MS Defense

Modeling Individual Nodes In Dynamic Link Prediction

Maksym Morawski

2:00pm Thursday, 25 April 2013, ITE325b, UMBC

The question of how to predict which links will form in a graph, given the graph’s history, is an open research problem in computer science. There are many different approaches to the link prediction problem, one of which involves building a set of features for pairs of nodes and using supervised learning to build a model that predicts when these pairs of nodes will link. Typically, this model is learned over the entire graph. In this thesis, I investigate building this model over each individual node in an attempt to learn the particular ways in which that node behaves before making predictions about it. In addition, research into link prediction to date lacks intelligent ways of utilizing the graph over large timespans. To address this, I introduce a variety of ways to include temporality into the link prediction process by introducing new ways of using existing features.

Committee: Dr. Marie desJardins (Chair), Dr. Tim Oates, Dr. Tim Finin

UMBC's cyber defense team, the Cyber Dawgs, will host an interdisciplinary talk and screening of the film Game of Pawns at 7:00pm on Tuesday, April 30, 2013 in room 102 of the ITE building (LH8). The film is a true story of an American student who was recruited by the Chinese government to infiltrate a U.S. intelligence agency.

The event is sponsored and run by InfraGuard, an organization that acts as a partnership mediator between the FBI and US businesses. The talk will be nontechnical and will present an overview of the dangers that might arise when dealing with foreign businesses or representatives. It should be of interest to students considering studying abroad, pursuing international relations or business, or anticipating working for a government agency.

CSEE Colloquium

Queuing and Long Lines: How to run efficient elections

Dr. William A. Edelstein
Visiting Distinguished Professor of Radiology
Johns Hopkins School of Medicine

1:00pm Friday, 3 May 2013, ITE227, UMBC

Computerized touchscreen "Direct Recording Electronic" (DRE) voting systems have been used by over 1/3 of American voters in recent elections, including in Maryland. In many places, insufficient DRE numbers, in combination with lengthy ballots and high voter traffic, have caused long lines and disenfranchised voters who left without voting. We have applied computer queuing simulation to the voting process and conclude that far more DREs, at great expense, would be needed to keep waiting times low. Alternatively, paper ballot-optical scan systems can be easily and economically scaled to prevent long lines and meet unexpected contingencies. We have developed a heuristic "Queue Stop Rule" that can be applied to prevent long lines at voting stations. We have also carried out queuing simulations of other parts of the voting process, for example, voter check-in and ballot scanning. Our results can be used to help plan cost-effective election systems that will produce expeditious elections.

William Edelstein, physicist, received BS and PhD degrees in that subject from University of Illinois and Harvard, respectively. His career has principally focused on the development of MRI, starting in Scotland in 1977 and continuing from 1980 at the GE research lab in Schenectady, NY. He has been Visiting Distinguished Professor of Radiology at Johns Hopkins School of Medicine since 2007. His MRI work has been recognized with many honors, including the 2005 Industrial Applications of Physics Prize from the American Institute of Physics. His interest in election systems began several years ago in NY State during the debate to replace lever voting machines.

MS Thesis Defense

Text and Ontology Driven
Clinical Decision Support System

Deepal Dhariwal

9:00am Tuesday 23 April 2013, ITE325b, UMBC

This thesis discusses our ongoing research in the domain of text and ontology driven clinical decision support system. The proposed framework uses text analytics to extract clinical entities from electronic health records and semantic web analytics to generate a domain specific knowledge base (KB) of patients’ clinical facts. Clinical Rules expressed in the Semantic Web Language OWL are used to reason over the KB to infer additional facts about the patient. The KB is then queried to provide clinically relevant information to the physicians. In the first phase, standard text pre processing techniques such as section tagging, dependency parsing, gazetteer lists are used filter clinical terms from the raw data.

In the second phase, a domain specific medical ontology is used to establish relation between the extracted clinical terms. The output of this phase is a Resource Description Framework KB that stores all possible medical facts about the patient. In the final phase, an OWL reasoner and clinical rules are used to infer additional facts about patient and generate a richer KB. This KB can then be queried for a variety of clinical tasks. To demonstrate a proof of concept of this framework, we have used discharge summaries from the cardiovascular domain and determined the TIMI Risk Score and San Francisco Syncope Score for a patient. The goal of this research is to combine factual knowledge about patients, procedural knowledge (clinical rules), and structured knowledge (medical ontologies) to develop a clinical decision support system.

Committee: Dr. Anupam Joshi (chair), Dr. Michael Grasso, Dr. Tim Finin, Dr. Yelena Yesha

MS Thesis Defense

Text and Ontology Driven
Clinical Decision Support System

Deepal Dhariwal

9:00am Tuesday 23 April 2013, ITE325b, UMBC

This thesis discusses our ongoing research in the domain of text and ontology driven clinical decision support system. The proposed framework uses text analytics to extract clinical entities from electronic health records and semantic web analytics to generate a domain specific knowledge base (KB) of patients’ clinical facts. Clinical Rules expressed in the Semantic Web Language OWL are used to reason over the KB to infer additional facts about the patient. The KB is then queried to provide clinically relevant information to the physicians. In the first phase, standard text pre processing techniques such as section tagging, dependency parsing, gazetteer lists are used filter clinical terms from the raw data.

In the second phase, a domain specific medical ontology is used to establish relation between the extracted clinical terms. The output of this phase is a Resource Description Framework KB that stores all possible medical facts about the patient. In the final phase, an OWL reasoner and clinical rules are used to infer additional facts about patient and generate a richer KB. This KB can then be queried for a variety of clinical tasks. To demonstrate a proof of concept of this framework, we have used discharge summaries from the cardiovascular domain and determined the TIMI Risk Score and San Francisco Syncope Score for a patient. The goal of this research is to combine factual knowledge about patients, procedural knowledge (clinical rules), and structured knowledge (medical ontologies) to develop a clinical decision support system.

Committee: Dr. Anupam Joshi (chair), Dr. Michael Grasso, Dr. Tim Finin, Dr. Yelena Yesha

MS Thesis Defense

Text and Ontology Driven
Clinical Decision Support System

Deepal Dhariwal

9:00am Tuesday 23 April 2013, ITE325b, UMBC

This thesis discusses our ongoing research in the domain of text and ontology driven clinical decision support system. The proposed framework uses text analytics to extract clinical entities from electronic health records and semantic web analytics to generate a domain specific knowledge base (KB) of patients’ clinical facts. Clinical Rules expressed in the Semantic Web Language OWL are used to reason over the KB to infer additional facts about the patient. The KB is then queried to provide clinically relevant information to the physicians. In the first phase, standard text pre processing techniques such as section tagging, dependency parsing, gazetteer lists are used filter clinical terms from the raw data.

In the second phase, a domain specific medical ontology is used to establish relation between the extracted clinical terms. The output of this phase is a Resource Description Framework KB that stores all possible medical facts about the patient. In the final phase, an OWL reasoner and clinical rules are used to infer additional facts about patient and generate a richer KB. This KB can then be queried for a variety of clinical tasks. To demonstrate a proof of concept of this framework, we have used discharge summaries from the cardiovascular domain and determined the TIMI Risk Score and San Francisco Syncope Score for a patient. The goal of this research is to combine factual knowledge about patients, procedural knowledge (clinical rules), and structured knowledge (medical ontologies) to develop a clinical decision support system.

Committee: Dr. Anupam Joshi (chair), Dr. Michael Grasso, Dr. Tim Finin, Dr. Yelena Yesha

UMBC CSEE Colloquium

Large Data Transfer over the Wide Area Network

Jim Finlayson
Laboratory for Physical Sciences
Advanced Computing Systems Group

1:00pm Friday, 26 April 2013, ITE 227, UMBC

The Department of Defense has challenges related to the transfer of large data sets over distance. This talk will go over some of the investigations into potential solutions in this space.

Jim Finlayson is a File Systems and I/O researcher for the Laboratory for Physical Sciences' Advanced Computing Systems Group. Mr. Finlayson has a long history in data storage infrastructure. He graduated from the University of Maryland, College Park with a BS in Computer Science and later received his MS in Computer Science from The Johns Hopkins University's Whiting School of Engineering.

Center for Hybrid Multicore Productivity Research
Distinguished Computational Science Lecture Series

Quantum Computer Compilers

Professor Alfred V. Aho

Department of Computer Science, Columbia University

3:00pm Thursday, 25 April 2013, ITE 456, UMBC

Quantum computing is an exciting emerging field that offers great potential for next generation information processing but also presents great scientific and engineering challenges. Assuming that someday we will be able to build scalable and reliable quantum computers, we will need to create programming languages and compilers that will allow programmers to harness quantum phenomena. In this talk, Alfred Aho will look at quantum computing from a compiler writer's perspective and discuss some of the formidable challenges that face quantum computer compilers.

Alfred Aho is the Lawrence Gussman Professor of Computer Science at Columbia University. He received a B.A.Sc. in Engineering Physics from the University of Toronto and a Ph.D. in Electrical Engineering/ Computer Science from Princeton University. Prior to his current position, he served as vice president of the Computing Sciences Research Center at Bell Labs, the lab that invented UNIX, C and C++. He is the "A" in AWK, a widely used pattern-matching language. His current research interests include programming languages, compilers, algorithms, software engineering and quantum computing. He has won the IEEE John von Neumann Medal and is a Member of the National Academy of Engineering and of the American Academy of Arts and Sciences. He is a Fellow of the AAAS, ACM, Bell Labs and IEEE. In 2003 he received the Great Teacher Award from the Society of Columbia Graduates.

Host: Professor Milton Halem

We are now offering the UMBC Cybersecurity MPS program at Shady Grove in Montgomery County, MD.

The Cybersecurity Master’s in Professional Studies degree provides students the essential knowledge required to serve in leadership and operational roles throughout the industry. Through the program, students will learn how to analyze cybersecurity risks and assess available countermeasures. The program will expose students to practical managerial and operational considerations needed to conduct cybersecurity activities for large organizations.