NLP Applications for Scientific Review Assistance
As the world’s largest biomedical research agency, the National Institutes of Health (NIH), funds research performed across the country through thousands of competitive grants.
To support NIH’s rigorous scientific review process, scientists and reviewers have to sift through large volume of materials from various sources (e.g., grants, resumes, publications), often manually.
Leidos has successfully implemented Artificial Intelligence (AI) and machine learning (ML) solutions to improve the efficiency and accuracy of every stage of the current review cycle—saving time and money for the NIH, freeing up scientists to focus on more creative and strategic work, and accelerating research and discovery.
Kwan-Yuet (Stephen) Ho
Data Scientist Leidos
Kwan-Yuet (Stephen) Ho, Ph.D. is an applied quantitative researcher with 10-year experience in machine learning, text mining, and other related data science and quantitative fields.
He possesses exceptional mathematical abilities, and extensive experience with software development. He is seeking to advance his careers in machine learning, data analytics, and software engineering, aiming at solving business analytical (research and development) and engineering (automation) problems.
Currently he is doing ground breaking research leveraging NLP, AI/ML techniques to provide solutions for National Institute of Health (NIH) Center for Scientific Review (CSR) mission of ensuring NIH grant applications receive fair, independent and timely scientific reviews and are free from inappropriate influences so NIH can fund the most meritorious research.
