Bruno Alano
CTO & Co-Founder at ABRIA - Associação Brasileira de Inteligência Artificial
São Paulo, Brazil
Summary
👤
Senior🎓
Top SchoolBruno Alano is a CTO and serial founder with 11 years of experience building AI-driven products and engineering teams across Brazil and beyond. As co-founder and CTO of Avra he’s reshaping credit intelligence for SMEs, drawing on prior leadership of AI/product teams at Jusbrasil and founding roles at Leadster and ABRIA. He blends research pedigree from an early stint as an External Researcher at OpenAI with hands-on ML and backend work—contributing to notable open-source projects like HDBSCAN (distance metrics) and AWS Lambda-compatible Python packages. Comfortable across the stack, Bruno has improved React diagram UX, implemented clustering distance aliases, and delivered production fraud and churn models as a staff data scientist. He’s known for bridging deep research (NLP, time series, RNNs) with pragmatic engineering that ships in production for business impact.
11 years of coding experience
10 years of employment as a software developer
Bacharelado Ciência da Computação, Bacharelado Ciência da Computação at University of London
English, Spanish, Portuguese
Stackoverflow
643reputation
118kreached
22answers
20questions
Github Skills (32)
algorithm10
distance-matrix10
python10
package-management10
scikit10
aws-lambda10
typescript10
diagram10
machine-learning-algorithms10
typescript-types10
scikit-learn10
typescripts10
clustering10
react10
metric10
Programming languages (13)
Github contributions (5)
Miserlou/lambda-packages
Feb 2017 - Feb 2017
Various popular python libraries, pre-compiled to be compatible with AWS Lambda
Role in this project:
Back-end Developer
Back-end DeveloperContributions:6 commits, 3 PRs, 7 comments in 1 day
Contributions summary:Bruno contributed to adding and managing Python packages for use in AWS Lambda functions. Their work involved adding new packages like `datrie` and `python-Levenshtein`, modifying package versions, and reorganizing the package manifest. The user also addressed issues with package names and versions, ensuring the packages are compatible with the intended deployment environment. The primary focus was on providing various python libraries pre-compiled for AWS Lambda.
pythonpypibotoserverlessaws-lambda
scikit-learn-contrib/hdbscan
Aug 2016 - Aug 2016
A high performance implementation of HDBSCAN clustering.
Role in this project:ML Engineer
ML EngineerContributions:7 commits, 2 PRs, 8 comments in 13 days
Contributions summary:Bruno primarily focused on enhancing the distance metric functionalities within the HDBSCAN library. They implemented the arccos distance metric and subsequently refactored the code to leverage the cosine distance, addressing an initial issue with the former implementation. Further contributions included adding an alias for the cosine distance, indicating a focus on providing users with more flexibility in distance metric selection for clustering tasks. These improvements are directly related to the core functionality of the HDBSCAN clustering algorithm.
clustering-evaluationk-meansmachine-learning-algorithmsmachine-learningclustering
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