Ben Fox, MS
Ben Fox, MS, is a fourth-year student in the PhD Biomedical Sciences at the Icahn School of Medicine at Mount Sinai. He is in the Artificial Intelligence and Emerging Technologies (AIET) training area.
In this Q&A, he discusses how Mount Sinai’s access to data and clinicians enabled his research with wearables to better monitor patients in the ICU in real-time.
What is your education and career background?
My research journey began while studying physics at Pepperdine University, where I investigated geophysical phenomenon manifesting in the aurora borealis measured from observatories in Svalbard, Norway. This introduced me to research, allowed me to attend my first conferences, and travel throughout Norway to collect data.
Shortly after, I began working with Sarah Faubel, MD, at the University of Colorado Anschutz Medical Campus in Aurora, Colorado, for a summer research internship, where I was first introduced to AI and machine learning to interpret metabolomics data from different organs in mice with acute kidney injury. This resulted in my first, “first author” publication and the thesis of my masters project at the University of California, Santa Barbara (UCSB), where I studied computer science.
Following graduating from UCSB, I switched from studying omics and joined Evidation Health where I worked for three years as a data scientist building new health metrics from consumer wearables (such Fitbit, Garmin, Oura) and analyzing sleep and activity data for new drug clinical trials. My work at Evidation inspired me to pursue a PhD in the AIET program at Mount Sinai to continue my work in AI, signal data, and wearable devices.
“I knew that I needed to work in the health care space, and I was particularly drawn to Mount Sinai being primarily a hospital, with access to health data, high-performance computing, clinicians, and other experts to drive research.”
Why continue your education with a PhD in Biomedical Sciences?
I wanted to continue my education to become an expert in my field and open doors to teach and become an independent researcher in my future career. Further, I wanted to learn more about the fields I was working in (wearables/signal data/AI/omics) and find ways to contribute to bettering human health and well-being. I was inspired by my previous colleagues (who had PhDs) and their ability to drive new research projects, teach complex topics, and help others. Lastly, I knew that I wanted to work in health and given that the PhD in Biomedical Sciences is physically located at a hospital, I knew it was a near perfect match.
Why did you choose to study at Mount Sinai?
I knew that I needed to work in the health care space, and I was particularly drawn to Mount Sinai being primarily a hospital, with access to health data, high-performance computing, clinicians, and other experts to drive research. While interviewing for schools, it was noticeable how happy the students at Mount Sinai seemed compared to other schools. Also, I wanted to move to New York City. I am originally from Colorado, studied in California, and was excited to live in New York for a few years.
What made you interested in the Artificial Intelligence and Emerging Technologies training area?
The AIET training area did feel like a perfect fit for me, given my background in health and computer science/machine learning research. Beyond that, the faculty research was the main appeal. Many faculty were doing research that matched my interests. I spoke with some of them while deciding if I should come to the program, and they assured me that I could devise my own projects with wearables/signal data and work across a multitude of different health domains. At some other programs, I did not envision getting the same support, nor having the access to data or clinicians that have been essential to my research.
Who are your mentors, and what is the focus of your research?
My mentors are Girish Nadkarni and Ankit Parekh. My research uses AI-derived representations of signal data, from sleep studies and bedside monitoring data in the ICU, to estimate risk. Signal data is routinely collected in the sleep clinic, at home, and via wearable devices. However, links between that signal data and disease risk has not been established. Similarly, in the ICU, the bedside monitor collects extensive information about a patient over time; however, the data is typically not used. My work aims to utilize this data to better monitor patients in the ICU in real-time.
How have the resources at Mount Sinai contributed to your success in the program?
The resources at Mount Sinai have substantially contributed to my success. Specifically, the high-performance computing team and the Minerva supercomputer have made building scale AI models doable. Additionally, the data access and faculty connections have enabled more efficient data acquisition for developing my work. Clinician connections, internal Mount Sinai conferences, and the TL1 predoctoral fellowship have also allowed me to continue to learn about the medical domains I study and continue to get feedback on my work from a multitude of perspectives. Outside of research, the Mount Sinai climbing club and running club have helped me through the ups and downs of the program and meet some of my closest friends.
What are your plans after you complete your PhD?
After I finish my PhD, I plan to do a postdoc at Mount Sinai and switch projects to more wearable focused research, potentially alongside omics data. After that, I hope to eventually secure a faculty position at a university where I can teach and build my own research projects.