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Upcoming Events

9th International ACM Web Science Conference 2017
The conference brings together researchers from multiple disciplines, like computer science, sociology, economics, information science, or psychology. Web Science is the emergent study of the people and technologies, applications, processes and practices that shape and are shaped by the World Wide Web. Web Science aims to draw together theories, methods and findings from across academic disciplines, and to collaborate with industry, business, government and civil society, to develop our knowledge and understanding of the Web: the largest socio-technical infrastructure in human history.

Dates: June 26, 2017 - June 28, 2017

Recent Events

European Semantic Web Conference - ESWC 2017
TWed Lightning Talks (Spring 2017)
TWed Lightning Talks (Spring 2017)
When: Wednesday, 03 May 2017 (6p)
Where: Winslow 1140, RPI Campus, Troy, NY
VIDEO: TWed video streams
Google Event: YouTube

Plan to join us this WEDS, 03 MAY (6p) for a very special TWed as the Tetherless World Constellation holds its end-of-term Graduate Research "Lightning Talks" TWed session. This special TWed is a great way for the TWC community to learn of the wide range of amazing research happening at the Tetherless World, and "a good time is had by all!"

BACKGROUND: Lightning talks are VERY short --- approx. 2 minute! --- summaries by our students of current research work, with no NO SLIDES and only brief "crib notes."

"The point (of a lightning talk) is to make a point, and explain it as quickly (and clearly) as possible. That's it..." Don't caught up in the whole idea of providing background information or explaining other issues.

STUDENTS: See this helpful guide BUT remember that there will be NO SLIDES for your talk; lightning talks are about YOU clearly describing YOUR work.

TWed Logistics (Spring 2017):
  • TWed schedule
  • 6p-7p, 1st floor Winslow (1140)
  • Pizza or snacks will be provided for TWed Talks

Dates: May 3, 2017 - May 3, 2017
TWed Discussion: Fun with GANs
TWed Talk: Weds, 26 Apr (6p Winslow 1140)
TITLE: "Fun with GANs"
LEADER: Matt Klawonn
VIDEO: TWed video streams
EVENT: YouTube
KEYWORDS: Machine Learning, Computer Vision and Pattern Recognition; Neural and Evolutionary Computing

Please join us next Weds, (26 Apr, 6p Winslow 1140) as TWC Ph.D. student Matt Klawonn leads us in a discussion of an exciting new area of machine learning known as Generative Adversarial Networks (GANs). One well-known application of GANs has been the creation of photorealistic images.

DESCRIPTION: According to Yann LeCun, "There are many interesting recent developments in deep learning ... The most important one, in my opinion, is adversarial training (also called GANs for Generative Adversarial Networks). This, and the variations that are now being proposed is the most interesting idea in the last 10 years in ML, in my opinion." In this talk we will introduce GANs, starting with some theory and moving to implementation tips and techniques. We will then take a look at some GAN demonstrations and code, moving from image to sequence generation.

BIO: Matt Klawonn is a third-year PhD student working under adviser Jim Hendler in various deep learning areas. His most recent project, one which he hopes to turn into a thesis, involves the use of GANs to create knowledge graphs from images.

  1. "An introduction to Generative Adversarial Networks (with code in TensorFlow)."
  2. Goodfellow, Ian J.; Pouget-Abadie, Jean; Mirza, Mehdi; Xu, Bing; Warde-Farley, David; Ozair, Sherjil; Courville, Aaron; Bengio, Yoshua (2014). "Generative Adversarial Networks".
  3. Salimans, Tim; Goodfellow, Ian; Zaremba, Wojciech; Cheung, Vicki; Radford, Alec; Chen, Xi (2016). "Improved Techniques for Training GANs"
  • 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
TWed Logistics (Spring 2017):

Dates: April 26, 2017 - April 26, 2017
Concepts: Computer Vision, Neural Computation, Pattern Recognition, Evolutionary Computation, Machine Learning
Data Analytics 2017
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
Ontology Engineering Spring 2017
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