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AutoML-engineering
πŸ”¬
AutoML-engineering

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simonprovost/README.md
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πŸ’‚πŸ» About Me

#!/usr/bin/python
# -*- coding: utf-8 -*-

class PhDStudent:

    def __init__(self):
        self.name = "Simon Provost"
        self.role = "PhD Student in Computer Science"
        self.research_interest = "Automated Machine Learning (Auto-ML) applied Medicine"
        self.language_spoken = ["fr_FR", "en_US"]
        self.preferred_stack = ["cpp", "python", "c", "javascript"]
        self.open_for_summer_phd_internship = True

    def say_about_me(self):
        print(f"Hi, I'm {self.name}, a {self.role} with a deep fascination "
              f"for problem solving, which led me to undertake a graduate "
              f"degree in Advanced Computer Science and Research.")
        print(f"My preferred tech stack includes {', '.join(self.preferred_stack)} "
              f"and I'm proficient in the following languages: "
              f"{', '.join(self.language_spoken)}.")
        print("My love for Computer Science was ignited by a profound "
              "interest in research and machine learning, earning me "
              "outstanding distinction for my work.")
        print(f"Today, my research focuses on {self.research_interest}, "
              "aiming to pioneer unexplored areas and revolutionise how "
              "Computer Science is perceived and utilised in our "
              "digitalised world.🌍")

    def say_hi(self):
        print("Thanks for dropping by, hope you find some of my work "
              "interesting and inspiring. πŸ‘Œ")

    def buy_me_a_coffee(self):
        print("If you appreciate my work, I would tremendously enjoy a "
              "β˜•οΈ CafΓ© au lait πŸ‡«πŸ‡· to keep-it up!")
        print("https://buymeacoffee.com/simonprovost")


me = PhDStudent()
me.say_about_me()
me.say_hi()
me.buy_me_a_coffee()

Google Scholar Research Papers

  1. (Co-Author)Provost, S. with Tighe, D., Ho, M., Puglia, F., McMahon, J., 2023. Risk Adjusted Cumulative Sum chart methodology to monitor of free flap failure rates in the QOMS national audit. British Journal of Oral and Maxillofacial Surgery VOLUME 61, ISSUE 10, E5.

  2. (Co-Author)Provost, S. with Tighe, D., Sasson, I., Ho, M., 2023. Technical appendix-Validating risk-adjustment models used in QOMS. British Journal of Oral and Maxillofacial Surgery (QOMS).

  3. (Co-Author)Provost, S. with Tighe, D., McMahon, J., Schilling, C., Ho, M., and Freitas, A., 2022. Machine learning methods applied to risk adjustment of cumulative sum chart methodology to audit free flap outcomes after head and neck surgery. British Journal of Oral and Maxillofacial Surgery, 60(10), pp.1353-1361.

  4. (Co-Author)Provost, S. with Tighe, D., Tekeli, K., Gouk, T., Smith, J., Ho, M., Moody, A., and Walsh, S., Freitas, A., 2023. Machine learning methods applied to audit of surgical margins after curative surgery for facial (non-melanoma) skin cancer. British Journal of Oral and Maxillofacial Surgery, 61(1), pp.94-100.

Medium Blogs

Medium Blogs

  1. Provost, S., Introduction to Automated Machine Learning with Auto-Sklearn βš™οΈ.

  2. Provost, S., Diversity and a novel Deep Learning model called Deep Forest: Applying an Important Concept to a Promising Framework.

  3. Provost, S., Classification of Sleep-Wake states with the use of a novel Deep-Learning approach.

  4. Provost, S., Michele, L., Classification of Sleep-Wake states with the use of a novel Deep-Learning approach.

  5. Provost, S., Michele, L., Computer Science Students take a step in the sleep-medicine field with Actigraphy.

  6. More can be seen here.

πŸ“Š GitHub Stats

Most Used Languages

🌍 Connect With Me

Feel free to get in touch with me on these platforms:

Pinned

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    Forked from openml-labs/gama

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    Automated Machine learning applied to medical data while benchmarking frameworks of Auto-ML - Master's thesis study πŸ”¬

    Python 3

  3. awaketogether/AI awaketogether/AI Public

    Machine Learning & Neural Networks

    Python

  4. Machine-learning-classifier-for-COVID-19-H1N1 Machine-learning-classifier-for-COVID-19-H1N1 Public

    Machine learning model for Covid-19 and Flue disease classification

    HTML 1