Problem Solver

Bassem Sabra

Bassem Sabra

Areas Bassem Sabra is Knowledgeable in:

Data analysis, mathematical modelling, machine learning
Applied cubesat space mission analysis and design

Techniques Bassem Sabra Uses:

I have the Swiss-army knife of problem solving tools: physics, and the soft skills that come with it. I apply it to resounding success in ALL the problems that I encounter. Solving problems in physics requires one to look at whole problem, dissect it into its individual working parts, work on the individual smaller problems, synthesize the solutions of the the many sub-problems into a unified solution for the original problem, and finally communicate the solution, and its rationale, and justification of the sub-steps in the most effective and efficient way possible. I do all of that, without loosing track of the bigger picture and also without loosing focus on the smaller problems.

I am versatile thinker. My broad experience in a variety of areas has given a allows me to introduce lateral thinking when solving any problem at hand. I can grasp the full picture and delve into the minute details at the same time. My analytical, mathematical, and coding skills are themselves problem solving techniques. Couple this to my communication skills (more than two decades in the higher education sector), and you've got a sharp problem solver who can communicate his solutions easily.

Bassem Sabra's Problem Solving Skills:

  1. Spectroscopy
  2. Space Sciences & Technology
  3. Astrophysics
  4. Spectroscopy
  5. Green Engineering
  6. Machine Learning
  7. Mathematical Modelling
  8. Data Analysis

Bassem Sabra's Problem Solving Experience:

  1. Data Analysis: Use spectroscopic data to study environments close to supermassive blackholes in the nuclei of distant galaxies.
  2. Outcomes Assessment: Devised and implemented several the outcomes assessment tools to track criteria -meeting goals at the course and program levels in academia.
  3. Curriculum Development: Developed curricula for undergraduate physics and graduate astrophysics programs.
  4. Program Reviews: Undertaken reviews of undergraduate and graduate physics and astrophysics programs.
  5. Computational & Mathematical Modeling: Use photoionization computer models to simulate environment near supermassive blackholes.
  6. Machine Learning: Used supervised machine learning methods (regression, mostly) to determine masses of supermassive blackholes.