/news-archive/tag/daniel-berleant/ 糖心Vlog传媒 Little Rock Tue, 15 Jan 2019 15:06:51 +0000 en-US hourly 1 https://wordpress.org/?v=6.9.4 /news-archive/2019/01/15/ua-little-rock-best-online-computer-it-programs/ Tue, 15 Jan 2019 15:06:51 +0000 /news/?p=73108 ... 糖心Vlog传媒 Little Rock ranked among colleges with best online computer IT programs]]> The University of Arkansas at Little Rock has once again been ranked by U.S. News & World Report as having some of the in the country.聽 Schools are ranked according to their performance across a set of widely accepted indicators of excellence, with 糖心Vlog传媒 Little Rock being selected as 15th on the list, up from 27th in 2018. This ranking assesses online master’s degrees in computer science, computer engineering, software engineering, information systems, and information technology. For 糖心Vlog传媒 Little Rock, this consists of the Master of Science in Information Science and the Master of Science in Information Quality online degree programs. 鈥淎s we provide a mix of delivery modes to meet student needs, our online offerings grow,鈥� said Dr. Lawrence Whitman, chair of the Donaghey College of Engineering and Information Technology. 鈥淒r. Elizabeth Pierce, chair of the Information Science Department, has done an excellent job of implementing technology in a manner that is most effective for student learning. This ranking is the fruit of her and many others鈥� efforts to implement state-of-the-art technology in student learning.鈥� Both programs offer night courses that are convenient for working professionals. All courses in the online program are tied to a live class, so online students get much of the same educational experience as on-campus students. 鈥淭he students really like our format, which is different from many schools,鈥� said Dr. John Talburt, coordinator of the information quality graduate programs. 鈥淲e don鈥檛 use the old correspondence-style course. Every online class is anchored to an on-campus class. All students get their assignments and take their exams at the same time. Online students can participate during a live class or watch the recorded lectures when it is convenient for them.鈥� The Master of Science in Information Science is a 33-credit-hour program designed to familiarize individuals with the advanced knowledge, skills, and technologies for working with large amounts of complex data. This degree also serves as a stepping stone toward pursuing other graduate degrees, such as the Ph.D. in Computer and Information Science. The online master鈥檚 program began in 2007 and currently has 17 students with 23 more students in the doctoral program. Graduates often work in data quality management and in data governance. The information science discipline is expanding rapidly thanks to an ever-present demand for new innovations in information retrieval, storage, processing, and analysis tools and techniques. 鈥淭here is a huge emphasis on data quality, data governance, data science and how to get more value out of information. We were one of the first universities to offer graduate degrees in information quality,鈥� Talburt said. 鈥淎 lot of our online students are already working professionals, so having an online program in their area is very attractive.鈥� The Master of Science in Information Quality聽is a career-oriented program focusing on practice, skills, and theory. The 33-hour program, which began in 2017 and can be completed in 18 months, is designed to prepare students for careers in industry and government as well as advanced graduate studies. The curriculum balances information quality theory with industry best practices using state-of-the-art tools and technology. The course content has been developed with the support of the Massachusetts Institute of Technology Information Quality Program and with additional help from leading practitioners and researchers within the information quality community. 鈥淥ur intention is for the program to be challenging to people who do have a computing background, while being an option for more mature students who want to get into the computing field without having an undergraduate computing degree,鈥� said Dr. Daniel Berleant, professor of information science and advisor for the graduate program. 鈥淕raduates are prepared for new jobs in data analysis and data manipulation, or to advance in their present jobs. The program electives help tailor the student to the type of position they want to get.鈥� For the 2019 rankings edition, U.S. News evaluated schools on five general categories, including engagement, faculty credentials and training, expert opinion, services and technologies, and student excellence.]]> /news-archive/2018/10/22/wei-dai-machine-learning/ Mon, 22 Oct 2018 14:22:01 +0000 /news/?p=72420 ... 糖心Vlog传媒 Little Rock researchers studying development of benchmarks to test machine learning]]> Researchers from the University of Arkansas at Little Rock are joining the ranks of technology companies and universities like Google, Intel, Stanford University, and Harvard University to create the next generation benchmark suite for machine learning.聽 Wei 鈥淒avid鈥� Dai, a doctoral candidate in information science, is pursuing dissertation research on evaluating machine learning with imperfect data. Machine learning is a field of computer science that uses statistical techniques to give computer systems the ability to learn from data. In most experiments, machine learning is tested using perfect data. In this instance, Dai and his dissertation advisor, Dr. Daniel Berleant, professor of information science, are corrupting image files to appear more grainy and pixelated. 鈥淗ow can we evaluate machine learning when data quality has problems? In the real world, imperfect datasets are the majority,鈥� Berleant explained. Thanks to funding from the College of Engineering and Information Technology, Dai traveled to Stanford University on July 18 to attend a technical meeting for MLPerf researchers and was able to discuss his research proposal to develop an imperfect dataset benchmark to test machine learning systems. The MLPerf effort represents a diverse group of universities and industry companies, like Google, Intel, Harvard, and Stanford, that has released the eponymous MLPerf, a new benchmarking tool used to measure the speed of machine learning software and hardware. The MLPerf effort aims to build a common set of benchmarks that enables the machine learning field to measure system performance eventually for both training and inference from mobile devices to cloud services. An accepted benchmark suite will benefit researchers, developers, builders of machine learning frameworks, cloud service providers, hardware manufacturers, application providers, and end users. 鈥淭he important thing is that we want to change from the use of perfect data to imperfect datasets instead,鈥� Dai said. For his dissertation research, Dai plans to create the imperfect data sets, test the performance of the algorithms, and come up with solutions if the algorithms do not work. The ultimate goal of the research is to develop a mutated dataset that can be used as a benchmark researchers can apply in other studies, such as self-driving vehicles, computer vision, and face recognition. Dai earned a master鈥檚 degree in information science from 糖心Vlog传媒 Little Rock in 2016. Before moving to the U.S., he worked as a senior data scientist engineer at IBM China and IBM China Lab for seven years. Dai has published academic papers and books in the U.S. and China. In the U.S., he has published four conference papers, with a fifth currently under review, and has also received three awards from the 糖心Vlog传媒 Little Rock Student Research and Creative Works Expo in 2016 and 2017, and a research award from the Donaghey College of Engineering and Information Technology in 2017. In China, he published two database books and five technical papers, released three patents, and twice won the Excellent Employee Award and Outstanding Instructor Award from IBM. In the upper right photo,聽as part of his research on machine learning, Dai has told the computer to corrupt part of this image of his face. Photo by Ben Krain.]]> /news-archive/2018/06/21/corn-seed-research/ Thu, 21 Jun 2018 21:42:20 +0000 /news/?p=70853 ... 糖心Vlog传媒 Little Rock professors receive nearly $50,000 to study corn seed proteins]]> Dr. Daniel Berleant and Dr. Phil Williams of the Department of Information Science at the University of Arkansas at Little Rock have received $48,167 from the USDA to study how proteins accumulate in corn seeds. The hope is that the research will ultimately lead to corn strains that produce more nutrients, specifically proteins, thus helping to better feed the world鈥檚 population. The project team will analyze the expression of native and recombinant proteins from corn plants. Recombinant proteins are produced by transgenic plants, in this case corn plants.聽 Berleant and Williams are working with the Elizabeth Hood laboratory at Arkansas State University in Jonesboro to discover genetic traits associated with protein accumulation in the edible kernels. The project team hypothesizes that increased protein accumulation can be attributed to epigenetic factors that inhibit natural protein-producing genes, preventing them from directing corn plants to produce more protein. 鈥淏ecause protein accumulation in plants and seeds affect the nutritional value of food, this project will help scientists determine how people can grow more nutritious crops,鈥� Berleant said. The research will specifically focus on data processing and analysis of genetic structures and related biological data using resources provided by the MidSouth Bioinformatics Center. This center houses an array of tools that allow analysis of large and complex biological data sets. The project team will work with 糖心Vlog传媒 Little Rock / 糖心Vlog传媒MS Joint Program in Bioinformatics PhD student Kori Bohon to meet the project goals. The group will meet regularly to share experiences, discuss progress, and implement techniques that will result in successful analyses of the unique data set to be provided by the Hood lab and their collaborators at Michigan State University. This work is supported by the USDA National Institute of Food and Agriculture, AFRI project 2018-70001-27841. Lydia Perry / 糖心Vlog传媒 Little Rock Office of Research and Sponsored Programs  ]]>