In the spring semester of 2018, Will Shaprio and I co-taught a new class, Data Science Projects for Social Good. This course was a unique offering at Cooper, and was taken by juniors, seniors and graduate students from all three schools.

When performing cohort retrieval, the goal is to find patients who have EEGs that are similar to other patients EEGs. If such a system existed, it would enable a clinician or researcher to quickly look up patients who have EEGs similar to each other. This could then assist them in diagnosing and treating seizures or other conditions, for example. A cohort retrieval system is a little bit different than a traditional classification method. The goal is not to classify a particular EEG, but to find other patients(i.e. the cohort), who are similar to the one in question.

Since the schedule is up now, I guess I can say that I’‘m excited to be offering 2 new interdisciplinary electives this spring.

This semester, I advised a collaborative project between an electrical engineering student (Jessica Marshall) and an art student (Emily Adamo). The goal of this project was to get an engineer and an artist talking about algorithms and data, and how they are applied and interpreted.

One of my graduate students, Neema Aggarwal, focused her graduate thesis on an important area of research, causal inference. Neema right now is working as a consultant for the Boston Consulting Group, so we wanted to find her a thesis topic that was relevant to buisness work, but also an electrical engineering topic. Causal inference, with its roots in machine learning, was a perfect fit.