Recent graduate with bachelor’s and master’s degrees in Electrical Engineering and Computer Science from MIT. Experience with software development, machine learning, and product management.
Message me at: antonioberrones.ab@gmail.com
Engaged in thesis research by developing data processing pipelines to detect learning behaviors from joint handwriting and eye-tracking data captured from subjects performing cognitive tests to aid in the diagnosis of neurodegenerative disease.
Supported the Product Management team in the design and development of data analytics and digital assets applications for the asset management industry.
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Investigated and documented the flow of financial data received by the firm’s portfolio companies in order to identify key pain points and improve overall data analytics operations.
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Conducted research, participated in 3rd-party product demos and compiled weekly reports on emerging start-ups and ongoing developments in generative AI with potential for integration into the firm’s AI strategy.
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Developed a software tool in Python that explores optimizing the allocation of assets into leverage facility portfolios aimed at maximizing various health metrics while adhering to covenant requirements.
Worked as part of the engineering team to develop, improve and maintain a cross-platform full-stack application in an agile environment.
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Developed asynchronous Python scripts using celery in the Django backend (along with a suite of unit tests) that compute and update a Postgres database to support a highly requested customer feature.
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Delivered several major and minor UI/UX features using TypeScript and React Native in the web app frontend to enhance user engagement and experience.
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Updated GraphQL requests and introduced Python type checking as part of a larger strategy to mitigate errors and improve performance.
Utilized geographic data from residential air quality dataset to capture additional demographic information to support further analysis.
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Led the creation of a dataset of air quality monitors listed on Amazon with information on cost, star rating, and customer reviews by utilizing web-scraping tools.
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Leveraged NLP techniques to develop topic models from the air quality monitor dataset to determine the factors and use-cases most significant to customers at different levels of satisfaction.
