Grant Brown
Grant Brown is an Assistant Professor of Biostatistics in the College of Public Health at the University of Iowa. His primary area of research is spatiotemporal epidemic modeling, which draws on aspects of Bayesian hierarchical models, statistical computing, simulation, and data visualization. He is also interested in the analysis of correlated data more generally, and statistical learning techniques. Grant earned his PhD in Biostatistics from the University of Iowa in 2015, and has contributed to numerous research projects across campus. Among other pursuits and consulting work, he has evaluated a program to encourage patient engagement with pharmacists, contributed to the development of data entry and program management websites for programs promoting colorectal cancer screening and breast/cervical cancer screening programs in Iowa, and implemented predictive enrollment models for Enrollment Management at the university.

Dunn Lopez
Karen Dunn Lopez is the Director of Research for the Center for Nursing Classification and Clinical Effectiveness. Her scholarship focuses on nursing informatics. She has authored and co-authored over 35 peer-reviewed articles and has been cited over 750 times. Using a wide range of methods including human factors engineering, human centered design approaches, systematic review methods, her program of research focuses on identifying factors that can improve the usability and usefulness of health information technologies with the overarching goals of improving decision making and health outcomes. Her current work focuses on tailoring technologies to individual characteristics in order to facilitate rapid cognition of complex health data. Within Dr. Dunn Lopez’s program of research, many of the technologies are grounded in the use of nursing standardized terminologies to generate new knowledge. To date this research involves novel application of NANDA-I (Nursing Diagnosis), NIC (Nursing Intervention Classification) and NOC (Nursing Outcome Classification) to develop clinical decision support technologies. This includes 3 federally funded grants. In addition, as part of her program of research, Dr. Dunn Lopez led the first systematic review of clinical decision support that targets decision making by acute care nurses. She found that although technologies designed to support nurse decision making has lagged behind medical decision supports, that decision support designed to support nurse decision making is associated with improved patient outcomes. She also co-led a project to develop and test an algorithm to determine the differences between nurse and physician use of terminologies that provided quantitative evidence of the differences between nurse and physician care. 

Hans Johnson
Hans J. Johnson, Ph.D.,  Associate Professor Electrical and Computer Engineering (Primary), Biomedical Engineering, Psychiatry -- His primary research interest involves accelerating research discovery through the efficient analysis of large scale, heterogeneous, multi-site data collections using modern High Performance Computing (HPC) resources.  Specifically, he directs research efforts that deploy software-engineered solutions that harness the power of modern HPC infrastructures (many-core laptops/accelerator cards, distributed storage solutions, centralized data repositories, and large cluster computing resources) so that well established single-user analysis tools can be repurposed and deployed for analysis and knowledge extraction from large data repositories. His current projects are interdisciplinary collaborations that have resulted in many funded grants.  These collaborations allow him to be significantly involved in software engineering and informatics projects.  His primary contributions to those efforts focus on developing and deploying the tools necessary to monitor, manage, analyze and foster collaborative data sharing for large-scale multi-site projects. His formal training in Biomedical, Electrical and Computer Engineering provide a solid foundation for his academic research objective of accelerating research by employing rigorous software engineering practices and leveraging high performance computing. His efforts have been widely acknowledged as he is the lead developer on 14 projects hosted by the Neuroinformatics Tools and Resources Clearing House, he is the most prolific contributor to the Insight Toolkit v4 package, and President of the Insight Software Consortium. He has also been elected to leadership roles on several international multi-site studies (PREDICT-HD, TRACK-HD, ITK). He is excited by the recent initiatives that that the University of Iowa has undertaken, and believe that the need for strong software engineering and informatics collaborations are necessary for their success. In particular, the Aging Mind and Brain, Genetics, and Water Sustainability cluster hires each have significant needs for managing the complexities of leveraging large data for the generation of new discoveries.  The recent investment in HPC resources (Helium/Neon computational clusters, centralized storage solutions, and upgrades to core networking capabilities) provides a modern platform for conducting research. It is incredibly exciting to have skills at the nexus of these two initiatives where I can apply software engineering and informatics technologies to leverage the research infrastructure for solving the complex scientific problems of tomorrow.

Caglar Koylu
​Caglar Koylu is an Assistant Professor of GIScience with a joint appointment at the Iowa Informatics Initiative and the department of Geographical and Sustainability Sciences. He received his Ph.D. in Geography in 2014 from University of South Carolina (USC). Before joining UI, he worked as a Postdoctoral researcher at USC. While his research interests are in the broad areas of spatial data mining, space-time analysis and visualization, human-computer interaction and visual analytics, his particular focus is developing new theories, visual and computational approaches to understand complex patterns from large (Big) geo-social networks, i.e., networks embedded in geographic space and time such as migration, human mobility, networks of social media, commodity flows and information flows. Caglar has published in peer-review journals, presented at various conferences, and served as a reviewer for journals such as IJGIS and CaGIS. 

Yang Liu joined UI3 in February, 2019 as the twentieth cluster faculty member. Dr. Liu has a PhD in Biomedical Engineering, and has spent the last decade perfecting augmented reality (AR) goggles used for medical diagnostic and surgical guidance purposes. His specialties include AR, virtual reality, cyber-physical systems, Internet of things (IoT), computer vision, medical imaging, medical informatics, and computer-aided surgeries. Prior to joining the University of Iowa (UI) in 2019, Dr. Liu served as an assistant professor in the Department of Biomedical Engineering at the University of Akron in Ohio from 2013-2018. UI3 Director Greg Carmichael believes Dr. Liu’s strengths complement those of incumbent cluster faculty specialists and more than 300 affiliate members from most UI colleges and departments. “With access to unprecedented computational capacity and interfaces that make it easier for more communities of practice to command this power, the research community generates more data than ever. Our goal is to prepare the future workforce with the skills needed to transform this information into meaningful knowledge and actions,” he said. “The ability to visualize data through AR/VR is useful for a variety of life-saving applications in many domains,” he added.

Daniel Sewell
Daniel Sewell received his PhD in statistics from the University of Illinois in 2015.  He is currently an assistant professor of Biostatistics in the College of Public Health at the University of Iowa.  His primary area of research is in statistical models and inference for network data, and in particular the statistical analysis of dynamic social networks.  He has also contributed to other subfields of statistics, such as clustering and particle filtering, and holds interest in broad research topic areas such as Bayesian statistics and statistical computation.  As a graduate student, he was selected as a student presenter at the Midwest Statistical Research Colloquium, was a finalist for the Norton Prize for Outstanding Doctoral Thesis in Statistics, and, along with his collaborators from the University of Illinois, won the Patrick J. Fett Award for best paper on the scientific study of Congress and the Presidency.  He has collaborated with and provided consulting for a large number of researchers in over fifteen distinct fields of study.  He has presented at various conferences and universities, acted as a reviewer for several statistical journals, is a member of the American Statistical Association, the Institute of Mathematical Statistics and the International Society for Bayesian Analysis.

M Zubair Shafiq
M. Zubair Shafiq received his PhD degree in computer science from Michigan State University in 2014. He is an assistant professor of computer science at the University of Iowa. He is part of the Iowa Informatics Initiative. His research interests are in the broad areas of networking and security, with a focus on measurement and performance evaluation of wireless networks, content delivery networks, and online social networks. He received the best paper award from the 2012 IEEE International Conference on Network Protocols. He was also honored with the 2013 Fitch Beach Outstanding Graduate Research Award, which is the most prestigious award given annually for graduate research by the College of Engineering, Michigan State University.

Sanvesh Srivastava
​Sanvesh Srivastava is an Assistant Professor the Department of Statistics and Actuarial Science and a member of Iowa Informatics Initiative.  His research aims to develop flexible Bayesian methods and efficient computational algorithms for big data sets, tailored for both their complexity and size.  Motivating examples include big data in genomics, medical imaging, and recommender systems.  Simultaneously optimizing for the size and complexity is a challenge with current Bayesian methods. He is developing novel and computationally tractable Bayesian methods using principles from machine learning and optimal transportation. Before coming to the University of Iowa, Sanvesh received his Ph.D. in Statistics in August, 2013 from Purdue University, where he also won I.W. Burr Award for "promise of contribution to the profession as evidenced by academic excellence in courses and exams, by the quality of research, and by excellence in teaching and consulting." After Ph.D., he spent two years at Duke University and Statistical and Applied Mathematical Sciences Institute (SAMSI) as a postdoctoral researcher.  He has extensive experience in collaborating with scientists and teaching statistics to students from diverse areas and varied expertise.

Fatima Toor
Fatima Toor is an Assistant Professor at the electrical and computer engineering department with a joint appointment at the Iowa Informatics Initiative and the Optical Science and Technology Center. Her current research involves the design, fabrication, and testing of cutting edge photonics devices for applications in the health, environment, and energy industries. Prior to the University of Iowa, she was a Research Analyst at Lux Research, a multinational technical advisory firm, where she helped global clients – Innovation 1000 corporations, leading institutional investors, utilities, and public policy makers – make better strategic decisions and monitor the ever changing global solar market. Before joining Lux, she was a Postdoctoral Researcher in the Silicon Materials and Devices group at the National Renewable Energy Lab (NREL).  Professor Toor obtained her Ph.D. and M.A. in electrical engineering from Princeton University where she developed spectrally high performing InGaAs/InAlAs/InP based mid-infrared wavelength quantum cascade lasers (QCLs). Professor Toor is a member of APS, IEEE, OSA, Sigma Xi, and SWE.  She has published in many peer-reviewed scientific journals, presented at various scientific conferences across the globe, and a reviewer for several APS, IEEE, OSA and ACS journals.

Jun Wang
Spring 2016 Jun Wang, Professor, Chemical and Biochemical Engineering.                                                                                                                                                  Background is in satellite remote sensing, atmospheric science, climate change.

Tianbao Yang
Tianbao Yang joined the Computer Science Department at UI in 2014. He received his Ph.D. in Computer Science in 2012 from the Michigan State University. Before joining UI, he worked as a researcher at the NEC Laboratories America and GE Global Research. His research interests lie at the crossroads of machine learning and big data analytics. He has focused on several research topics, including deep learning, distributed optimization, stochastic optimization, and randomized algorithms in machine learning. He has published over 40 papers in prestigious machine learning conferences and journals. He has won the Mark Fulk Best Student paper award at the 25th Conference on Learning Theory (COLT) in 2012. Dr. Yang also served as (senior) program committee or reviewer for several conferences and journals, including AAAI, CIKM, IJCAI, ACML, NIPS, TKDD, TKDE. 

Xun Zhou
Xun Zhou is an Assistant Professor of Management Sciences at the Tippie College of Business, University of Iowa. Prior to joining UI, he received a Ph.D. in Computer Science from the University of Minnesota in June 2014. His general research interests are data mining and data management, with an emphasis on spatio-temporal big data analytics and mining, spatial database and Geographic Information Systems (GIS). Xun’s work has been recognized with best paper awards at international conferences and workshops such as BigSpatial’13 and SSTD’11. Xun served as program committee members in SSTDM’14, ACM SIGSPATIAL PhD Symposium’14. He also served as a reviewer for conferences and journals including IEEE TKDE, Geoinformatica, ACM KDD, IEEE ICDM, ACM SIGSPATIAL.