specific analyses and that improved outcomes for all patients might be accomplished by tailoring treatment to sex differences in molecular mechanisms.

Skills

Complex AI Systems

Expertise in designing and implementing advanced AI models and algorithms.

Deep Learning

Deep expertise with neural networks (CNN, GPT, Transformers, etc.) and frameworks like TensorFlow and PyTorch.

Data Science

a solid foundation in statistical analysis and machine learning techniques for designing machine learning models for production systems.

Classical Machine Learning

Proficient in traditional machine learning algorithms and techniques for data analysis and predictive modeling.

Open Source

Active Open Source contributor with a focus on data science and machine learning projects.