|Mahdi Ahmadi||Alireza Borhani||Daniel Cook|
|Bradford Eilering||Erika Helgeson||Dennis Linders|
|Kin Gwn Lore||Sushant Mahajan||Josh Melander|
|Fred Morstatter||Julie van der Hoop|
Mechanical and Energy Engineering
Big Data Mining Methods for Accurate Spatial Interpolation of Ozone Pollution
This paper explains the importance of using big data methods to extract accurate spatial interpolation functions for ozone pollution prediction.
I am a Master student in Mechanical and Energy Engineering at the University of North Texas (Denton, TX). I am interested in air quality modeling and forecasting. For my thesis I've been photochemical modeling codes and statistical data analysis tools to model formation and transportation of ozone pollution for the purpose of air quality regulation. I'm minoring in computer science with emphasize on data mining. I'm interested in using data mining tools and techniques in air quality forecasting and analysis.
|air pollution modeling||air pollution data mining||air pollution statistical analysis|
Department of Construction Management
Building User Audit: Capturing Behavior, Energy, and Culture
Data analytics is emerging as an important management tool for the built environments. Utilizing data gathering and processing techniques result in dramatic improvements in building performance. However, studies show a significant discrepancy between predicted and actual performance mainly because of a gap in analysis of the occupant's impacts. Therefore, the main objective of this interdisciplinary research is to provide an audit tool that characterizes the building user behaviors and determine their influences on energy consumption. This audit tool (named Building User Audit Procedure, BUAP) introduces a procedure for an optimized data collection and analysis. The proper implementation of BUAP leads to more sustainability through improving energy efficiency and minimizing environmental impacts in building industry.
I am a second-year master student at the University of Washington, department of Construction Management. My research interests include sustainability and BIM. Particularly, I am interested in applying sustainability strategies in the field of built environment. My current research focuses on developing a building audit tool that analyzes user behavior impacts on the building performance with the purpose of reducing energy consumption and greenhouse gas emissions.
|Sustainability||Building Information Modeling (BIM)|
Improving data-management and integration within resequencing-pipelines
A powerful strategy for identifying the genetic basis of phenotypes is to perform genome-wide association (GWA) analysis. GWA studies that utilize massively parallel sequencing rely on population resequencing pipelines to identify genetic variants. Resequencing pipelines require precise data handling and integration of data generated across a large series of steps from multiple programs to identify issues, confounding factors in analysis, or to identify interesting associations. However, integrating this data is challenging as it requires extensive file parsing, manipulation, and merging. Here, I propose the development of a database schema resembling an entity-attribute-value (EAV) model for storage of summary data generated at different steps within resequencing pipelines and a set of tools enabling integration of this system. This system improves data-handling within resequencing pipelines and facilitates comparison of variables across tools, samples, and between pipeline configurations.
I am interested in identifying the genetic basis of complex traits
|Bioinformatics||Genetics and Genomics||Programming|
Fine Arts - Art Studio
Wave Pool: an environmental art installation.
I would like to address the challenges of our time and to assume the role of global citizen through avenues of public engagement with environmental artworks. Wave Pool is a visual aid with the substance to engage the public on many levels, including the artistic, the scientific and the environmentally minded.
Large scale public artworks that encourage public engagement as a means of communicating environmental issues through an interdisciplinary approach.
|Environmental Art||Environmental Science||Architectural Science|
Department of Biostatistics
Nonparametric Cluster Significance Testing
We describe a proposed method for testing the statistical significance of putative clusters. Cluster analysis is an unsupervised learning strategy that can be used to identify groups of observations in data sets of unknown structure. Few methods are available that can assess the strength of clusters identified in a data set. The methods that are available often rely on distributional assumptions or are not optimized for high dimensional settings. We propose a novel non-parametric method for testing the null hypothesis that no clusters are present in a given data set which can be used in both high and low dimensional settings with optimal accuracy.
I am a fourth year PhD student in biostatistics at the University of North Carolina, Chapel Hill. I earned my bachelor’s degree in mathematics and biology from Gonzaga University. I found biostatistics to be the perfect field for pursuing my interest in solving complex biological problems through the use of mathematical tools. Currently I am working on methodological research in cluster analysis with specific application to genetics and pain research.
|Cluster Analysis||Machine Learning|
The Smart City as a Platform for Collaboration on Climate Change
Cities are at the forefront of the fight against climate change, because their concentration of resources provides the most environmentally-friendly way of delivering a high quality of life. Sustainable cities combine this advantage with a society-wide commitment to a low-carbon lifestyle. Yet the traditional tools of public administration are poorly equipped to facilitate this collaborative approach. Fortunately, advancements in Information and Communication Technologies (ICT) hold tremendous potential to address these shortcomings. Most promisingly, innovative urban leaders have begun to reshape both government and governance around a vision of a “Smart City” that collects vast amounts of data on the state and performance of its communities and then translates this data into actionable insights. Yet the adoption of these “smart city” innovations remains best described as experimental, as blind aspirations continue to far exceed validated best practices or proven implementation strategies. To bridge this gap, the proposed research project will conduct holistic case studies of three pioneering "smart cities" to identify effective business models for using "smart" infrastructure, data science, and connected citizens to promote community-wide action on climate change.
My research examines how decision-makers can make use of “smart city” infrastructure, big data analytics, and connected citizens to better plan and manage sustainable cities. I am a PhD Candidate at the University of Maryland's iSchool, a data scientist ("CountyStat") in the Office of the Montgomery County Executive, and a "smart city" consultant to the World Bank and its 2016 World Development Report on "Internet for Development."
|Smart Cities||Data-Driven Government|
Pattern Discovery from Large-scale Computational Fluid Dynamic Data using Deep Learning
This paper outlines our research in solving an inverse fluid dynamics design problem using large-scale simulation data. The forward problem of sculpting fluid flow by placing a set of pillars in a fluid channel has been simulated and experimentally validated. We now explore the applicability of machine learning models (specifically deep learning) in the inverse problem to serve as a map between user-defined flow shapes and the corresponding sequence of pillars in the design of microfluidic devices.
Currently an M. Sc. graduate student in Iowa State University. From a mechanical engineering background, the main research areas include multi-agent systems with emphasis on network graphs (e.g. proximity networks), stochastic systems modeling, and machine learning (especially Deep Learning) with engineering applications (early detection of combustion stability, fluid flow-sculpting in microfluidic platforms) and image processing (low-light video and image denoising). Extremely interested in integrating machine learning and big data into various engineering applications.
|Machine Learning||Large-scale simulation data||Deep Learning|
Department of Physics & Astronomy
Automatic Detection and Characterization of Solar Filaments
The Solar Dynamics Observatory sends 1.5 TB data every day back to Earth. In this data lie the observations of various solar phenomena. The observations of filaments are crucial for space weather. We are trying to improve an automated code that detects and analyzes solar filaments so that it can be adopted for the next solar observatory in Hawaii which will record 5 TB of data every day.
I work on observations and simulations of the solar magnetic field, the solar interior and data analysis of various phenomena like sunspots, filaments, solar flares and coronal mass ejections. The ultimate goal is to understand the physics of magnetic fields and plasma flows inside and outside the Sun in order to predict space weather around the Earth.
From Data to Knowledge: Temporal Network Analysis towards Implementing Robust Large-Scale Societal Changes
The past century has seen an unparalleled increase in technology, from the invention of the transistor to the enumerable services provided by the Internet it has become obvious that technology has a great effect on our lives. Some of the consequences of technological progress are obvious—increased connectivity, automation, new industries, etc.—while others are more subtle, whose connections and implications may go unnoticed. Being aware of these changes is dependent on our ability to understand and model the enumerable relations present in all aspects of society. Ultimately understanding the large scale implications (e.g. social, economical, environmental, etc.) of our actions, be they technological, legislative, or political, is going to take a shift in how we approach problems. It is not sufficient to take a reductionist point of view in understanding the world around us, we need to focus on the interactions between the various entities and how they give rise to large-scale, emergent behavior.
I was raised in Gresham, Or, a suburb of Portland. After high school I attended Linfield College and obtained a B.S. in physics and mathematics. The summer of 2013, following my graduation, I moved to the San Francisco Bay Area and worked for the company BrightSign LLC where I developed a program to automate regression testing. Although I enjoyed my time in California I knew that higher education was for me and so after a year I decided to pursue my PhD at Kansas State University in the Electrical Engineering department. My current research interests involve network science and modeling/simulating infectious diseases; specifically I'm researching how the time dynamics of networks affect the evolution and spreading of diseases.
School of Computing, Informatics, and Decision Systems Engineering
Discovering Bias in Big Social Media Data
One fundamental problem with social media mining is getting access to representative, reliable data. While companies like Facebook have massive amounts of data, they do not share this data with the research community at large. For the few sites that do share their data, they do so through the use of APIs that allow the researcher access to a portion of the overall data generated on the site. Twitter, one example of a social media site that shares its data, allows researchers to at most 1% of all of the posts generated on the site each day through its API. Twitter is perhaps the most lenient when it comes to sharing data with the research community. While Twitter’s APIs come as a welcome relief to those in the area of social media mining, their ability to represent the true activity on the social media site has become a concern to researchers in recent years. The problem of finding representative samples of social media is a widely accepted and necessary problem that researchers must address in order to ensure the veracity of their research results. Herein we define the problem and outline two state-of-the-art solutions.
Fred Morstatter is a PhD student in computer science at Arizona State University in Tempe, Arizona. Fred won the Dean's Fellowship for outstanding leadership and scholarship during his time at ASU. Among his publications is an ICWSM paper that investigates the representativeness of Twitter's Streaming API, a WWW Web Science paper that seek to find periods of bias automatically in streaming Twitter data, 2 KDD demo papers, an article in IEEE Intelligent Systems, and a book: Twitter Data Analytics. He has served as a PC member of ICWSM 2014, IEEE/CIC ICCC 2014 Symposium on Social Networks and Big Data, and has been a co-chair of the Social Computing, Behavioral-Cultural Modeling and Prediction Conference's Grand Challenge organizing committee in 2014 and 2015. He has been a Visiting Scholar at Carnegie Mellon University as well as a Research Intern at Microsoft Research. He is the Principal Architect for TweetXplorer, an advanced visual analytic system for Twitter data. A full list of publications can be found at http://www.public.asu.edu/~fmorstat. Contact him at email@example.com.
|Data Mining||Machine Learning||Big Data|
Joint Program in Oceanography (Biology)
Integrating animal sensing systems
The next breakthroughs in wearable technology, for humans or animals, require integrated sensing systems.
My research focuses on the metabolic, kinematic, and behavioural effects of drag in marine mammals. Specifically, I study the effects of drag from scientific instruments (i.e., tags) on small cetaceans, and from entangling fishing gear on North Atlantic right whales.