Tariq Mahassen

Master's graduate in Theoretical Physics specialising in Cosmology, with a solid foundation in advanced mathematics and computational methods. Coauthoring a research paper and gained further research experience through a summer internship. Proficient in Python, with experience in Machine Learning and Deep Learning, as well as integrating computational tools into complex frameworks. Developed exceptional communication skills through teaching, public speaking, and an entrepreneurial fellowship, excelling in collaboration and scientific outreach. Driven to advance cutting-edge research alongside world-renowned experts in a field that demands both creativity and analytical precision

• Developing and implementing quantum machine learning algorithms in Julia and Python, using modules such as Yao.jl, Pennylane, and Qiskit to simulate and optimize quantum circuits.
• Applying hybrid quantum-classical techniques to tackle complex problems in high energy physics, including the modeling of quantum field dynamics and exploring non-perturbative phenomena.
• Investigating the potential of quantum-enhanced learning algorithms to accelerate computations and improve scalability.

"Exploring the CPL parameterization of Dark Energy, to ease the Hubble and S8 tensions." 

• Conducted in depth analysis, investigating the potential dynamical nature of dark energy, co-authoring a paper in the field.

• Modified the Boltzmann code CAMB using Python and Fortran for improved model analysis.

• Applied Bayesian inference and MCMC techniques to evaluate and compare dynamical dark energy using a combination of datasets, using HPC resources and writing optimized Bash scripts.

• Leveraged GitHub for version control and collaborative workflows, strengthening software practices in computational physics.

'Exact solutions to Einstein's Field Equations - from Mercury's Orbit to black holes and gravitational
waves'

• Collaborated with a PhD supervisor to study general relativity, starting with Mercury's orbital precession and expanding to broader theoretical problems.

• Modeled orbital trajectories around static and rotating black holes using Mathematica and motivated gravitational waves from linearized gravity, linking theory with observation.

• Presented research findings to faculty and students, sharpening science communication and simplifying complex concepts for diverse audiences

"Comparing the efficacy of the dark matter model, ΛCDM, with the alternate gravitational theory, MOND/TeVeS, in explaining the Universe's missing mass." 

• Conducted research comparing the standard cosmological model with modified gravity theories to address discrepancies in the Universe's observed and theoretical mass.

• Investigated the mathematical foundations and theoretical motivations behind complex models while critically evaluating observational and experimental evidence.

• Assessed both frameworks' strengths and limitations through the philosophy of science, enhancing critical thinking and evidence-based reasoning.

• Completed the SELL Fellowship at the University of Texas, developing a mental health app for students.
• Collaborated with psychology, medical, and IT professionals, showcasing strong teamwork, communication, and organizational skills.
• Gained experience in pitching to investors, delivering presentations, and coordinating cross-disciplinary efforts for an impactful proposal.

Public Speaking 

• Participated in Education Outreach, delivering exciting presentations to students.
• Participated in UT Toastmasters club, working in a team to fine-tune public speaking, communication, leadership, and presentation skills.

Climbing 

• Competed for UT Austin's climbing team in national competitions including the USAC circuit.