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Faculty Research Projects & Opportunities for Students
Department of Computing Sciences September 16, 2013
Faculty are full-time and part-time members Interests range from theoretical foundations
to practical applications Some research is sponsored – funding for
assistantships sometimes available Actively seeking external sponsorship and
partnership Interdisciplinary research promoted Student involvement is welcome and
encouraged!
Overview
CSC 9025 – Often called “Independent Study” Mandatory for graduate students Conduct independent research under
guidance of a faculty advisor Encouraged to tackle topics in our discipline
that interest you AND your advisor Intended for completion in a single semester Extension to second semester possible Keep your eyes open for interesting topics!
What is the “Grand Challenges of Computing” course?
• Software Project Management • Web Design• Database Systems• Inter-discipline applications of database
- Manchester Mummy project - Egypt- Alaska- South America
Current Interest
DATABASE
Database designed and implemented Programs to enter data completed
2012 Documentation begun Egyptian data entered into database
REMAINING WORK
Update database with Alaskan mummies
Update database with North and South American mummies
Transfer the database to Manchester England
Train the researchers in England to use and update the database
Coordinate with researchers using the database
RESEARCHERS USING OUR DATA 2013 Dr. Frank Ruhli, Head of Centre of
Evolutionary Medicine in Zurich Searched the Database and found
specimens for DNA studies Collected the Paraffin blocks from
Manchester and have found DNA evidence in our mummy tissues
Dr. Randall Thompson, Saint Luke’s Ancient Mummy Research Searched the database for diagnosis of
Atherosclerosis He will confirm using CT scans, tissue
samples and microscopic slides
Web Site Design Categories of web sites Design principles for a particular category Systematic evaluation against design
principles Automatic measurements
Web Site Renovation Help nonprofit corporations, usually small
ones, upgrade their web sites Student works with “technical” person at
nonprofit Gather data for web site evaluation Challenges
◦ Communicating with the representatives◦ Developing with a variety of tools◦ Navigating the politics of the nonprofit
Web Site Renovation (2) Current requests include:
◦ Nancy’s House, a one-person corporation that arranges respite for caregivers
◦ Brain Injury Association of Pennsylvania Video based educational material on the site Improved web site design Marketing strategies to drive users to the site
◦ College of Liberal Arts and Sciences Better navigation
Social Network Analysis Mesh models of conflict resolution with
models of systems thinking for applications to◦ Nation building◦ Co-opetition in SOA system building
Examine and model social network strategies for promoting a cause◦ Flash mob◦ Philanthropy◦ “Pipeline” maintenance
Computing in Context Computing and music through inquiry-based
learning (IBL)◦ More generally, IBL for computing◦ More specifically, strategies for using ChucK, the
language of the laptop orchestra
Packing Problems
Pack n equally sized spheres into the unit sphere and calculate the radius of the small spheres as a function of n.◦ Alternatively, use an ellipsoid of revolution instead
of the unit sphere◦ Alternatively, solve the problems in two dimensions◦ Use a heuristic approach◦ Use a genetic algorithm
Mathematical Structures on the Web Strategies for calculating, storing, and
viewing mathematical structures such as:◦ Finite rings◦ Small Lie algebras◦ Lie algebra representations
Dr. Lillian CasselResearch interests:
Digital LibrariesComputing OntologyInformation and the
WebInterdisciplinary
ComputingSome Current Projects
Computing PortalConnecting Computing Educators
A large digital library project for computing education, funded by NSF.
Computing Ontology A complete definition of the computing disciplines, in collaboration with ACM
www.computingportal.org
www.distributedexpertise.org/computingontology
Just starting
Earlier and Broader Access to Machine LearningWith Dr. Way, Dr. Matuszek, and help from Dr. Papalaskari, funded by NSFWe will hire undergraduate help
Information Management Data Modeling Data Warehousing Data Mining Information Metrics
Interests and Projects
Healthcare Applications
“…degradation of service can have serious consequences, especially when the medical device relies heavily on the wireless connection. Such situations can compromise the wireless transmission of high-priority medical device alarms, …” FDA, August 13, 2013
CPN Model of WMDN
Alarms
Alarms
Nurses
Nurses
Network
Network
ResetQS
1`[]
ALIST
AlarmQR
1`[]
ALIST
ResetQR
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ALIST
AlarmQS
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ALIST
Network NursesAlarms
DataGen
DataGen
Patient10
Patient10
Patient9
Patient9
Patient8
Patient8
Patient7
Patient7
Patient6
Patient6
Patient5
Patient5
Patient4
Patient4
Patient3
Patient3
Patient2
Patient2
Patient1
Patient1
AlarmQSOut
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Results and ImplicationsHeart Alarm Max Delay
0
5000
10000
15000
20000
25000
30000
35000
1 2 3 4 5 6 7 8
Number of Patients Monitored
Sim
ula
tion
Tim
e U
nit
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Non QoS Max Delay
QoS Max Delay
Need for QoS requirement for medical applications
Similar situation in other application domains
Model Components
F ill B a tch P
F ill B a tch P T
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S ha red P T
S ha red E
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P _ L O W
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Ins tument F ree
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ResultsNumber of samples = 16where Sample Set = [[(1,[]),(2,[]),(3,[]),(4,[12])],[(1,[]),(2,[]),(3,[9]),(4,[11])],[(1,[]),(2,[]),(3,[]),(4,[])],[(1,[]),(2,[]),(3,[]),(4,[11])],[(1,[]),(2,[]),(3,[]),(4,[])],[(1,[]),(2,[]),(3,[8]),(4,[])],[(1,[]),(2,[]),(3,[8]),(4,[11])],[(1,[]),(2,[]),(3,[]),(4,[12])],[(1,[]),(2,[]),(3,[]),(4,[])],[(1,[]),(2,[]),(3,[9]),(4,[11])],[(1,[]),(2,[]),(3,[]),(4,[11])],[(1,[]),(2,[]),(3,[]),(4,[12])],[(1,[]),(2,[]),(3,[]),(4,[12])],[(1,[]),(2,[]),(3,[]),(4,[11])],[(1,[]),(2,[]),(3,[]),(4,[12])],[(1,[]),(2,[]),(3,[]),(4,[12])]]Number of unique requests = 3where Request Set = [[(3,[8])],[(4,[12])],[(4,[12])],[(3,[8]),(4,[11,12])]]Number of matched samples = 8where Matched Samples = [([13],[(4,[12])]),([15],[(4,[12])]),([16],[(4,[12])]),([8],[(4,[12])]),([7],[(3,[8]),(4,[11])]),([6],[(3,[8])]),([1],[(4,[12])]),([12],[(4,[12])])]Number of pantry samples = 5where Pantry Samples = [([14],[(4,[11])]),([4],[(4,[11])]),([2],[(3,[9]),(4,[11])]),([10],[(3,[9]),(4,[11])]),([11],[(4,[11])])]Number of hold samples = 0where Hold Samples = []Number of discarded samples = 3where Discarded Samples = [([([9],[])],12),([([5],[]),([3],[])],12)]Number of unique assigned requests to samples = 2where Assigned Samples = [([(4,[12])],[([13],[(4,[12])]),([15],[(4,[12])]),([16],[(4,[12])])]),([(4,[12])],[([13],[(4,[12])]),([15],[(4,[12])]),([16],[(4,[12])])]),([(3,[8])],[([7],[(3,[8]),(4,[11])]),([6],[(3,[8])])]),([(4,[12])],[([8],[(4,[12])]),([1],[(4,[12])])]),([(4,[12])],[([8],[(4,[12])]),([1],[(4,[12])])]),([(4,[12])],[([12],[(4,[12])])]),([(4,[12])],[([12],[(4,[12])])])]Number of unique possibly unmatched requests = 2where Possibly Unmatched Requests = [([(3,[8])],[]),([(3,[8]),(4,[11,12])],[]),([(3,[8]),(4,[11,12])],[]),([(3,[8])],[]),([(3,[8]),(4,[11,12])],[])]Number of actual unmatched requests = 1where Actual Unmatched Requests = [[(3,[8]),(4,[11,12])]]
Network Data Analysis and Modeling
Develop a set of tools and techniques for Network Performance Management and Service Assurance.
Create a generalized and extensible framework to accommodate future needs and expansion.
Build a unified dashboard that facilitates the understanding of the relationships between network resources, customer services and their respective performance indicators.
Databases for Many Majors: A Student-Centered Approach (Dietrich & Goelman) – through 2/2013
Expansion of the Project (? – keep your fingers crossed - !)
Update 5/2013 – not enough crossed fingers for funding, but interest continues in using and customizing animations to bring database appreciation to the world
Funded Projects (Sort of)
Collaborative research with Prof. S. Dietrich, Arizona State University
Calendar: March, 2010 – February, 2013 Curriculum development for database
education to diverse majors Software development: two animations
◦ Advantages of (normalized) database technology over loser (I mean non-normalized) alternatives
◦ Introduction to querying
Funded Project (NSF DUE): Databases for Many Majors
Technical issues◦ Programming in FLASH/FLEX◦ Porting to mobile devices◦ Customization of the animations to majors
Driven by producers (Goelman/Dietrich) and consumers
XML-based Rollout of animations - pretty mature Home page:
http://databasesmanymajors.faculty.asu.edu/
Databases for Many Majors (continued)
Databases: conceptual modeling Databases: schema integration Databases: XML for non-majors Databases: NoSQL databases Data Science and Big Data
Other Interests and Projects
◦Ramya Numboori: NOSQL Data Stores◦Hao Zhang: Database Querying in C#◦Takashi Binns: DB Systems for Geographical
Applications◦Shishir Kaushik: Online Marketing◦Sruthi Cherukuri: Utilities for Data
Warehouses◦Kartheek Chiluka: Rapid Application
Development Frameworks◦Sudha Palivela: NoSQL Databases,
Exemplified by MongoDB
Current and Recent Independent Studies
Develop algorithm visualizations along with mini-tutorials for computer aided instruction in Data Structure and Algorithm classes.
Visualizations as a mini-tutorial with animations portraying different parts of the algorithm.
Sample of five animations of ADT’s (and looking for more) http://www.csc.villanova.edu/~helwig/index1.html
Graph algorithms at http://algoviz.org/fieldreports AlgoViz.org is supported by the National Science
Foundation under a grant
Algorithm Visualizations for Teaching and Learning
J2 Micro Edition (J2ME) which is the version of the Java 2.1 platform that is designed for use with smaller devices such as PDA’s, mobile phones etc.
Since the size of small devices varies greatly, there are two profiles provided by the J2ME. The first,CLDC configuration , has a unique profile for Mobile Information Device Profile (MIDP toolkit).
Lab for Data Structures and Algorithms III developing a small app for the Blackberry.
Developing applications (games) on Mobile Phones and Small Devices
Computational Theory Artificial Intelligence Logic Projects
◦ Computability Logic◦ Interactive Computation
Interests and Projects
Interests and Projects Department Web Team Lead Programming Team Coach Graduate Independent Study / Grand Challenges Coordinator
◦ http://csc.villanova.edu/academics/gradIS ◦ have contacts/ideas BEFORE your final semester starts
Research Interests◦ Software development/engineering◦ Web programming◦ Security◦ Computer Science Education
Research Project Ideas◦ Collecting and analyzing data related to the software development
process◦ Report on the use of a new technology to create a system, perhaps
comparing it to use of a different technology Development Project Ideas
◦ Camp Registration Site◦ Use of Kinnects
AI, Robotics & Simulation
Virtual Reality◦ CAVEs◦ Immersive Video◦ Web-based Experiences
Mobile Apps
Interests and Projects
Anany Levitin
Algorithm design techniques are general strategies for algorithmic problem solving (e.g., divide-and-conquer, decrease-and-conquer, greedy, etc.)
paramount for designing algorithms for new problems provide a framework for classifying algorithms by design idea
Algorithmic puzzles are puzzles that requires design or analysis of an algorithm
illustrate algorithm design and analysis techniques as general problem solving tools (computational thinking)
some puzzles pose interesting and still unanswered questions entertainment technical job interviews
Anany Levitin (cont.)
Algorithm design techniques projects thinking backward; design by cases how to solve it (G. Polya) vs.
how to solve it by an algorithm
Algorithmic puzzles projects a few specific puzzles (research and visualization) taxonomies of algorithmic puzzles
• Artificial Intelligence– knowledge-based systems– ontologies and the semantic web– knowledge capture and sharing– Machine learning
• Natural Language Processing/Text Mining– Computer understanding of natural (human) languages– Finding, extracting, summarizing, visualizing information from
unstructured text• Project
– Broader and Earlier Access to Machine Learning: NSF project to develop machine learning materials for non-computer science students.
Interests and Projects
Systems Programming Systems Administration
◦ Linux◦ Solaris◦ Mac OS X
Web Application Development Current projects:
◦ Systems setup for upcoming programming contest◦ IBM ThinkPad Linux configuration for cityteam
ministries◦ Thin Client performance analysis◦ VU community Dropbox
Interests and Projects
Artificial Intelligence: - Augmented reality - Conversational agents - Reasoning with incomplete information - Machine learning - Computer Vision
Computer Science Education: - Teaching and learning computer science through service to the community - Computing for non-CS majors - Computer science through media computation - PACSE: Philadelphia Area Computer Science Educators
Interests and Projects
Cyber Security◦ Adaptive Network Defense◦ Data Protection and Privacy◦ Security within the Smart Grid◦ Ethical Hacking
Modeling and Simulation◦ Software Architectures as Executable Models◦ Security Modeling for Service Oriented
Architectures◦ Discrete Event Simulation
Interests and Projects
Department of Computing Sciences 72
Active Projects
Parsing & Translation Nanocompilers & Nanocomputers (Nanotech)
Sentiment Analysis & Tracking (AI)
Tremor Filtering Wii Pointer (Rehab Engr)
SNITCH plagiarism analyzer (Sim & Tools)
CS Education Distributed Expertise learning modules (CS
Ed) Machine Learning modules (CS Ed)
ACT Lab (CS Education)
Department of Computing Sciences 73
ACT Lab Research GroupsApplied Computing Technology Laboratory
Director of Research
Dr. Tom Way
Com. Sci.
Education
High Perf.
Computing
Rehab. Engineeri
ng
Simulation & Tools
Information
Fluency
Databases
Other Groups..
.
Nanotech
Department of Computing Sciences 74
Back-burner Projects
Using Magic to Teach CS (CS Education)
Green Computing (Green Comp.)
Speech Recog. for note-taking (Rehab Engr)
Info. literacy using science satire (Info. Fluency)
Many other ideas
actlab.csc.villanova.edu