Computing

Ali Akoglu is an Associate Professor in the Department of Electrical and Computer Engineering and the BIO5 Institute at the University of Arizona. He received his Ph.D. degree in Computer Science from the Arizona State University in 2005. He is the site-director of the National Science Foundation (NSF), Industry-University Cooperative Research Center on Cloud and Autonomic Computing regarding the design and development of architectures for achieving self-management capabilities across the layers of cloud computing systems. Dr. Akoglu is an expert in high performance scientific computing and parallel computing with a primary focus on restructuring computationally challenging algorithms for achieving high performance on field programmable gate array (FPGA) and graphics processing unit (GPU) hardware architectures. Recently he has contributed to the scientific computing domain with: 1) design and development of novel computational methods on T-Cell Receptor (TCR) synthesis for studying the immune systems of complex organisms, which led to reducing the time scale of determining all possible ways (several trillions of sequences) in which proteins can be encoded from 62 months scale to 19 hours on a single GPU; 2) investigation of electrophysiological behavior of the heart by coupling tissue and cell models in a such a way that through physics aware programming (PAP) paradigm 3D heart simulations become parallelizable without sacrificing model accuracy, which led to reducing the time scale of such simulations from 453 hours to 1.66 seconds with a simulation accuracy of 99.9% Dr. Akoglu has been involved in many crosscutting collaborative projects with the goal of solving the challenges of bridging the gap between the domain scientist, programming environment and emerging highly-parallel hardware architectures. His research projects have been funded by the National Science Foundation, Defense Advanced Research Projects Agency, Office of Naval Research, US Air Force, NASA Jet Propulsion Laboratories, Army Battle Command Battle Laboratory, and industry partners such as Nvidia and Raytheon.

Tyson L. Swetnam Ph.D., is Research Assistant Professor of Geoinformatics in the BIO5 Institute. Dr. Swetnam's interests include the applied use of cyberinfrastructure for spatial analysis in the earth and life sciences. His broad and collaborative research portfolio spans dendrochronology, dendroecology, disturbance and landscape ecology, ecohydrology, geographic information systems, geoinformatics, geomorphology, natural resource management, and remote sensing. He is currently lead scientist for the development of spatial data infrastructure with CyVerse, a National Science Foundation (NSF) supported cyberinfrastructure project. Dr. Swetnam holds a joint faculty appointment in the School of Natural Resources and the Environment, where he originally received a Ph.D. and M.S. in Watershed Management. His current research involves funded projects with the NSF, Nature Conservancy, and USDA Agricultural Research Service. He an active member of Data7 Data Science Institute at UA; a volunteer instructor and lessons maintainer for The Carpentries teaching foundational coding and data science skills; and Research Bazaar Arizona, a digital literacy group emerging at the center of modern research.

Dr. Bane Vasic is a Professor of Electrical and Computer Engineering and Mathematics at the University of Arizona. He is affiliated with BIO5, the Institute for Collaborative Beoresearch, and is a Director of the Error Correction Laboratory.Dr. Vasic is an inventor of the soft error-event decoding algorithm, and the key architect of a detector/decoder for Bell Labs read channel chips which were regarded as the best in industry. His pioneering work on structured low-density parity check (LDPC) error correcting codes and invention of codes has enabled low-complexity iterative decoder implementations. Dr. Vasic is known for his theoretical work in error correction coding theory and codes on graphs which has led to analytical characterization of the hard decision iterative decoders of LDPC codes, and design of codes with best error-floor performance known today. He has led a project "Error Correction Algorithms for DNA Repair: Inference, Analysis, and Intervention" funded by the NSF.He is an IEEE Fellow and da Vinci Fellow.

Ram’s research spans enterprise data management, business intelligence and predictive analytics. She is director of both the Advanced Database Research Group and INSITE: Center for Business Intelligence and Analytics at the Eller College. The latter seeks to harness exploding amounts of data online – including through social media – for better decision making. Among her many honors include a 2012 IBM Faculty Award. Her research has been highlighted in several media outlets including NPR news. She has received more than $50 million in research funding from several different agencies including the National Science Foundation, National Institutes of Health, and NASA. Keywords: Big Data Analytics, Chronic health Conditions, Network Science, Machine Learning, Prediction Modeling

Mr. Todd Proebsting is Department Head and Professor of Computer Science at the University of Arizona. Mr. Proebsting worked for Microsoft for fifteen years, and is the Founder of Microsoft's efforts in using prediction markets to help forecast future events. He received his B.S. in Business Administration and Operations & Management from Lincoln University. Mr. Proebsting's research interests include cloud computing, programmer productivity and making resources even more accessible to developers. His knowledge on Cloud computing enables people to harness the power of networked resources productively and can overcome the inherent complexities of large-scale processing done on commodity hardware, allowing applications to scale elastically and reliably. This software knowledge allows Mr. Proebsting to focus on investigating how to get rid of all the clutter in most statically-checked computer programs without sacrificing the benefits of static analysis.

Over the last two decades my work has focused on developing computational platforms and enabling technologies, primarily directed towards improving research productivity and collaboration for interdisciplinary teams and virtual organizations. The key thrust areas for my work encompass life cycle management for: 1. High throughput and automated bio sample processing systems 2. Highly scalable data and metadata management systems 3. High throughput and performance computing systemsMy recent work has been directed towards supporting pervasive computing needs for mHealth (mobile health) initiatives and health interventions, with focus on developing study management platforms that leverage cloud based telephony, messaging and video in conjunction with wearable’s and sensors.Platforms and tools developed by team are utilized in: 1. Managing samples and data for Clinically certified (CAP/CLIA) NGS pipelines 2. Large scale genotyping (million+ samples) with robotic automation 3. National Cyberinfrastructure iPlant; facilitates researchers to effectively manage their data, computation and collaborations using a cohesive computational platform 4. Health interventions and patient monitoring I firmly believe that measured adoption of emerging computational technologies and methods are essential for life scientist to successfully operate at the scale and complexity of data they are constantly encountering. This can only happen if there is continuing education and practical training focused around the use of Cyberinfrastructure and computational thinking. I have developed and taught workshops, graduate and undergraduate project based learning courses with emphasis on these topicsMy team (Bio Computing Facility) engages with the campus community at various levels ranging from multi- institutional collaborative projects, graduate and undergraduate courses for credit and special topic seminars and workshops. With emphasis on enabling digital discoveries for the life sciences.

Dr. Bonnie Hurwitz is an Assistant Professor of Biosystems Engineering at the University of Arizona and BIO5 Research Institute Fellow. She has worked as a computational biologist for nearly two decades on interdisciplinary projects in both industry and academia. Her research on the human/earth microbiome incorporates large-scale –omics datasets, high-throughput computing, and big data analytics towards research questions in “One Health”. In particular, Dr. Hurwitz is interested in the relationship between the environment, microbial communities, and their hosts. Dr. Hurwitz is well-cited for her work in computational biology in diverse areas from plant genomics to viral metagenomics with over 1200 citations

Dr Chen's areas of expertise include:Security informatics, security big data; smart and connected health, health analytics; data, text, web mining.Digital library, intelligent information retrieval, automatic categorization and classification, machine learning for IR, large-scale information analysis and visualization.Internet resource discovery, digital libraries, IR for large-scale scientific and business databases, customized IR, multilingual IR.Knowledge-based systems design, knowledge discovery in databases, hypertext systems, machine learning, neural networks computing, genetic algorithms, simulated annealing.Cognitive modeling, human-computer interactions, IR behaviors, human problem-solving process.