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Hello, and welcome to my profile. My name is Eduardo — grab a cup of coffee and allow me to introduce myself.
I build Machine Learning & Data Systems where Biology, Medicine, and Software Engineering meet
(and occasionally argue — that’s fine, I’m a trained diplomat).
My work focuses on end-to-end ML systems. In practice, that means taking messy real-world data, ingesting it (a fancy name for ETL), and serving it hot and fresh for:
- Large-scale processing — things need to flow smoothly.
- Model training — using math to reward or punish learning machines
(I once broke a gradient in an RNN’s head, but it was already vanishing). - Deployment — when ideas meet reality and still have to behave.
I care deeply about reliability, clarity of design, and systems that age well — like a good Madeira wine.
I have experience designing modular data pipelines, scalable data engineering architectures, cloud-orchestrated systems, and ML workflows using Python-centric stacks and modern deep learning frameworks. This usually translates to:
- Production ML and data pipelines with strong reproducibility and auditability
- Scalable processing for high-volume analytical workloads
- Feature engineering layers serving both training and inference
- Bioinformatics workflows integrated with HPC and cloud environments
I often work at the boundary between scientific complexity and engineering constraints, translating domains such as
Chromatin Biology, Cancer Immunology, Gene Regulation, Microscopy, Spatial Transcriptomics, and Precision Medicine into clear, testable, and auditable computational systems.
I value clean design, explicit trade-offs, and systems that are understandable by humans — not just machines.
Ethics, reproducibility, and long-term sustainability are not optional; they are part of the job.
Currently open to on-site or hybrid roles and long-term projects.
Relocation and onboarding take planning — good systems (and good moves) benefit from doing things properly.
Cheers.
I would like to know more...
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Name: Eduardo G Gusmao
Role: Senior Machine Learning Researcher | Applied Scientist
Contact: eduardo@gusmaolab.org | English, Portuguese
Education: 2 x PhD | Machine Learning & Precision Medicine
Research_Profile: 5+ years experience on Translational & Production-Aware Method Development
Development_Environment:
Infrastructure: AWS | HPC | GCP
Languages: Python | SQL | C/C++ | R | Bash/Shell
MLStack: PyTorch | TensorFlow | JAX | Spark | Grafana
DataStack: PostgreSQL | MongoDB | Pinecone | REST/GraphQL | Pandas
SysOps: (Micro)Mamba | Docker | Kubernetes | GH Actions | PrometheusI would like to know more...
Name: Eduardo G Gusmao
Role: Senior Machine Learning Researcher | Applied Scientist
Contact: eduardo@gusmaolab.org | English, Portuguese, German, Spanish
Education:
- PhD: 2017 | Harvard University & RWTH Aachen University | Machine Learning & Precision Medicine | Summa Cum Laude
- BSc_MSc: 2013 | Federal University of PE | Computer Science & Machine Learning | GPA 3.96/4.00
Core_Expertise:
- "Machine Learning"
- "Deep Learning"
- "Statistical Modeling"
- "Bioinformatics"
Research_Profile:
- "Method Development"
- "Translational Modeling"
- "Production-Aware Research"
Development_Environment:
Hardware: ["AMD", "ARM", "NVIDIA", "Intel"]
OS: ["MAC OS X", "Ubuntu", "Debian", "Fedora", "Windows"]
Infrastructure:
- "Bare-Metal Servers"
- "VMs"
- Cloud_Computing: ["AWS", "GCP", "Azure"]
- HPC_Paradigm: ["Slurm", "OpenPBS", "MPI"]
- Infra_as_Code: ["Terraform", "CloudFormation", "Pulumi"]
Languages:
- Multi_Paradigm: ["Python", "C/C++", "R", "Bash/Shell", "Julia", "Go", "Rust", "Kotlin"]
- Web_OO: ["TypeScript", "Java", "Javascript", "C#", "Ruby", "PHP"]
- Markup: ["YAML", "Quarto", "LaTeX", "HTML/CSS/Markdown"]
- Declarative: ["SQL", "HCL"]
Runtimes: ["CPython", "JVM", "Node.js"]
ML_Stack:
- Frameworks: ["PyTorch", "JAX", "TensorFlow", "Keras", "Hugging Face", "NLTK", "Scikit-Learn"]
- Engines: ["Spark", "Ray", "TensorRT"]
- Models: ["Generative Models", "Variational Inference", "Graph Neural Networks", "Attention Hypergraph"]
Data_Stack:
- Databases:
- SQL: ["PostgreSQL", "MySQL"]
- NoSQL: ["MongoDB", "Arangodb"]
- Vector: ["Pinecone", "FAISS"]
- APIs: ["REST", "GraphQL", "gRPC"]
- Data_Software: ["Power BI", "Microsoft Suite", "HDF5/Parquet/Zarr"]
- Data_Tools:
- Basic: ["Pandas", "NumPy", "Scipy", "Bioconductor", "PySAM"]
- Big_Data: ["Dask", "Polars"]
- Specialized: ["PyCaret", "OpenCV"]
Web_Stack:
- Frameworks: ["Django", "React", "Next.js", "Express.js"]
- Dashboards: ["Dash", "Streamlit", "Gradio"]
Systems:
- Version_Control: ["Git", "Github"]
- Packaging: ["pip", "(micro)mamba", "(mini)conda", "poetry", "npm"]
- Containers: ["Docker", "Singularity", "Podman"]
- Orchestration: ["Kubernetes", "Helm"]
- CI_CD: ["GitHub Actions", "Jenkins", "GitLab CI"]
- Observability: ["Prometheus", "Grafana"]
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Apple |
Python |
PyTorch |
GATK |
Git |
Snakemake |
Gradio |
Docker |
AWS |
Jira |
Linux |
R |
TensorFlow |
Bioconduct |
GitHub |
Nextflow |
FastAPI |
K8s |
Terraform |
Grafana |
VsCode |
Bash |
JAX |
Ruff |
GActions |
Mamba |
Postgres |
Redis |
DtBricks |
Prometheus |
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Flagship: 🏳️⚧️ | 🏳️🌈 | 🇺🇳
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