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James Anibal

James Anibal

Scholar Type:

NIH Oxford Scholar

Entry Year: 2021
Degrees:

B.S. Computer Science, Case Western Reserve University, 2020

Mentors:

Dr. Bradford Wood (NCI) and 
Prof. David Clifton (Oxford)

Research Interest:

Precision medicine, Deep learning, Signal Processing

James Anibal attended Case Western Reserve University (CWRU) from 2016-2020, earning a B.S. in Computer Science. After his second year at CWRU, James began his research career at the National Cancer Institute under the mentorship of Dr. Grégoire Altan-Bonnet. His projects involved (I) developing machine learning algorithms for single-cell data analysis and (II) developing natural language processing (NLP) methods for biomedical text mining. From this research, James has published in Science and Journal of Clinical Investigation.  He has also published a biomedical question-answering system on the Hugging Face NLP platform. In summer 2021, James worked as a research scientist at Afiniti, a multinational company aiming to improve business outcomes through AI-driven pairing of agents and customers.  

Beyond research, James is passionate about improving access to STEM education. As an instructor for FAES@NIH, he designed multiple courses/workshops focusing on computer programming and machine learning. He also partnered with OITE to design a Python programming course for summer interns at NIH. Finally, James founded EAST, an organization of scientists and educators with the shared goal of creating STEM opportunities for underserved groups, particularly incarcerated individuals. EAST partners with outreach organizations and technology companies to make these resources available for a national audience. 

For his graduate studies, James plans to develop scalable deep learning methods for noisy data from devices such as wearables, sensors, and smartphones. His research will be applied in two primary areas: (I) personalized healthcare for low-resource settings and (II) minimally invasive surgical procedures.  He also plans to develop multimodal analysis methods to learn detailed insights from increasingly diverse data. Upon graduation from Oxford, James plans to build accessible, cost-effective systems for precision oncology.

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