The SCI Institute is pleased to announce five new faculty hires whose combined expertise will not only enhance the current research at the Institute, but also lay the path for future endeavors. The SCI Institute’s overarching vision is the transformation of science and society through translational research and innovation in computer, computational, and data science. Its mission is to bring together excellence in diverse domains applied to multidisciplinary and interdisciplinary problems of scientific and societal importance. The Institute accomplishes these goals through the collaborative development, assembly, and/or application of applied scientific and data computing, imaging, and visualization tools. These new hires will expand the core research expertise at SCI, and will continue to broaden the diversity of faculty, students, and staff.
In her research, Dr. Isaacs develops visualization approaches that address the complex analysis scenarios of active research teams, often with application to software and computing systems. She works closely with collaborators from those research teams to identify and push past the limitations of available visual tools and develop more interpretable and scalable visual representations that fit into their workflow. Dr. Isaacs deeply believes that pushing past the analysis challenges of exploratory analysis in large and complex data requires meeting users where they are and thus her solutions aim to identify and then computationally enhance their analysis processes.
Dr. Elhabian has established her research program around biomedical problems that entail collaborating with scientists and domain experts in different disciplines and backgrounds to conduct interdisciplinary research projects. Her research spans foundational and translational advances at the intersection of image analysis and statistical machine learning with a focus on clinical and biomedical applications.
Dr. Elhabian’s vision is that deployable image analysis systems empowered by machine learning can transform the way biomedical researchers and clinicians interpret imaging data in an objective, thorough, efficient, and reproducible manner, thereby maximizing the benefit-to-cost of imaging technologies and enabling early diagnosis and patient-specific treatment and prognosis. Her long-term goal is to accelerate the adoption and increase the clinical utility of machine-learning-based image analysis systems. Progress in this domain will mitigate critical bottlenecks in attaining an expert-level understanding of the complexities of imaging data and have a broad impact in a range of clinical and biomedical research disciplines. To attain this goal, Dr. Elhabian has been establishing foundational methods to solve complex problems in image analysis and quantitatively interpret imaging data using minimal expert supervision, then translating these methods to application domains through robust, flexible, and usable open-source software packages.
In his research, Dr. Rosen studies approaches to improving the efficacy of visualization tools by utilizing a mix of human-centered design and geometry- and topology-based methods to extract and emphasize important data features. He has studied these techniques in the context of many data types, including scalar and vector fields, multidimensional data, and graphs. Further, these techniques have been used to solve important problems in wide-ranging collaborations, including in affective computing, nuclear engineering, biomedical engineering, radio astronomy, software performance analysis, and 3D printing. Dr. Rosen believes strongly in the need for visualization capabilities with robust theoretical and practical capabilities that simultaneously consider the needs of visualization users and enable the process of developing insights about data that are as unambiguous as possible.
In his research, Dr. Arzani develops computational models to fundamental understanding of blood flow and cardiovascular disease. Broadly, he is interested in understanding the role of blood flow in the heart and cardiovascular system, developing predictive computational models that can predict disease, and using modern data-driven modeling techniques for improving the fidelity and accuracy of current experimental and computational blood flow models.
His research is highly interdisciplinary and integrates fluid mechanics, solid mechanics, mass transport, scientific machine learning, computational mechanics, and dynamical systems theory. Dr. Arzani is also interested in fluid flow problems beyond cardiovascular flows, for example chaotic advection in unsteady flows, respiratory flows, and convective heat transfer.
Dr. Arzani highly supports student success in research. Prior to joining SCI, his students at NAU had published 12 first-author journal papers. In 2022, he received NAU’s College of Engineering, Informatics, and Applied Sciences Distinguished Mentorship Award. Dr. Arzani has recently received an NSF CAREER award from NSF’s Office of Advanced Cyberinfrastructure.
Dr. Arzani’s research fits with the key strengths of SCI (imaging, visualization, and scientific computing). Specifically, Dr. Arzani and his collaborators are creating machine learning approaches for improving blood flow quantification from medical imaging. Additionally, Amir has previously collaborated with SCI alumni and visualization experts in surface, vector-field modeling and visualization. Finally, his research heavily involves a wide range of scientific computing approaches, such as computational mechanics and scientific machine learning.
Tan received his Bachelor of Engineering from the University of Technology Malaysia, and earned his PhD from the University of Glasgow in computer science and bioinformatics. He received post-doctoral fellowships at Johns Hopkins University Whiting School of Engineering and School of Medicine.
As a scientist in cancer translational bioinformatics and cancer systems biology, Tan’s research focuses on computational and statistical learning methods to overcome treatment resistant barriers in cancer. He is most interested in research that goes from the lab to bedside, providing data-driven precision oncology for patients. With grants from the National Institutes of Health and Florida Biomedical Research Program, Tan has focused his research on understanding tumor microenvironment such that effective drug combinations could be delivered to patients based on their individual genomic profiles. He has published more than 200 articles.
Tan previously served as vice chair of the Department Biostatistics and Bioinformatics at Moffitt Cancer in Tampa, Florida.