MAYOWA ADEWUYI

PHD CANDIDATE (PHYSICS)

CONTACT

** **** ******

Newmarket, NH. 03857.

973-***-****

ad40xj@r.postjobfree.com

https://github.com/Tomi-unh

https://kaggle.com/madewuyi

PROFILE

PhD researcher adept at analyzing complex systems and phenomena. Demonstrated ability to analyze large datasets and translate findings through impactful data visualizations. Proficient in employing deep learning algorithms for predictive modelling and forecasting in various domains. I am actively seeking opportunities in data science and related fields where I can apply my analytical skills to tackle engaging projects and get impactful outcomes.

EDUCATION

PHD PHYSICS• AUG. 2018 - PRESENT

University of New Hampshire. Durham, New Hampshire.

B.S. PHYSICS

June 2018

New Jersey Institute of Technology.

Newark, New Jersey.

KEY SKILLS

Data analytics

Excellent communication

Critical Thinking

physics

SQL

Python

Pytorch

Tensorflow

Keras

Pandas

Numpy

Scipy

Matlab

NLP

Latex

INTERESTS

Football (COYG!)

Cooking

Basketball

Painting

RESEARCH EXPERIENCE

RESEARCH ASSISTANT • AUG. 2018 – PRESENT (EXPECTED JUNE 2024).

University of New Hampshire, Durham, NH.

Drove research aimed at understanding the energy transfer from the sun to earth.

Cleaned and processed large dataset from various scientific satellite missions.

Applied various analysis techniques such as canonical cross correlation to generate network maps of magnetometer which enhanced simulation results.

Created global maps of the space around earth based on the processed data to extract meaningful information for deep learning models.

Leveraged machine learning models for predictions based on global maps.

Produced graphical representations of research findings for presentation at national conferences and group meetings.

Published findings in peer reviewed journals.

NASA Space Grant Fellow.

UNDERGRADUATE RESEARCHER • AUG. 2015 – JULY 2018

New Jersey Institute of Technology, Newark, NJ.

Investigated ultra-low frequency waves in the magnetopause.

Their spatial and temporal dynamics in response to interplanetary shocks were analyzed through Superposed Epoch Analysis.

Presented the results at AGU 2017 and documented procedure for future work.

College of science and Liberal Arts Outstanding Student Award (2018).

Boulder Space Weather Summer School • 2019

Collected and analyzed data from the sun all the way to earth.

Worked in groups to process and analyze said data.

Presented the findings to colleagues and instructors.

NASA 2023 SPACEAPPSs Challenge (Hackathon)

Worked with a team of 6 to clean, analyze and make use of machine learning algorithms (mainly ANN) to predict geomagnetic storms based on data from the L1 point of the solar system.

Contact this candidate