- Associate Director, UA Data Science Institute; Professor, Computer Science
Stephen Kobourov completed B.S. degrees in Mathematics and Computer Science at Dartmouth College in 1995, and a Ph.D. in Computer Science at Johns Hopkins University in 2000. He was a Research Scientist at AT&T Research Labs, a Hulmboldt Fellow at the University of Tübingen in Germany, and in 2015-16 is working as a Distinguished Fulbright Chair at Charles University in Prague. His areas of expertise include data analysis and visualization, human-computer interaction, design and analysis of algorithms, and algorithm engineering.
- Director, UA Data Science Institute (Data7)
He received his undergraduate degree in Industrial engineering from the University of Pune, India, and graduate degree in Systems and Industrial Engineering from the University of Arizona (1994).
Over the last two decades his research has been directed towards developing scalable computational platforms for supporting open science and open innovation, with emphasis on improving research productivity for geographically distributed interdisciplinary teams.
His interests include data science literacy, large-scale data management platforms, data delivery technologies, managed sensor and mobile platforms for health interventions, workforce development, and project based learning.
- Associate Professor, Computer Science
Dr. Surdeanu earned a Ph.D. in Computer Science from Southern Methodist University, Dallas, Texas, in 2001. He has more than 15 years of experience in building systems driven by natural language processing (NLP) and machine learning. His experience spans both academia (Stanford University, University of Arizona) and industry (Yahoo! Research and two NLP-centric startups). During his career he published more than 80 peer-reviewed articles, including two articles that were among the top three most cited articles at two different NLP conferences. He was a leader or member of teams that ranked in the top three at seven highly competitive international evaluations of end-user NLP systems such as question answering and information extraction. His work was funded by several government organizations (DARPA, NIH), as well as private foundations (the Allen Institute for Artificial Intelligence, the Bill & Melinda Gates Foundation).
Dr. Surdeanu's current work focuses on using machine reading to extract structure from free text, and using this structure to construct causal models that can be used to understand, explain, and predict hypotheses for precision medicine.
- Professor, Mathematics
Hao Helen Zhang is a Professor of Department of Mathematics at University of Arizona, as well as a faculty member of Statistics Graduate Interdisciplinary Program (GIDP). Dr. Zhang obtained a Ph.D. in Statistics from University of Wisconsin at Madison in 2002. She was assistant and associate professor of Statistics at North Carolina State University 2002-2011. Dr. Zhang’s research areas include statistical machine learning, high-dimensional data analysis, nonparametric smoothing, and biomedical data analysis. Her research was funded by NSF, NIH, NSA, including a NSF CAREER Award. Dr. Zhang is currently Associate Editor of Journal of American Statistical Association, Journal of Computational and Graphical Statistics, and Statistical Analysis and Data Mining. She is a Fellow of the American Statistical Association, Fellow of the Institute of Mathematical Statistics, as well as elected member of the International Statistical Institute.
- Associate Professor - School of Plant Sciences, BIO5 Institute, Agricultural and Biosystems Engineering, CyVerse (formerly the iPlant Collaborative), College of Agriculture and Life Sciences
Eric is an expert in plant comparative genomics and life-science cyberinfrastructure. He has published over 30 peer-reviewed research articles, four book chapters, and maintains the widely used comparative genomics platforms CoGe and EPIC-CoGe. He is a Co-PI on the NSF funded iPlant Collaborative and is dedicated to democratizing access to cyberinfrastructure for all life science research. His research group has been supported by the Gordon and Betty Moore Foundation, the US National Science Foundation, and the US Department of Agriculture. He is a triple graduate (BA, MS, PhD) from the University of California, Berkeley, and spent several years working in pharmaceutical, biotechnology, bioinformatic companies. He teaches a project-based learning course called Applied Concepts in Cyberinfrastructure. Areas of Interest: Comparative Genomics and Genome Evolution Computational systems and cyberinfrastructure for biological research Data Science Literacy Educational Background: 2006-2008 PhD: University of California, Berkeley (Plant Biology) 1997-1999 MS: University of California, Berkeley (Microbial Biology) 1993-1997 BA: University of California, Berkeley (Immunology)
- Deputy Director for Research Cyberinfrastructure, Data7
Susan Miller's experience includes data analysis, High Performance Computing, conducting Computing Workshops for Scientists, and collaborations with CyVerse, AGAVE, UAMap, and Space Objects Behavioral Sciences projects. Susan has completed HIPAA training and participated in the 2016-2017 cohort of the UA IT Leadership Academy. Susan also serves on the UA Research Computing Governance Committee and several related subcommittees.
Susan earned a M.S. in Computer Science from UA in 1983 and a B.S. in Molecular & Cellular Biology in 2002. Her other interests include public speaking, writing, and cat rescue. She is a member of the UA Biosciences Toastmasters club. With Nirav Merchant, she has co-authored a chapter on Bioinformatics Programming in David Mount's Bioinformatics text book.
Collaborations on analysis of NextGen Sequencing data or Whole Genome Analyses can be arranged through UA Genome Analytics Services. Assistance with High Throughput Computing for Life Sciences is also available through UA GAS.
- Data Science Specialist, Office of Digital Innovation & Stewardship, University Libraries
A field biologist turned programmer, I provide support in computational literacy and data science applications. My background is primarily in phyloinformatics and population genetics, and I have expertise in R, Python, and Java. I work with CyVerse and the Carpentries to transform the University of Arizona into a data-literate campus that can capitalize on the big data revolution. Everyone can learn to code. I'm here to help.
Ph.D. Graduate Interdisciplinary Program in Insect Science, University of Arizona, 2007