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)

Scalable monitoring and kernel learning for energy grids

Vassilis Kekatos
Department of Electrical and Computer Engineering
University of Minnesota

12:00pm-1:00pm, Thursday, 13 March 2014, ITE 325b, UMBC

The smart grid vision urges for enhanced situational awareness, sustainability, and economics over our energy systems. While meters are being installed throughout the grid, algorithms that can effectively process this big data deluge are now demanded. Aligned to that end, this talk focuses first on scalable grid monitoring. Albeit control centers monitor their local grids independently, deregulation and renewables call for power system state estimation (PSSE) at the interconnection level. To address the complexity and communication challenges involved, a decentralized PSSE framework based on the alternating direction method of multipliers has been developed. Beyond conventional least-squares, our framework can identify outliers and circuit breaker statuses as verified on IEEE grids having thousands of nodes. Electricity market inference is the second theme of this talk. We will first demonstrate how grid topologies can be revealed using only publicly available real-time energy prices. This becomes feasible after recognizing that the price matrix can be factorized as the product of the grid Laplacian times a low-rank and sparse matrix. Leveraging the link between energy markets and the underlying physical grids, we will then cast day-ahead price forecasting as a kernel learning task. Through a novel nuclear norm-based regularization, kernels across pricing nodes and hours are systematically selected. Numerical tests using real data from the Midwest ISO market corroborate the interpretative merits of our schemes.

Dr. Vassilis Kekatos is currently a postdoctoral associate with the ECE Dept. of the University of Minnesota, Minneapolis. He obtained his Ph.D. in Computer Engineering and Science from the University of Patras, Greece, in 2007. In 2009, he received a Marie Curie fellowship. During the summer of 2012, he worked as a consultant for Windlogics Inc. His current interests lie in the areas of signal processing, optimization, and statistical learning towards modernizing our energy systems.

Host: Tulay Adali

Scalable monitoring and kernel learning for energy grids

Vassilis Kekatos
Department of Electrical and Computer Engineering
University of Minnesota

12:00pm-1:00pm, Thursday, 13 March 2014, ITE 325b, UMBC

The smart grid vision urges for enhanced situational awareness, sustainability, and economics over our energy systems. While meters are being installed throughout the grid, algorithms that can effectively process this big data deluge are now demanded. Aligned to that end, this talk focuses first on scalable grid monitoring. Albeit control centers monitor their local grids independently, deregulation and renewables call for power system state estimation (PSSE) at the interconnection level. To address the complexity and communication challenges involved, a decentralized PSSE framework based on the alternating direction method of multipliers has been developed. Beyond conventional least-squares, our framework can identify outliers and circuit breaker statuses as verified on IEEE grids having thousands of nodes. Electricity market inference is the second theme of this talk. We will first demonstrate how grid topologies can be revealed using only publicly available real-time energy prices. This becomes feasible after recognizing that the price matrix can be factorized as the product of the grid Laplacian times a low-rank and sparse matrix. Leveraging the link between energy markets and the underlying physical grids, we will then cast day-ahead price forecasting as a kernel learning task. Through a novel nuclear norm-based regularization, kernels across pricing nodes and hours are systematically selected. Numerical tests using real data from the Midwest ISO market corroborate the interpretative merits of our schemes.

Dr. Vassilis Kekatos is currently a postdoctoral associate with the ECE Dept. of the University of Minnesota, Minneapolis. He obtained his Ph.D. in Computer Engineering and Science from the University of Patras, Greece, in 2007. In 2009, he received a Marie Curie fellowship. During the summer of 2012, he worked as a consultant for Windlogics Inc. His current interests lie in the areas of signal processing, optimization, and statistical learning towards modernizing our energy systems.

Host: Tulay Adali

UMBC Eminent Scholar Program

Smart Distribution Systems

Dr. Karen Butler-Purry
Texas A&M University

11:00-12:00 Thursday, 13 March 2014, ITE 325B

Smart Grid refers to the computerizing of the grid via the addition of monitoring, analysis, control, and communication capabilities to improve its reliability, efficiency, and security. Smart meter devices, that include sensors to gather data and two-way digital communication between the smart meters in the field and the utility’s grid operations center, are associated with the grid. The smart grid can take advantage of new technologies, such as plug-in hybrid electric vehicles, various forms of renewable and conventional distributed generation, lighting management systems, automation technology that lets the utility adjust and control each individual device or millions of devices from a central location, and many more. This presentation will discuss some of the current research projects being investigated by Butler-Purry’s group on smart distributions systems, in grid or island operation. One project investigates the impact of cyber attacks on the operation of smart distribution systems. The second project developed two new approaches to enhance the protection of smart distribution systems. One approach uses smart meters during distribution planning to improve selectivity of protection, and the other approach uses smart meters during operation to improve the sensitivity of protection.

Karen L. Butler-Purry, PhD, PE, is Associate Provost for Graduate and Professional Studies and Professor in the Department of Electrical and Computer Engineering at Texas A&M University where she has served on the faculty since 1994. She received a B.S. in Electrical Engineering in 1985 from Southern University in Baton Rouge, Louisiana. She was awarded a M.S. degree in 1987 from the University of Texas at Austin and a Ph.D. in Electrical Engineering in 1994 from Howard University in Washington, D.C. Her research interests are in the areas of protection and control of distribution systems and isolated power systems such as all electric power systems for ships, mobile grids, and microgrids; cybersecurity protection; and intelligent systems for equipment deterioration and fault diagnosis.

Host: Prof. Gymama Slaughter, Sorry, you need javascript to view this email address.

UMBC Eminent Scholar Program

Smart Distribution Systems

Dr. Karen Butler-Purry
Texas A&M University

11:00-12:00 Thursday, 13 March 2014, ITE 325B

Smart Grid refers to the computerizing of the grid via the addition of monitoring, analysis, control, and communication capabilities to improve its reliability, efficiency, and security. Smart meter devices, that include sensors to gather data and two-way digital communication between the smart meters in the field and the utility’s grid operations center, are associated with the grid. The smart grid can take advantage of new technologies, such as plug-in hybrid electric vehicles, various forms of renewable and conventional distributed generation, lighting management systems, automation technology that lets the utility adjust and control each individual device or millions of devices from a central location, and many more. This presentation will discuss some of the current research projects being investigated by Butler-Purry’s group on smart distributions systems, in grid or island operation. One project investigates the impact of cyber attacks on the operation of smart distribution systems. The second project developed two new approaches to enhance the protection of smart distribution systems. One approach uses smart meters during distribution planning to improve selectivity of protection, and the other approach uses smart meters during operation to improve the sensitivity of protection.

Karen L. Butler-Purry, PhD, PE, is Associate Provost for Graduate and Professional Studies and Professor in the Department of Electrical and Computer Engineering at Texas A&M University where she has served on the faculty since 1994. She received a B.S. in Electrical Engineering in 1985 from Southern University in Baton Rouge, Louisiana. She was awarded a M.S. degree in 1987 from the University of Texas at Austin and a Ph.D. in Electrical Engineering in 1994 from Howard University in Washington, D.C. Her research interests are in the areas of protection and control of distribution systems and isolated power systems such as all electric power systems for ships, mobile grids, and microgrids; cybersecurity protection; and intelligent systems for equipment deterioration and fault diagnosis.

Host: Prof. Gymama Slaughter, Sorry, you need javascript to view this email address.

Learning and Optimization for Complex Dynamic Networks: The
Cases of Future Power Systems and Cognitive Wireless Networks

Dr. Seung-Jun Kim, University of Minnesota

11:45-12:45 Tuesday, 11 March 2014, ITE325b, UMBC

With enormous growth in sensing and communication capabilities as well as processing power to analyze collected data, we are witnessing exciting opportunities in diverse disciplines to study complex interactions of networked entities. The overarching theme is to explore cutting-edge computational intelligence tools from signal processing, machine learning, optimization, and control to make sense of amassed data and exploit complex interactions to make significant real-world impacts. In this talk, I will make cases for two prime examples, namely, future power systems and cognitive wireless networks. The role of contemporary tools including online learning, sparse and low-dimensional models, distributed and robust algorithms, will be emphasized.

Seung-Jun Kim received his B.S. and M.S. degrees from Seoul National University in Seoul, Korea in 1996 and 1998, respectively, and his Ph.D. from the University of California at Santa Barbara in 2005, all in electrical engineering. From 2005 to 2008, he worked for NEC Laboratories America in Princeton, New Jersey, as a Research Staff Member. He is currently with the Department of Electrical and Computer Engineering and the Digital Technology Center at the University of Minnesota, where he is a Research Associate Professor and a Research Associate. His research interests lie in applying signal processing, optimization, and machine learning techniques to various application domains including wireless communication and networking and smart power grids.

Host: Tinoosh Mohsenin, Sorry, you need javascript to view this email address.

Learning and Optimization for Complex Dynamic Networks: The
Cases of Future Power Systems and Cognitive Wireless Networks

Dr. Seung-Jun Kim, University of Minnesota

11:45-12:45 Tuesday, 11 March 2014, ITE325b, UMBC

With enormous growth in sensing and communication capabilities as well as processing power to analyze collected data, we are witnessing exciting opportunities in diverse disciplines to study complex interactions of networked entities. The overarching theme is to explore cutting-edge computational intelligence tools from signal processing, machine learning, optimization, and control to make sense of amassed data and exploit complex interactions to make significant real-world impacts. In this talk, I will make cases for two prime examples, namely, future power systems and cognitive wireless networks. The role of contemporary tools including online learning, sparse and low-dimensional models, distributed and robust algorithms, will be emphasized.

Seung-Jun Kim received his B.S. and M.S. degrees from Seoul National University in Seoul, Korea in 1996 and 1998, respectively, and his Ph.D. from the University of California at Santa Barbara in 2005, all in electrical engineering. From 2005 to 2008, he worked for NEC Laboratories America in Princeton, New Jersey, as a Research Staff Member. He is currently with the Department of Electrical and Computer Engineering and the Digital Technology Center at the University of Minnesota, where he is a Research Associate Professor and a Research Associate. His research interests lie in applying signal processing, optimization, and machine learning techniques to various application domains including wireless communication and networking and smart power grids.

Host: Tinoosh Mohsenin, Sorry, you need javascript to view this email address.

From Terabyte-Sized Stem Cell Images to Knowledge

Peter Bajcsy, PhD
Information technology Laboratory
National Institute of Standards and Technology

10:00am Monday, 10 March 2014, ITE 346, UMBC

This talk will present the computational challenges and approaches to knowledge discovery from terabyte-sized images. The motivation comes from experimental systems for imaging and analyzing human pluripotent stem cell cultures at the spatial and temporal coverage of colonies that lead to terabyte-sized image data. The objective of such an unprecedented cell study is to characterize pluripotency of stem cell colonies over time at high statistical significance in order to understand the stem cell culture quality parameters and guide a repeatable growth of high quality stem cell colonies. The terabyte- sized images represented a stem cell line that was engineered to produce green fluorescent protein (GFP) under the influence of Oct4 promoter and then imaged in a mosaic of contiguous frames covering approximately 180 square millimeters, over five days under both phase contrast and GFP channels.

We overview multiple computer and computational science problems related to correcting (flat-field, dark current and background), stitching, segmenting, tracking, re-projecting and then representing large images for interactive visualization and sampling in a web browser. We researched extensions to Amdahl’s law for Map-Reduce computations, established benchmarks for image processing on a Hadoop platform, and introduced cluster node utilization coefficients for modeling memory demanding computations running on a computer cluster/cloud. The theoretical aspects of algorithmic complexity and cluster utilization at terabyte scale are extended to the experimental aspects of efficient image representation and client-server workload distribution in the context of visualization interactivity and image sampling. We report such experimental results for the NIST extensions to the Deep Zoom paradigm. The presentation will conclude with illustrations of enabled stem cell discoveries and collaboration opportunities to create a reference resource not only for cell biologists but also for computer scientists focusing on terabyte scale image analyses.

Peter Bajcsy received his Ph.D. in Electrical and Computer Engineering in 1997 from the University of Illinois at Urbana-Champaign and a M.S. in Electrical and Computer Engineering in 1994 from the University of Pennsylvania. He worked for machine vision, government contracting, and research and educational institutions before joining the National Institute of Standards and Technology (NIST) in 2011. At NIST, he has been leading a project focusing on the application of computational science in biological metrology, and specifically stem cell characterization at very large scales. Peter’s area of research is large-scale image-based analyses and syntheses using mathematical, statistical and computational models while leveraging computer science fields such as image processing, machine learning, computer vision, and pattern recognition. He has co-authored more than more than 24 journal papers and eight books or book chapters, and close to 100 conference papers.

Host: Yelena Yesha (Sorry, you need javascript to view this email address. )

From Terabyte-Sized Stem Cell Images to Knowledge

Peter Bajcsy, PhD
Information technology Laboratory
National Institute of Standards and Technology

10:00am Monday, 10 March 2014, ITE 346, UMBC

This talk will present the computational challenges and approaches to knowledge discovery from terabyte-sized images. The motivation comes from experimental systems for imaging and analyzing human pluripotent stem cell cultures at the spatial and temporal coverage of colonies that lead to terabyte-sized image data. The objective of such an unprecedented cell study is to characterize pluripotency of stem cell colonies over time at high statistical significance in order to understand the stem cell culture quality parameters and guide a repeatable growth of high quality stem cell colonies. The terabyte- sized images represented a stem cell line that was engineered to produce green fluorescent protein (GFP) under the influence of Oct4 promoter and then imaged in a mosaic of contiguous frames covering approximately 180 square millimeters, over five days under both phase contrast and GFP channels.

We overview multiple computer and computational science problems related to correcting (flat-field, dark current and background), stitching, segmenting, tracking, re-projecting and then representing large images for interactive visualization and sampling in a web browser. We researched extensions to Amdahl’s law for Map-Reduce computations, established benchmarks for image processing on a Hadoop platform, and introduced cluster node utilization coefficients for modeling memory demanding computations running on a computer cluster/cloud. The theoretical aspects of algorithmic complexity and cluster utilization at terabyte scale are extended to the experimental aspects of efficient image representation and client-server workload distribution in the context of visualization interactivity and image sampling. We report such experimental results for the NIST extensions to the Deep Zoom paradigm. The presentation will conclude with illustrations of enabled stem cell discoveries and collaboration opportunities to create a reference resource not only for cell biologists but also for computer scientists focusing on terabyte scale image analyses.

Peter Bajcsy received his Ph.D. in Electrical and Computer Engineering in 1997 from the University of Illinois at Urbana-Champaign and a M.S. in Electrical and Computer Engineering in 1994 from the University of Pennsylvania. He worked for machine vision, government contracting, and research and educational institutions before joining the National Institute of Standards and Technology (NIST) in 2011. At NIST, he has been leading a project focusing on the application of computational science in biological metrology, and specifically stem cell characterization at very large scales. Peter’s area of research is large-scale image-based analyses and syntheses using mathematical, statistical and computational models while leveraging computer science fields such as image processing, machine learning, computer vision, and pattern recognition. He has co-authored more than more than 24 journal papers and eight books or book chapters, and close to 100 conference papers.

Host: Yelena Yesha (Sorry, you need javascript to view this email address. )

Predictive Analytics for Insider Threats

Ben Shariati
Visiting Lecturer & Interim Assistant CYBR GPD

4:00pm Wednesday, 5 March 2014, ITE325b

This talk will discuss how using operational cyber analytics for predictive security intelligence support a powerful defensive cybersecurity capability. Specifically, I will share elements of my commercial research on how organizations can predict malicious behavior (both user and digital) on their networks by incorporating tailored algorithms and artificial intelligence capabilities as part of an overall cybersecurity sensor architecture. Additionally, this talk will briefly discuss the impact that mobile devices have on the insider threat vulnerability within the government and private sector.

Ben Shariati is a Ph.D. candidate in Information Assurance/Cybersecurity at the George Washington University, where his dissertation work examines the analysis and management of cybersecurity concerns of critical infrastructures. His research and professional interests include mobile device security, emerging technology evaluation, risk analysis, audit, and compliance. For AY13-14, Mr. Shariati is a visiting lecturer and Interim Assistant Director of the Graduate Cybersecurity Program overseeing program activities at The Universities at Shady Grove. In addition to teaching several courses at UMBC, Mr. Shariati has taught graduate cybersecurity courses at George Washington University and undergraduate technical certification classes Hagerstown Community College.

Mr. Shariati is a technology and business executive with over 20 years of experience specializing in strategic and operational cybersecurity program activities and development for international organizations. His career highlights include serving as a lead enterprise security architect at the Federal Reserve Bank in Richmond, VA, a cybersecurity advisor at Cell Trust Corporation, and Technology Advisor/Cybersecurity Architect for the United Nations Pan American Health Organization.