Computer Science Seminar Series
|Date||Presenter||Title and Abstract|
|02/23/18||Dr. Jen Li||Personalized Proactive Diabetes Self-Management and Social Networking for American Indians|
This project proposes a proactive diabetes self-care mobile platform based on the unique socio-economic, cultural, and geographical status of AI patients living in a reservation community in the upper Midwest. The mobile platform connects AI diabetic patients to their medical devices, healthcare team and similar patients, and offers personalized prediction, recommendation, and social networking regarding diabetes care. It transforms diabetes management from the traditional reactive and hospital-centered care to preventive, proactive, evidence-based, and person-centered care.
|01/31/18||Dr. Simone Ludwig||Classification in Action - From Brain-Computer Interface to Intrusion Detection|
Dr. Ludwig will present research results from two research projects. The first research project investigates different classification algorithms applied to BCI data, and the second project applies a deep neural network ensemble to Intrusion detection data.
|11/01/17||Dr. Dean Knudson and Alex Radermacher||Computer Science Capstone Projects at NDSU|
The CS Capstone project course has evolved considerably over the past few years. (Course structure, tools, processes, IP, etc.) This talk will go over that history and also describe some exciting things happening with international projects and the establishment of an International Capstone Project Exchange.
The exchange presently includes universities in Germany, Sweden, Austria, Netherlands, Colombia, and Australia. US universities include the University of IL - Urbana/Champaign, Texas A&M, the University of Pittsburgh, the University of Colorado - Colorado Springs, North Carolina State University, Oregon State University, Mississippi State University, etc. In addition to Computer Science we are also doing projects in ME, EE, and International Marketing. Several more universities are investigating pairings in these and other disciplines.
Webcast on the fundamentals of machine learning, deep learning and artificial intelligence
|10/04/17||Dr. Anne Denton||Challenges in the Data Science for Food|
Data science for food, energy, and water is increasingly recognized as a topic of high global relevance. However, it may not always be clear what types of research results will ultimately improve the outlook for our planet. The prediction of agricultural yield is a natural starting point for data scientists, because it resembles data mining tasks in other areas. An increasing availability of large data quantities indeed allows answering questions that have previously been the subject of educated guesses, for example how the temporary distribution of precipitation affects yield. In a changing climate, yield prediction and optimization can result in counterproductive recommendations that aggravate the problems that motivated the research, prompting an examination of the ethics of scientific problem statements. Prediction of soil health emerges as an important and ethically robust research topic that offers fundamentally and practically relevant data science research questions. Window-based derived attributes, in particular fractal dimensions, are shown to be especially promising for this purpose.
|03/22/17||Dr. Gursimran Walia||Empirical Research Methods in Software Engineering|
Dr. Walia's presentation will illustrate the ability and value of applying human error research (borrowed from Cognitive Psychology) to a software engineering problem through close interaction between software engineering experts and cognitive psychology experts. He will also discuss research results on how to staff and improve inspection performance in the Software Industry.
|03/01/17||Drs. Nygard, Salem, Kotala||Cyber Security|
Dr. Nygard will address the needs for cyber education and characterize the field. Drs. Salem and Kotala will provide an overview of the Cyber Security course that they are currently teaching.
|11/16/16||Dr. Changhui Yan||Graph methods for the discovery of function-associated structural patterns in biological molecules|
With the advances of various genomic and proteomic projects, biomedical research has entered a data-driven era, in which computational methods are urgently needed for the task of mining complicated biological data to discover knowledge that can be used for disease treatment and drug design. In this talk Dr. Yan will present the graph methods that his group is developing for the tasking of discovering function-associated structural patterns in biological macromolecules. Using a succinct graph representation the Yan group discovered structural patterns that were enriched in the functional sites. The biological significance of these patterns was evaluated using statistical test, molecular docking, and expert knowledge.
|10/19/16||Dr. Saeed Salem||Significant subnetworks for biological network analysis|
Complex networks appear in a wide spectrum of fields, including bioinformatics and neuroscience. These systems (networks) are governed by intricate webs of interactions among constituent elements. Entities and relationships in networks are increasingly being annotated with content, thus giving rise to rich attributed graphs. In this talk, we will present graph mining algorithms for mining multiple gene expression datasets, and mining discriminative subnetworks for gene coexpression networks.
|04/28/16||Dr. Ken Magel|
A Preliminary Theory of Software Complexity
|03/08/16||Dr. Donald Costello|
*** ACM Distinguished Lecture - Donald Costello ***
Cryptography: From Enigma to Elliptical Curve Cryptography
|11/18/15||Dr. Jun Kong||A framework for cross-device interactions|
A shared interactive display (e.g., a tabletop) provides a large space for collaborative interactions. However, a public display lacks a private space for accessing sensitive information. On the other hand, a mobile device offers a private display and a variety of modalities for personal applications, but it is limited by a small screen. We have developed a framework that supports fluid and seamless interactions among a tabletop and multiple mobile devices. This framework can continuously track each user’s action (e.g., hand movements or gestures) on top of a tabletop and then automatically generate a unique personal interface on an associated mobile device. This type of inter-device interactions integrates a collaborative workspace (i.e., a tabletop) and a private area (i.e., a mobile device) with multimodal feedback. To support this interaction style, an event-driven architecture is applied to implement the framework on the Microsoft PixelSense tabletop. This framework hides the details of user tracking and inter-device communications. Thus, interface designers can focus on the development of domain-specific interactions by mapping user’s actions on a tabletop to a personal interface on his/her mobile device. The results from two different studies justify the usability of the proposed interaction.
|04/10/15||Dr. Wei Jin|
Creation, Visualization, and Mining of Domain Textual Graphs: Integrating Domain Knowledge and Human Intelligence
|03/13/15||Dr. Jen Li|
Semantics-enhanced Online Intellectual Capital Mining for Enterprise Customer CentersOne of the greatest challenges of an enterprise's service center is to ensure that their engineers and customers are provided with the right information in a timely fashion. For this purpose, modern organizations operate a wide range of information support systems to assist customers with critical service requests. It is often the case that relevant information is scattered over the Internet and/or maintained on disparate systems, buried in large amount of noisy data, and in heterogeneous formats, thereby complicating the access to reusable knowledge and extending the response time to reach a resolution. To address these challenges, in this project, we propose an effective knowledge mining solution to improve service request resolution success rates and response times.
|02/27/15||Dr. Simone Ludwig|
MapReduce-based Fuzzy C-Means Clustering AlgorithmThe management and analysis of big data has been identified as one of the most important emerging needs in recent years. This is because of the sheer volume and increasing complexity of data being created and/or collected. Current clustering algorithms cannot handle big data, and therefore, scalable solutions are necessary. Since fuzzy clustering algorithms have shown to outperform hard clustering approaches in terms of accuracy, this presentation will outline the parallelization and scalability of a common and effective fuzzy clustering algorithm named Fuzzy C-Means (FCM) algorithm. The algorithm is parallelized using the MapReduce paradigm. A validity analysis is conducted in order to show that the implementation works correctly achieving competitive purity results compared to state-of-the art clustering algorithms. Furthermore, a scalability analysis is conduced to demonstrate the performance of the parallel FCM implementation with increasing number of computing nodes used.
|11/12/14||Dr. Hyunsook Do|
Offline-based social media marketing approach and empirical evaluation
A new proposed social media marketing approach addresses the existing social media marketing tools' limitations and links customers' offline experiences and actions online. This approach will allow us to track and collect the customers' offline experiences in real-time and to obtain good quality data. We evaluated the proposed approach through several case studies that were applied to real marketing events and presented the results of the case studies. Through these studies, we demonstrated that our method can be more effective and efficient in increasing the online exposure effect and more useful in increasing the participation rate of customers compared to existing social media marketing methods.
|10/08/14||Dr. Ken Nygard|
Data Science, Analytics, Mathematical Modeling, and Visualization in International Development and Policy
At the United State Agency for International Development (USAID) and at the Department of State, data science, analytics, visualization, and mathematical modeling all play important roles in the conducting and evaluating of international projects and programs. To illustrate, we describe several example projects, such as understanding resilience to drought in Ethiopia, how people perceive biotechnology risk worldwide, energy development in Armenia and in Pakistan, and infrastructure development in Ghana.
|03/26/14||Dr. Ken Magel|
Theory-Based Software Design Complexity
Given a set of alternative designs for the same application, how do we determine which should be employed for a specific purpose. This talk starts by presenting a purpose- oriented theory of software artifacts. This theory is compared with the two major efforts to develop theories of software development. This theory then is used to develop two software complexity metrics for different purposes. Preliminary results are presented indicating the potential usefulness of these metrics for their intended purposes.
|05/07/14||Dr. Saeed Salem|
Mining interesting patterns from biological and information networks
Graphs are increasingly being used to represent systems of interacting entities. In addition to the structural relationship defined by edges, these graphs can have other sources of data annotating entities and relationships. In this talk, we will present graph mining algorithms for integrating gene's attribute data in the module discovery problem from protein-protein interaction networks by integrating gene regulation in different experiments. The second part of the talk will focus on mining graphs with edge attributes. We will present two algorithms: Mining coexpression patterns from multiple gene expression datasets, and mining cross graph communities from social and collaborative networks.
|04/11/13||Dr. Gursimran Walia|
Research in Software Engineering
Dr. Walia's research interests lie in software engineering, particularly software quality improvement and measurement, software inspections and software errors, and software engineering education. The goal of his research is to improve the quality and reliability of software through the use of Empirical Software Engineering. Empirical research is based on observation and measurement of different aspects of software development in the context of human-subject experiments. Dr. Walia's approach to empirical software engineering involves conducting empirical studies to study how people (i.e. developers) use processes and tools in different settings and to objectively evaluate these technique(s). His research is multidisciplinary, i.e., it uses approaches that have been applied successfully in other domains and adapts them for the task of improving and managing software quality. He will talk about the results from empirical studies that have been conducted at NDSU to 1) develop a deeper understanding of human errors and develop techniques to detect and prevent errors early in the software development lifecycle; 2) evaluate the use of the Capture-Recapture method in software organizations, and 3) to improve the state of software engineering education.
|10/10/12||Dr. Hyunsook Do|
Context-aware Regression Testing Techniques and Empirical Evaluations of Their Economic Impact
Successful software systems evolve: they are enhanced, corrected, and ported to new platforms. To ensure the quality of modified systems, software engineers perform regression testing, but this can be expensive depending on the size of the systems and their complexity and it is responsible for a significant percentage of the costs of software. For reasons such as this, researchers have spent a great deal of time creating and empirically studying various techniques for improving the cost-effectiveness of regression testing. Despite the progress this research has achieved to date, several important aspects have not been considered, such as factors involving testing context and the system lifetime view. In this talk, I'll present research activities that can address these limitations as follows: (1) creating cost-effective regression testing techniques that address the testing process and domain contexts, and (2) creating regression testing strategies that address system lifetimes, (3) creating economic models that enable the adequate assessment of techniques and strategies, and (4) evaluating and refining these techniques and strategies through rigorous empirical approaches.
|09/12/12||Dr. Anne Denton|
Challenges in Data-driven Agriculture
Many data sources can contribute to an understanding of agriculture. We will look at how data mining techniques can help understand the relevance of agricultural choices and help predict outcomes. The presentation will also show where questions that were originally driven by companies can result in data mining problems that have not previously been discussed by either the data mining or statistics communities. Considering the large amounts of satellite, weather, soil, and other data, the problem of data-driven agriculture cannot be addressed without modern computing infrastructure. Parallel data processing, as it was introduced by Google, and later continued as part of the open source Hadoop effort, can help with processing of large image files. We will also see how such processing can be done with general purpose departmental computing infrastructure, i.e., using the infrastructure that is now commonly referred to as cloud.
|04/11/12||Dr. Jun Kong|
Efficient Mobile Browsing
Wireless network and mobile devices make it possible to access information from anywhere at anytime. However, most Web pages are tailored to personal computers. Without an adaptation, it is frustrating to browse those pages on mobile devices since users have to frequently scroll the display window to find the content of interest. Keeping two versions of presentations, one for desktops and the other for mobile devices, is time-consuming and error-prone. This project will investigate an innovative approach to automatically adapting Web pages from a desktop presentation to a mobile presentation, and address two challenging issues: fine-grained page segmentation (i.e. discover closely related information) and human-centric context-aware adaptive layouts.
|02/29/12||Dr. Ken Nygard|
Research Issues in a Smart Grid
The basic concept of a Smart Grid is to add intelligent capabilities to the systems that produce, deliver, and consume electrical power. Potential benefits are many, and include gains in efficiency, conserving of natural resources, improving the utilization of alternative energy sources, cost containment, and advances in sustainability. There are many computer science and software engineering research issues in a Smart Grid operation, including 1) data mining, networking, sensing, and data fusing in system monitoring, 2) pattern recognition for fault management, 3) decision support methodologies for fault management, 4) Self-healing, 5) management of devices, including dynamic pricing, 5) management and control of distributed systems, and 6) intelligent autonomy. In this talk we describe our recent efforts to address these research issues.
|02/08/12||Dr. Juan Li|
Community-based cloud for emergency management
Natural and man-made emergencies pose an ever-present threat to the society. In response to the growing number of recent emergencies, and in particular, the Red River crest that causes flood here in Fargo, North Dakota, we propose a community-based scalable cloud computing infrastructure for large-scale emergency management. This infrastructure will maximally utilize all of the available information and human power from within and outside of a community to effectively deploy personnel and logistics to aid in search and rescue. The infrastructure also will aid in damage assessment, enumeration, and coordination to support sustainable livelihood by protecting lives, properties and the environment.
|11/09/11||Dr. Gregory Wettstein|
IDfusion: A Strategy for Implementing Perimeter Security and Identity Protection for the National Health Information Network
With implementation of the National Health Information Network (NHIN) the United States is poised to create a massive cyber-infrastructure resource, which presents both opportunity and peril. Opportunity with respect to the desired outcomes of reducing health-care costs and increasing quality of care. Peril with respect to privacy threats and the potential for creating a new cyber-threat vector with unknown implications. The IDfusion project is a multi-state platform for evaluating an integrated hardware, software and network infrastructure solution to address the security implications inherent in this national strategy. In developing this solution the project has addressed issues ranging from security protocol design to embedded system development and network design. All within the political context of the high profile socio-economic debate over unified health-care.
The presentation will feature Dr. Greg Wettstein, R.Ph.,Ph.D. who has authored formal responses to the President's Commission on Science and Technology (PCAST) in the field of health information systems. The presentation will include a brief review of the NHIN strategy and how IDfusion was developed and implemented to support this national initiative.
Dr. William Perizo
P-tree Vertical Data Mining Technology
Vertical technologies represent and process data differently from the ubiquitous horizontal data technologies. In vertical technologies, the data is structured column-wise and the columns are processed horizontally (typically across a few to a few hundred columns), while in horizontal technologies, data is structured row-wise and those rows are processed vertically (often down millions, even billions of rows). The patented P-tree technology is a vertical data technology. P-trees are lossless, compressed and data-mining ready data structures. P-trees are data-mining ready because the fast, horizontal data mining processes involved can be done without the need to decompress the structures first. P-tree vertical data structures have been exploited in various domains and data mining algorithms, ranging from classification, association rule mining, to outlier analysis, as well as other data mining algorithms. P-tree technology is patented in the United States by NDSU.
Typically, a new data mining technology will either tout improved speed or improved accuracy. P-trees can facilitate both. In fact, the Closed Nearest Neighbor Classification P-tree technology, has been shown to do both simultaneously.
Speed improvements are very important in data mining because many quite accurate algorithms require an unacceptable amount of processing time to complete, even with today's powerful computing systems and efficient software platforms. Undoubtedly the most important breakthrough offered by P-tree technology is the ability to process all instances (even billions) of an entity with one horizontal pass across a small number (a few to a few hundred) vertical, compressed P-tree data structures.
Treeminer Inc. has licensed the P-tree patents while Dr. William Perrizo's DataSURG group is further developing the technology, including better algorithms for P-tree processing and processing on multi-core CPUs, GP-GPUs and FPGAs
Jonathan Tolstedt, Patent Agent and Licensing Associate for the NDSU Technology Transfer Office, gave a short presentation on intellectual property policies at NDSU, how those policies apply to Computer Science discoveries, and how best to protect software-based intellectual property (patent versus copyright). Jonathan has a Bachelors in Electrical Engineering and a Masters in Computer Science.
|09/14/11||Dr. Ron Vetter|
Building Mobile Phone Applications
The ACM Distinguished lecture by Dr. Vetter will describe the development of interactive short message service applications, which range from simple data access applications to a novel discovery game designed for the freshman experience. Several of these applications are now being sold commercially via a novel technology transfer agreement with the University of North Carolina Wilmington. In addition, several iPhone applications also have been developed. A discussion of the relative advantages, costs and lessons learned while developing mobile phone applications will be presented.
Dr. Vetter earned his bachelor's and master's degrees in computer science from NDSU and his doctorate in computer science from the University of Minnesota. He has published more than 100 journal, conference and technical papers. He has served as the principal investigator or co-principal investigator on grants and contracts exceeding $5 million dollars.