"I am a young researcher interested in the theoretical characterization of neural computations, towards application in cognitive brain-machine interfaces and biomimetic artificial intelligence.

I have graduated summa cum laude from the University of Pennsylvania with a dual degree in Computer Science and Cognitive Neuroscience, which I followed up with a Masters of Science degree at Graduate Training Centre of Neuroscience at the University of Tuebingen (Germany), where I will stay working towards my doctorate.

I have over three semesters of experience as a teaching assistant: 2 semesters as a TA for our introductory Theoretical Computer Science course and 1 semester for the graduate classical artificial intelligence course. Additionally, I participated in the creation of the Mathematical Foundations of Computer Science online masters course, boosting my remote and online teaching skills. The extensive experience on the teaching side provided me with a clear understanding of the academic standards and the exact expectations of students' performance at the highest level. On Wyzant, I usually teach Theoretical Computer Science (covering topics such as automata, DFA, NFA, formal languages, computability, complexity, Turing machines, algorithms, data structures...) and programming in different languages (Python, Java, C, C++ , C#, Matlab, OCaml, etc). Over the years I worked with many students from top schools (Ivy League, Carnegie Mellon, Georgia Tech, etc.) firmly grasp the core concepts of theoretical computer science and algorithms.

In my teaching, I seek to fully engage the students. My favorite method of doing so is constantly facing students with variations of the original problem, waiting for their ideas, and challenging them. I always try to explain the same principle from different perspectives, often using cross-disciplinary analogies. These allow for a deeper understanding of the material and also provide students with a broader arsenal on their exam day." less...