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๐Ÿ’‚๐Ÿป 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.๐ŸŒ")

    def say_hi(self):
        print("Thanks for dropping by, hope you find some of my work "
              "interesting and inspiring. ๐Ÿ‘Œ")

    def buy_me_a_coffee(self):
        print("If you appreciate my work, I would tremendously enjoy a "
              "โ˜•๏ธ Cafรฉ au lait ๐Ÿ‡ซ๐Ÿ‡ท 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 โš™๏ธ.

  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.

๐Ÿ“Š GitHub Stats

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auto-skwe's People

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auto-skwe's Issues

Comparing AutoWEKA and AutoSklearn

[Feedbacks from Alex] [Early Deliverable]

The literature review should be expanded to include another section. A second section will discuss the distinctions between AutoWEKA and AutoSklearn. A good starting point would be to identify the classification algorithms that are used in both or just one of them.

AutoSklearn experimentation with Medical Dataset

The task's objective is to master AutoSklearn framework. The following task will make use of the dataset provided by Alex Freitas.

Experimentation's consignes are as follows:

  • Require a period of time for the system to perform a search for the spatial dimension available: A one-hour search.
  • Experiments multiple times on each dataset: Three or five distinct runs.
  • Variation of the random seeds used to initialise the system on each new experiment: 42;21;11

Desired outcomes:

Each dataset is represented within a table, with the following represented in it too:

  • A list of the optimal algorithms and their optimal hyper-parameter settings as determined by auto-sklearn.
  • One set of results per row for each random seed used to demonstrate whether the results are "consistent" across different random seeds.

Document every class/methods

The task's objective is to provide thorough documentation for the solution, such that any contributor or reader will understand the meaning of this class or method.

Citation final writing

[Feedbacks from Alex]ย [Early Deliverable]

On the final writing, cite proper references particularly when defining or introducing a topic.

Examples are as follows.

  • In the current text, section 2.1, you put a definition of machine learning in terms of E, T, P without a citation, you should cite here the Machine Learning book by Mitchel 1997, where that definition was introduced.

  • In section 2.2, when you first mention the CASH problem, cite the paper by Thornton et al. 2013 introducing Auto-WEKA.

  • At the start of section 2.3, cite a reference for NAS.

  • Section 4.1, cite a reference specific for meta-learning.

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