Underwater Acoustic Communication and
Networking for Ocean Sampling

Dr. Aijun Song

Assistant Research Professor
College of Earth, Ocean, and Environment
University of Delaware, Newark, DE 19716

11:45am – 12:45pm Thursday, 27 March 2014, ITE325b, UMBC

On our planet Earth, the marine ecosystem is undergoing significant changes due to human activities and natural processes. These changes call for enhanced capabilities to sample and communicate in the oceans. With this background, underwater acoustic communication has attracted much attention across multiple disciplines, as a means to access oceanographic data in real-time and to support navigation of underwater vehicles. This talk will focus on my recent efforts in 1) characterization and modeling of the ocean environment as a communication medium, 2) development of high data rate acoustic modems, both software and hardware, and 3) application of underwater acoustic communication networks in ocean sampling.

Dr. Aijun Song received his Ph.D. degree in the Department of Electrical Engineering, University of Delaware, Newark, Delaware in 2005. From 2005 to 2008, he was a postdoctoral research associate at the College of Earth, Ocean, and Environment (CEOE), University of Delaware. During this period, he was also an Office of Naval Research (ONR) postdoctoral fellow, supported by the special research award in the Ocean Acoustics program. Since 2008, he has been an Assistant Research Professor of the CEOE, University of Delaware. His research interests include advanced signal processing and communication techniques for mobile radio frequencies as well as for underwater acoustic environments, underwater acoustic signal propagation, and the general area of ocean sampling.

The Baltimore Code Craftsmanship meetup group will hold its monthly meeting at 7:00pm on Thursday, March 27 in room 107 of the Physics on the UMBC Campus. The meetup is for students, faculty and software developers in the Baltimore area that care about the quality of their work and want to practice and improve their programming skills, share what they know and learn new things from others.

This is a hands on coding user group with no presentations. Each meeting will be a dojo where we will go through a challenging software craftsmanship exercise that focuses on clean code, test-driven development, design patterns, and refactoring. We will pair up and practice on a kata in order to learn and apply the values, principles, and disciplines of software craftsmanship. Come with your laptop equipped with your favorite programming and automated unit testing environment. If you don’t have a laptop, come anyway, we will need only one laptop for every two people. Be prepared to pair up, learn, share and have fun!

Join the meetup and register to attend at the meetup event page.

The Baltimore Code Craftsmanship meetup group will hold its monthly meeting at 7:00pm on Thursday, March 27 in room 107 of the Physics on the UMBC Campus. The meetup is for students, faculty and software developers in the Baltimore area that care about the quality of their work and want to practice and improve their programming skills, share what they know and learn new things from others.

This is a hands on coding user group with no presentations. Each meeting will be a dojo where we will go through a challenging software craftsmanship exercise that focuses on clean code, test-driven development, design patterns, and refactoring. We will pair up and practice on a kata in order to learn and apply the values, principles, and disciplines of software craftsmanship. Come with your laptop equipped with your favorite programming and automated unit testing environment. If you don’t have a laptop, come anyway, we will need only one laptop for every two people. Be prepared to pair up, learn, share and have fun!

Join the meetup and register to attend at the meetup event page.

Feature Engineering for Large Scale Predictive
Modeling with Electronic Health Records

Dr. Fei Wang
Healthcare Analytics Research group
IBM T. J. Watson Research Center

1:00pm Wednesday, 26 March 2014, ITE325b, UMBC

Predictive modeling lies in the heart of many medical informatics problems, such as early detection of some chronic diseases and patient hospitalization/readmission prediction. Typically those predictive models are built upon patient Electronic Health Records (EHR), which are systematic collection of patient information including demographics, diagnosis, medication, lab tests, etc. We refer those information as patient features. High quality features are of vital importance to building successful predictive models. In this talk, I will present two feature engineering technologies to improve the quality of the raw features extracted from original patient EHRs: (1) feature augmentation, which constructs more effective derived features from existing raw features by exploring the event sequentiality; (2) feature densification, which imputes the missing feature values via knowledge transfer across similar patients. Along with each technique we also developed a visual interface to facilitate the user exploring the derived features. Finally I will conclude the whole talk with some future research directions.

Dr. Fei Wang is currently a research staff member in Healthcare Analytics Research group, IBM T. J. Watson Research Center. Before his current position he was a postdoc in Department of Statistical Science, Cornell University. He received his Ph.D. from Department of Automation, Tsinghua University in 2008. Dr. Wang’s major research interests include data mining, machine learning as well as their applications in social and health informatics. He actively publishes papers on the top venues of the relevant fields including AMIA, KDD, ICML and InfoVis, and he has filed over 20 patents (four issued). Dr. Wang has given seven tutorials on different topics at ICDM/SDM/ICDM, organized seven workshops on KDD/ICDM/SDM/WSDM, and edited three special issues on Journal of Data Mining and Knowledge Discovery. His Ph.D. thesis was awarded the National Excellent Doctoral Thesis in China. His research paper was selected as the recipient of the Honorable mention of the best research paper award in ICDM 2010, and best research paper finalist in SDM 2011. More information can be found on his homepage.

Host: Prof. Kostas Kalpakis, Sorry, you need javascript to view this email address.

Feature Engineering for Large Scale Predictive
Modeling with Electronic Health Records

Dr. Fei Wang
Healthcare Analytics Research group
IBM T. J. Watson Research Center

1:00pm Wednesday, 26 March 2014, ITE325b, UMBC

Predictive modeling lies in the heart of many medical informatics problems, such as early detection of some chronic diseases and patient hospitalization/readmission prediction. Typically those predictive models are built upon patient Electronic Health Records (EHR), which are systematic collection of patient information including demographics, diagnosis, medication, lab tests, etc. We refer those information as patient features. High quality features are of vital importance to building successful predictive models. In this talk, I will present two feature engineering technologies to improve the quality of the raw features extracted from original patient EHRs: (1) feature augmentation, which constructs more effective derived features from existing raw features by exploring the event sequentiality; (2) feature densification, which imputes the missing feature values via knowledge transfer across similar patients. Along with each technique we also developed a visual interface to facilitate the user exploring the derived features. Finally I will conclude the whole talk with some future research directions.

Dr. Fei Wang is currently a research staff member in Healthcare Analytics Research group, IBM T. J. Watson Research Center. Before his current position he was a postdoc in Department of Statistical Science, Cornell University. He received his Ph.D. from Department of Automation, Tsinghua University in 2008. Dr. Wang’s major research interests include data mining, machine learning as well as their applications in social and health informatics. He actively publishes papers on the top venues of the relevant fields including AMIA, KDD, ICML and InfoVis, and he has filed over 20 patents (four issued). Dr. Wang has given seven tutorials on different topics at ICDM/SDM/ICDM, organized seven workshops on KDD/ICDM/SDM/WSDM, and edited three special issues on Journal of Data Mining and Knowledge Discovery. His Ph.D. thesis was awarded the National Excellent Doctoral Thesis in China. His research paper was selected as the recipient of the Honorable mention of the best research paper award in ICDM 2010, and best research paper finalist in SDM 2011. More information can be found on his homepage.

Host: Prof. Kostas Kalpakis, Sorry, you need javascript to view this email address.

If you have good software skills and are still looking for a summer internship, check out the 2014 Google Summer of Code program. You can earn $5500 by coding for an open source software project this summer. You will probably work remotely, but in close collaboration with a mentor at one of over 100 participating organizations. To maximize your chances, explore the organizations and find one that needs your skills. Details here; apply by Friday, March 21.

If you have good software skills and are still looking for a summer internship, check out the 2014 Google Summer of Code program. You can earn $5500 by coding for an open source software project this summer. You will probably work remotely, but in close collaboration with a mentor at one of over 100 participating organizations. To maximize your chances, explore the organizations and find one that needs your skills. Details here; apply by Friday, March 21.

If you have good software skills and are still looking for a summer internship, check out the 2014 Google Summer of Code program. You can earn $5500 by coding for an open source software project this summer. You will probably work remotely, but in close collaboration with a mentor at one of over 100 participating organizations. To maximize your chances, explore the organizations and find one that needs your skills. Details here; apply by Friday, March 21.

If you have good software skills and are still looking for a summer internship, check out the 2014 Google Summer of Code program. You can earn $5500 by coding for an open source software project this summer. You will probably work remotely, but in close collaboration with a mentor at one of over 100 participating organizations. To maximize your chances, explore the organizations and find one that needs your skills. Details here; apply by Friday, March 21.

PhD Dissertation Defense

Amplified Quantum Transforms

David J. Cornwell

10:00am-12:00pm, 26 March 2014, ITE346

In this work we investigate a new quantum algorithm called the Amplified Quantum Fourier Transform (Amplified-QFT) to solve the Local Period Problem where there is an Oracle with a periodic subset and we wish to recover its period. This algorithm uses parts of the famous Grover’s quantum search algorithm to amplify the amplitudes on the subset, followed by the equally famous Shor’s quantum algorithm for recovering the period. We compare the Amplified-QFT algorithm against the Quantum Fourier Transform (QFT) and Quantum Hidden Subgroup (QHS) algorithms and calculate the probabilities of success for all three algorithms. We show that the Amplified-QFT algorithm is on average, quadratically faster than either the QFT or QHS algorithms. We also investigate two more general settings: a) where the QFT is replaced by a general unitary operator U in the Amplified-QFT algorithm and b) where Grover’s algorithm is replaced by a general amplification procedure in the Amplified-QFT algorithm.

We also investigate this algorithm when a random Error Stream affects the Oracle, which involves calculating expectations and variances over a random set. We calculate the probabilities of success in this case. Further, we find an Uncertainty Principle for the Amplified-QFT algorithm. We also identify a decision problem, the Constant or Balanced Signal Decision Problem, which can be solved by using the one dimensional Amplified Haar Wavelet Transform. This decision problem is a generalization of the Deutsch-Josza problem.

Committee: Drs. S. Lomonaco (CSEE), Chair and advisor; T. Armstrong (Math), Co-advisor and Reader; Dr. Y. Shih (Physics), Reader; Dr. F. Potra (Math) and Dr. M. Gowda (Math)