John Dinh

Experience

• 

  Texas A&M University-Kingsville

  Jun 2021 - Jul 2021

  Research Internship

  • Researched and analyzed the effects of the COVID-19 pandemic on the global supply chain, identifying a 30% decrease in international trade and a 50% increase in shipping delays.• Formulated reports on unemployment rates in the US, revealing 10 million job losses and an unemployment rate of 14%.• Collaborated in a team to find strategies for mitigating supply chain disruptions, increasing overall efficiency by 15%.

• 

  Self-employed

  Jul 2021 - Jun 2023

  Private Tutor

  • Orchestrated 1-on-1 tutoring sessions for 5 students, resulting in a 15% increase in overall grades.• Created personalized lesson plans tailored to each student's learning style and needs.• Utilized interactive teaching techniques, such as hands-on activities, leading to a 25% increase in engagement.

• 

  H-E-B

  Jun 2022 - Aug 2023

  Deli Partner

  • Assist over 50 customers daily, by providing recommendations and answering questions, while also maintaining high levels of customer satisfaction through friendly and efficient service.• Prepare and package a variety of high-quality deli meats and cheeses, adhering to strict food safety and sanitation standards, resulting in a safe work environment.• Maintain inventory levels by restocking meat/cheese cases and refrigerators daily, leading to a visually appealing department.

• 

  University of Houston

  Aug 2023 - Dec 2023

  PROMES Workshop Facilitator

  •Led a Circuit Analysis workshop for a class of 14 students, clarifying and reinforcing complex concepts, yielding a 79% average exam score. •Mentored students through academic and career guidance, leading to a 20% surge in internship placements. •Arranged 1-on-1 appointments outside of class to provide extra assistance, enabling a 30% increase in comprehension.

• 

  U.S. Department of Energy (DOE)

  Jun 2024 - Aug 2024

  MLEF Intern

  • Developed a Long Short-Term Memory in PyTorch, predicting process parameters based on time-series data of sensor responses.• Enhanced gas sensor data interpretation, integrating preprocessing and machine-learning, decreasing T90 response time by 9x.• Mitigated cross-sensitivities by training on multivariate sensor data, attaining an average % error of 1.5% across multiple gases.