Hi, my name is Carlos Heredia Pimienta, and I earned my Ph.D. in (Mathematical) Physics under the guidance of Prof. Josep Llosa at the University of Barcelona.
My passion for finding answers to complex and intriguing problems drives my intellectual curiosity. Instead of being intimidated by the abundance of questions that remain unanswered, I find it fascinating as it presents endless opportunities for learning and discovery.
In addition to my academic pursuits, I have several years of experience in various physics fields, including String Theory, Classical electrodynamics of dispersive media, modified Gravity, and Mathematical Physics. I have also worked as a Business Intelligence consultant at EY (Ernst & Young), specializing in Data Science and Analytics.
I am currently employed as a Senior Data Scientist at DAMM, while also actively engaged in research focused on applied and mathematical physics at the University of Barcelona and NTT Research.
I am a determined and optimistic person, and I love to travel and meet people from different cultures. I have learned that teamwork is key to success and that each team member should contribute responsibility, honesty, initiative, and humility to the group.
Academic interest
My research has pivoted towards the intriguing domain of the learning dynamics of deep learning, emphasizing the exploration of local and nonlocal Lagrangian and Hamiltonian formulations. This shift reflects a keen interest in understanding the intricate mechanisms that govern how deep learning models learn and adapt.
The application of Lagrangian and Hamiltonian perspectives, borrowed from classical mechanics, provides a structured way to analyze the trajectory and energy conservation within neural networks. It enables a mathematical dissection of the learning process, focusing on minimizing loss functions and understanding the distribution of information
The study of symmetries within these frameworks further enriches our comprehension of the invariances that deep learning algorithms exploit. My goal is to meld these theoretical insights with practical machine learning challenges, aiming to enhance the robustness and generalizability of deep learning models through a foundational understanding of their dynamic behavior.
Business interest
In my trajectory, I have cultivated a strong interest in the business sector, focusing on the growing importance of data in strategic decision-making. I recognize that currently, data is an invaluable currency that drives the success of any company. My studies and experiences have allowed me to deeply understand that algorithms, essential in this context, are actually a form of applied mathematics that unravel the hidden value in large sets of information.
I fully understand that companies rely heavily on this information to make informed and competitive decisions in an ever-changing business environment. It is evident that the access and correct interpretation of this data can make the difference between success and stagnation in the business world.
In view of all this, it is evident why my interests are focused on applying artificial intelligence (AI) tools to this business world. AI represents a powerful way to enhance data analysis capability and to discover patterns and trends that often escape human perception. This convergence between data, algorithms, and artificial intelligence presents itself as an extremely promising combination and a cornerstone in my approach to the business sector.
«In my heart, a strong desire burns. It’s where my academic pursuits align with my professional aspirations. A dream where knowledge guides every step, and my passions drive me forward.»