Zack Lipton always wanted to be a musician, and for a period of time he was. He took saxophone lessons from Branford Marsalis while in high school and proceeded to Columbia University where he studied math and economics during the day and participated in jazz jam sessions throughout New York City most nights. After graduating, Zack stayed in New York City and pursued his passion for music.
On the side, Zack dabbled in computer science and learned enough web programming to build websites and make a few dollars. Through contacts, Zack was introduced to and built websites for professors and a PI. While he had no background in computer science or medical research, he was attracted to the open, inquisitive culture in these researchers’ labs. While building their websites he started hanging out in this environment, reading, and attending discussions. He found the experience stimulating.
At some point Zack visited a friend who was getting a PhD at the Universty of California Santa Cruz, which Zack described as “the most beautiful place in the history of the world.” Having spent years as a jazz musician in New York City hemorrhaging money, Zack decided that he too wanted to move to California to pursue a PhD (He had other friends who had also gone the PhD route and thought it would be a good fit for him.)
After deciding to move to California to get a PhD, Zack had to decide what field to focus on. At the time he had never heard of biomedical informatics, but was interested in life sciences and working on healthcare problems. He also knew he wanted to acquire skills in computer science, probability and statistics, and machine learning. He concluded he wanted to do a PhD in computer science with a focus on machine learning.
“I settled on applying to computer science and decided to learn machine learning and use it to do something useful for medical care.”
Zack moved to California and undertook applying to PhD programs. While completing his applications he spent time working to become a better programmer. He ultimately was accepted and chose to attend the University of San Diego.
In retrospect, Zack says he was incredibly naïve about making the jump from music to a computer science PhD program. However, at times detailed plans and formal qualifications are less important than interest, curiosity, hard work, and good fortune.
He was fortunate to get taken under the wing of faculty members who served as advisors and mentors. He was also lucky that early in his PhD he identified a problem to research that others had overlooked. This research led to Zack getting a paper published in just a few months.
Suddenly it seemed the ridiculous idea of becoming a professor was no longer so ridiculous. He found the academic environment appealed to him in that it provided a mandate to work on interesting problems, with autonomy and flexibility.
“I went from being really unfocused, having no plans out more than maybe one year, to being in a PhD program and having this ridiculous idea of ‘I’m going to be a faculty member.’”
Zack found research exciting and addictive. The culture of research is figuring out what other people are missing, deciding what problems to explore, and then researching these questions.
While Zack was working on his PhD, breakthroughs were taking place in machine learning and natural language processing, as people figured out how to train massive neural networks and deep learning emerged. It was an exciting time.
Zack was subsequently invited to interview for a faculty position at Carnegie Mellon, which happened sooner than he thought. He got the position, which is a joint appointment in the school of business and the machine learning department. Zack loves the freedom and autonomy of deciding research questions to focus on. He finds the academic environment and the interaction with amazing students exhilarating.
In addition, one of his focus areas is using his knowledge and tools to work on meaningful problems in the world of healthcare, which are some of the most important problems in society. These problems are a place where machine learning can make a meaningful difference.
“The cool thing about working in the medical informatics space is that the problems actually matter.”