PhD proposal: Data, Energy, and Privacy Management Techniques for Sustainable Microgrids, 11am 8/11

Ph.D. Proposal Defense

Data, Energy, and Privacy Management
Techniques for Sustainable Microgrids

Zhichuan Huang

11:00am Tuesday, 11 August 2015, ITE 325b

Sustainable microgrids have gained increasing attention recently, because they can provide the power supply to places i) where the traditional power grid does not exist due to the poor economy or limited number of residences (e.g., islands); and ii) when the traditional power grid is temporally not functioning due to severe weather conditions (e.g., storms). However, in order to achieve sustainability, there are a lot of challenges to be addressed. In this thesis, we propose to investigate three key techniques in sustainable microgrids. First, we investigate the big energy data management problem and present E-Sketch, a middleware for utility companies to gather data from smart meters with much less storage and communication overhead. E-Sketch utilizes adaptive sampling to compress power consumption changes in time domain. Then frequency compression is applied to further compress the sampled data.

The second key technique is the energy management in microgrids. Because energy generation and demand in each individual home and microgrid is not matching, the key challenge of the energy management is to model the existing energy demand and propose novel energy management to reduce the overall energy usage and cost in microgrids. In this technique, we study the theoretical, technical, and economic feasibility of sustainable microgrids. To enable distributed energy management, energy consumption data of different homes needs to be shared in the microgrid. Thus an important problem is how we guarantee that the shared data can only be used for energy management but not revealing the privacy of individual homes in the microgrid. To address this problem, we leverage the unique feature of hybrid AC-DC microgrids and propose the third technique — Shepherd, a privacy protection framework to effectively protect occupants’ privacy. In Shepherd, we provide a generic model for energy consumption hiding from different types of detection techniques.

Committee: Drs. Ting Zhu (chair), Nilanjan Banerjee, Chintan Patel, and David Irwin (UMass Amherst)

PhD proposal: Holistic Home Energy Management: From Sensing to Data Analytics, 2pm 8/11

Ph.D. Proposal Defense

Holistic Home Energy Management:
From Sensing to Data Analytics

David Lachut

2:00pm Tuesday, 11 August 2015, ITE 325b

As home automation tools become more prevalent, they provide great potential to assist energy conservation and promote sustainable energy use in a way that increases users’ quality of life. This paper proposes the Greenhome System: a software system for using off-the-shelf home automation components and back-end data analytics to provide intelligent home energy management capabilities primarily targeted to renewable powered homes. The system will take input from various sensors and user input to detect user activities, predict home energy consumption, and make energy consumption recommendations to users. To accomplish the project goals, the Greenhome system requires in-home hardware and software components, a mobile component for user interaction, and a server component to tie them together. These components will accomplish tasks of data collection and analysis, activity and anomaly detection, prediction, planning, and recommendation.

This project builds on prior research in several areas, combining such diverse fields as predictive analytics, data visualization and annotation, planning, and recommender systems into a holistic approach. Combining these fields will result in new adaptations and make the overall Greenhome System a novel contribution. Work has begun on the Greenhome System preliminary to this proposal, with published work on residential sensor system design and implementation, data annotation collection, and energy demand prediction. It remains to incorporate automated self-maintenance, user activity detection, and personalized recommendations into a holistic system for home energy management.

Committee: Drs. Nilanjan Banerjee (chair), Ting Zhu, Charles Nicholas, Nirmalya Roy

MS Defense: Cybersecurity Assessment Tools, 11am 8/7

Computer Science and Electrical Engineering
University of Maryland, Baltimore County

MS Thesis Defense in Computer Science

Identifying Significant, Difficult and Timeless
Concepts for Cybersecurity Assessment Tools:
Results and Analysis of Two Delphi Processes

Geet Parekh

11:00am Friday, 7 August 2015, ITE 228

As part of our ongoing project to create Cybersecurity Assessment Tools (CATs), we carried out two Delphi processes to help cybersecurity experts and educators build a consensus about the core concepts and skills in the field. We present and analyze the results of these processes.

The first process identified fundamental concepts for our Cybersecurity Concept Inventory to be given to students completing any first course in cybersecurity. The second process identified skills for our Cybersecurity Curriculum Assessment, which will be given to students graduating from college headed for their first job in cybersecurity. These tests will provide infrastructure for evidence-based improvement of cybersecurity education to help universities better prepare the substantial number of cybersecurity professionals needed in America.

Thirty-six experts participated in four to five rounds of data collection. By the end of the processes, experts reached a consensus, as indicated by decreasing variations in their scoring of the importance, difficulty, and timelessness of concepts and topics that they identified. Participation by these diverse cybersecurity experts should help increase adoption of the tests.

Keywords: Cybersecurity Education, Cybersecurity Assessment Tools (CATs), Delphi Method, Concept Inventory

Committee: Drs. Alan T. Sherman (chair), Linda Oliva, Dhananjay Phatak, Chintan Patel

Remote Participation: If you wish to attend the defense remotely via Skype, please email Geet ().

Note: The Department of Defense has funded continuation of this research under BAA-003-15 via a collaborative award to UMBC and The University of Illinois at Champaign-Urbana (PIs Alan Sherman and Geoffrey Herman; CoPIs Dhananjay Phatak and Linda Oliva).

GRA sought for DoD-funded cybersecurity education project


A 12-Month Graduate Research Assistant (GRA) is sought for 2015-2016 to work on a DoD-funded cybersecurity education project at UMBC.

Position Highlights

  • 9-month stipend: $18,752.94; 2.5 month summer stipend: $8,000.
  • Hours: 20 hours/week (September 2015-May 2016)
  • Benefits: tuition and mandatory fees, health insurance
  • Eligibility: MS or PhD student at UMBC in CS, CE, EE, or related field (IS, math, education, physics).
  • INS Requirements: USA citizen or permanent resident
  • Source of funding: Department of Defense via a grant under BAA-003-15 (PI Alan Sherman)

Skills Needed

The GRA will (1) transcribe and analyze interviews of students to uncover their misconceptions about cybersecurity, (2) suggest interview prompts and test questions, and (3) help prepare publications describing the results. This work will include some statistical analysis and use of SurveyMonkey on-line questionnaires.

The GRA should bring knowledge and passion for cybersecurity, excellent communication skills, a strong work ethic, and a willingness, ability, and eagerness to learn whatever is needed to complete the project successfully.

The GRA will work closely with the investigators, including Drs. Alan Sherman, Dhananjay Phatak, Linda Oliva, Geoffrey Herman, and a post-doc in engineering education to be hired at The University of Illinois, Champaign-Urbana.

Project Summary

Professors Alan Sherman (CSEE), Dhananjay Phatak (CSEE), and Linda Oliva (Education) have been awarded a research grant from the Department of Defense to create two educational cybersecurity assessment tools, to help improve the way cybersecurity is taught. The $294,016 one-year project is joint with Geoffrey Herman at the University of Illinois, Champaign-Urbana (UMBC portion $146,917). The research is being carried out at the UMBC Cyber Defense Lab at the UMBC Center for Information Security and Assurance and will fund a 12-month GRA in 2015-2016.

This project is creating infrastructure for a rigorous evidence-based improvement of cybersecurity education by developing the first Cybersecurity Assessment Tools (CATs) targeted at measuring the quality of instruction. The first CAT will be a Cybersecurity Concept Inventory (CCI) that measures how well students understand basic concepts in cybersecurity after a first course in the field. The second CAT will be a Cybersecurity Curriculum Assessment (CCA) that measures how well curricula prepared students graduating from college on fundamentals needed for careers in cybersecurity. Each CAT will be a multiple-choice test with approximately thirty questions.

Inspired by the highly influential Force Concept Inventory from physics, the investigators are following a three-step process: In fall 2015, with MS student Geet Parekh, they carried out two Delphi processes to identify important and difficult concepts in cybersecurity. Next, they will interview students to uncover their misconceptions about these concepts. Finally, they will draft and psychometrically evaluate questions whose incorrect answers are driven by the uncovered misconceptions. For more information, see here and here.

In 2015-2016, the project will focus on (1) interviewing students and analyzing the results, and (2) developing draft questions.

How to Apply

Interested graduate students should email Dr. Sherman () a resume, unofficial transcript, and statement of interest and qualifications. Include “CATs GRA” in subject header.


MS defense: Lianjie Sun, Assessing Confidence in Relation Extraction, 2pm 7/23

Computer Science and Electrical Engineering
University Of Maryland, Baltimore County

M.S. Thesis Defense

Assessing Confidence in Relation Extraction Systems

Lianjie Sun

2:00ppm Thursday, 27 July 2015, ITE 325b, UMBC

In information extraction, a central and challenging task is extraction of relations. Systems that extract relations from text tend to be very productive, so it is important to quantify confidence or certainty in what is extracted. In this thesis we introduce a framework to assess confidence in relation extraction systems. We trained our system using a logistic regression model based on manually tagged sentences from the New York Times Annotated Corpora. Empirical results based on ROC curves show that our system performs better at computing confidence than previous systems such as Reverb. We conclude with a detailed analysis of the features used in our system and explain how these features might be tailored for use in other relation extraction systems.

Committee: Drs. Tim Oates (chair), Charles Nicholas and Matt Schmill

CSEE PhD student Kavita Krishnaswamy interviewed by Dr. Renetta Tull


CSEE Ph.D. student Kavita Krishnaswamy is interviewed by Dr. Renetta Tull, UMBC Associate Vice Provost for Graduate Student Development and Postdoctoral Affairs. The taped interview includes several graduate students from UMBC, UMBC's Graduate Dean, Dr. Janet G. Rutledge and Kavita’s parents. Kavita discussed her research and answered questions from the audience regarding graduate school tips, information about research by and for people with disabilities, and motivation to work toward the goal of the PhD. A transcript is available.

Nielsen Audio Data Science Day event, Thr. June 25

Nielsen Audio, a consumer research company that collects and analyzes listener data on radio broadcasting audiences, invites UMBC faculty and students to attend events focused on data science from 11:00am-2:30pm on Thursday June 25 at its headquarters in Columbia MD.

In the past few years, data science has become one of the top career opportunities for students with a background in computing or mathematics, offering interesting challenges and top salaries. Nielsen has been actively recruiting on campus and has hired three graduating UMBC students into its leadership rotational program, as well as several summer interns. They will be recruiting for full-time positions in the Fall.

The Nielsen Data Science Day event will take place in lobby and auditorium of Nielsen Audio's headquarters at 9705 Patuxent Woods Dr #200, Columbia, MD 21046 (map). Activities will include presentations, data science themed games and group discussions.

Between 11:00 and 12:00 participants can engage with interactive games with a Math/Data Science/Audio theme, including Data Science Jeopardy, Name that Tune, and Sampling Marbles. In the auditorium, a short video on data science produced by Nielsen will play continuously.

Lunch is available at Noon, followed by an introduction to data science at Nielsen Audio and presentations from managers of Nielsen's data science groups.

In the afternoon there will be a chance to meet with data scientists and find out what they do and opportunities for internships and positions.

If you have questions, contact the Columbia Data Science Day Committee leads: Kelly Dixon () or Freddie Navarro ()

Summer text analysis research jobs, on-campus

Hiring Students for Summer Text Mining Project

A new, interdisciplinary research project offers a total of five positions to graduate or undergraduate students from multiple disciplines who exhibit the right combination of initiative, skills and reliability. The project investigates undergraduate teaching at UMBC, and is led by the combined expertise of the Shriver Center, Interdisciplinary Studies Program and the Honors College. The students will work as an integrated, interdisciplinary team, and may expect to expand their own knowledge, skills and experience as a result. UMBC graduate students and exceptionally qualified undergraduate students will be considered.

A group of Academic and Student Affairs Division faculty and staff are working to better track, assess, strengthen and increase, and recognize and reward applied learning experiences across the UMBC Campus. In FY15, this group’s work has focused on creating a plan for assessing the impact of applied learning experiences (broadly defined) on students’ affective development.

Each position offers partial summer support funded by a Hrabowski Fund for Innovation: Implementation and Research grant.

80 hours (may span across 4-8 weeks depending on availability-no more than 20 hrs/week)

Stipend of $1150

The five graduate students we seek will have a background in the following skills/disciplines: Information Systems; Computer Science; Human Centered Computing; Instructional Systems Design; Applied Mathematics; Statistics; Human Services Psychology; Applied Sociology; Education; Language, Literacy & Culture; and Public Policy.

The project aims to continue for a minimum of three years, expanding subject to further successful funding. Students who perform well may be invited to extend their role.

One goal is to explore, through written student feedback, how diverse courses have contributed to their perception of growth in key attributes.  This first summer of the project will look at pilot data, exploratory analysis of scrappy, inconsistent, self-reported text, in order to figure out how to collect better, more uniform data in the Fall semester. One of the skill sets we wish for in the team would be a computer scientist able to mine text to seek the suggestion of themes, patterns or clues to what we would need to ask to get such patterns more clearly in the future.

All interested candidates should send a letter of interest, resume with references, and a statement with general summer weekly availability (e.g., Monday, Wednesdays & Fridays from 9am – 2pm) to Michele Wolff at by Wednesday, June 24.  Exceptionally qualified undergraduate students will also be considered.

Robotic assistive devices for independent living

Accessibility symbol

CSEE PhD student Kavita Krishnaswamy and Prof. Tim Oates write about their research using brain-computer interfaces and speech recognition tools to control robotic to assist individuals with reduced muscular strength. The piece, Robotic assistive devices for independent living, appeard in Robohub, "a non-profit online communication platform that brings together experts in robotics research, start-ups, business, and education from across the globe."

They describe their motivation as follows.

"One of the most craved aspects of the human experience is to be independent: the abilitiy to take care of one's self establishes a sense of dignity, inherent freedom, and profound independence. Our goal is to bring robotic assistive devices into the real world where they can support individuals with severe disabilities and alleviate the workload of caregivers, with the ultimate vision of helping people with severe physical disabilities to achieve physical independence without relying on others. As robotic assistive devices become ubiquitous, they will enable people with severe physical disabilities to confidently use technology in their daily lives, not just to survive, but to flourish."

They demonstrated the feasibility of integrating a brain-computer interface with speech recognition for self-directed arm repositioning tasks through a robotic interface for repositioning the simulated arm of an avatar using a Emotiv Epoc headset and Dragon NaturallySpeaking voice recognition software.

PhD proposal: Real-time Spectral Rendering of Atmospheric Optical Phenomena, 2pm 6/10

Ph.D. Dissertation Proposal

Real-time Spectral Rendering of Atmospheric Optical Phenomena

Ari Blenkhorn

2:00pm Wednesday, 10 June 2015, ITE 352

Glories, rainbows, and coronas are colorful atmospheric effects which occur when sunlight interacts with cloud droplets. Adding these effects to digital cloud environments will provide increased realism and a greater sense of immersion. Furthermore, these phenomena are the subject of active scientific research.  In both communities, high-resolution real-time rendering is desirable.

The color distribution of these phenomena is typically calculated using the Mie scattering theory, Debye series, or Airy theory. The calculations give the intensity of a single wavelength of light at a single scattering angle. They must be repeated for all desired wavelengths at all desired pixels of the final image.

I propose accelerating the calculations by using general-purpose GPU computing to transform a single-threaded, CPU-based Mie scattering application into a collection of highly-parallel GPU calculations.  I also propose to reduce the number of wavelengths required by using importance sampling, a monte-carlo selection method which concentrates the computing resources on the wavelengths belonging to the most important regions of the visible spectrum.

Planned work includes development of both numerical and perceptually-based image quality metrics, of interest to optical physicists and interactive application developers, respectively. These metrics will guide development of the GPU kernel parallel structure and the selection of a suitable estimator for importance sampling.

Committee: Drs. Marc Olano, Penny Rheingans, Curtis Menyuk, Matthias Gobbert (Mathematics), Raymond Lee (USNA)

1 5 6 7 8 9 36