Max did his undergraduate work at MIT, majoring in electrical engineering and computer science (EECS) and in mathematics, and he stuck around for his M.Eng. in EECS. His thesis, which applied machine learning techniques to predict a key diagnostic parameter using physiologic signals, was awarded the Adler M.Eng. thesis prize. After working in industry for two years as a signal processing and machine learning engineer, he decided to transition back into academia. As a research affiliate, he is continuing his master’s research. Next year, he will begin a PhD in applied math.
HIs studies at an institution whose motto is “Mens et Manus” have taught him that applying knowledge can deepen understanding. As a teaching assistant for the introductory EECS sequence, Max learned to ask questions that not only probe understanding but get students to think of the material in a different way, connecting their current assignment to real-world problems and to ideas covered in other classes. While he never tires of, say, understanding an equation differently by rearranging its terms or drawing a new picture that captures its truth, he is most concerned with efficiently helping students meet their learning goals. Based on feedback from students, he received the department’s Undergraduate Teaching Award.
When not tutoring or doing research, he hosts a jazz program on MIT’s radio station and enjoys surfing north of Boston, volunteering on political campaigns, and making short films.