Zhiyuan Zhang, a computer science Ph.D. candidate, has received an IBM Ph.D. Fellowship, an intensely competitive worldwide program that honors exceptional Ph.D. students who have an interest in solving problems that are fundamental to innovation. The IBM Ph.D. Fellowship Awards is a highly prestigious annual competition. Less than 10% of world-wide applicants make it to the 2nd selection phase, and even fewer make it to the final award. The competitive fellowships provide a stipend, tuition, and fees to the awardees.

Zhiyuan Zhang works in the Visual Analytics and Imaging Lab led by Professor Klaus Mueller. Zhang's research interests include Information Visualization and Visual Analytics, with a special focus on healthcare informatics, multivariate data visualization, and correlation analysis.

In the modern information technology era, huge amounts of structured and unstructured data are captured from myriad sources. The huge amounts of data offer tremendous opportunities for both researchers and policy makers to study existing behavioral patterns and to predict future developments. At the same time, the information explosion also poses great challenges in terms of selecting the useful information, understanding and making discoveries from the data. Information visualization and visual analytics provide solutions by combining the computation capabilities of machines with the perceptual and reasoning strengths of humans. They use interactive graphical representations to help researchers to understand and gain insights into the data in a more efficient way.

Most state-of-the-art Electronic Health Record (EHR, EMR) systems face the problem of high costs, lack of speed, non-intuitive interfaces, and inefficient, fragmented display of patient information. Zhang has been working on an emerging visual analytics system dedicated to clinical encounters. It unifies all EMR information fragments into a single interactive visual framework, which provides diagnostic support and helps the clinicians to efficiently understand the patient(s) histories and make predictions. Zhang spent the summer of 2012 as an intern in the healthcare analytics research group at IBM T.J. Watson Research Center. He worked with David Gotz and Adam Perer, developed a system that helps clinicians to interactively visualize and refine patient cohorts, request analytics on those cohorts, and make new discoveries.

As for big data, the ultimate goal for data analysts and researchers is to understand the data collected and make useful decisions from them. More often than not, the most valuable insight comes from intricate inter-relationships among data attributes, and extracting these relationships requires skills in multivariate data understanding. Zhang developed an interactive framework that helps researchers to visually learn about and gain insights from their multivariate data. The framework has been utilized in the areas of climate research, finance, and healthcare informatics.