Colocation of Tests: A Cross-Language Perspective
How Different Languages Handle Test Organization and What We Can Learn
5 min read
AI & Software Engineer
Hi, I'm Mario Dias.
I'm an AI engineer with 4 years of industry experience and a strong background in backend and data engineering. At UBS, I built scalable systems using Java, Spring, Python, and Databricks, and integrated AI into enterprise workflows using LangGraph, FastAPI, and Azure OpenAI, developing intelligent systems and agents that solve real-world business problems.
Writing
Latest posts
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From Text to Image: How do image generators like DALL-E 2 work?
How do image generator models actually generate images from captions?
7 min read
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Navigating Concurrency: A Dive into PostgreSQL Locking Mechanisms
Unlocking the Power of Advisory Locks for Seamless Concurrency Management
5 min read
Portfolio
Featured work
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It's a My Money!
2026A personal finance app for Android built with Expo. Track income, expenses, and account balances, fully offline with no accounts and no cloud.
- React Native
- Expo
- TypeScript
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Designing an Effective Framework for Visual Question Answering using Deep Learning
2022Designed a novel framework for open-ended visual question answering using deep-learning and compared it with existing state-of-the art in order to check its effectiveness. Visual question answering is a well-known problem in the field of computer vision and natural language processing where an image and a question related to the image are given and the machine has to generate a natural language answer. We explore the advancements made in this domain and evaluate and modify existing frameworks to improve their performance. Our best single model delivers 71% overall accuracy on the test-std split of the VQA 2.0 dataset.
- Deep Learning
- Computer Vision
- Natural Language Processing
- TensorFlow
- Keras
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First Aid and Emergency Assistance Robot using Deep Learning
2021A smart IOT based robot system that aims to assist people, especially elderly, in case they are injured or in a state of emergency and when no one else is present to assist them. We divided the overall functioning into three stages: distress detection, navigation and searching and assistance and feedback. The robot detects distress in the form of audible screams and then navigates throughout the household. After successfully locating the person, it begins communication and attempts to get feedback to provide appropriate remedy. If victim is unconscious, it then contacts emergency services immediately. The prototypes were designed and tested with different test cases to draw conclusions and evaluate limitations and scope for future improvements.
- Object Detection
- Speech Recognition
- IoT
- ROS
- Natural Language Processing