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Jacopo Di iorioAssistant Teaching Professor

Biography

Dr. Jacopo Di Iorio clearly recalls when, as an undergraduate student, he used to go to class and sit in the second row to take notes. He did not sit in the first row; that made him feel too exposed, as if the instructor could notice his yawns or “read” the confusion in his mind. And sometimes that was in fact the case; Di Iorio was taking notes as fast as he could, his hand moving automatically over the note pad, but he was rolling his eyes and asking himself, “Why is this so boring?” or “Why can’t I grasp anything?” It did not occur to him then that he could raise his hand and ask questions — or rather, it kind of did, but the instructor was almost always in a rush, and he did not want to look stupid. Now that he's older (and perhaps wiser) and had moved from the second row of the classroom to the blackboard, he finally understands two things. First, that trying not to look stupid is, in the end, really stupid; and second, that if the instructor had transmitted to the younger him the message that “stupid is ok” (and mostly normal), he would have raised that hand. As an instructor, Dr. Di Iorio always remembers how hard it was to be a student and how good it felt to be noticed and cared for, especially when one got lost in a particularly complicated lecture. Teaching is not the act of showing off one’s shimmering knowledge hoping that students will shine of reflected light; it is instead trying as hard as one can to act as a lighthouse, pointing towards the harbor as those in front of you move across the waves.

Education

  • Ph.D., Penn State University, 2024
  • Ph.D., Mathematical Models and Methods in Engineering, 2020
  • M.Sc. , Mathematical Engineering, 2016
  • B.Sc., Mathematical Engineering, 2014

Research

Jacopo Di Iorio's research is sitting at the intersection of Functional Data Analysis (FDA), unsupervised learning and statistical computing – motivated by complex, challenging applications, as well as a passion for Statistics Education.

Teaching

  • Fall QTM 220 Regression Analysis

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    Spring QTM 220 Regression Analysis and QTM 465 Semiparametric Statistics