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FMCalisto/README.md

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✌ Synopsis

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Francisco Maria Calisto holds a PhD in Computer Science and Engineering from Instituto Superior TΓ©cnico (IST) at University of Lisbon (ULisboa). He is a Human-Computer Interaction (HCI) researcher and an Artificial Intelligence (AI) enthusiast. More specifically, he is working on the intersection of HCI and AI in Health Informatics (HI), designing intelligent agents that aid clinicians as a second opinion during breast cancer diagnosis. He holds a Bachelor of Engineering degree in Computer Science and Engineering, as well as a Master of Engineering in Information Systems and Computer Engineering at IST. Qualified and experienced at working as a Researcher & Software Engineer, his work is settled in several directions. He is a system design thinker who is passionate about exploring how new interactive ideas, technologies, and business models can address the most pressing problems, particularly in healthcare. Francisco has been working with several Associated Laboratories of IST since 2015. Between 2015 and 2017, Francisco was working at INESC-ID and, since then, he is working at ISR-Lisboa in collaboration with ITI. Both ISR-Lisboa and ITI are associate laboratories of LARSyS, currently being the host laboratories for his Doctoral Program. Researching & Developing (R&D) in areas such as HCI, User-Centered Design (UCD), Information Visualization (InfoVis), and HI, understanding and reducing the user burdens of interactive technologies for health professionals, patients, and people with disabilities through the design of future applications. Also lecturing on those areas during this path. Prior to IST, he was part of several Entrepreneurship projects, from Agriculture to Advertisement, building systems for both business areas. He has designed, developed, and evaluated mobile, sensor, and collaborative applications for helping users (e.g., physicians, patients, and so on). His primary research methods involve UCD, InfoVis, technology development, as well as a mix of qualitative and quantitative methods.

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πŸ“ˆ GitHub Stats

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Francisco's GitHub Stats

⚑ Recent GitHub Activity

  1. πŸŽ‰ Merged PR #21 in FMCalisto/redux-get-started

πŸ™Œ Helping Others

Contributing to the open source community can be challenging and sometimes terrifying. One particular challenge is how to expose your problem in a clear way. There are a plenty of online articles explaining how to contribute and support the open source community. However, the best way for that, is to understand how could we help the community by giving some examples of Questions & Answers. Next, there are several o my recent examples of helping others through this path.

πŸ‘€ StackOverflow Activity

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✭ Total Contributions and Streaks

βš’ Contributed Repositories

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✍ Blog & Writing

Apart from being an Open Source'rer, I also write on Medium and DEV.to platforms. Please, be free to follow my work there. Hope to help you!

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πŸ“Š Time Spent Coding

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β™Ύ Research

As researcher, Google Scholar is the most representative platform for my current publications. However, it could be interesting to follow my ResearchGate account. You can also find me on Publons, where the ResearcherID for my Web of Science account is following the O-7933-2017 reference code. Finally, my ORCID is represented by the 0000-0001-8179-7872 reference number.

List of research initiatives:

✍ Discussions

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πŸ’¬ Channels

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BreastScreening-AI Final Presentation | EIA 2023 | One Year Later
Aug 2, 2024
Francisco Maria Calisto Live Stream
Aug 10, 2022
Medical Imaging Multimodality Annotating Framework
Oct 14, 2020
BreastScreening: On the Use of Multi-Modality in Medical Imaging Diagnosis
Oct 8, 2020

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  1. welcome welcome Public

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  2. about about Public

    πŸ’« About

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  3. FMCalisto.github.io FMCalisto.github.io Public

    🌐 FMCalisto GitHub Pages

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  4. dash-tutorial dash-tutorial Public

    Dash Tutorial

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  5. machine-learning-stanford-coursera machine-learning-stanford-coursera Public

    Exercises and source code of the MOOC Course on Coursera for Machine Learning by Stanford University. The course was taught by Prof. Andrew Ng.

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  6. neural-networks-deep-learning-coursera neural-networks-deep-learning-coursera Public

    Exercises and source code of the MOOC specialization on Coursera for Deep Learning. The course was taught by the DeepLearning.AI team.

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