UCLA Ph.D. program in Bioengineering: Medical Imaging Informatics track

This fall, I’ll be starting a Ph.D. program in Bioengineering at UCLA, where I’ll work in the Medical Imaging Informatics (MII) Group. The core curriculum of this program is a great fit for me; it covers medical signal processing, statistics, software development, and medical networks and information systems. For my electives, I’m planning to choose courses on machine learning and databases. I’d like to be prepared for big data—especially in the medical domain. I really appreciate that Prof. Alex Bui and Prof. William Hsu volunteered so much of their time to discuss their work while I was choosing a graduate program.

As I read the MII Group’s publications, I’m becoming increasingly interested in medical applications of natural language processing. This paper, for example, describes how to extract statistical data from clinical studies with enough detail to perform Bayesian analysis. Even though I’m an engineer, I always enjoyed my English classes, and I appreciate the technicality of grammar. It’s exciting to think that I could apply my language skills to engineering research.

In case you’d like to know what motivated my decision to earn a Ph.D., I’ve included my statement of purpose below.

Statement of Purpose

I am applying to the Ph.D. program in Medical Imaging Informatics at UCLA because I want to develop technologies that improve both the quality and efficiency of healthcare. In my career, I wish to develop medical solutions that hospitals can quickly incorporate into their existing infrastructures and methodologies. I can achieve this goal either in hospitals as a biomedical engineer or in the R&D departments of medical device companies. This work will require expertise that only advanced graduate training can provide, and I am confident that I would be prepared having earned a Ph.D. in bioengineering at UCLA.

To envision successful healthcare solutions, I must not only master various aspects of biomedical and electrical engineering but also understand the underlying biological systems that produce physiological signals. To implement effective healthcare technologies, I must strengthen my awareness of the engineering, medical, legal, and even political factors that affect the development and application of technology. The interdisciplinary environment of the Medical Imaging Informatics Group offers the ideal context for these pursuits. The potential for collaboration between researchers with diverse expertise fosters creativity. The technology-driven culture of the program offers unique networking opportunities in both industry and academia, which will ensure that I apply my skills optimally after graduation.

I became interested in medical applications of electrical engineering during my senior year of undergraduate study at Tufts when I worked with two students to advance the state of the art in electrolarynx devices. At the suggestion of our adviser, Dr. Karen Panetta, my team-mates elected me project manager because of my strong organizational and communication skills. Electrolarynx technology had remained virtually unchanged since its creation in the 1940s; we developed a new interface to improve the device’s intonation capability, allowing laryngectomees to communicate more expressively. Throughout the project, we aggressively pursued all of the resources that could aid our work. My team won $1,200 of Dean’s Grant funding, which allowed us to purchase a top-selling electrolarynx model so that we could reverse-engineer it. We also worked with the Tufts University Office for Technology Licensing and Industry Collaboration to patent our design. To date, we have filed both a U.S. provisional patent application and a PCT application. The acknowledgement of our work by school administrators and legal professionals encouraged me to pursue biomedical research.

During my graduate study at Tufts, I was fortunate to work with another professor who focuses on biomedical technology, Prof. Carla Brodley. As an electrical engineering student working for a professor in the Department of Computer Science, I adapted to learn new machine learning and software engineering concepts very quickly. Prof. Brodley showed me the power of collaborating with researchers from other domains when she paired me with computer scientists and neuroscientists to develop seizure prediction algorithms. The neuroscientists provided invaluable perspectives on our EEG data because of their understanding of the biological systems that produced these signals. This application of biological knowledge to engineering problem solving further motivated me to apply my skills to medical technology. I also became interested to learn more about the acquisition of biological signals from living subjects.

Although it might seem unorthodox for an electrical engineering student to work in a psychology lab, my work as a research assistant in the lab of Prof. Nalini Ambady was a natural extension of my seizure prediction research. The neuroscientists I had worked with piqued my interest in the mind sciences, and their fluency in the biological aspects of our work encouraged me to study the physiological and instrumental sources of biological signals. The psychologists and I benefited significantly from our collaboration. I wrote MATLAB scripts to expedite their data analysis tasks, and my graduate student adviser, Jon Freeman, provided me with valuable, hands-on training in EEG and fMRI data collection. Engineers can process signals having never witnessed their acquisition, but psychologists do not just collect data. They provide an experience for the participant and must be mindful of not only the experiment’s protocol but also the participant’s comfort. This experience has made me more aware of the human element in biomedical engineering and has strengthened my motivation to create technologies that enhance patients’ well-being. During my time in the Department of Psychology, I gained new perspectives on the EEG data from my seizure prediction work and a greater appreciation for interdisciplinary research.

My current work in the Department of Neurology Research at the Massachusetts General Hospital consists of an apt combination of my academic interests and research experiences. Over the course of a typical day, I apply skills from every aspect of my training as an engineer, including software development, signal processing, circuit design, soldering, and CAD. My principal investigator, Stephen Gomperts, M.D., Ph.D., is a neurologist who has given me a physician’s perspective on clinically motivated research. I have also benefited from my immersion in the culture of the nation’s top-ranked hospital, and I am now more eager than ever to develop medical technologies.

The above experiences have engendered my desire to synthesize clinical knowledge of biological systems and engineering methods of signal processing. I am particularly interested in engineering solutions that are not only technically elegant and robust but also medically interpretable and intuitive for clinicians who do not have an engineering background. I am most interested in information theoretic and data visualization approaches to physiological time series analysis. These techniques will continue to be used in medical solutions such as wearable computing and personalized medicine, which may lower the world’s healthcare costs significantly. Having witnessed the application of statistics in psychological and clinical research, as well as the infusion of statistical methods into digital signal processing and machine learning, I am also looking forward to making statistics a focus of my graduate study. I am therefore interested in working with either Prof. Aberle, Prof. Bui, Prof. Hsu, or Prof. Taira for my thesis. I am most interested in the following projects: Retrieving Understandable Medical Information (RUMI), Data Exchange Platform for Structuring and Sharing Clinical Data, and Data Structuring and Visualization System for Neuro-Oncology. These projects would allow me to employ skills I already have while strengthening others that I would like to develop, including software development, data visualization, and decision science.

I am certain that obtaining a Ph.D. in bioengineering is the most logical next step in my professional development. The breadth and depth of my academic experiences demonstrate not only that I have a sustained, genuine interest in bioengineering but also that I am capable of being a successful graduate student. As this country’s population ages and healthcare costs climb, a Ph.D. from UCLA would provide the preparation I need to become a technological leader who can skillfully address a variety of healthcare challenges.

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