Over the past decade, machine learning has achieved remarkable successes in various research domains, including computer vision, natural language processing, and robotics learning. Artificial intelligence driven by machine learning algorithms is rapidly gaining popularity in the field of drug discovery and development. However, given the complexity of human biology, applying AI to drug discovery is significantly more challenging than image classification! Our group is formed by members with diverse expertise and research background to fullfil three key objectives:
Develop innovative approaches to incorporate biochemical knowledge into the development of AI algorithms, enable data-to-knowledge transition, and exploit the knowledge derived from data for drug development.
Nurture multidisciplinary collaborations among the different stakeholders in the drug discovery fields, and unite researchers of similar interests at Connecticut to turn the impossible into possible.
Train the next generation of life science professionals to be prepared for the big data and AI revolution. Educate computational scientists with biological and chemical knowledge for data-driven drug discovery research.
Professor at UCONN CSE. Ph.D. in Mathematics. Expertise in machine learning, big data analytics, computer vision, and medical informatics.
Yale Center of Molecular Discovery, Ph.D. in Organic Chemistry from RPI and M.S. in Data Science from UC Berkeley. Expertise in computer-aided drug discovery.
Postdoctoral at University of Connecticut. He obtained his Ph.D. in Computer Science at the City University of Hong Kong, expertise in data science and data analytics.
Ph.D. Student at University of Connecticut. Expertise in machine learning and deep graph learning using large-scale datasets, with an emphasis on healthcare informatics.
Ph.D. student in CSE, University of Connecticut. M.S. in Statistics and B.S. in Mathematics. Expertise in machine learning, algorithm and statistical inference.
Undergraduate student from the University of Connecticut. Interested in machine learning, data analysis, mathematical modeling and drug discovery.
Undergraduate student at the University of Connecticut. Interested in dimension reduction, computer vision, autoencoders, and software development.
Undergraduate student from the University of Connecticut. Interested in machine learning and doing work on classification algorithms for a drug discovery project.
Undergraduate student at the University of Connecticut. Interested in machine learning, software development, and medical informatics.