Two  Mechanical Engineering students who are currently in the Maryland Elite Researchers-in-Training (MERIT) Program under Dr. Carlos Romero-Talamás, won 1^st and 2^nd place in the Innovative STEM Conference 2014 held this year at Morgan State University. This is a young conference, and all the previous winners in this category have been from the host school, so home field advantage was alive and present before this year. 

More information about the Innovative STEM Symposium can be found here: http://www.istemfoundation.org/isc2014/about_iss.html

Dr. Romero-Talamás came to UMBC from Cal Tech just over a year ago and has been bringing dusty plasma and quality research to the Mechanical Engineering Department ever since.

UMBC Center for Hybrid Multicore Productivity Research
Distinguished Computational Science Lecture Series

Tales of Scientific Computation at Ames and in NASA

Dr. Hans Mark

University of Texas Cockrell School of Engineering, Austin, TX

4:00pm Thursday, 22 May 2014, ITE 456, UMBC

This is personal story about how high performance computing was developed at the NASA-Ames Research Center and elsewhere in NASA. There were people at Ames who were first class aerodynamic scientists and who could use computers. Thus, it was decided that some procurement short cuts were justified. We acquired computers in three quantum steps. First, in 1969, there was an IBM duplex 360/67 which was captured by a “midnight supply operation” from the Air Force. Next, in 1972, the ILLIAC IV at the University of Illinois became available because of an act of domestic terrorism and financial help from DARPA. Finally in 1975, there was one of Seymour Cray’s CDC 7600s, also from an Air Force source. In 1981, by which time Seymour Cray had his own company, a Cray 1S appeared at Ames, followed in 1984 by CDC Cyber 205 and a Cray X-MP/22. The last named machines were made available because of shameless earmarking by NASA Headquarters. However, confession being good for the soul, the NASA-Goddard Space Flight Center also benefited from the earmarking with twenty million dollar fund to develop a truly massively parallel computer, the Goodyear MPP with 16,000 processors, which was delivered in 1984. Now we are working with people at Ames on a quantum computer manufactured by D‐Wave Systems, Inc. The machine was installed at Ames last year and we are now working on various “benchmark” tests and developing operating systems for the machine. We believe that there is great promise for much more capable computing machines in this new quantum technology.

Dr. Hans Mark is a leading expert in the fields of both aerospace design an national defense policy. For fourteen years Dr. Mark was associated with the University of California’s Nuclear Weapons Laboratory at Livermore, serving as Physics Division Leader from 1960 to 1964. He was named Under Secretary of the Air Force an Director of the National Reconnaissance Office in 1977. While Director of the National Reconnaissance Office, he initiated the development of a new reconnaissance satellite system an the upgrade of two others. As Secretary of the Air Force (1979 to 1981), Dr. Mark initiated the establishment of the U.S. Air Force Space Command. During his tenure as Deputy Administrator of NASA from 1981 to 1984, Dr. Mark oversaw the first fourteen Space Shuttle flights and was a leading contributor to the establishment of the U.S. Space Station Program. Over the past twenty years, Dr. Mark has served as Chancellor of the University of Texas System (1984 to 1992) and is still actively involved in research and teaching at the University of Texas Cockrell School of Engineering in Austin, TX. From 1998 to 2001, Dr. Mark was on leave from the University to serve in the Pentagon as Director of Defense Research and Engineering. Dr. Mark received an A.B. Degree in physics from the University of California, Berkeley and a Ph.D. in physics from the Massachusetts Institute of Technology (MIT). He has been member of the National Academy of Engineering for three years an holds six honorary doctorates.

Host: Prof. Milton Halem, Sorry, you need javascript to view this email address.

Computer Science and Electrical Engineering
University of Maryland, Baltimore County

Data Analytics for Sustainability

Professor Katharina Morik
TU Dortmund University, Germany

11:00am-12:30pm, Thursday 22 May 2014, ITE 456, UMBC

Sustainability has many facets and researchers from many disciplines are working on them. Particularly knowledge discovery always considered sustainability an important topic (e.g., special issue on data mining for sustainability in Data Mining and Knowledge Discovery Journal, March 2012).

• Environmental tasks include risk analysis concerning floods, earthquakes, fires, and other disasters as well as the ability to react to them in order to guarantee resilience. The climate is certainly of influence and the debate on climate change received quite some attention.
• Energy efficiency demands energy-aware algorithms, operating systems, green computing. System operations are to be adapted to a predicted user behavior such that the required processing is optimized with respect to minimal energy consumption.
• Engineering tasks in manufacturing, assembly, material processing, and waste removal or recycling offer opportunities to save resources to a large degree. Adding the prediction precision of learning algorithms to the general knowledge of the engineers allows for surprisingly large savings.

Global reports on the millennium goals and open government data regarding sustainability are publicly available. For the investigation of influence factors, however, data analytics is necessary. Big data challenges the analysis to create data summaries. Moreover, the prediction of states is necessary in order to plan accordingly. In this talk, two case studies will be presented. Disaster management in case of a flood combines diverse sensor data streams for a better traffic administration. A novel spatiotemporal random field approach is used for smart routing based on traffic predictions. The other case study is in engineering and saves energy in the steel production based on the multivariate prediction of the processing end-point by the regression support vector machine.

Further reading:

• Katharina Morik, Kanishka Bhaduri, Hillol Kargupta “Introduction to Data Mining for Sustainability”, Data Mining and Knowledge Discovery Journal, Vol. 24, No.2, pp. 311 – 324, 2012.
• Nico Piatkowski, Sangkyun Lee, Katharina Morik “Spatio-Temporal Random Fields: Compressible Representation and Distributed Estimation”, Machine Learning Journal Vol.93, No. 1, pp: 115-139, 2013.
• Jochen Streicher, Nico Piatkowski, Katharina Morik, Olaf Spinczyk “Open Smartphone Data for Mobility and Utilization Analysis in Ubiquitous Environments” In: Mining Ubiquitous and Social Environments (MUSE) workshop at ECML PKDD, 2013.
• Norbert Uebbe, Hans Jürgen Odenthal, Jochen Schlüter, Hendrik Blom, Katharina MorikA novel data-driven prediction model for BOF endpoint. In: The Iron and Steel Technology Conference and Exposition in Pittsburgh (AIST), 2013.
• Alexander Artikis, Matthias Weidlich, Francois Schnitzler, Ioannis Boutsis, Thomas Liebig, Nico Piatkowski, Christian Bockermann, Katharina Morik, Vana Kalogeraki, Avigdor Gal, Shie Mannor, Dimitrios Gunopulos, Dermot Kinane, “Heterogeneous Stream Processing and Crowdsourcing for Urban Traffic Management” Procs. 17th International Conference on Extending Database Technology, 2014.

Katharina Morik is full professor for computer science at the TU Dortmund University, Germany. She earned her Ph.D. (1981) at the University of Hamburg and her habilitation (1988) at the TU Berlin. Starting with natural language processing, her interest moved to machine learning ranging from inductive logic programming to statistical learning, then to the analysis of very large data collections, high-dimensional data, and resource awareness.

Her aim to share scientific results strongly supports open source developments. For instance, RapidMiner started out at her lab, which continues to contribute to it. She was one of those starting the IEEE International Conference on Data Mining together with Xindong Wu, and was chairing the program of this conference in 2004. She was the program chair of the European Conference on Machine Learning (ECML) in 1989 and one of the program chairs of ECML PKDD 2008. She is in the editorial boards of the international journals “Knowledge and Information Systems” and “Data Mining and Knowledge Discovery”. Since 2011 she is leading the collaborative research center SFB876 on resource-constrained data analysis, an interdisciplinary center comprising 12 projects, 19 professors, and about 50 Ph. D students or Postdocs.

Host: Hillol Kargupta, Sorry, you need javascript to view this email address.

PhD Dissertation Proposal

Learning and Executing Energy Efficient, Context-Dependent
Rules for Privacy and Security Management on Mobile Devices

Prajit Kumar Das

8:00am Friday, 16 May 2014, ITE325b

There are ongoing security and privacy concerns around mobile platforms that are increasingly being used by citizens. For example a newly discovered security flaw in WhatsApp that allows hackers using a malicious app to read chat messages stored on the SD card. The Brightest Flashlight application was reported to have logged precise location and a unique user identifier, which have nothing to with its intended functionality. Current mobile platform privacy and security mechanisms are limited to an initial installation phase permission acquisition method. In addition to that, the permissions are of the all or none form. This means that either the users accept all the permissions requested by the mobile app or they cannot use the app in question. Even if permissions were not structured as such, typically, users do not understand the permissions being requested or are too eager to use the application to even care to read them. These issues are present in all major mobile operating systems. Given the penetration of mobile devices into our lives, a fine-grained context-dependent security and privacy control approach needs to be created.

We propose a framework that will allow us to learn the privacy and security rules for a particular user, on their mobile devices. We do this by employing a simple user feedback mechanism. The rule learning framework consists of a “learning mode” where it observes and learns from user behavior and a “working mode” where it implements the learned rules to protect user privacy and provide security. The rules are represented to the user in plain English using an easily understandable construct. The rules are internally written in a logic based language and using Semantic Web technologies. The antecedents of the rules are context elements that are derived from an ontology using a query engine and an inference mechanism. The main contributions of our work include learning modifications to current rules and learning new rules to control the data flow between the various data providers on the user’s mobile device, including sensors and services and the consumer of such data. The privacy and security rule execution consumes significant energy due to the context detection. We create an energy model that allows us to make energy cost optimizations with regards to rule execution. We use a three-fold solution for achieving the said energy cost optimizations.

Committee: Drs. Anupam Joshi (chair), Nilanjan Banerjee, Dipanjan Chakraborty (IBM), Tim Finin, Tim Oates, Arkady Zaslavsky (CSIRO)

The Biomedical Sciences Graduate Programs Preview Weekend is a unique visit to the University of Minnesota campus for prospective graduate students interested in one or more of the following Ph.D. programs:

• Biochemistry, Molecular Biology & Biophysics (BMBB)
• Integrative Biology & Physiology (IBP)
• Microbiology, Immunology & Cancer Biology (MICaB)
• Molecular, Cellular, Developmental Biology & Genetics (MCDB&G)
• Neuroscience (NSCI)
• Pharmacology (PHCL).

When will the Preview take place?
Thursday, September 25 - Saturday, September 28, 2014

Who is eligible to attend?
Those who will be invited to the Preview must be US citizens or permanent residents who will be applying for entry into graduate school in Fall of 2015. The program is most interested in students enrolled at institutions outside the upper midwest who may be unfamiliar with the outstanding training environment provided by the University of Minnesota. Students from under-represented racial and ethnic groups, low-income family backgrounds, first-generation U.S. citizens, and students with disabilities are especially encouraged to apply.

What are the benefits of attending?
The Preview activities will provide opportunities for students to meet informally with the directors of graduate study, faculty and current graduate students of the six biomedical sciences doctoral programs. There will be tours of campus that will let students gain a feel for the research and laboratory environments where they would work. There will be time for the attendees to learn about the graduate programs so that they can decide which one might best meet their career goals and interests. There will also be activities to introduce you to the quality of life in the metropolitan area of Minneapolis - St. Paul. Any participants who apply to one of the biomedical sciences graduate programs will have their application fee paid. If you are selected to attend, pending the availability of funds and the results of your GRE scores, you will also be invited to participate in the admission interview weekend for the program or programs you apply to.

What is the Cost to Attend?
Visit expenses (including airfare, lodging, and on-campus meals) are provided for those who are selected to attend. We expect to invite 15 students to attend the Preview.

Application & Selection Timeframe
The online application is open and invitations will be made in the summer of 2014.

Name: Robert Roselle