Empowerly

Student profile

Describe something outside of your intended academic focus about which you are interested in learning.

At first glance, machine learning seems just a series of complex algorithms run one after another, but it isn’t just mathematics -- it is history, digging into the past for patterns to understand the present. Last year, I researched the application of machine learning to predict epileptic seizures after witnessing my cousin suffer from a seizure firsthand. 

The algorithms I used analyzed previous epilepsy brainwave data, classifying seizure versus non-seizure events for the selected features of energy and root-means-squared.  As I think back to my history class, I see the parallels between my algorithm and the historical method. I select political, economic, and cultural features and classify different historical periods, naming them Bronze, Edwardian, Paleolithic.

Running the model, I predict when seizures will occur in the future, drawing upon conclusions of the retrospective data. I am a historian, making sense of contemporary world problems by searching for truths in past patterns of actions.

Although I intend to pursue computer science and engineering, I wish to study history as well. Being a lover of both machine learning and history, I am able to realize how the two seemingly polar fields have more in common than I initially thought. Studying one will only increase my expertise in the other -- as I run machine learning models of predictive analysis, I am not just a computer scientist, but also a historian.

Required for all applicants:

Describe how you plan to pursue your academic interests at USC. Please feel free to address your first- and second-choice major selections. (250 word limit)

For the past two years, I have researched the implementation of artificial intelligence in healthcare, using machine learning and deep learning as diagnostic tools. With these projects, I have realized that I aspire to be an artificial intelligence researcher at the intersection of biology and computer science, working on solutions to the most pressing healthcare problems. 

As such, the USC Computer Science major is the perfect fit for me, with its emphasis on interdiscliplinary research for undergraduates. Research conducted at labs such as the Computational Neuroscience iLab are directly in line with my interest in machine learning for medical application, with projects in areas such as neuroimaging and computer vision. I look forward to conducting undergraduate research at these labs and with summer opportunities such as the Natural Language Processing internship. This research as an undergraduate will provide the foundations of my career in artificial intelligence, allowing me to gain valuable experience at cutting-edge laboratories.

Beyond research, I am excited to learn computer science through the hackathons held at USC. As the Head Organizer of defHacks Seattle, I recognize the value of hackathons in creating innovative projects for a hands-on learning experience. With hackathons such as AthenaHacks at USC, I will have the opportunity to work on exciting projects, and hope to one day join the organizing teams of these events! Through USC’s commitment to research and opportunities for hackathon project-based learning, I know I will be able to follow my career aspirations and thrive. 

While scientists yearn to discover the world that exists, Engineers and Computer Scientists seek to create the world that never was. Our faculty and students believe collaborative teams are the key to great accomplishments. Please describe a time in your life (academic, co-curricular, or otherwise) where you had to collaborate to accomplish more than you could alone. (250 word limit)

Last summer, I was honored to be a Fellow of the Simons Summer Research Program, interning at the Stony Brook University’s Department of Radiology under Dr. Timothy Duong. I worked on the automatic segmentation of multiple sclerosis lesions on magnetic resonance imaging (MRI) scans using deep learning (a type of artificial intelligence), testing the accuracy of a U-Net implementation of a convolutional neural network.

I was determined to leave the program with a working prototype of my hypothesis, improving the diagnostic capabilities of radiologists to identify the hard-to-detect multiple sclerosis lesions. Alone, this task was impossible in my short span of 6 weeks at the program. But in a lab group setting, I realized research is a collaborative effort. When my neural network required heavy computing resources, I asked the Computer Science Department for access to their supercluster and for feedback on my deep learning models. Further into the project, I would present my research progress at the weekly lab meetings, after which members of the lab would question my methodology and findings. I learned to welcome constructive critcism and feedback; with their help, I could analyze my work at different angles and find solutions to problems much faster than I could have alone.

By the end of the program, I presented my working algorithm at the closing symposium, proud to have developed it in just a few weeks, knowing that I couldn’t have done it without the help of my mentors within my lab group.

While the world as a whole may be more technologically advanced than ever before, the National Academy of Engineering (NAE) has outlined 14 Grand Challenges that engineers should focus on to improve life on the planet. Learn about the Grand Challenges at www.engineeringchallenges.org and tell us which challenge is most important, and why. (250 word limit)

Artificial intelligence (AI) is the future, as everything from the cars we drive to the robots in factories becomes automated. Reverse engineering the brain will allow us a better understanding of artificial intelligence, with far reaching applications not only in industry, but also in healthcare, as it is increasingly being used as medical tool. 

For example, great strides have been made in the diagnosis, treatment, and prevention of cancer, but much work is still to be done until cancer can be considered a threat of the past. Perhaps the answer will lie in AI, as one important area of research in the path towards a cure for cancer is the prediction of the effect of the medications prescribed. With a deeper knowledge of AI, researchers can predict how these treatments will affect the body before they even enter the patient, using certain features of imaging technology to predict patient symptoms and chance of success. This will ensure that, for example, the right amount of radiation therapy is given to the patient, enough to kill the cancerous cells, but not too much to kill the healthy ones. Any number of drugs and treatments may be developed, but knowing which ones are ideal for the patient significantly increases their chances of survival. 

As such, it is vital that AI researchers such as myself better understand how to engineer computer systems with greater intelligence, as it will it can have significant impacts in medicine to improve the quality of life for many.