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Khairi Reda and Jingwen Yan

Two School of Informatics and Computing researchers receive prestigious NSF CAREER grants

April 10, 2020

Both Khairi Reda, assistant professor of data science and human-computer interaction (HCI), and Jingwen Yan, assistant professor of bioinformatics, recently were awarded 5-year CAREER grants by the National Science Foundation (NSF).

According to the NSF, The Faculty Early Career Development (CAREER) Program is a Foundation-wide activity that offers the National Science Foundation’s most prestigious awards in support of early-career faculty who have the potential to serve as academic role models in research and education and to lead advances in the mission of their department or organization.

IU School of Informatics and Computing at IUPUI Senior Executive Associate Dean Mathew J. Palakal, said, “We are enormously proud of Khairi and Jingwen, who received the first two NSF CAREER Awards in the history of our school at IUPUI. These are highly esteemed grants, awarded only to those proposals that can demonstrate high-quality original research combined with promising educational opportunities for students.”

Khairi Reda

The goal of Reda’s $538,140 grant project, titled, “Towards Trustworthy Analytics,“ is to reduce the risk of “false discovery” from data.

“Data visualization is increasingly important for discovery and decision-making in a range of domains, from science and engineering to commerce. However, visualizations can also expose random data fluctuations, which could be mistaken for real patterns. If analysts are not careful in interpreting these apparent patterns, they could inadvertently make false discoveries or take incorrect decisions, ” Reda said.

Reda’s team will develop tools to aid analysts in assessing the reliability of data patterns, while guarding against visualizations that seem convincing but that are likely to be misleading. The project is expected to help broaden the adoption of visual analysis tools, increase the confidence in conclusions, and potentially reduce the incidence of false discovery.

As part of this research, the team will develop interactive educational materials for training students in reliable data-driven inference, which will be disseminated to data science instructors for inclusion into existing curricula. The project will also provide opportunities for graduate research training and incorporate K-12 outreach activities that introduce young learners to data science.

Davide Bolchini, chair of the Department of Human-Centered Computing, said, “Khairi’s NSF CAREER project is very exciting and gives him a solid foundation for career-long scientific and educational contributions at the forefront of HCI and Human-Centered Data Science. With the proliferation of tools and dashboards for data-intensive interactive visualizations of broad interest to the world population, it is now of paramount importance to determine when and how our micro-interactions with visualizations increase the probability of errors in interpreting data.  His project seeks to fundamentally advance our understanding of the analyst’s intent in interactive data analysis and form the research basis to develop a new generation of interactive tools that better match error-free visualizations to that intent. We are all very proud of his significant achievement.”

Jingwen Yan

Yan’s $549,909 grant project, titled “Computational strategies for incompleteness and heterogeneity in multi-omic data,” will provide novel perspectives in handling the incompleteness and heterogeneity problems in multi-omics data and hereafter allow biomedical researchers to gain more insights from rapidly growing yet imperfect biomedical data. Multi-omics refers to the integrative analysis of multiple types of -omics data, for example, genotype, gene expression, and protein expression.

“Increasing multi-omic data provides opportunities for discovery of disease biomarkers from multiple molecular scales and therefore can further our understanding of underlying disease mechanisms. Despite this great potential, existing multi-omic data collections are mostly incomplete and of heterogeneous types, such as continuous and categorical numbers. Integrating these data for joint analysis typically requires exclusion of many subjects with missing values; as a consequence, a large chunk of data remains unused, “ Yan said.

Yan’s team aims to develop new classes of computational methods to enable the joint mining of incomplete and heterogeneous multi-omic data by leveraging various biological networks for discovery of functionally connected biomarkers. This will be accomplished through development of a multi-task joint network module detection and feature selection model and a novel multi-task sparse association model. This research effort will lead to discovery of more reliable biomarkers for further validation and better understanding of their relationships with disease traits than currently possible.

The research will also support several outreach activities including developing a project-based curriculum and hosting an annual summer workshop on multi-omics for high school students, as well as providing advanced research opportunities to undergraduates from biomedical informatics and related disciplines.

Huanmei Wu, chair of the Department of BioHealth Informatics, said, “Dr. Yan’s CAREER grant aligns well with her long-term research goal and will fulfill her research vision in translational bioinformatics. The funded project will apply innovative network science and emerging -omics technology to translational life science research by making the connections from genes to protein to metabolites, then to individual health. Her project will help us to better understand the influential factors of health, disease, and biological systems, from individuals to population outcomes. I can foresee the research outcome improving human health care in our daily lives.”

Acknowledgement

This material is based upon work supported by the National Science Foundation under Grant No. 1942429 and Grant No. 1942394. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.​

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