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Current Events

Ontology Engineering Spring 2017
Description:
To learn how to build computer understandable definitions of terms for usage in automated systems.
This course provides an introduction to ontologies, their uses, and an overview of their application in semantically enabled systems. Ontologies encode term meanings. Ontologies with their declarative encodings of meaning can be used to improve communications between people and can enable computer programs to function more effectively. They provide the foundation for clear and unambiguous interaction. Ontologies have become increasingly common on the web, and class participants will not only learn about the use of ontologies in web-based applications but how to evaluate ontologies for reuse in such applications. Participants will read relevant papers, learn how to critically review ontology papers as well as ontologies themselves, and will participate in at least one group project designing, using, and evaluating ontologies.

Dates: January 1, 2017 - May 31, 2017
Concepts: Rule Modeling, Semantic Reasoning, Information Model, Linked Data, Taxonomy, Controlled Vocabulary, , Ontology, Semantic Web Services, Semantic Web, Inference, Vocabulary, Schema, Provenance
Data Analytics 2017
Description:
Data and Information analytics extends analysis (descriptive and predictive models to obtain knowledge from data) by using insight from analyses to recommend action or to guide and communicate decision-making. Thus, analytics is not so much concerned with individual analyses or analysis steps, but with an entire methodology. The world at-large is confronted with increasingly larger and complex sets of structured/unstructured information; from sensors, instruments, and generated by computer simulations; data is "hidden" in websites, application servers, social networks and on mobile devices. As a nation, assimilating information across disparate domains (e.g., intelligence, economics, science) has the potential to provide improved capabilities for decision makers. In commerce and industry, analytics-driven enterprises are becoming mainstream. Yet, there is a shortfall in the key education skills needed to meet the growing needs. Traditional enterprises are moving toward analytics-driven approaches for core business functions. In the government and corporations, cybersecurity problems are prevalent. The investment in advanced analytics capabilities could potentially be more broadly leveraged today and greater than any prior government investments in computing. Emphasis is now placed on disruptive data and information sources on the Web and Internet: using Web Science and informatics to explore social networks, platform competition, the "long tail" and economic or resource impacts of the search for new findings. Key topics include: advanced statistical computing theory, multivariate analysis, and application of computer science courses such as data mining and machine learning and change detection by uncovering unexpected patterns in data.
  • Introduce students to relevant methods to recognize and apply quantitative algorithms, techniques and interpretation
  • To develop students' strategic thinking skills, combined with a solid technical foundation in data and model-driven decision-making.
  • Develop ability to apply critical and analytical methods to formulate and solve science, engineering, medical, and business problems
  • Students will examine real-world examples using modern cyberinfrastructure to place statistical and data-mining techniques in context, to develop data-analytic thinking, and to illustrate that proper application is as much an art as it is a science.
  • By the end of the course, students can effectively communicate analytic findings to non-specialists

Dates: January 16, 2017 - May 5, 2017
Concepts: Predictive Analytics, Big Data, Data Science, Analytics, Data Visualization


Upcoming Events

TWed discussion: Using Information Centrality for detecting systemic anomalies in large homogeneous networks
Description:
TWed Talk: Weds, 22 Feb (6p Winslow 1140)
TITLE: "Using Information Centrality for detecting systemic anomalies in large homogeneous networks"
LEADER: Nidhi Rastogi
VIDEO: TWed video streams
EVENT: YouTube

Please join us TOMORROW (Weds, 22 Feb, 6p, Winslow 1140) as TWC PdD student Nidhi Rastogi leads us in a discussion of her recent progress in performing anomaly detection in large networks using graph analytics.

DESCRIPTION: Modern networked systems are constantly under threat from systemic attacks. There has been a massive upsurge in the number of devices connected to a network as well as the associated traffic volume. This has further led to heightened awareness as well as inclusion of most potential attack vectors during system design and implementation. The impact of this remodeling can be seen in the increased amount of time required to detect various cyber attacks. Since this is an undesirable outcome, there is a need to identify gaps in existing intrusion detection systems that can be filled using novel approaches. Information Centrality based Anomaly Detector (IC-AD) identifies anomalous activities in large, homogeneous, and static networks. It labels network nodes with better vantage points for detecting network-based anomalies as central nodes and uses them for attack detection. The intuition is that since these central nodes already see a lot of information flowing through the network, they are in a good position to detect anomaly. This research first dives into the important role played by graph based anomaly detection in existing communication networks. We then introduce IC-AD as a centrality index based approach, one that belongs to the field of graph analytics. Through simulation, we demonstrate that IC-AD is able to detect anomalous behavior using central nodes, given the anomaly is systemic in nature.

BIO: Nidhi Rastogi is a Ph.D. candidate in the Computer Science Department of Rensselaer Polytechnic Institute, Troy, NY, where she is leading innovation in anomaly detection in large networks using graph analytics. She also holds a master=E2=80=99s in computer science from the University of Cincinnati. She has extensive work experience in networks at Verizon Wireless, NJ, and GE Global Research, NY. She is also committed to social good by using her skills in securing cyberspace, networks, graph analytics, machine learning, and AI.

REMINDERS:
  • ALL TWC STUDENTS are STRONGLY encouraged to attend, regardless of whether the talk is in your specific research area.
  • SIGN UP NOW for your Spring 2017 LIGHTNING TALK!
  • Sign up for RPIrates, the RPI R Users Group here:
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TWed Logistics (Spring 2017):

Dates: February 22, 2017 - February 22, 2017
Concepts:


Recent Events

TWed Discussion: Data science and the future of the built environment: Applying the work of Tetherless World to CASE's Active Modular Phytoremediation System (AMPS)
Description:
TWed Talk: TONIGHT, Weds, 08 Feb (6p Winslow 1140)
TITLE: "Data science and the future of the built environment: Applying the work of Tetherless World to CASE's Active Modular Phytoremediation System (AMPS)"
LEADER: Josh Draper (RPI CASE)
VIDEO: TWed video streams
EVENT: YouTube

Please join us Wednesday, February 8, 2017 (6p, Winslow 1140) as we welcome a special guest speaker, Josh Draper from RPI's Center for Architecture Science and Ecology (CASE). Josh will discuss CASE's innovative collaboration with TWC centering on data science and the built environment. Special thanks to Paulo Pinheiro for arranging with Josh to give this talk!

DESCRIPTION: Data science and the future of the built environment: Applying the work of Tetherless World to CASE's Active Modular Phytoremediation System (AMPS)

Integrating green walls in buildings raises some interesting questions: How and to what degree might they improve indoor air quality (IAQ) ? How does IAQ play a role in executive function and health? What are the plants doing to the building's microbiome? How do you prevent mold spores? How do greenwalls affect the energy use of HVAC systems?

CASE, the Center for Architecture Science and Ecology, is performing studies with human subjects and greenwalls to begin to answer the above questions. Data Science plays a central role in the work helping to understand an inherently complex, integrated problem that requires a range of domain expertise to address. Working with Paulo Pinhero and the Tetherless World team, we are using Data Science to enable better analysis, provenance and integration.

Josh Draper, PI on CASE's current greenwall studies using AMPS, will discuss progress on our latest experiments and data science's emerging, critical role.

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BIO: Josh Draper received his BA in Classics from St. John's College and his M.Arch from GSAPP, Columbia University. He works at the intersection of computation and craft, with broad experience in digital fabrication, computational design and rapid prototyping. Joining CASE in 2014, Josh teaches Advanced Prototyping, Data Visualization and Research Investigations with CASE's Doctoral Students. Josh's research at CASE focuses on advanced forming technologies and agricultural by-products as building materials. More info...

REMINDERS:
  • ALL TWC STUDENTS are STRONGLY encouraged to attend, regardless of whether the talk is in your specific research area.
  • SIGN UP NOW to give YOUR Spring 2017 TWed talk!
  • Sign up for RPIrates, the RPI R Users Group here
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TWed Logistics (Spring 2017):

Dates: February 8, 2017 - February 8, 2017
Concepts: eScience, Data Science
TWed Discussion: Semantic Markdown: Embedding Workflow Semantics via R Markdown
Description:
TWed Talk: Weds, 01 Feb (6p Winslow 1140)
TITLE: "Semantic Markdown: Embedding Workflow Semantics via R Markdown"
LEADER: John Erickson
VIDEO: (TWed video streams)
EVENT: You Tube
KEYWORDS: Semantic Workflows, Reproducibility, Data Analytics

Please join us this Weds (6p, Winslow 1140) as I discuss recent thoughts on using esp. R Markdown to extend the RStudio environment to enabling data analysts to directly generate and publish RDF that richly describes the semantics of their scripts. This is a possible next step towards best practices for "in situ" embedding of appropriate concepts and vocabulary from established ontologies (including ProvONE and domain ontologies) into practical workflows.

DESCRIPTION: I'll discuss new work that aims to explore extending markdown syntax (esp. R Markdown) in concert with 'knitr' to directly produce workflow markup, in a human-compatible way. One example of an outcome: An RStudio user can "knit" a markdown rendition that, instead of generating (e.g) PDF or HTML, an extension will generate RDF (TTL or JSON-LD) or HTML+RDFa. By "human readable," we mean markdown best practices will be developed that are reasonable for a data analyst to use; methods (possibly based on templates) must be developed that do not require the user to "know" RDF. Today we can create cumbersome R Markdown (Rmd) files that produce HTML+RDFa outputs with correct embedded workflow semantics, but the user must be an HTML and RDFa hacker to understand the code. Workflow reproducibility requires tools that data analysts will actually use.

This work will be an advancement of the semantic workflow work inspired by YesWorkflow, and leverages an approach using standard practices for R extensions, markdown and publication, creating a direct path for data analysts to get their workflows represented in knowledge graphs. This approach broadens the potential user base by helping to ensure their workflows and results are easier to discover, conceptually easier to understand, and therefore increasing the likelihood they will be cited, reused and reproduced.

BIO: John S. Erickson, Ph.D. has spent over two decades studying the unique social, legal, and technical problems that arise when managing and disseminating information in the digital environment. Currently Director of Research Operations for the Rensselaer Institute for Data Exploration and Application (The Rensselaer IDEA) and Deputy Director of the Web Science Research Center of the Tetherless World Constellation (TWC) at Rensselaer Polytechnic Institute (RPI), John coordinates, contributes, and teaches.

REMINDERS:
  • ALL TWC STUDENTS are STRONGLY encouraged to attend, regardless of whether the talk is in your specific research area.
  • SIGN UP NOW to give YOUR Spring 2017 TWed talk!
  • Sign up for RPIrates, the RPI R Users Group
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TWed Logistics (Spring 2017):
  • TWed schedule
  • Snacks will be provided for TWed Talks
  • Live video streams of TWed Talks are now available via YouTube
  • TWed Talks from previous terms are archived

Dates: February 1, 2017 - February 1, 2017
Concepts: Semantic Web
Better Searching through Reformulated Queries
Description:
TWed Talk: Weds, 25 Jan (6p Winslow 1140)
TITLE: "Better Searching through Reformulated Queries"
LEADER: Amar Viswanathan
VIDEO: TWed video streams
EVENT: YouTube
KEYWORDS: SPARQL, Knowledge Graph, Linked Data

Do search engines always give you the right information? How long does it take to get an answer you are looking for? Would it have been better if the system 'talked' to you. Why can't we build such a system now? Please join us next Wednesday (6p, Winslow 1140) as TWC PhD Student Amar Viswanathan talks about how query reformulation can bridge the gap between systems and humans.

DESCRIPTION: This work focuses on addressing the problem of query failure using the Gricean maxim of cooperative answering as amotivating foundation. More specifically, using query reformulations that abide by data- and schema-awareness, we show that failed user queries can be given more context. We discuss the results for the same and argue that such reformulations help in providing a better interaction with the user.

BIO: Amar Viswanathan is an n-th year graduate student in the Tetherless World Constellation who has at last finished his thesis proposal. He has worked on sentiment analysis, event summarization, entity summarization and Linked Data Analysis. Currently he is focusing on query reformulation and dialog based Knowledge Graph Search.

REMINDERS:
  • ALL TWC STUDENTS are STRONGLY encouraged to attend, regardless of whether the talk is in your specific research area.
  • SIGN UP NOW to give YOUR Spring 2017 TWed talk!
  • Sign up for RPIrates, the RPI R Users Group here
====
TWed Logistics (Spring 2017):
  • TWed schedule
  • Snacks will be provided for TWed Talks
  • Live video streams of TWed Talks are now available via YouTube
  • TWed Talks from previous terms are archived

Dates: January 25, 2017 - January 25, 2017
Concepts:
Advanced Semantic Technologies (Fall 2016)
Description:
  • Prepare students for research in semantic technologies
  • Teach students how to
    • Read papers,
    • Present research ideas,
    • Synthesize material,
    • Critically review (as one might do for a publication venue)
  • Teach students how to develop a literature corpus for use in research
This course will discuss emerging trends in semantics research, focusing on knowledge representation, management, and modeling, including applications of knowledge graphs and ontologies. This is a seminar course, not a lecture course. We will have many presentations and discussions throughout the course that help you to understand, conduct, and evaluate academic research while we discuss the emerging trends in semantic technologies. This course is intended to allow students to produce a research survey that can fulfill their research qualifying examination. Participants will read relevant papers, learn how to critically review ontology papers as well as ontologies themselves, and will participate in at least one group project designing, using, and evaluating knowledge representation systems.

Dates: August 1, 2016 - December 31, 2016
Concepts: Ontology, Semantic Web, , Taxonomy, Vocabulary, Semantic Reasoning, SPARQL, Linked Data, Controlled Vocabulary, Inference, Information Model
Geographic Information Systems in the Sciences (2016)
Description:
Introduction to relational analysis and interpretation of spatial data and their presentation on static and interactive maps using PostGIS, qGIS, Leaflet.js and Geoserver. Geographic spatial data concepts covered are map projections, vectors & geoprocessing, raster analsysis, interpolation, collaborative mapping, GIS on the cloud and web mapping. Database concepts of building and manipulating a spatial database, SQL, spatial queries, and integration of graphic and tabular data are also covered. During each class we will discuss topics and do examples. Related take-home exercises will be assigned. Depending on class size, students may be asked to present assignments to the rest of the class. Each student will do a semester-long project on some topic of particular interest to them, but also of relevance to the class. These projects will be presented to the class during the last week. 4 credit hours.

Each Topic requires 3-4 hours per week of effort, comprising 1.5 hours of reviewing materials and approximately 2 hours of lab work.
  1. To provide students an opportunity to learn geospatial applications and tools.
  2. To introduce relational analysis and interpretation of spatial data and presentation on maps.
  3. Introduce spatial database concepts and technical aspects of query languages and geographic integration of graphic and tabular data.
  4. To introduce intermediate aspects of geospatial analysis: map projections, reference frames, multivariate analysis, correlation analysis, regression, interpolation, exptrapolation, and kriging.
  5. To gain experience in an end-to-end GIS application via a term project.

Dates: August 1, 2016 - December 31, 2016
Concepts: Geophysical Science, Geoinformatics, Geoscience, Geographic Information System