ELogs/Febian

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This is Febian's Electronic Log in completing PhD at UCL from September 2023 to September 2027

To Do

Last updated 22/07/24’’

  • Upcoming Event:
    • DKFZ 6th Summer School
  • Coding FPGA to interface the new daughter board utilising Zmod Port

Dates and Progress

Date Event
2 October 2023 Starting PhD Journey in PBT Group at UCL
9 November 2023 MAThRAD 2nd Workshop at Teddington
8 January 2024 Starting Industrial Project with Peak.AI
18 - 31 January 2024 Away for family vacation in US
21 - 23 February 2024 Valencian Proton Therapy Facility Workshop
25 April 2024 CDT Industrial Partner Presentations (Peak.AI)
30 April - 1 May 2024 Beam Test at UCLH: Quarc, Temperature (NPL) and Pixel Sensor (Birmingham)
14 & 17 May 2024 Science Communication Workshop
8 - 17 June 2024 PTCOG62 Annual Conference in Singapore
17 - 30 June 2024 Away on Vacation in Indonesia
14 - 19 July 2024 STFC Summer School in Data Intensive Science Liverpool
9 - 12 Sep 2024 Phystat - Statistic meet Machine Learning Workshop, Imperial College London
26 Aug - 27 Sep 2024 DKFZ 6th Summer School in Medical Physics

PhD Completed

This section highlights the events/activities that have been done throughout the PhD. It also includes what have been learned and important key takeaways.

  • Phystat - Statistics meets Machine Learning Workshops by Imperial College London (Online)
    • Various algorithm, statistical model and Machine Learning application in HEP and Astrophysics world
    • Discussion on interpretability, data sources, samples relevance, and mismodelling.
  • DKFZ 6th Summer School in Medical Physics.(Online)
    • Recapped on Radiobiology and particle interactions
    • Scintillators and luminescent materials for detector
    • Monte Carlo Simulation
  • Initiate the creation of PBT Group Github organisation for Collaborative workspace.
  • Practical FPGA and Implementation in Vivado
    • Installation and run through the current Nexys Video FPGA code for DaQ (assisted by Matt)
    • Follow through the STFC Training books for RTL to FPGA Basics.
    • Implementation of FPGA code on easy applications (LED and Gates)
    • Creating a github repository on the FPGA end-to-end guide on debugging and implementation
    • Learning on Hierarchical programming and IP components in Vivado
    • Learning FTDI (USB driver management)
  • STFC Summer School in Liverpool:
    • Networking with people from the same cohort
    • Highlighting session about: Neural Network, Clustering and Data Visualisation, Publishing Code
  • PTCOG62 Conference in Singapore
    • Education session includes medical aspects of PBT, standard procedure in QA, etc
    • Open source data/tools and AI discussion in PBT
    • Networking with people from the same field of research
  • Science Communication Workshop
    • Tips for good presentation slides and skills
    • Body language impact on communication
  • Beam Testing at UCLH
    • Helping and learn logical step by step set up
    • Taking notes of the experiments taken
  • Data Acquisition Process and Plotting the data using the provided code by Sonia
    • Marking the USB-C configuration (which side to plug)
    • Testing supply voltage in each node of DaQ
    • Formulating consistent working DaQ with the latest Matt Version FPGA code
  • Setting up data acquisition process on MacBook M2 Chips
  • Testing and Categorising types of different USB-C 3.x Cable regulating in the market
    • 16 Cable was tested and 3 general classification of USB-C 3.x were found
  • Doing literature review as part of MPHY0038 Assignment on IMPT and the emergence of AI in the field.
  • Reading Saad (Previous PhD student) Thesis
  • Modules Completed:
    • PH4515 Statistics Course Royal Holloway (Mathematical theory behind ML)
    • PHAS0102 High-Performance Computing (GPU optimisation)
    • COMP0233 Research Software using Python (Basic Python and Colaborative work)
    • MPHY0038 Treatment Radiation (Radiotherapy theory and highlights)
    • COMP0210 Research Computing with C++ (C++ introduction)
    • SPCE0038 Machine Learning with Big Data (Using python to implement ML algorithm on big data)