Members

Benefits of Membership in Data7 include access to shared meeting space, shared postdocs, invitations to events, student funding opportunities.

PhD student in Systems and Industrial Engineering (Minor in Computer Science)

I am a member of Computational Medicine and Informatics for Neurological Health (COM-IN) Collaboratory. We work on different kind of problems related to data science. My main focus is on Medical Image Processing. I am in an early stage of my learning and I wish to be a member of such a good group to excel my knowledge in data science.

Educational Background:

B.S. Industrial Engineering, University of Kurdistan, 2010
M.S. Industrial Engineering, Kharazmi University, 2013
PhD. Systems and Industrial Engineering (Minor in Computer Science).

Area(s) of Interest: Natural Language Processing, Image Analysis

Michael Barker

Associate Professor, Department of Ecology & Evolutionary Biology; Director, Bioinformatics Degree Program

Educational Backgroun:

    B.S. Biology, Denison University, 2001
    M.S. Botany, Miami University, 2003
    Ph.D. Evolutionary Biology, Indiana University, 2009

Bio:

Mike Barker is an evolutionary biologist studying the origins of biological diversity, particularly how abrupt genomic changes such as polyploidy, chromosomal change, and hybridization have contributed to the evolution of plant diversity. Biologists have long been fascinated by these processes because they create unique opportunities for the evolution of ecological and phenotypic novelty with the potential for relatively rapid speciation. Although assessing the importance of these abrupt changes has historically been a difficult task, advances in genomics and bioinformatics have created new opportunities for addressing these longstanding questions. To address these questions, the Barker Lab has developed new approaches to infer ancient and recent polyploidy across the tree of life. These include new machine learning methods that have increased the efficiency of genomic analyses of polyploidy. Ongoing research in the lab is focused on developing new models and theory to understand the evolution of chromosome numbers, deep learning approaches to study ancient whole genome duplications, and new population genomic methods for the analysis of contemporary polyploid species. Mike’s research efforts have been recognized with the NSERC-BRITE Biodiversity Postdoctoral Fellowship at the University of British Columbia (2010) and the Emerging Leader Award from the Botanical Society of America (2016).

Area(s) of Interest:

Machine Learning
Large Scale Visualization
Data Science Literacy

Steven Bethard

Assistant Professor at the University of Arizona School of Information

Steven Bethard is an Assistant Professor at the University of Arizona School of Information, where he co-directs the Computational Language Understanding Lab (CLU Lab). His research and teaching lie in the areas of machine learning and natural language processing. He was previously an assistant professor in Computer and Information Science at the University of Alabama at Birmingham and before that, worked as a postdoctoral researcher at Stanford University's Natural Language Processing group, Johns Hopkins University's Human Language Technology Center of Excellence, KULeuven's Language Intelligence and Information Retrieval group in Belgium, and the University of Colorado Boulder's Center for Language and Education Research. He received his Ph.D. in Computer Science and Cognitive Science from the University of Colorado Boulder in 2007. His areas of interest include Machine Learning and Natural Language Processing.

Rahul Kumar Bhadani

Research Assistant, Electrical and Computer Engineering

Rahul Kumar Bhadani is a graduate student in the Department of Electrical and Computer Engineering and Statistics GIDP, at The University of Arizona. He did his undergraduate degree in Information Technology with an emphasis in Computer Sciences and Software Engineering. In his PhD program, he is working under the supervision of Dr. Jonathan Sprinkle to develop novel control and learning algorithms for autonomous driving technology and intelligent transportation systems. He has project management and leadership experience gained while serving as a mentor for REU undergraduate interns every Summer since 2016, supervising them on projects related with controller design for autonomous driving and detection/perception algorithms using range of sensors including LIDAR, Stereocamera and GPS.

Educational Background

MS, Electrical and Computer Engineering, The University of Arizona, 2017
MS, Statistics, The University of Arizona, 2019
PhD, Electrical and Computer Engineering, The University of Arizona, 2020

Area(s) of Interest

Machine Learning
Image Analysis
Large Scale Visualization
Data Science Literacy

Ronald Breiger

Regents' Professor; Professor, School of Sociology; Affiliate, Graduate Interdisciplinary Program in Statistics; Professor (by courtesy), School of Government and Public Policy

Dr. Ronald Breiger, Regents' Professor and Professor of Sociology at the University of Arizona, holds joint affiliations with the Interdisciplinary PhD program in Statistics and with the School of Government and Public Policy. He is a leading contributor to theory and methods in social network analysis, and he has substantial strengths in the sociology of culture, organizations, stratification, theory and methods. He served as Editor of the journal Social Networks (1998-2006) and is currently (2016- ) Edtior for Social and Political Science of the journal Network Science. He is the 2005 recipient of the Simmel Award of the International Network for Social Network Analysis, and was elected Chair of the Section on Mathematical Sociology of the American Sociological Association (2009-10). He chaired a 2002 National Academy of Sciences workshop on dynamic social network modeling and analysis, which was focused on the contributions of that area to national needs and especially to national security. The proceedings (edited by R.L. Breiger, K.M. Carley, and P.E. Pattison) were published in 2003 by National Academies Press (http://www.nap.edu/catalog/10735/). Dr. Breiger has been named a Fellow at the Center for Advanced Study in the Behavioral Sciences and a Fulbright Senior Scholar, and he is currently an editorial board member of the American Journal of Cultural Sociology, Poetics: Journal of Empirical Research in Culture, the Media, and the Arts, and the American Sociological Association journal Socius. He currently holds or has recently held federal research grants, from the Defense Threat Reduction Agency (PI), the Air Force Office of Scientific Research (co-PI), and the National Science Foundation (co-PI on two separate grants). Dr. Breiger earned a Ph.D., Sociology, Harvard University, 1975. His interests include Machine Learning, Natural Language Processing, and Data Science Literacy.

Robert Canales

Assistant Professor, Environmental Health Sciences

Prior to joining the University of Arizona, Dr. Canales was a researcher at Stanford University - focusing on computational exposure simulation - and an instructor at the New School - working with faculty and students across Parsons School of Design and Eugene Lang College. Currently at the University of Arizona, he collaborates with investigators and teams of researchers interested in machine learning, data science, and the development of mechanistic models of health, risk, and environmental systems. Robert mentors students from diverse backgrounds that are motivated to learn about interdisciplinary science, applied statistics, and computational methods in environmental science and health.

Educational Background:

BS, Civil Engineering, University of Texas-Austin
MS, Civil Engineering, Stanford University
MS, Statistics, Stanford University
PhD, Environmental Engineering, Stanford University

Area(s) of Interest:

Machine Learning
Image Analysis
Data Science Literacy

Tracy Crane

Assistant Professor Biobehavioral Health Sciences Division University of Arizona, Associate Member University of Arizona Cancer Center

My research program designs and tests integrated lifestyle behavior and symptom management interventions to reduce symptom burden and increase adherence to cancer-preventive lifestyle behaviors including diet, physical activity and tobacco use. This research utilizes real-time, multi-modal methods for assessing and intervening on both symptoms and lifestyle behaviors to improve patient reported outcomes and ultimately prevent cancer in cancer survivors and their informal caregivers (family, friends). Utilizing digital voice recordings from intervention sessions, my team and I are also interested in associating adherence to lifestyle behavior interventions and language employing techniques such as Natural Language Processing and Machine Learning.

Educational Background

BS - Nutritional Sciences - Dietetics
MS - Nutritional Sciences
PhD - Nurse Science minor Psychology
RDN - Registered Dietitian Nutritionist

Alexander Danvers

Postdoctoral Researcher, Department of Psychology

Educational Background:

PhD Psychology, Arizona State University, 2017

Bio:

Dynamics of emotions and social interactions

Area(s) of Interest:

Machine Learning
Natural Language Processing
Data Science Literacy

Arthur Delsing

Manager of Business Analytics Projects, Eller College of Management (MSBA)

Arthur Delsing has been working in Information Technology since he started his career in the US Navy. From there he worked through IT from Network Administration to IT Security at Motorola. He has had the opportunity to work at IBM working with their re-sellers and design and implementing server and data storage arrays. He had the opportunity to go to college later in life and received his undergraduate degree in Psychology and a Professional Science Master’s in Human-Computer Interaction from SUNY Oswego. After his degree he worked as an Adjunct professor for SUNY Oswego in the MBA department introducing the MBA students to Business Analytics as well as working for UA and Intuit as a Senior Data Analyst developing dashboards, sourcing data, and developing algorithms for predicative planning. He currently is the Manager of Business Analytics Projects at UA Eller MIS’s new Masters of Science in Business Analytics.

Educational Background:

B.A. Psychology, SUNY Oswego University, 2013
M.S. Human Computer Interaction, SUNY Oswego University, 2015

Area(s) of Interest:

Machine Learning
Natural Language Processing
Large Scale Visualization
Data Science Literacy

Neng Fan

Assistant Professor, Systems and Industrial Engineering; Assistant Professor, Applied Mathematics - GIDP; Assistant Professor, Statistics-GIDP

Dr. Neng Fan is an assistant professor at Department of Systems and Industrial Engineering at the University of Arizona (UA), Tucson, Arizona. He received his bachelor degree in computational mathematics from Wuhan University in China, and master degree in applied mathematics from Nankai University in China. He also received his master and PhD degrees from Department of Industrial and Systems Engineering at University of Florida. Before joining UA, he worked in Los Alamos National Laboratory, Los Alamos, New Mexico and Sandia National Laboratories, Albuquerque, New Mexico.

His research interests include (1) Methodologies in Optimization; (2) Applied Operations Research: energy systems, water systems, renewable energy integration, interdependent infrastructures, healthcare processes; and (3) Data Analytics. Areas of interest include Image Analysis and Machine Learning.

Joe Galaskiewicz

Professor, School of Sociology; Director of the Certificate Program in Computational Social Sciences

After graduating with a Ph.D. in Sociology, Galaskiewicz joined the Sociology faculty at the University of Minnesota where he achieved the rank of Professor and also became a faculty member in the Carlson School of Management. He came to the University of Arizona's Sociology department in 2001 and is the founding Director of the Certificate Program in Computational Social Sciences. Since coming to Arizona he has worked with large data sets of establishments in the Phoenix metro area using GIS methods and spatial econometrics to explain changes in organizational populations from 2003 to 2013. Also he has studied the ties between households and these establishments and their spatial capital. He also is doing research on dynamic models of network change using new visualization techniques (NDTV). He has been funded by NSF almost continuously since 1980. Areas of interest include Large Scale Visualization and Data Science Literacy.

Gus Hahn-Powell

Assistant Professor, Linguistics; Assistant Professor, Cognitive Science - GIDP; Affiliated Faculty, Computational Social Science Certificate Program

Educational Background:

PhD in Computational Linguistics (University of Arizona, 2018)
MS in Human Language Technology (University of Arizona, 2014)
MA in Applied Linguistics (University of Alabama, 2010)
BA in Japanese (University of Alabama, 2008)

Bio:

My research centers around machine reading for scientific discovery. In other words, I build and design intelligent systems to help researchers surmount the problem of information overload by scouring the vast body of scientific literature, analyzing findings, and synthesizing discoveries to generate novel hypotheses. Please see my website (https://parsertongue.com) for details on my research and teaching.

Area(s) of Interest:

Machine Learning
Natural Language Processing
Literature-based Discovery (LBD)

Mike Hammond

Professor, Linguistics; Professor, Cognitive Science - GIDP; Professor, Second Language Acquisition / Teaching - GIDP

Hammond earned a PhD in Linguistics from UCLA in 1984. He has published extensively on stress, syllabification, prosodic morphology, computational phonology, and Optimality Theory. His work has focused on the phonology of English and more recently Welsh and Scottish Gaelic. His work over the last ten years has dealt with: a) on-line judgments of grammaticality as a function of phonotactic probability, and the computational modeling of those judgments; and b) the relationship between lexical and syntactic frequency and phonological well-formedness.

P. Bryan Heidorn

Professor, School of Information Director; Center for Digital Society and Data Studies

Educational Background:

B.S. Psychobiology, MS and PhD in Information Science, University of Pittsburgh

Bio:

P. Bryan Heidorn is a Professor at the University of Arizona School of Information, where he directs the Center for Digital Society and Data Studies: https://cdsds.arizona.edu. From 2009-2018 he served as the Director of the School of Information Resources and Library Science and the School of Information. Prior to that he served as a program officer at the National Science Foundation in the Division of Biological Infrastructure and participated in NSF cyberinfrastructure initiatives though Computer, and Information Science and Engineering and other programs. He served for ten years on faculty of the University of Illinois School of Information Sciences.

His current research grants include Astrolabe, a project to create active data repositories for data in astronomy that might otherwise be lost (dark data). He also is brining and NSF Harnessing the Data Revolution project titled: Converging Genomics, Phenomics, and Environments Using Interpretable Machine Learning Models. He is also working on a project to establish sensor networks to help predict species interaction.

Area(s) of Interest:

Machine Learning
Natural Language Processing
Data Science Literacy

Robert Henderson

Assistant Professor, Linguistics; Assistant Professor, Second Language Acquisition / Teaching - GIDP

My research focuses on natural language semantics---what words mean and how those meanings are combined to generate meanings for larger expressions, like sentences. I also study natural language pragmatics---how people use context and world knowledge to enrich the literal meanings of utterances. Modern semantics and pragmatics has deep ties to formal logic. This is because to understand these puzzles it is often helpful to treat human languages as if they were formal languages like you might encounter in mathematics or computer science. The way my research unfolds is to design logics (or other formal systems) in which one can define representations and operations over those representations that closely mimic what is observed in human languages.

Recently my work has taken a more computational turn. I am interested in using tools from natural language processing to build and refine theories of natural language processing. I am currently working on a project to model long distance semantic dependencies using LSTMs. I am also working with a student constructing a corpus of child speech involving numerals and quantifiers and their various interpretations.

M. Sriram Iyengar

Director, Clinical Outcomes Research

Director, Clinical Outcomes Research
Associate Professor, Research Scholar - Internal Medicine
Associate Professor, BIO5 Institute

Educational Background:

B.Tech., Electrical Engineering, Indian Institute of Technology; Madras, India

M.Sc. (Engg), Electrical Communications Engineering, Indian Institute of Science; Bangalore, India

M.S. (Statistics), The Ohio State University; Columbus, OH

Ph.D. (Computer Science), The Ohio State University; Columbus, OH

Areas of Specialty:

Internal Medicine
Clinical Informatics,
Mobile Learning,
Mobile Health

Peter Jansen

Assistant Professor, School of Information

Peter Jansen is a broadly interdisciplinary artificial intelligence researcher specializing in natural language processing and methods inspired by cognition and the brain. He apply these to application areas in science and health care. A central focus of his science research is on how we can teach computers question answering in the form of passing standardized science exams, as written. In particular, he focus on methods of automated inference that generate explanations for why the answer is correct, largely using graph-based methods. In terms of health care, he studies how we can use natural language processing and inference to improve electronic health records and improve nurse communication, as well as detect potentially dangerous clinical events before they happen. His areas of interest include Machine Learning and Natural Language Processing.

Jong-Hyun Jeong

PhD Candidate, Civil & Arch. Engineering & Mechanics

Bio:

Research focuses on Structural Health monitoring and engineering application of AI in civil engineering problems
I have been focusing on my research in broad area including wireless sensor hardware development, signal processing, and machine learning-based infrastructural health evaluation technique development, and reinforcement learning-based design automation.

Area(s) of Interest:

Machine Learning
Image Analysis
Engineering applications

Blake Joyce

Assistant Director, Research Computing, UITS

Blake is a code-curious biologist and works with a lot of biology-curious coders. He started his scientific career as an ecologist and realized they needed to learn genetics. Then he became a geneticist and learned that they needed to learn bioinformatics. Then he became a bioinformaticist and realized they needed to learn data science and research computing. 15 years later, he started working for CyVerse to deliver data science, cyberinfrastructure (computing resources), and advanced data visualizations to ecologists, geneticists, and other life scientists. After hours, he dabbles with Jupyter notebooks et al., precision agriculture, zymurgy, Software/Data Carpentry, and Research Bazaar Arizona. Blake's interests include Machine Learning, Image Analysis, Large Scale Visualization, Data Science Literacy. Blake's current position is Assistant Director, Research Computing, UITS, University of Arizona: https://it.arizona.edu/blake-joyce

Educational Background:

PhD Plants, Soils, and Insects, University of Tennessee, 2013
Master's, Plant Sciences, minor: Statistics, University of Tennessee, 2008
BS Ecology, University of Georgia, 2006
BS Biology, University of Georgia, 2006

Ahyoung Amy Kim

Ph.D. Student, Statistics GIDP; Graduate Research Assistant, UA TRIPODS

Ahyoung Amy Kim is a Ph.D. student in the Statistics GIDP and a Graduate Research Assistance in the TRIPODS program at the University of Arizona. She earned a B.S. in Applied Mathematics and Statistics at the Johns Hopkins University, and after completing her undergraduate degree, she worked at a consulting firm analyzing a large-scale compensation data to design the pay structures for various companies in Korea and abroad. At the University of Arizona, she is working under the supervision of Dr. Helen Zhang. Amy has been working on three major projects that develop and apply various statistical machine learning algorithms to analyze high dimensional karyometric cancer data for precision medicine, advance spatial and temporal models including graphical models to make predictions of atmospheric phenomena such as hurricanes, and establish methodologies for automated constructions of diverse networks.

Educational Background:

Ph.D. Student in Statistics GIDP
M.A. Economics, the University of Arizona, 2015
B.S. Applied Mathematics and Statistics, the Johns Hopkins University, 2011

Area(s) of Interest:

Machine Learning
Large Scale Visualization
Data Science Literacy

Hongseok Ko

Graduate candidate/Graduate Research Assistant, Ecology and Evolutionary Biology (EEB)

Hongseok Ko is a Ph.D. candidate in Ecology and Evolutionary Biology (EEB) program at the University of Arizona. He completed his B.S. in Environmental Science and Ecological Engineering (EEE) at Korea University in South Korea. After working as a manager of R & D and planning departments at an international company, Hong became a graduate researcher working in both Ferriere (The director of iGLOBES) lab and Martinez lab. For his research projects, Hong has developed 1) mathematical models for integrating human impact on ecological networks to detect the possibility of achieving sustainability in socio-ecological systems, 2) methodologies to assemble and visualize ecological networks of the Polynesian islands which are including thousands of species, and 3) automated methods to reconstruct biological networks based on empirical data. For many of his projects, Hong has been using classical mathematical models, statistical machine learning methods, and various neural networks algorithms.

Educational Background:

Ph.D. Candidate in Ecology and Evolutionary Biology (EEB), University of Arizona
B.S. Environmental science and ecological engineering, Korea University, Korea, Republic of

Area(s) of Interest:

Machine Learning
Large Scale Visualization
Data Science Literacy

David LeBauer

Director of Data Science for the Arizona Experiment Station

Dr. David LeBauer is the director of Data Science for the Agricultural Experiment Station at the University of Arizona. His research is focused on using science to engineer more sustainable and productive crops and agricultural systems. To support this effort, he develops open software and data to integrate data and knowledge across disciplines. Key projects include the Predictive Ecosystem Analyzer (PEcAn) framework for data synthesis and forecasting with crop and ecosystem models and the TERRA Reference phenotyping database and computing pipeline.

Education:

Ph.D. Earth System Science, University of California at Irvine, 2008

Area(s) of Interest:

Image Analysis
Data Science Literacy

Tao Liu

Postdoc, Geosciences; Hydrology & Atmospheric Sciences

Educational Background:

PhD. Hydrology, UofA

Area(s) of Interest:

Machine Learning
Geographic Information Systems
High Performance Computing

Joseph Long

Ph.D. Student, Department of Astronomy / Steward Observatory

Joseph Long uses data science techniques to pose and answer questions about planets outside our solar system (and the instruments we use to find them). His research interests include the application of dimensionality reduction techniques to high-contrast exoplanet direct imaging data, as well as enabling efficient processing of embarrassingly parallel workloads with careful software design and cloud services. He is advised by Dr. Jared Males, an Assistant Astronomer at the Steward Observatory in the University of Arizona. His interests in data science include machine learning techniques, image analysis, and the promotion of data science literacy.

Educational Background

B.A. in Physics (Astrophysics track), Pomona College, 2014

Area(s) of Interest

Machine Learning
Image Analysis
Data Science Literacy

Eric Lyons

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)

Fiona McCarthy

Associate Professor, School of Animal and Comparative Biomedical Sciences

My main area of research is providing functional annotations to support modeling of functional genomics data sets for non-model organisms. This includes aspect of biocuration, ontology design, gene identification and functional prediction and functional genomics. I am also interested in integrating different date types and the visualization of these results in a way that enables researchers to turn genomics data into practical knowledge that can be applied to agricultural systems.

Educational Background:

B.Sc. Molecular Biology & Microbiology, University of Queensland, 1991
B.Sc. (Honours) Genetics, University of Queensland, 1992
Ph.D. Virology, University of Queensland, 2003

Vivian Miranda

Steward Observatory, Astronomy Department

Educational Background:

Ph.D. University of Chicago 2015, Postdoc Fellow University of Pennsylvania 2018

Bio:

Vivian Miranda joined Steward Observatory on September 1, 2018, to work with Prof. Elisabeth Krause. Vivian's research is focused on probing inflation, the epoch of reionization and dark energy with the Cosmic Microwave Background. She has done her Ph.D. at the University of Chicago under the guidance of Prof. Wayne Hu. Vivian is also keen on understanding how extensions of the LCDM model can be constrained via the combination of the CMB with low redshift probes. As a postdoc at the University of Pennsylvania, she has developed research on how to test fundamental assumptions about the standard model using model-independent techniques. At the University of Arizona, Vivian intends to dedicate her time to the Dark Energy Survey (DES), in particular, to cluster cosmology and weak lensing. Vivian's work stands on the bridge between theory and data, and she is open to radically new ideas, as long as it can be falsified by either the CMB or the DES/LSST/WFIRST surveys. In 2019, Vivian was awarded the Leona Woods Distinguished Postdoctoral Lectureship Award.

Area(s) of Interest:

Machine Learning
Image Analysis

Clayton Morrison

Associate Professor UA School of Information; Statistics Graduate Interdisciplinary Program faculty

Clayton T. Morrison is an Associate Professor in the School of Information at the University of Arizona and faculty member of the Statistics Graduate Interdisciplinary Program. He leads the Machine Learning for Artificial Intelligence Laboratory (ml4ai.org). Professor Morrison received his Ph.D. in Philosophy from Binghamton University in 1998 in the area of computational cognitive modeling and received his M.Sc. in computer science from University of Massachusetts in 2004. He spent five years at the University of Southern California Information Sciences Institute, for two years as a Director of Central Intelligence Postdoctoral Fellow, and then as a Research Computer Scientist . He joined the University of Arizona in 2008. His current research focuses on developing machine learning and statistical modeling approaches to learning structured representations from unstructured, semi-structured and time series data. Applications include natural language processing and machine reading, biological structure and processes, computational music analysis, and modeling the relationships between human facial expressions and decision-making. His work has been funded by multiple grants from NSF, DARPA, AFOSR, and ONR. His areas of interest include Machine Learning, Natural Language Processing, and Image Analysis.

Laurent Pagnier

Visiting Assistant Professor, Department of Mathematics, U of A

Educational Background:

PhD in Theoretical Physics, École polytechnique fédérale de Lausanne (EPFL), 2019.

Bio:

Laurent Pagnier received M.S. and Ph.D. degrees in theoretical physics from the EPFL, Lausanne, Switzerland, in 2014 and 2019, respectively. His research interests include the development of new renewable energy sources and their effects on transmission grids. He focuses his efforts on developing new methods and models to describe power systems.

Area(s) of Interest:

Machine Learning
Large Scale Visualization

Alex Pakalniskis

PhD Student and Graduate Teaching Assistant, School of Geography and Development

Hi, my name is Alex and I’m a plant biogeographer. I’ve conducted and supported UAV remote sensing and fire ecology field work in sites across southern California, United States, North America, Earth and Koulikoro and Sikasso regions, Mali, West Africa, Earth.

Educational Background:

MA, Geography, California State University, Long Beach (CSULB), 2018
BS, Biochemistry, University of California, Los Angeles (UCLA), 2014

Areas of Interest:

Image Analysis
Data Science Literacy

Duke Pauli

Assistant Professor, School of Plant Science

I am a plant breeder and geneticist interested in harnessing big data to help understand abiotic stress physiology in order to develop new plant varieties that are more environmentally resilient and better able to cope with changing climatic conditions.

Educational background:

PhD Plant Science, University of Montana, 2014

Areas of interest include Machine Learning, Image Analysis, Data Science Literacy

Walter Piegorsch

Professor/Dept. of Mathematics; Director of Statistical Research & Education/BIO5 Institute

Walter W. Piegorsch is the Director of Statistical Research & Education at the University of Arizona’s BIO5 Institute. He is also a Professor of Mathematics, a Professor of Public Health, a Member and former Chair of the University’s Graduate Interdisciplinary Program (GIDP) in Statistics, and he holds accreditation as a Professional Statistician (PStat) from the American Statistical Association. Dr. Piegorsch’s research focuses on data science for biomedical and environmental problems, with emphasis on informatics in risk assessment and precision medicine. He has been supported via external funding for this work by the U.S. National Institutes of Health and the U.S. Environmental Protection Agency. His interests also include data analytics in public health risk assessment, including geo-spatially referenced disaster informatics, multiple/simultaneous inferences for toxicological and genetic endpoints, and the historical development of statistical thought as prompted by problems in the biological and environmental sciences. Dr. Piegorsch has held a number of professional positions, including Chairman of the American Statistical Association Section on Statistics & the Environment (2004); Vice-Chair of the American Statistical Association Council of Sections Governing Board (1997-1999), and election to the Council of the International Biometric Society (2002-2005). Since 2010 he has served as Editor-in-Chief of Environmetrics, and since 2014 as a Founding Editor for the online encyclopedia WileyStatsRef: Statistics Reference Online. He also has served as Joint-Editor of the Journal of the American Statistical Association (Theory & Methods Section), the flagship journal of the association; as Co-Editor-in-Chief of the Encyclopedia of Environmetrics, 2nd Edition, published in 2012 by John Wiley & Sons; and as a member of many journal editorial boards, including Environmental and Ecological Statistics, Environmetrics, Environmental and Molecular Mutagenesis, Mutation Research, the Journal of the American Statistical Association, and Biometrics. Dr. Piegorsch has been honored as a Fellow of the American Statistical Association (1995), a Member (by Election, 1995) of the International Statistical Institute, and has received the Distinguished Achievement Medal of the American Statistical Association Section on Statistics and the Environment (1993), and the University of South Carolina Educational Foundation Research Award for Science, Mathematics, and Engineering (2000). Interests include Machine Learning and Data Science Literacy.

Educational Background:

Ph.D. Statistics, Cornell University, 1984
M.S. Statistics, Cornell University, 1982
B.A. Mathematics, Colgate University, 1979

Julian Pistorius

Software Engineer, BIO5 Institute & CyVerse

Julian is a software engineer who specializes in scaling software 'up and out' using High Throughput Computing as well as cloud infrastructure. He has extensive experience in debugging, profiling and optimization of software. He also teaches these techniques to researchers. In addition he has experience testing, deploying, and supporting systems at scale on POSIX platforms. Julian's areas of interest include Machine Learning and Data Science Literacy.

He works on the CyVerse project's Atmosphere cloud computing product. He has a peculiar interest in decentralized, 'organic', anti-fragile computing systems.

He received a Bachelor of Science (B.Sc.), Computer Science in 1998 from the University of Auckland, New Zealand.

Samir Rachid Zaim

Graduate Research Assistant, Center for Biomedical Informatics & Biostatistics; Graduate Researcher in Lussier Lab

Samir Rachid Zaim is a PhD student in Statistics and a Graduate Research Assistant in the Lussier Group. After completing his Bachelor's Degree in Mathematics/Statistics at Carleton College, Samir spent two years as a collegiate research fellow with the Analytics Department at the Parkland Center for Clinical Innovations (PCCI) developing real-time statistical models to predict adverse events such as the onset of Sepsis in the ER or 30-day hospital readmissions. In the Lussier group, Samir has worked in a variety of projects ranging from detecting environmental associations with hospital visits to extending the single-subject analytics framework in differential gene expression. Samir is interested in advancing the development of statistical and decision support algorithms in precision medicine, particularly in methods development for differential expression in single-subject analytics, EMR-based case-based reasoning, and high-dimensional feature selection in machine learning.

Educational Background:

PhD Student in Statistics
B.A. Mathematics/Statistics

Area(s) of Interest:

Machine Learning
Data Science Literacy

Joellen Russell

Thomas R. Brown Distinguished Chair of Integrative Science; Assoc. Professor in the Department of Geosciences and jointly appointed in the Department of Planetary Science/ LPL

Prof. Joellen Russell is the Thomas R. Brown Distinguished Chair of Integrative Science and Associate Professor at the University of Arizona in the Department of Geosciences. Her research uses global coupled climate models and earth system models to simulate the climate and carbon cycle of the past, the present and the future, and develops observationally-based metrics to evaluate these simulations. Before joining the University of Arizona, Dr. Russell was a Research Scientist at Princeton University and the National Ocean and Atmospheric Administration’s Geophysical Fluid Dynamics Laboratory (NOAA/GFDL). Prior to that, Dr. Russell was a fellow at the Joint Institute for the Study of Atmsophere and Oceans at the University of Washington. Prof. Russell currently serves as a member of the NOAA Science Advisory Board’s Climate Working Group, as an Objective Leader for the Scientific Committee on Antarctic Research’s AntarcticClimate21, and on the World Climate Research Program’s Southern Ocean Region Panel. She is also Associate Editor for the Americal Geophysical Union’s journal, Paleoceanography and Paleoclimatology. Prof. Russell is one of the 14 scientists behind an amicus curiae brief supporting the plaintiff in the historic 2007 U.S. Supreme Court decision on carbon dioxide emissions and climate change, Commonwealth of Massachusetts, et al. v. U.S. Environmental Protection Agency. And in 2011, the American Association of Petroleum Geologists appointed her a Distinguished Lecturer. She received her A.B. in Environmental Geoscience from Harvard and her PhD in Oceanography from Scripps Institution of Oceanography, University of California, San Diego. Areas of interest include Machine Learning, Image Analysis, Large Scale Visualization, and Data Science Literacy.

Yotam Shmargad

Assistant Professor, School of Government & Public Policy

Educational Background:

Ph.D. Marketing, Kellogg School of Management, Northwestern University, 2014

Bio:

Yotam Shmargad is a computational social scientist and Assistant Professor in the School of Government & Public Policy at the University of Arizona. His research focuses on understanding how social media platforms shape political life in the United States. He uses a mix of statistical and computational methods, including social network analysis, online data collection, virtual experimentation, sentiment analysis, and machine learning techniques. His research speaks to questions about how social media dampen certain disparities while magnifying others, and how social media can both fuel political polarization and incivility as well as extinguish their flames. Shmargad’s work has appeared in the Journal of Politics, the Journal of Information Policy, the Journal of Political Marketing, the Journal of Interactive Marketing, the International Journal of Communication, and the Journal of the Association for Information Science and Technology, among other venues. Before joining the University of Arizona as an Assistant Professor, Shmargad received his Ph.D. in Marketing from Northwestern University’s Kellogg School of Management, an M.S. in Operations Management from Columbia University, and a B.S. in Mathematics and Applied Sciences from the University of California, Los Angeles.

Area(s) of Interest:

Digital Trace Data
Social Network Analysis
Virtual Experiments

Vignesh Subbian

Assistant Professor of Biomedical Engineering, Systems and Industrial Engineering

Vignesh Subbian is an Assistant Professor of Biomedical Engineering, Systems and Industrial Engineering, member of the BIO5 Institute, and a Distinguished Fellow of the Center for University Education Scholarship (CUES). He is also the Director of Computational Medicine and Informatics for Neurological health (COM-IN) Collaboratory, focused on transforming neurologic care and health through engineering-driven and integrative research as well as training next-generation scientists, engineers, clinicians, and leaders through personalized mentorship and true multidisciplinary immersion. His research and training areas include medical informatics, healthcare systems engineering, traumatic brain injury (TBI), mental health, and computing applications for critical care medicine. His areas of interest include Machine Learning and Data Science Literacy.

Tyson Swetnam

Science Informatician, CyVerse, BIO5 Institute

My first career was as a wildland firefighter and fire management specialist for the US DOI National Park Service and USDA Forest Service.

My education in natural resource management and dendrochronology came with a strong emphasis on Geographic Information Systems and remote sensing which became the focus of my current work.

Today, I work mostly on spatial data infrastructures as a data scientist for CyVerse. Areas of Interest include Image Analysis, Large Scale Visualization, Data Science Literacy.

Educational Background:

PhD Watershed Management, University of Arizona 2013,
MS Watershed Management, University of Arizona, 2006,
BS Ecology and Evolutionary Biology, University of Arizona, 2002

Ravi Tandon

Assistant Professor, Department of Electrical and Computer Engineering, UA

Ravi Tandon is an Assistant Professor in the Department of ECE at the University of Arizona. Prior to joining the University of Arizona in Fall 2015, he was a Research Assistant Professor at Virginia Tech with positions in the Bradley Department of ECE, Hume Center for National Security and Technology and at the Discovery Analytics Center in the Department of Computer Science. He received the B.Tech. degree in Electrical Engineering from the Indian Institute of Technology, Kanpur (IIT Kanpur) in 2004 and the Ph.D. degree in Electrical and Computer Engineering from the University of Maryland, College Park (UMCP) in 2010. From 2010 to 2012, he was a post-doctoral research associate at Princeton University. He is a recipient of the 2018 Keysight Early Career Professor Award, NSF CAREER Award in 2017, and a Best Paper Award at IEEE GLOBECOM 2011. He is a Senior Member of IEEE and currently serves as an Editor for IEEE Transactions on Wireless Communications. His current research interests include information theory and its applications to wireless networks, communications, security and privacy, machine learning and data mining.

Educational Background:

B.Tech, Electrical Engineering, Indian Institute of Technology (IIT), Kanpur 2004
Ph.D., Electrical and Computer Engineering, University of Maryland, College Park 2010
Post-doctoral Fellow, Princeton University, 2010-2012.

Area(s) of Interest:

Machine Learning
Image Analysis

Elmira Torabzadeh

Statistician at BIO5

I am a statistician working in the Statlab at BIO institute. I have a bachelor in Math and a Master in Statistics. I work as a statistical consultant and mostly working on big data. Interested in learning more about machine learning. I use R and SAS as tools for the analysis.

Educational Background:

MS Statistics

Area(s) of Interest:

Machine Learning
Large Scale Visualization
Data Science Literacy

Guillermo Uribe

Data Analyst and Instructor, Department of Mathematics

Educational Background:

Ph.D. Applied Mathematics, University of Arizona, 1993

Bio:

Manager, Systems Support, University Learning Center 1993-2003
Director, University Learning Center 2003-2006
Assistant Director, Institutional Research, 2006-2016
Data Analyst, Instructor, Mathematics 2016-2019

Areas of Interest:

Machine Learning
Large Scale Visualization
Data Science Literacy

Sayyed Mohsen Vazirizade

Department of Civil Engineering and Engineering Mechanics. Minor in System and Industrial Engineering.

My bachelor degree is in Civil Engineering; my master is in risk and earthquake engineering, Sharif University 2015. Furthermore, I have a certificate from Johns Hopkins University,2018, for data science, including the following topics: The Data Scientist’s Toolbox, R Programming, Getting and Cleaning Data, Exploratory Data Analysis, Reproducible Research, Statistical Inference, Regression Models, Practical Machine Learning, Developing Data Products. I'm a Ph.D. Candidate at the University of Arizona, Department of Civil Engineering and Engineering Mechanics. Supported by NSF, my focus of research is risk estimation, uncertainty modeling, and artificial intelligence. The aim of this project is developing an intelligent and integrated software for reliability estimation of dynamic nonlinear complex systems in time-domain. Simultaneously, I teach 2 engineering courses each semester; my research is concerned with Engineering Statistics and machine learning.

Area(s) of Interest:

Machine Learning
Large Scale Visualization
Data Science Literacy

Francesca Vitali

Research Assistant Professor, Center for Biomedical Informatics and Biostatistics

After completing her Bachelor's Degree and Master's Degree in Biomedical Engineering at the University of Pavia, Dr. Vitali received her PhD in Bioinformatics and Biomedical Engineering from the University of Pavia, Italy. Before joining the Lussier Research Group, she was a postdoctoral researcher at the Bioinformatics, Mathematical Modelling and Synthetic Biology (BMS) Lab at the University of Pavia under the mentorship of prof. Riccardo Bellazzi. Her work mainly focused on developing computational methods to support drug repurposing and polypharmacology.

During her career, Dr. Vitali developed strong programming experience with different languages and solid knowledge of data mining, statistics, graph theory, machine learning, and data integration techniques. One of the key aspects in these methods is their flexibility to make them suitable for use in different contexts. Her research was conducted in an expert multidisciplinary collaborative team, which included collaborations with international laboratories and pharmaceutical industries such as Sanofi and AstraZeneca.

Educational Background:

B.S. Biomedical Engineering, University of Pavia, 2009
M.A. Biomedical Engineering, University of Pavia, 2012
PhD in Bioinformatics and Bioengineering, University of Pavia, 2015

Area(s) of Interest:

Machine Learning
Natural Language Processing
Image Analysis
Large Scale Visualization
Data Science Literacy

Shu-Fen Wung

Associate Professor, Nursing

Educational Background:

PhD Nursing, University of California San Francisco, 1999

Bio:

Nursing Research
Acute Care Nurse Practitioner

Area(s) of Interest:

Machine Learning
Natural Language Processing
Image Analysis
Data Science Literacy

Ann Zabludoff

Professor, Department of Astronomy; Astronomer, Steward Observatory

Ann Zabludoff has led studies across extragalactic astronomy and cosmology, including analyses of large observational datasets and theoretical simulations. She has worked to adapt astronomical instruments for new science. She was a Guggenheim Fellow in 2013-14, a TEDx speaker in 2012, and the Caroline Herschel Distinguished Visitor at the Space Telescope Science Institute in 2011–2013. Her service includes advising the NSF, NASA, and international research institutes. She has mentored numerous junior scientists and was Graduate Program Director for UA Astronomy from 2005 to 2013, supervising 40-50 PhD students at a time.

Educational Background

Ph.D. Astronomy, Harvard University, 1993
A.M. Astronomy, Harvard University, 1988
S.B. Mathematics, Massachusetts Institute of Technology, 1987
S.B. Physics, Massachusetts Institute of Technology, 1986

Area(s) of Interest

Machine Learning
Image Analysis
Large Scale Visualization

Qiang Zhou

Assistant Professor, Department of Systems and Industrial Engineering; Faculty member of the UA Statistics GIDP (graduate interdisciplinary program); Director, Reliability & Intelligent Systems Engineering Lab

His research focuses on industrial data analytics for engineering decision making and system performance improvement,using methodologies from applied statistics, data mining, machine learning and signal processing. Specifically, he has been working in the areas such as experimental design and analysis of computer simulations, engineering system monitoring, fault diagnostics and failure prognostics, statistical quality control for manufacturing processes. Application areas of his research include semiconductor manufacturing, nanomaterial fabrication, automobile industry, telecommunications, computer simulations, etc. Interests include Machine Learning and Image Analysis. For more details, please go to my personal webpage: http://qzhou.faculty.arizona.edu/

Educational background:

B.E. in Automotive Engineering (2005), Dept of Automotive Engineering, Tsinghua Univ, Beijing, China
M.S. in Mechanical Engineering (2007), Dept of Automotive Engineering, Tsinghua Univ, Beijing, China
M.S. in Statistics (2010), Dept of Statistics, University of Wisconsin-Madison
Ph.D. in Industrial Engineering (2011), Dept of Industrial and Systems Engineering, University of Wisconsin-Madison

Last Updated: Mon, 03/16/2020 - 10:24am

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