Department of Computing Sciences
September 29, 2014
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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!
Devices
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CAVE
Object capture rig
Oculus Rift
Google Glass
Mindstorm robots
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Kinect
Raspberry Pi
Finch
IR keyboard
Research Outlets & Support
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Conferences
Research Projects
Fun Projects
Reading Day Events
CS Ed Week Events
Sigma Xi Event
Many others
• Travel funds
• Equipment funds
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Grad Office has some
Undergrad Office also
Department might too
Research grants as well
CSC 9025
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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!
Listen for opportunities to
get involved in research
Projects
Parsing & Translation
 Google Glass, Machine Learning & Memory
 Sentiment Analysis & Tracking
 Misc. NLP Parsing Projects
 Tremor Filtering Wii Pointer
 SNITCH plagiarism analyzer
CS Education
 Loosely-Coupled Interdisciplinary Teaching
 Machine Learning modules
 Distributed Expertise learning modules
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Department of Computing
Sciences
High Perf.
Computing
Nanotech
Simulation
& Tools
Com. Sci.
Education
Databases
Information
Fluency
Director of Research
Dr. Tom Way
Rehab.
Engineering
Other
Groups...
Department of Computing
Sciences
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Nanocompilers & Nanocomputers
Using Magic to Teach CS
Green Computing
Speech Recog. for note-taking
Info. literacy using science satire
Many other ideas
actlab.csc.villanova.edu
click on "Idea Incubator"
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Department of Computing
Sciences
Sentiment Analysis & Tracking
 Tremor Filtering Wii Pointer
 Tremor Quantification
 Plagiarism detection
 Fake research paper detection
 Social network extraction from novels
 Machine Learning education modules
 Google Glass
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Department of Computing
Sciences
Projects
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Constructing and maintaining wireless network
topologies.
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Folding and unfolding polyhedra.
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DNA Computing: How can DNA molecules
solve computational problems?
Projects
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Department Web Team Lead
Programming Team Coach
Graduate Independent Study / Grand Challenges Coordinator
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Research Interests
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◦ http://csc.villanova.edu/academics/gradIS
◦ have contacts/ideas BEFORE your final semester starts
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Software development/engineering
Web programming
Security
Computer Science Education
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Research Project Ideas
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Development 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
◦ Camp Registration Site
◦ Use of Kinnects
Projects
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Systems Programming
Systems Administration
◦ Linux
◦ Solaris
◦ Mac OS X
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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
Dr. Robert Beck
A Sampling of Projects
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
• Computational sustainability
– Figuring out what this means
Chronozoom
• Check out chronozoom.com, an open source
system for displaying time lines
– Create content, and enhance the content creating
process
– Develop programs for Big History
– Investigate a 3-D timeline in the CAVE
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
• Map communities as social networks
UX of Smart Things
• Interacting with the internet of things
– Mobile Wallet Worth Having (MWWH)
– Apple Watch
– Smart home monitoring
– Smart driving
– Smart touring: QR codes, cell phone tours
• More generally, gesture interfaces
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
Cliques, etc
• Finding a maximal clique (largest complete
subgraph) in a given simple graph
– Fred’s strategy
– More generally, strategies for NP-hard problems
– Involves creative programming and
experimentation with heuristics
Projects
Dr. Lillian Cassel
Research interests:
Digital Libraries
Ensemble
Marconi Museum Library
Computing Ontology
Resources for computing education
Data Science
Information and the Web
Interdisciplinary Computing
Interested graduate students meet at 1:30 on Tuesday
afternoon, Mendel 290
Undergraduates welcome then or at other times.
Ensemble Computing Education
Portal
www.computingportal.org
Computing Portal
Connecting Computing Educators
• Well established, but with many
opportunities for refinement.
• Original funding has ended, so mostly
volunteer work at this time.
• Opportunities for research projects as we
attempt to solve some interesting
problems.
• Proposals under development to obtain
more funding.
More Digital Libraries/Web
Information
• Marconi Museum
– We have large collection of pictures
– How do you make a good representation of
a physical museum on the web?
– Possible CAVE application, as well as regular
digital library
Computing Ontology
www.distributedexpertise.org/computingontology
Computing Ontology A complete definition of the
computing disciplines, in
collaboration with ACM
• Status
– Still an interesting problem.
– On the list of applications to develop for the
CAVE
– Needs people with good imaginations and
creativity
Educational Resources
• Earlier and Broader Access to Machine
Learning
– With Dr. Way, Dr. Matuszek, Dr. Papalaskari
• Data Science
– With Dr. Goelman, Dr. Posner (statistics)
Projects
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Research topics related to ethical issues and
themes
Privacy, Surveillance, and Big Data
Lethal Autonomous Robotic Weapons
Electronic voting
Outreach activities
Dr. Vijay Gehlot
Projects
Systems Modeling
• Behind every data there is a process that
generates/consumes it
• To effect changes, understating of processes is
crucial
• Process mining
• Holistic vs reductionist
• Systems thinking
Systems
Model Components
ToT
In
runConfigs( ["r1c1","r1c2","r1c3","r1c4","r2c1","r2c5","r2c6","r3c1","r3c7","r3c7e",
"r5c1-corr","r5c2-corr2","r5c3-corr","r5c4-corr","r8c1","r8c8","r8c8e"],
[(32,128), (64,256)],2)
Samples
Fusion 23
PoolListxNodeIDTimed
SampleSet
s
runConfigs(["r1c1","r5c1-corr"],[(2,16), (4,16)],2)
Pantry
if #1(plnH)<>[] then (plnH::mpl1)
else mpl1
rs
p2
Fusion 19
Initialize Sample Set
Route Table and
Node Attr Table
discardAllEmpty(rs)
RequestSet
p2^^plP
1`[]
PoolList
Request
Fusion 24
1`()
Condition Pools
Start
ConditionedPools
ConditionPools T
UNIT
PoolListxNodeIDTimed
input ();
output (s,rs);
action
(init_routing(!rt_file_name);
setCurSampleSet(genSampleSet(!sSize));
(getCurSampleSet(), genSampleSet(!rSize)))
1`[]
Hold
Fusion 21
mpl1
()
PLNIDLst
Fill Batch P
ToP
Fill Batch P T
1`[]
p1^^plM
Matched
Router
(if stP=T then (if #1(plnToP)<>[]
then 1`plnToP else empty) else empty) ++
(if stN=T then (if #1(plnToN)<>[]
then 1`plnToN else empty) else empty)
PoolListxNodeIDTimed
Fusion 22
PLNIDLst
Shared P
PLNIDLstTimed
PoolListxNodeIDTimed
am3
Shared E
1
1`[]
am3^^am4
Shared E T
PLNIDLstTimed
R Ready
INT
input (p, nodeFr, rs);
output (plnToP, plnToN, stP, stN, plM, plP, plnD, plnH,am2,am4);
action
route(p,nodeFr,rs);
(p,nodeFr)
ToType
1`12
1`Limit_R
1`[]
Possibly
Unmatched
Fusion 26
AssignmentMatrix
am1^^am2
Assigned
Samples
Fusion 25
AssignmentMatrix
ToE
Shared P T
ToS
(if stP=S then (if #1(plnToP)<>[]
then 1`plnToP else empty) else empty) ++
(if stN=S then (if #1(plnToN)<>[]
then 1`plnToN else empty) else empty)
am1
PLNIDLstTimed
1`[]
Discard
mpl2
p1
Fusion 20
PoolList
ToT
if #1(plnD)<>[] then (plnD::mpl2)
else mpl2
i
Type
@+procTime(40,20)
Type
Setup R
T
ToRouter
T
S
i
S
PoolListxNodeIDTimed
(p,~1)
R Setup
Injector
ConditionedPools
In
PoolListxNodeIDTimed
INTTimed
i i-1
p
[List.length(mpnlst) < Limit_R, i > 0]
[initPool()]
Available PoolList
pn
PoolList
In
Limit Batch
Combined
Amplicon
Pools to R
(mpl,il)
Accept
P_HIGH
PLNIDLstxIntListTimed
mpnlst
batchPoolList(pn, BatSizeMax_E)
(mpl,il)::mpnlst
[List.length(#1(pn1)) > BatSizeMin_E]
Fusion 3
pn1
Ready To
Batch
Pass
Through
[]
1`[]@0
1
Accepted
[pn1]
PLNIDLstxIntListLstTimed
PoolListxNodeIDTimed
[]
mpnlst
pn
[mpnlst<>[]]
[mplLength(mpl) + List.length(#1(pn)) <= BatSizeMax_E]
(if mpl=[] then 1`() else empty)@+Timer_P
Combined
Batched
Pools
Add to
Batch
Fusion 5
P_LOW
mpl
mpl
PLNIDLstTimed
Move To
Shared P
()
mpl
ToP
Out
i
Start R
Limit_R
PLNIDLstTimed
mpnlst@+(procTime(20,10)+150)
pn::mpl
1`()
1
Cancel
Timer
()
Timer
Fusion 6
UNITTimed
1`[]
Hold
Fusion 4
1`[]
[mplLength(mpl) >= BatSizeMin_E]
[]
Forward
Ready
Batch
mpl
PLNIDLst
mpl
1`()
1
Instument
Free
Active R
UNIT
()
[mpl1<>[]]
[]
Forward
Timedout
Batch
mpl1
()
P_HIGH
PLNIDLstxIntListLstTimed
mpnlst
mpl1
Un Batch
list2ms(getMPL(mpnlst))
Unbatched
Pools
pn1
PoolListxNodeIDTimed
Done R
pn2
input (pn1);
output (pn2);
action
processType(pn1);
ToRouter
Out
PoolListxNodeIDTimed
ToRouter
Out
PoolListxNodeIDTimed
Tools/Approaches
Projects
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Databases for Many Majors: Customizable
Visualizations to Improve STEM Learning
(Dietrich & Goelman) – NSF IUSE project:
9/2014 through 8/2017
Data Computing for All: Developing an
Introductory Data Science Course in Flipped
Format (Cassel, Posner, Dichev, Dicheva &
Goelman) – NSF IUSE project: 9/2014 through
8/2017
Details in next slides
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Collaborative research with Prof. S. Dietrich,
Arizona State University
Enhancement of visualizations for promoting
database education to diverse majors
 Visualizations from the last grant: intro to relational
databases and intro to querying
 Add a third visualization: conceptual modeling
 Add functionality for self-assessment by students
 Add functionality for educators to customize the
setting to diverse domains (FlashBuilder and
ActionScript)
◦ Home page:
http://databasesmanymajors.faculty.asu.edu/
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Collaborative research with Profs. L. Cassel
and M. Posner, Villanova University; and
Profs. C. Dichev and D. Dicheva, WSSU
Curricular development: an introductory
course in data science
Pedagogical development: inverted classroom
approach
Research assistance: information gathering
and presentation
Databases: conceptual modeling
 Databases: schema integration
 Databases: XML for non-majors
 Databases: NoSQL databases
 Data Science and Big Data
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◦ Anusha Chenreddy: NoSQL Databases
◦ Sai Viswa Teja Mitta: Object Relational
Mapping
◦ Dinesh Paladugu: Big Data and Real-Time
Applications
◦ Nagasaiteja Popuri: Distributed File
Systems for Big Data – Exemplified by
Hadoop
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MohanKumar Puttasidaiah: Big Data
Processing: Applications of MapReduce and
Hadoop in Industry
Swathi Vangala: Data Warehousing Solutions
for Big Data
Akhila Yarlagadda: Technology and Health
Care Data Management
Siva Sindhuri Yenamaladoddi: Processing
and Analysis of Big Data Using Hadoop
Projects
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Computational Theory
Logic
Projects
◦ Computability Logic
◦ Cirquent Calculus
◦ Interactive Computation
Projects
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Computer Vision
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Computer Game Development
Projects
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Virtual Reality
◦ CAVE
◦ Immersive Video
◦ Web Experiences
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AI, Robotics, and Simulation
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Mobile Apps
Projects
Anany Levitin
Algorithm design techniques are general strategies for
algorithmic problem solving (e.g., divide-and-conquer,
decrease-and-conquer, greedy, etc.)
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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
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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
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thinking backward; design by cases
how to solve it (G. Polya) vs.
how to solve it by an algorithm
Algorithmic puzzles projects
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a few specific puzzles (research and visualization)
taxonomies of algorithmic puzzles
Projects
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Artificial Intelligence:
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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
Projects
Current Interest
Software Project Management
• Web Design
• Database Systems
• Inter-discipline applications of
database
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Manchester Mummy project
Egypt
Alaska
South America
Manchester Mummy Database
Update
2013 Status
Database
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Database designed and implemented
All programs to enter data completed
Documentation completed
Egyptian, Alaskan, North and South American
mummies data entered into database
• Transferred the database to Manchester
England
Remaining Work
• Train the researchers in England to use and
update the database
• Coordinate with researchers using the
database
Researchers Using our Data 2014
• Giada Ferrari and 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
Still more opportunities
Dr. Paula Matuszek
Projects
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Interests and Projects
• Artificial Intelligence
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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.
Projects
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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
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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.
Projects
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Information Management
Data Modeling
Data Warehousing
Data Mining
Information Metrics
Projects
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Cyber Security
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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