Cognitive Computing and Visualization at IBM Research/RPI CISL

Dr. Hui Su, IBM Research

10:00-11:00am, Thursday, 19 May 2016, ITE 325b

Dr. Hui Su will talk about Cognitive and Immersive Systems Lab, a research initiative to develop the new frontier of immersive cognitive systems that explore and advance natural, collaborative problem-solving among groups of humans and machines. This lab is a collaboration between IBM Research and Rensselaer Polytechnic Institute. Dr. Su will talk about why the research for human computer interaction is extended to build a symbiotic relationship between human beings and smart machines, what research is going to be important to build immersive cognitive systems in order to transform the way professionals work in the future.

Dr. Hui Su is the Director of Cognitive and Immersive Systems Lab, a collaboration between IBM Research and Rensselaer Polytechnic Institute. He has been a technical leader and an executive at IBM Research. Most recently, he was the Director of IBM Cambridge Research Lab in Cambridge, MA and was responsible for a broad scope of global missions in IBM Research, including Cognitive User Experience, Center for Innovation in Visual Analytics and Center for Social Business. As a technical leader and a researcher for 19 years at IBM Research, Dr. Su has been an expert in multiple areas ranging from Human Computer Interaction, Cloud Computing, Visual Analytics, and Neural Network Algorithms for Image Recognition etc. As an executive, he has been leading research labs and research teams in the US and China. He is passionate about game-changing ideas and fundamental research, passionate in speeding up the impact generation process for technical innovations, discovering and developing new linkages between innovative research work and business needs.

Host: Jian Chen (Sorry, you need javascript to view this email address. )

America is ‘dropping cyberbombs’ – but how do they work?

Richard Forno and Anupam Joshi

Recently, United States Deputy Defense Secretary Robert Work publicly confirmed that the Pentagon’s Cyber Command was “dropping cyberbombs,” taking its ongoing battle against the Islamic State group into the online world. Other American officials, including President Barack Obama, have discussed offensive cyber activities, too.

The American public has only glimpsed the country’s alleged cyberattack abilities. In 2012 The New York Times revealed the first digital weapon, the Stuxnet attack against Iran’s nuclear program. In 2013, former NSA contractor Edward Snowden released a classified presidential directive outlining America’s approach to conducting Internet-based warfare.

The terms “cyberbomb” and “cyberweapon” create a simplistic, if not also sensational, frame of reference for the public. Real military or intelligence cyber activities are less exaggerated but much more complex. The most basic types are off-the-shelf commercial products used by companies and security consultants to test system and network security. The most advanced are specialized proprietary systems made for exclusive – and often classified – use by the defense, intelligence and law enforcement communities.

So what exactly are these “cyberbombs” America is “dropping” in the Middle East? The country’s actual cyber capabilities are classified; we, as researchers, are limited by what has been made public. Monitoring books, reports, news events and congressional testimony is not enough to separate fact from fiction. However, we can analyze the underlying technologies and look at the global strategic considerations of those seeking to wage cyber warfare. That work allows us to offer ideas about cyber weapons and how they might be used.

Read more @ The Conversation and also on the Scientific American Web site.

UMBC Information Systems Department

Predicting Demographics and Affects in Social Networks

Dr. Svitlana Volkova
Pacific Northwest National Laboratory

11am Friday, 13 May 2016, ITE 459

Social media predictive analytics bring unique opportunities to study people and their behaviors in real time, at an unprecedented scale: who they are, what they like and what they think and feel. Such large-scale real-time social media predictive analytics provide a novel set of conditions for the construction of predictive models. This talk focuses on various approaches to handling this dynamic data for predicting latent user demographics, from constrained-resource batch classification, to incremental bootstrapping, and then iterative learning via interactive rationale (feature) crowdsourcing. In addition, we present the relationships between a variety of perceived user properties e.g., income, education etc. and opinions, emotions and interests in a social network.

Svitlana Volkova received her PhD in Computer Science from Johns Hopkins University. She was affiliated with the Center for Language and Speech Processing and the Human Language Technology Center of Excellence. Her PhD research focused on building predictive models for sociolinguistic content analysis in social media. She built online models for streaming social media analytics, fine-grained emotion detection and multilingual sentiment analysis, and effective annotation techniques via crowdsourcing incorporated into the active learning framework. She interned at Microsoft Research in 2011, 2012 and 2014 at the Natural Language Processing and Machine Learning and Perception teams. She was awarded the Google Anita Borg Memorial Scholarship in 2010 and the Fulbright Scholarship in 2008.

CHMPR Lecture Series

• Topic Modeling for Analyzing Document Collection

Mitsunori Ogihara
Department of Computer Science, University of Miami

11:00am Monday, 16 May 2016, ITE 325b, UMBC

Topic modeling (in particular, Latent Dirichlet Analysis) is a technique for analyzing a large collection of documents. In topic modeling we view each document as a frequency vector over a vocabulary and each topic as a static distribution over the vocabulary. Given a desired number, K, of document classes, a topic modeling algorithm attempts to estimate concurrently K static distributions and for each document how much each K class contributes. Mathematically, this is the problem of approximating the matrix generated by stacking the frequency vectors into the product of two non-negative matrices, where both the column dimension of the first matrix and the row dimension of the second matrix are equal to K. Topic modeling is gaining popularity recently, for analyzing large collections of documents.

In this talk I will present some examples of applying topic modeling: (1) a small sentiment analysis of a small collection of short patient surveys, (2) exploratory content analysis of a large collection of letters, (3) document classification based upon topics and other linguistic features, and (4) exploratory analysis of a large collection of literally works. I will speak not only the exact topic modeling steps but also all the preprocessing steps for preparing the documents for topic modeling.

Mitsunori Ogihara is a Professor of Computer Science at the University of Miami, Coral Gables, Florida. There he directs the Data Mining Group in the Center for Computational Science, a university-wide organization for providing resources and consultation for large-scale computation. He has published three books and approximately 190 papers in conferences and journals. He is on the editorial board for Theory of Computing Systems and International Journal of Foundations of Computer Science. Ogihara received a Ph.D. in Information Sciences from Tokyo Institute of Technology in 1993 and was a tenure-track/tenured faculty member in the Department of Computer Science at the University of Rochester from 1994 to 2007.

Beginning May 23, 2016 and running through the end of August, the Women's Center will be operating under limited hours and the Center may be closed during times that UMBC community members need access to the lactation room.

In order to ensure access to this safe and private space for nursing parents, the Women's Center is partnering with the Commons CIC. If you plan on using the lactation room over the summer, please contact Jess Myers (jessm@umbc.edu, 410-455-2714) for details and next steps.