The Heidelberg Laureate Forum is an extraordinary opportunity for a group of students and young researchers to interact with some of the greatest minds of Mathematics and Computer Science for a week of scientific exchange and inspiration.

It will bring together winners of the Abel Prize, the ACM Turing Award, and the Fields Medal with young scientists from Computer Science and Mathematics. The first forum will be held over a week in September 2013 in Heidelberg and will consist of presentations, workshops, panel discussions and social events, all of them involving both the laureates and the young scientists. Some travel support is available.

The Forum will invite undergraduate students, PhD candidates, and young researchers at the postdoctoral level. Apply online by submitting a statement of purpose, CV, and the names of 1-3 people who can write recommendations. PhD candidates and postdocs should provide more information on their research. The deadline for applications is February 15, 2013 but may close early if the maximum number of applications that can be reviewed is reached.

The Center for Hybrid Multicore Productivity Research is a collaborative research center sponsored by the National Science Foundation with two university partners (UMBC and University of California San Diego), six government, and seven industry members. The Center's research is focused on addressing productivity, performance, and scalability issues in meeting the insatiable computational demands of its members' applications through the continuous evolution of multicore architectures and open source tools.

As part of its annual industrial advisory board meeting next week, the center will hold an afternoon of public tutorials from 1:00pm to 4:00pm on Monday, 17 December 2012 in room 456 of the ITE building at UMBC. The tutorials will be presented by students doing research sponsored by the Center and feature some of the underlying technologies being used and some of their applications. The tutorials are:

• GPGPUs – Tim Blattner and Fahad Zafa
• Cloud Policies – Karuna Joshi
• Human Sensors Networks – Oleg Aulov
• Machine Learning Disaster Warnings – Han Dong
• Graph 500 – Tyler Simon
• HBase – Phuong Nyguen

The tutorial talks are free and open to the public. If you plan to attend, please RSVP by email to Dr. Valerie L. Thomas, Sorry, you need javascript to view this email address.

UMBC Computer Science Alumni Donald Miner (BS '06, PhD '10) (left) and Adam Shook (BS '09, MS expected '13) (right) have written a book on the popular MapReduce paradigm that has revolutionized the way collections of computers are used to process large amounts of data in parallel. Their book, MapReduce Design Patterns Building Effective Algorithms and Analytics for Hadoop and Other Systems, was published by O'Reilly Media in December.

“Adam and I were teaching Hadoop classes and we saw a gap: students would pick up on how hadoop worked mechanically, but struggled to understand how to solve problems with it,” explains Donald, who now works as a Solutions Architect at EMC Greenplum. “This book is intended for people who have a basic understanding of Hadoop, but want to start solving their problems effectively.”

Adam and Donald met in fall 2008 during an Artificial Intelligence class at UMBC. Donald, a Ph.D. student working with Dr. Marie desJardins on machine learning and multiagent systems research, was teaching the class. Adam, an undergraduate Computer Science student, was taking the class. Later, the pair ended up working together at ClearEdge IT Solutions.  

“We worked well together and our skills and interests complemented each other well, so when I had the opportunity to write this book I knew it would be a much better book doing it with him than doing it alone,” says Donald.

Now Adam works with big data technologies like Hadoop, Accumulo, Pig, and ZooKeeper as a Software Engineer at ClearEdge IT Solutions. He is working towards his Master’s in Computer Science at UMBC under Dr. Tim Finin. His research deals with developing an efficient in-memory distributed database for Semantic Web applications.

“I don’t know how I do it,” says Adam about working full time, being in graduate school, and writing a book. “Caffeine helps.” He plans on finishing up his degree in 2013.

In the meantime, Adam and Donald have started working on Mapreducepatterns.com. Still in its infancy, the website is meant to be a Wikipedia style site where the Hadoop community can rally around a well-defined set of standard MapReduce design patters.

Ph.D. Dissertation Defense

Supporting Citizen Science and
Biodiversity Informatics on the Semantic Web

Joel Sachs

10:00am Friday, 14 December 2012, ITE 325b

It is common for Semantic Web documents to use terms from multiple ontologies, with no expectation that the full semantics of each ontology will be imported by consuming applications. This makes sense, because importing all ontologies referenced by a document causes both practical and logical problems. But it has the drawback of leaving it to the consuming application to determine appropriate semantics for the terms being used. We describe 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.

The layers that we develop correspond to patterns in the RDF graph. This contrasts with typical approaches to modular ontology development, where the layers are domain based. The three primary motivations for this approach are i) preserving computational tractability; ii) enabling easy coupling and decoupling with foundational ontologies and iii) maintaining cognitive tractability. This third motivation is still under-studied in semantic web development; we consider it in relation to reducing the ease with which ontology users can publish data that accidentally implies things that they do not mean. This is important always, but becomes especially so in citizen science, where users will naturally bring intuitive semantics to the terms that they encounter.

We describe case studies that involved deploying our approach in the context of citizen science activities, and which provided opportunities to assess its capabilities and limitations. We also describe subsequent work aimed at addressing these limitations, and, by applying newly defined layers over the underlying data, show that we are able to improve the competency of our knowledge base. More generally, we show that appropriately combining triple-pattern-based layers allows us to support a wide variety of use cases with varied (and occasionally conflicting) requirements.

In addition to our approach to semantic layering, contributions include an improved understanding of how to blend social and semantic computing to support citizen science, and a collection of layers for representing biodiversity information in RDF, with a focus on invasive species. Compared with other proposed “semanticizations” of the Darwin Core standard for representing biodiversity occurrence data, these layers involve minimal modification to the Darwin Core vocabulary, and make maximal use of the Darwin Core namespace, thereby simplifying the transition of current practices onto the semantic web.

Committee: Drs. Tim Finin (Chair), Anupam Joshi, Tim Oates, Cynthia Parr, Yelena Yesha, Laura Zavala

On December 12, CSEE professor Hillol Kargupta will receive the 10-year Highest-Impact Paper Award from the IEEE International Data Mining Conference (ICDM) in Brussels, Belgium.

The winning paper—“On the Privacy Preserving Properties of Random Data Perturbation Techniques”—discusses privacy-preserving data mining and it also received the 2003 ICDM Best Paper Award. It is co-authored by former UMBC PhD student Souptik Datta (CS '08) and Dr. Kargupta’s colleagues at Washington State University—Qi Wang and Professor Krishnamoorthy Sivakumar.

Privacy Preserving Data Mining (PPDM) is important in many domains where the data is privacy sensitive and exposing the data to a third party for mining is not an option. Researchers have come up with many PPDM algorithms that attempt to protect data privacy while allowing analysis of the data for detecting patterns. Many of these algorithms make use of randomized techniques. This paper offers a perspective on the structure of random noise using theories of random matrices and their spectral properties in order to analyze their role in preserving data privacy while still keeping data patterns intact for analysis. It points out that spectral properties of random matrices can be exploited to create attacks on many commonly used privacy-preserving data mining algorithms.

Kargupta and his associates point out is that you must be very careful when using random noise to protect data, since it can be easily filtered out. “Random noise is really not that unpredictable,” explains Kargupta, since it has a pattern of its own.

Out of all of the papers on data mining published within the last ten years, this year Dr. Kargupta’s paper was chosen by IEEE as the most impactful paper in its field.

The TRUST Research Experiences for Undergraduates (TRUST REU) is a nine-week summer program in Cybersecurity, Privacy and Trustworthy Systems, was established to promote access to graduate education among undergraduates who have been educationally or economically disadvantaged and who may not have exposure to the academic environment of a research university.

The goal of the TRUST REU program is to increase the level of diversity among students entering graduate programs in computer science and engineering by providing research opportunities under the supervision of a faculty member and graduate student mentor. Program objectives are:

• To provide students with preparation to become research scholars
• To stimulate serious consideration of graduate study
• To increase the number of successful underserved applicants able to enroll in graduate school

Special consideration will be given to applicants who have shown potential for success, but may have had limited access to undergraduate research or other academic opportunities. Upon completion of this program students will be better prepared and motivated to attend graduate school.N/p>

There are five research program locations:

• Carnegie Mellon University (Pittsburgh, PA)
• Cornell University (Ithaca, NY)
• Stanford University (Palo Alto, CA)
• UC Berkeley (Berkeley, CA)
• Vanderbilt University (Nashville, TN)

The application deadline is 15 February 2012. For more information and to apply, see the TRUST REU site.

Candidate Requirements for Software Quality Assurance Engineer Opening

Qualcomm's Research & Development team in San Diego has a number of cutting edge projects lined up for next summer and will hire 20+ interns on their team during Summer 2013. The opportunities below are just a section. See the complete list here.

Intern Context Analysis and Reasoning (Qualcomm Research San Diego)

QUALCOMM Research is looking for highly motivated and self-driven systems engineering interns to work on challenging problems related to context awareness on mobile platforms.

Job Function

Mobile phones have transformed from being a communication device to smart and powerful personal computing platforms. Over recent years, with a wide variety of sensors available on smart phones, there has been an exploding interest in context aware applications that use sensor information to infer user situation and interest. These applications utilize different types of hard sensor data (such as GPS, WiFi, accelerometer, gyro, Bluetooth, pressure sensor, etc.) and soft sensor data (such as user calendar, user profile, data from social networks such as Facebook posts and Twitter feeds) to infer user situations and provide a useful service.

QUALCOMM Research is looking for highly motivated and self-driven systems engineering interns to work on challenging problems related to context awareness on mobile platforms.

Responsibilities

Positions are available in the following areas: Situation Inference on Mobile Devices

The goal of this intern project is to research algorithms for inferring user situations from smartphone sensor data. This involves developing machine learning and probabilistic graphical models to fuse low-level inferences from multiple sensor streams. The intern will work on developing techniques and on-device algorithms.

Always-On Context Awareness

Since context is fundamentally 24/7, one of the key challenges is to enable situation inference that is always-on, or “in the background,” and to do this within the power requirements for background tasks. The goal of this intern task is to analyze context aware algorithms and smartly optimize them for execution on QUALCOMM smartphone platforms.

Privacy

The goal of this internship task is to develop a framework for privacy in smartphones that provides a good tradeoff between the amount of private data collected and utility of the application. The intern will look into different aspects of privacy in smart phone and examine approaches to: (1) include privacy aspects into sensor data collection and inferences made from different types of data, (2) incorporate privacy in data models and ontologies, and (3) automatically learn user preferences and user privacy settings for different context aware applications.

Skills/Experience

Seeking candidates with strong background in one or more of the following areas:

• Machine learning, statistics, and signal processing
• Hands on experience with probabilistic inference techniques, Bayesian networks, graphical models, Hidden Markov models
• Working knowledge on semantics, reasoning, ontology definitions
• Experience working with rule based systems
• Privacy aspects of data acquisition
• Sensor data analysis and modeling
• System design and analytical reasoning
• Strong coding skills on C++, Java, and Android is a plus.

Qualcomm's Research & Development team in San Diego has a number of cutting edge projects lined up for next summer and will hire 20+ interns on their team during Summer 2013. The opportunities below are just a section. See the complete list here.

Intern Context Analysis and Reasoning (Qualcomm Research San Diego)

QUALCOMM Research is looking for highly motivated and self-driven systems engineering interns to work on challenging problems related to context awareness on mobile platforms.

Job Function

Mobile phones have transformed from being a communication device to smart and powerful personal computing platforms. Over recent years, with a wide variety of sensors available on smart phones, there has been an exploding interest in context aware applications that use sensor information to infer user situation and interest. These applications utilize different types of hard sensor data (such as GPS, WiFi, accelerometer, gyro, Bluetooth, pressure sensor, etc.) and soft sensor data (such as user calendar, user profile, data from social networks such as Facebook posts and Twitter feeds) to infer user situations and provide a useful service.

QUALCOMM Research is looking for highly motivated and self-driven systems engineering interns to work on challenging problems related to context awareness on mobile platforms.

Responsibilities

Positions are available in the following areas: Situation Inference on Mobile Devices

The goal of this intern project is to research algorithms for inferring user situations from smartphone sensor data. This involves developing machine learning and probabilistic graphical models to fuse low-level inferences from multiple sensor streams. The intern will work on developing techniques and on-device algorithms.

Always-On Context Awareness

Since context is fundamentally 24/7, one of the key challenges is to enable situation inference that is always-on, or “in the background,” and to do this within the power requirements for background tasks. The goal of this intern task is to analyze context aware algorithms and smartly optimize them for execution on QUALCOMM smartphone platforms.

Privacy

The goal of this internship task is to develop a framework for privacy in smartphones that provides a good tradeoff between the amount of private data collected and utility of the application. The intern will look into different aspects of privacy in smart phone and examine approaches to: (1) include privacy aspects into sensor data collection and inferences made from different types of data, (2) incorporate privacy in data models and ontologies, and (3) automatically learn user preferences and user privacy settings for different context aware applications.

Skills/Experience

Seeking candidates with strong background in one or more of the following areas:

• Machine learning, statistics, and signal processing
• Hands on experience with probabilistic inference techniques, Bayesian networks, graphical models, Hidden Markov models
• Working knowledge on semantics, reasoning, ontology definitions
• Experience working with rule based systems
• Privacy aspects of data acquisition
• Sensor data analysis and modeling
• System design and analytical reasoning
• Strong coding skills on C++, Java, and Android is a plus.

Qualcomm's Research & Development team in San Diego has a number of cutting edge projects lined up for next summer and will hire 20+ interns on their team during Summer 2013. The opportunities below are just a section. See the complete list here.

Intern Context Analysis and Reasoning (Qualcomm Research San Diego)

QUALCOMM Research is looking for highly motivated and self-driven systems engineering interns to work on challenging problems related to context awareness on mobile platforms.

Job Function

Mobile phones have transformed from being a communication device to smart and powerful personal computing platforms. Over recent years, with a wide variety of sensors available on smart phones, there has been an exploding interest in context aware applications that use sensor information to infer user situation and interest. These applications utilize different types of hard sensor data (such as GPS, WiFi, accelerometer, gyro, Bluetooth, pressure sensor, etc.) and soft sensor data (such as user calendar, user profile, data from social networks such as Facebook posts and Twitter feeds) to infer user situations and provide a useful service.

QUALCOMM Research is looking for highly motivated and self-driven systems engineering interns to work on challenging problems related to context awareness on mobile platforms.

Responsibilities

Positions are available in the following areas: Situation Inference on Mobile Devices

The goal of this intern project is to research algorithms for inferring user situations from smartphone sensor data. This involves developing machine learning and probabilistic graphical models to fuse low-level inferences from multiple sensor streams. The intern will work on developing techniques and on-device algorithms.

Always-On Context Awareness

Since context is fundamentally 24/7, one of the key challenges is to enable situation inference that is always-on, or “in the background,” and to do this within the power requirements for background tasks. The goal of this intern task is to analyze context aware algorithms and smartly optimize them for execution on QUALCOMM smartphone platforms.

Privacy

The goal of this internship task is to develop a framework for privacy in smartphones that provides a good tradeoff between the amount of private data collected and utility of the application. The intern will look into different aspects of privacy in smart phone and examine approaches to: (1) include privacy aspects into sensor data collection and inferences made from different types of data, (2) incorporate privacy in data models and ontologies, and (3) automatically learn user preferences and user privacy settings for different context aware applications.

Skills/Experience

Seeking candidates with strong background in one or more of the following areas:

• Machine learning, statistics, and signal processing
• Hands on experience with probabilistic inference techniques, Bayesian networks, graphical models, Hidden Markov models
• Working knowledge on semantics, reasoning, ontology definitions
• Experience working with rule based systems
• Privacy aspects of data acquisition
• Sensor data analysis and modeling
• System design and analytical reasoning
• Strong coding skills on C++, Java, and Android is a plus.