About Me

Who am I?

Hello! I’m Julián Vicente Miranda, a 24-year-old individual fueled by diverse passions including music, fitness, reading, and of course, machine learning. To deepen my understanding and engagement with this particular passion, I embarked on a journey to attain my MS in Applied and Computational Mathematics, graduating with honors magna cum laude. Additionally, I have devoted several years to immersing myself as a research assistant, honing my skills in computer science and seeking to grasp its nuances. My goal is to get to the ‘cutting edge’ of machine learning, and I’m prepared to dedicate years of my life in pursuit of this ambitious aspiration.

Why Machine Learning?

  • Machine Learning is a leading force in technological advancement, perceived as the future of problem-solving and decision-making.
  • Its remarkable capacity to surpass traditional limitations and address challenges in unprecedented ways is awe-inspiring.
  • The potential for Machine Learning to revolutionize industries and reshape societal landscapes is vast and transformative.
  • One of its most intriguing aspects is its continuous potential for improvement as algorithms and models evolve.
  • Machine Learning enables the extraction of meaningful insights from vast datasets, facilitating solutions with unparalleled accuracy and efficiency.
  • The practical applications of Machine Learning are extensive, spanning tasks such as automating processes, optimizing customer service, conducting sentiment analysis, managing traffic flows, and detecting fraudulent activities.
  • By streamlining operations and enhancing productivity across diverse sectors, Machine Learning yields substantial time and cost savings.
  • Its versatility transcends industry boundaries, promising significant enhancements in performance, effectiveness, and profitability.
  • Machine Learning offers boundless opportunities for innovation and progress, with the potential to drive economic growth and transform industries.
  • As an advocate for progress and innovation, I am drawn to Machine Learning and aspire to contribute to its transformative impact.

What's my Education?

  • Started academic journey at Texas A&M International University (TAMIU) in August 2017, majoring in Mathematics with a minor in Applied Physics.
  • Enrolled in a Programming class in summer 2021, igniting interest in coding and initiating research on Magnetohydrodynamics under Dr. Muhammad Mohebujjaman’s mentorship.
  • Honored with the Louis Stokes Alliance for Minority Participation (LSAMP) STEM Undergraduate Fellowship, supporting research endeavors.
  • Discovered passion for machine learning during studies in Numerical Analysis.
  • Graduated cum laude with a Major GPA of 4.0 in May 2022.
  • Offered the opportunity to continue research while pursuing a master’s degree at TAMIU.
  • Entered Master of Science in Applied and Computational Mathematics program in June 2022, focusing on diverse coursework including Machine Learning.
  • Began work on thesis titled “Two Novel Efficient Algorithms for Parameterized Magnetohydrodynamic Flow Ensemble Simulation.”
  • Presented research findings at conferences such as the 7th Coastal Bend Mathematics and Statistics Conference and the 2022 Fall Student Conference.
  • Received support from the National Science Foundation (NSF-DMS-2213274) for research endeavors.
  • Graduated magna cum laude in July 2023, characterized by a dedication to research excellence and academic growth.

Any Work Experience?

  • Commenced employment as a CS and Mathematics Tutor at TAMIU in September 2021 while undertaking the role of an undergraduate research assistant under Dr. Mohebujjaman’s guidance.
  • Tutored advanced-level courses ranging from Statistical Analysis to Numerical Analysis 2, imparting computational concepts such as regression modeling and graph theory.
  • Contributed to research on “A High Order Efficient Grad-Div Stabilized Algorithm for Parameterized Magnetohydrodynamic Flow Ensemble Simulation” as a research assistant.
  • Promoted to supplemental instruction (SI) instructor for Calculus 2 in January 2022, achieving an impressive 18% increase in overall class grades.
  • Transitioned into graduate school in June 2022, resigning from tutoring and SI instruction to focus on roles as a graduate research assistant.
  • Diligently worked on implementing theoretical concepts into practical simulations, demonstrating the efficacy of the algorithm.
  • Achieved breakthroughs in Magnetohydrodynamic (MHD) flow simulation, with applications spanning medical technology, weather forecasting, process metallurgy, and propulsion.
  • Demonstrated commitment to pushing the boundaries of knowledge and innovation through unwavering dedication and perseverance.

Now what?

  • Focused on enhancing proficiency in various libraries and expanding knowledge of machine learning algorithms since completing master’s degree.
  • Engaged in diverse projects showcasing competencies, including disease classification, residential property value forecasting, COVID-19 sentiment analysis, and more.
  • Prepared to transition into the industrial realm with confidence, leveraging strong academic foundation and practical experience.
  • Aspires to make meaningful contributions to the field of machine learning while embracing new challenges and growth opportunities.
  • Eager to collaborate with industry professionals, apply skills in real-world contexts, and advance the frontiers of machine learning.
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