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Dr. Karuna Joshi
Computer Science
Semantically Rich, Policy Based Framework to Automate Lifecycle of Cloud Based Services
Mentors: Yelena Yesha and Tim Finin
Thesis Topic: Dr. Joshi developed a new framework to automate the acquisition, composition, and consumption/monitoring of virtualized services delivered on the cloud. The lifecycle consists of five phases of requirements, discovery, negotiation, composition, and consumption. She has developed ontologies to represent the concepts and relationships for each phase using Semantic Web languages. She has also developed a protocol to automate the negotiation process when acquiring virtualized services.
"I chose to concentrate on Cloud Services automation for my Ph.D. thesis since I was able to draw upon my extensive experience as an IT Project Manager to determine open issues that need to be addressed for broader adoption of cloud computing."
Future plans: Dr. Joshi has received funding from NIST to continue her research on Cloud Computing and Big Data management. As part of this funding, she will be working as a research faculty member in the CSEE Department. In the spring, Dr. Joshi will teach a course on Software Design and Development.
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Dr. Phuong Nguyen
Computer Science
Data Intensive Scientific Compute Model For Multicore Clusters
Mentors: Milton Halem and Yelena Yesha
Thesis Topic: Dr. Phuong developed a scalable workflow system on top Apache Hadoop for orchestrating data intensive scientific workflows. New scheduling algorithms have been developed in the workflow system to manage and reduce latency of the workflow executions. The evaluations of the workflow system on the climate data processing and analysis application (several TB dataset) showed that it is feasible and improved. The scientific results of the application provide new global climate change indicators for the decade of 2002-2012.
"The Ph.D. topic came from the motivations related to our NASA and NOAA projects which need to process and analyze very large datasets to study climate change. My research contributions provide new tools for accelerating scientific discoveries from very large datasets and the scientific results."
Future plans: Work on research and development related to building large distributed systems or applications.
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Dr. David Chapman
Computer Science
A Decadal Gridded Hyperspectral Infrared Record for Climate
Mentors: Milton Halem
Yelena Yesha, Shujia Zhou, John Dorband, Joel Susskind (NASA)
Thesis Topic: Dr. Chapman helped improve our understanding of Global Climate Change by creating a Climate Data Record (CDR) of Outgoing Longwave Radiation (OLR) from 55 terabytes of NASA satellite weather observations from the Atmospheric Infrared Sounder (AIRS). He developed a parallel data-intensive scientific workflow infrastructure making use of Large Array Storage (LAS) in order to show the complete derivation these climate trending results.
"Global Climate Change and Global Warming are very important and controversial issues, and we need to measure if they have actually happened. AIRS is the first of its kind because it measures hyperspectral radiation. The trick is to take a Big Dataset, and squeeze it into something meaningful. This takes a lot of hardware, and typically a large software team to develop the processing system. I showed how the Large Array Storage (LAS) paradigm can simplify these calculations along with their derivation."
Future plans: Dr. Chapman has applied for a post doc in Climate Modeling at Columbia University. It would allow him to do interdisciplinary work to develop Big Data Analytics infrastructure alongside the statistical validation of climate models.
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Dr. Niyati Chhaya
Computer Science
Joint Inference for Extracting Soft Biometric Text Descriptors from Patient Triage Images
Mentors: Tim Oates
Thesis Topic: Dr. Chhaya's research was a combination of Soft biometrics, Probalistic Graphical Models, and Natural Language Processing techniques. The aim was to extract soft biometric text labels (using computer vision techniques) from images of mass disaster victims. The main contributions of the work include soft biometric feature extractors, a probalistic graphical model that exploits related appearance-related features, and a novel study of natural human descriptors using NLP techniques that help understand 1) how people describe other people and 2) order and structure of free text human descriptions.
"Socially, this work aims at addressing the issue of providing victim information to the public in a post disaster situation. It forms an important contribution to anonymize available image data using text labels to facilitate efficient search. Technically, this is the first work of its kind that aims at using Probabilistic Graphical Models to relate Soft biometric features, and in turn improve the overall accuracy of text label extraction. Also, the NLP study is a significant contribution along with the datasets gathered for this research. The key contribution is the use of techniques from computer vision, machine learning, and NLP to build a robust system that extracts soft biometric features."
Future Plans: Dr. Chhaya has moved back to India and will work as a Computer Scientist with Adobe Research Labs starting in January.
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Dr. Yasaman Haghpanah Jahromi
Computer Science
A Trust and Reputation Mechanism Through Behavioral Modeling of Reviewers
Mentors: Marie desJardins
Thesis Topic: Dr. Haghpanah introduced a novel mechanism to represent trust and reputation using behavioral modeling of online reviewers. Her approach helps decision makers utilize reputation information more effectively.
"Evidence shows that people are now relying more and more on other people's posted opinions for making decisions about which product to buy, which movie to watch, etc. So, I modeled the raters' or in general information providers' behavior and showed how we can improve our decisions by knowing the behavior of the online raters."
Future Plans: Dr. Haghpanah is currently interviewing for postdoctoral positions at universities and research labs to extend and broaden her knowledge.
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Dr. Ganesh Saiprasad
Electrical Engineering
Automatic Detection of Adrenal Gland Abnormality Using The Random Forest Classification Framework combined with Histogram Analysis
Mentors: Chein-I Chang
Thesis Topic: Dr. Saiprasad proposed a new, more accurate way to detect adrenal abnormalities: rather than using the popular Region of Interest (ROI) method, Dr. Saiprasad suggests segmenting the adrenal gland automatically using the random forest classification framework and then performing histogram analysis.
"Working with radiologists and surgeons at the University of Maryland Medical Center on my Master's research helped me pick a topic for my Ph.D. research. Adrenal gland abnormality detection is a very challenging problem and we have some preliminary results now to show that it can be done automatically. This is a very important step forward in using such systems as decision support tools and also the same methodology can be used for other smaller organs to detect abnormalities which are challenging to detect on CT."
Future Plans: Postdoc at the National Institute of Standards and Technology (NIST)
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Dr. Kevin Fisher
Computer Engineering
Real-Time Progressive Band Processing for Linear Spectral Unmixing and Endmember Extraction
Mentors: Chein-I Chang
Milton Halem (NASA)
Thesis Topic: Dr. Fisher developed three algorithms that work on hyperspectral images–pictures (often taken by satellites or airborne cameras) where each pixel is a spectograph of the materials in that part of the image. His algorithms work to reduce the amount of irrelevant data in the image, detect samples of pure materials in the image, and then estimate the abundance of those materials in each pixel in the image.
"In 2006, I finished a Master's thesis with Prof. Alan Sherman on electronic voting systems. It was an engaging project in a hot topic in computing, but it was not related to the work I was doing as an intern at NASA Goddard Space Flight Center. I sat down with my supervisor and some NASA technologists, and looked for common areas of interest between UMBC and NASA. Hyperspectral image processing was on the short list and that's when I contacted Prof. Chein-I Chang about potential research projects."
Future Plans: Dr. Fisher will continue working at NASA as a software systems engineer working on the ground antenna system for the Geostationary Operational Environmental Satellite, R-Series (GOES-R) spacecraft, a new line of weather satellites due to launch in 2015.
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Dr. Joel Sachs
Computer Science
Supporting Citizen Science and Biodiversity Informatics on the Semantic Web
Mentors: Tim Finin
Thesis Topic: Dr. Sachs introduces an approach to constructing ontologies by layer, designed to make it easier for both data publishers and application developers to tailor-fit semantics to use cases.
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