Learning from ancestors

Working with my doctoral advisor, Andrew J. Millis, was a formative experience. His depth of understanding and intuitive approach deeply influenced me. I often found myself absorbing best practices simply by observing—much like a child learns from a parent.

It led me to wonder: How many of these insights were passed down through generations of scholars before him? What ideas, values, and methods survive through academic lineages? I don’t have definitive answers, but my love of history compelled me to explore the connections. These subtle influences, I believe, help shape our scientific paths.

Quantum

Quantum physics beautifully unites the harmony of symmetry in nature with the unpredictability of statistics. When I arrived at Columbia, I was inspired to be part of a department connected to more than ten Nobel laureates in Physics.

Although I had explored quantum theory during my Master’s thesis at UNal, I chose to start fresh at Columbia—revisiting core courses to build a deeper, more principled understanding. The material was so rich that, paraphrasing Prof. Miklos Gyulassy: “Solving each homework problem was a matter of honor.”

Fig. 1: My node and parents in the Academic Family Tree.

As I studied further, I realized that foundational figures like Born, Ehrenfest, and Pauli weren’t just names in textbooks—they were academic ancestors, part of a lineage that now felt personal. The journey deepened when Andy suggested reading Hershfield’s paper, which launched me into an unexpected and fascinating direction in my research.

Many

Thermodynamics bridges microscopic reversibility with macroscopic irreversibility. I became obsessed with understanding how these scales coexist—how quantum rules give way to thermodynamic behavior.

This question, passed down from the founders of statistical mechanics, remains unresolved. Discovering that some of those pioneers—Maxwell, Boltzmann, and Kelvin—were part of my academic lineage only intensified my curiosity.

Fig. 2: Zooming out to view five generations back in the lineage.

My doctoral thesis addressed one facet of this puzzle: interpreting entropy as a quantum observable and deriving implications for thermodynamics. I was honored when Burmistrov and Lunkin referenced this work in their Lecture Notes on Statistical Mechanics.

Poly

Over time, I’ve broadened my research scope. Today, my work focuses on the information-theoretic foundations of artificial intelligence. I seek a holistic view that enables collaboration across diverse domains.

The physicist’s search for simplicity, harmony, and elegance in complexity continues to guide me. I’m proud to carry that legacy into new fields—bringing a scientific mindset into machine learning, AI, and beyond.

Fig. 3: My extended lineage includes the polymath Carl Friedrich Gauss.

I’ll close with one of my favorite discoveries: Carl Friedrich Gauss appears ten academic generations above me. To me, he represents what science can be—curious, rigorous, and beautifully broad. His example reminds me that the freedom to explore is itself a core part of research, no matter the discipline.