Armando Xique Trujillo: Training Machine Learning Models with Uncertainty

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In this project, Armando explored ways to make data processing in machine learning more efficient in order to save time and resources. Here is what Armando had to say about the experience:

What I Did:

"For my project, I focused on optimizing bi-linear classification using support vector machines (SVMs) in machine learning. The challenge was that existing methods, which account for a certain amount of errors in data sets, often result in excessively large data sets. This impacts the speed of software execution and complicates running multiple tests. My goal was to explore ways to reduce the data required for accurate results, making the testing process more efficient. Although the investigation is ongoing, the progress made has already had a significant impact. By improving the efficiency of data processing, the project could streamline machine learning models, saving time and resources for future applications. This work also delves into complex machine learning theories that are typically taught at advanced academic levels, adding valuable insights to the field."

What I Learned:

"The most significant takeaway from this experience has been learning how to conduct independent research. While I've worked on group research projects before, this experience marked a shift in my development as a researcher. Conducting research on my own gave me more autonomy and taught me to rely on my own knowledge and judgment.  The project helped me develop strong literature review skills, as I relied on academic papers from experts in the field. I am proud of my progress in understanding and applying these complex theories. Gaining a deep understanding of these topics and applying them to my work. These projects push you to test your problem-solving skills, expose your weaknesses, and ultimately help you grow as a researcher."