Dr. Eugene Pinsky
Dr. Eugene Pinsky Brings 20 Years of Experience to Courses in Data Science and Python
Associate Professor of the Practice of Computer Science
BA, Harvard University; PhD, Columbia University
What is your area of expertise?
My research area is in performance analysis, data science, and machine learning. Since completing my PhD in computer science at Columbia University, I have been teaching throughout my career within both academia (Boston University, MIT) and industry. For almost twenty years, I worked in data science and machine learning areas developing applications that utilize machine learning to solve real-life practical problems, primarily in computational finance and computational advertising.
Data science and machine learning require an understanding of many interdisciplinary concepts. The practice involves mining large amounts of data and applying machine learning methods to design new algorithms to solve important practical problems. As more and more tasks become automated, machine learning has an increasing role in our life. Thus, it is important that students are taught the practical elements of data science and machine learning.
What courses do you teach at MET? What “real-life” exercises do you bring to class?
At the present time, I teach Information Structures with Python (MET CS 521) and a new course, Data Science with Python (MET CS 677). In all of my courses, I try to present many simple examples with a strong emphasis on visualization. In addition to regular homework and exams, students choose a project and present it on the last day of class.
How do you see the curriculum evolving to stay current with industry trends?
Traditionally, machine learning and data science courses have been often limited to the arenas of computer science and engineering. Such courses focus on computer science and assume students have a background in advanced math and science courses. At present, there is a growing need for students to learn and use data science and machine learning in real-life applications. The challenge, therefore, is to develop courses that are application driven and can be utilized by students with diverse backgrounds. I am very interested in helping to develop such application-driven courses that use data mining and machine learning techniques, especially in computational finance and computational advertising. As an example, consider computational advertising. Skills in this area are in increasing demand as more advertising, commerce, and delivery of services are online. In computational advertising, we try to solve the problem of finding the best advertisement for a given target. The science behind solving this problem is very complex and requires different areas of expertise such as data mining, recommendation systems, optimization, and forecasting. As data science and machine learning algorithms are increasingly employed in advertising and marketing, it becomes essential to have a basic understanding behind this science.
I strongly believe that there is a tremendous opportunity to collaborate with my colleagues at the MET and with other faculty members across the university to help develop new and extend existing courses to incorporate opportunities provided by data mining and machine learning.