Hi! I like data and problem-solving.
- Led research projects in the field of Computer Vision, focusing on the application of machine learning techniques to analyze complex datasets.
- Developed and fine-tuned custom algorithms for unsupervised 3D image object detection.
- Collaborated closely with cross-functional teams, communicating technical concepts to non-technical stakeholders
- Developed 3D image processing techniques (Image Processing, Image Segmentation, Image registration)
- Proposed a lossless data format change that reduced memory storage by 35%, resulting in a savings of 70 gigabytes (70GB).
- Presented findings through interactive reporting for effective communication with both technical and non-technical stakeholders.
- Designed and deployed diverse machine learning models (Machine Learning, Tidymodels) to predict patient responses to immunotherapy, utilizing clinical data.
- Translated technical insights into a practical solution by developing and integrating predictive models into an API (API Development).
- Used Version Control (Git, GitHub, GitLab) to manage the codebase and enable efficient collaboration.
- Data Wrangling and Analysis (R, Pandas) for efficient data processing and interpretation
- Conducted statistical analysis to data-mine and leverage in-house database (RNA-seq and SNP analyses) using R and Galaxy (Bioinformatics Tools).
- Visualized and interpreted interaction networks to enhance understanding of the data and improve the presentation of findings.
2020/2022, Masters Degree in Computational Mathematical Biology - Bioinformatics, Aix-Marseille University
- Combined program in computer science, mathematics, and biology, with a focus on analyzing complex data
- Foundations in linear algebra, discrete and continuous dynamical systems, graph theory, and network analysis
- Expertise in statistical analysis of big data, advanced statistics, and analysis of OMIC data
- Overview of different biological fields (i.e. ecology, evolution, molecular biology, biochemistry, etc).
- 400+ hours of supplemental coursework in statistics and computer science
- Programming Languages: Python, R, Bash
- Machine Learning: Scikit-learn, SciPy, Scikit-image (Skimage), OpenCV, Tidymodels, TensorFlow
- Data Manipulation and Analysis: Pandas (Python), SQL, dplyr (R), purrr (R)
- Data Visualization: Plotly, Plotnine (ggplot2 for R), Matplotlib, Seaborn
- Version Control: Git, GitHub, GitLab
- Reporting: Jupyter notebooks, markdown, latex, Quarto
- Deployment: pickle, docker (basics)
- Other Tools: Unix Shell, VS Code, RStudio
- Benzekry, S., Grangeon, M., Karlsen, M., Alexa, M., Bicalho-Frazeto, I., Chaleat, S., Tomasini, P., Barbolosi, D., Barlesi, F. & Greillier, L. (2021). Machine learning for prediction of immunotherapy efficacy in non-small cell lung cancer from simple clinical and biological data. Cancers, 13(24), 621.
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2019 - "Clustering in microRNA in carcinomas highlights its role in hallmarks of cancers". Isabella Bicalho Frazeto and Bruno Magalhães, 5th Centro-Oeste Genetics Conference.
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2018 - "How to deal with redox potential and pH changes: insights from molecular dynamics". Isabella Bicalho Frazeto and Marcos Serrou do Amaral, X-Meeting 2018 - 14th International Conference of the Brazilian Association of Bioinformatics and Computational Biology (AB3C), São Pedro - Brazil.
- Topics: understanding bias and explainability in sensitive data models, challenges of bias in AI, fairness and unbiased systems, sensitive attributes, proxies in machine learning, the journey of model construction, metrics role of explainability and trustworthy AI.
- Topics: hands-on tutorial for dimensional reduction techniques (PCA, ICA, Multidimensional Scaling, t-sne, and U-MAP).
- Topics: introduction to R, data structures, introduction to tidyverse, data manipulation in dplyr, data visualization using ggplot2.
- 2018 - Undergraduate research outstanding award (Prêmio Destaque PIBIC/PIBITI ), Integra, UFMS, for the investigation of the effects of pH and redox changes on micro peroxidase-8 and Cytochrome using molecular dynamics. Work suppervised by Dr. Marcos Serrou do Amaral.
- Teaching Assistant to Statistics and Bioinformatics for undergraduate and master-level courses.
2021 - Present, Writer in SOMA (On Pause)
- SOMA is a project whose goal is to make biostatistics and programming easy for biologists. We create content in the form of tutorials, courses, and interviews in Portuguese and Spanish. This project is on pause as we are restructuring it
- Teaching Assistant in hands-on Data Science in R courses
- Spearheaded and orchestrated a dynamic three-month study group, catering to a diverse cohort of 60 women, where I delivered sessions on foundational machine learning concepts.
- Crafted and curated comprehensive course materials, ensuring accessibility and clarity for participants with varying levels of familiarity with the subject.
- Fostered a collaborative learning environment by facilitating discussions, encouraging active participation, and providing personalized guidance to enhance understanding and retention.
- Accountant: spreadsheet keeping, balancing books, reporting the financial status of the center
- Organized talks, volunteer opportunities, and student integration projects.
- Portuguese: Native.
- English: C1 (114/120 in TOEFL).
- French: B2 (DELF B2).
- Spanish: B2.