UMBC and UMB celebrate research partnerships with symposium and innovation grants.

A post curated by Women’s Center staff member, Daniel

This week is Critical Social Justice week!! Yay!! The Women’s Center will be occupying Main Street on Wednesday from 11am to 1pm by bringing our lounge out of the center and into the public! We’ll be doing a number of really cool activities including creating a scrapbook full of pages made by community members about their Feminist Click Moments.

What’s a Feminist Click Moment?????

Your Click Moment is the event or thought or moment when you realized the word “feminist” applied to you. Click is a book of essays about various authors’ Click Moments compiled by Courtney E. Martin and J. Courtney Sullivan. You can read an interview about the book here. Each of our staff members created their scrapbook pages for you all to see and get you thinking about how you want to express your Click Moment and add a piece of yourself to a Women’s Center project that will be available for all to see!

Amelia:

I don’t know if you can actually be a “natural born feminist,” but I was definitely raised as a feminist. My mom has always encouraged my social justice activism, and her constant cheering me on has been invaluable in my journey as a feminist. That might not be an actual “click” moment, but I feel like it’s one of the most important relationships I’ve had as a lil’ baby feminist person growing into a full on raging feminist scholar and activist.

Bria:

(You can read more about Bria’s Click Moment here.)

My click moments were clouds with silver linings.

Black

Woman

Comprehensive Health Care

Sex Positive

Activist

Reproductive Rights

Feminist

Yoo-Jin:

“So…are you a feminist now?”

When I first heard this question, one fateful day, I had to take a moment to pause. Why was this even a question? And why was this person asking like I had the plague? Most of my college experience has been me exploring my identities, values, and passions. My click moment can’t be pointed to just one event but many. Thanks to the amazing and strong women in my life, being a survivor and meeting other survivors, and Gender and Women’s Studies courses, I have learned and will continue to learn so much. So anyway, the answer is

“Damn right, I’m a feminist.”

Narges:

It has been few years since I had my feminist click moment, and identify as a feminist. I never forget the first time that I started thinking about this more in depth, asking myself if I am a feminist or not. It was during my Gender and Women’s Studies 100 class, when our professor asked the class “ Do you identify as a feminist?” This was something that I never really thought about, I asked myself that question and after a little while my answer was “no!” I don’t exactly remember why I chose that answer. Thinking about it now, it might be because of the fact that I wasn’t really sure of how I defined feminism and what my personal understanding of it was. Later on when I was able to explore my personal identity more and define feminism for myself I realize that I was and am a feminist!

(Narges wrote a longer blog about her journey to feminism last year! You can check it out here.)

Dan:

I was raised to politically conscious from the start. I went to my first protest at the age of 10 with my dad and my older sister. Once I realized I was bisexual at the age of 15, I began to get involved in gay rights activism by being the only student in my entire school to participate in the Day of Silence. But I never even considered the word “feminist” until I joined tumblr and a whole world of new ideas was opened up to me. I came into my feminist identity at the same time as I was coming into my queer/trans identity and the two have always been and will forever be tied to one another.

Understanding Social Spammers: A Data Mining Perspective
Xia “Ben” Hu

Computer Science and Engineering
Arizona State University

12:00-1:00 Tuesday, 24 February 2015

With the growing popularity of social media, social spamming has become rampant on all platforms. Many (fake) accounts, known as social spammers, are employed to overwhelm legitimate users with unwanted information. Social spammers are unique due to their coordinated efforts to launch attacks such as distributing ads to generate sales, disseminating pornography and viruses, executing phishing attacks, or simply sabotaging a system’s reputation. In this talk, I will introduce a novel and systematic analysis of social spammers from a data mining perspective to tackle the challenges raised by social media data for spammer detection. Specifically, I will formally define the problem of social spammer detection and discuss the unique properties of social media data that make this problem challenging. By analyzing the two most important types of information, network and content information, I will introduce a unified framework by collectively using heterogeneous information in social media. To tackle the labeling bottleneck in social media, I will show how we can take advantage of the existing information about spam in email, SMS, and on the web for spammer detection in microblogging. I will also present a solution for efficient online processing to handle fast-evolving social spammers.

Xia Hu is a Ph.D. candidate in Computer Science and Engineering at Arizona State University, supervised by Professor Huan Liu. His research interests include data mining, machine learning, social network analysis, etc. As a result of his research work, he has published nearly 40 papers in several major academic venues, including WWW, SIGIR, KDD, WSDM, IJCAI, AAAI, CIKM, SDM, etc. One of his papers was selected for the Best Paper Shortlist in WSDM’13. He is the recipient of IEEE “Atluri Award” Scholarship, 2014 ASU’s President’s Award for Innovation, and Faculty Emeriti Fellowship. He has served on program committees for several major conferences such as WWW, IJCAI, SDM and ICWSM, and reviewed for multiple journals, including IEEE TKDE, ACM TOIS and Neurocomputing. His research attracts wide range of external government and industry sponsors, including NSF, ONR, AFOSR, Yahoo!, and Microsoft.

– more information and directions: http://bit.ly/UMBCtalks –

UMBC’s Eco-Ambassadors welcome you back to campus and the spring semester!  We will continue our "Turn Down Tuesdays" series this semester with energy saving fun facts and tips!

As the semester’s workload begins to take off, we probably find ourselves using our computers more often.  Set your computer to automatically go to sleep after 10-15 minutes to save energy.  

According to energy expert Michael Bluejay, an average laptop uses 15-60 watts during normal use, but uses only 0-6 watts in sleep mode and 0 watts in hibernate.  That may not seem like a lot of savings, but imagine if everyone did this…it would add up!

Also, skip the screen saver – instead of saving energy, it consumes energy.  Setting your computer to sleep will still allow you to start up quickly where you left off.

Topic Modeling with Structured Priors for Text-Driven Science

Michael Paul, JHU

12:00pm – 1:00pm, Monday, 2 March 2015, ITE 325

Many scientific disciplines are being revolutionized by the explosion of public data on the web and social media, particularly in health and social sciences. For instance, by analyzing social media messages, we can instantly measure public opinion, understand population behaviors, and monitor events such as disease outbreaks and natural disasters. Taking advantage of these data sources requires tools that can make sense of massive amounts of unstructured and unlabeled text. Topic models, statistical models that describe low-dimensional representations of data, can uncover interesting latent structure in large text datasets and are popular tools for automatically identifying prominent themes in text. However, to be useful in scientific analyses, topic models must learn interpretable patterns that accurately correspond to real-world concepts of interest.

In this talk, I will introduce Sprite, a family of topic models that can encode additional structures such as hierarchies, factorizations, and correlations, and can incorporate supervision and domain knowledge. Sprite extends standard topic models by formulating the Bayesian priors over parameters as functions of underlying components, which can be constrained in various ways to induce different structures. This creates a unifying representation that generalizes several existing topic models, while creating a powerful framework for building new models. I will describe a few specific instantiations of Sprite and show how these models can be used in various scientific applications, including extracting self-reported information about drugs from web forums, analyzing healthcare quality in online reviews, and summarizing public opinion in social media on issues such as gun control.

Michael Paul is a PhD candidate in Computer Science at Johns Hopkins University. He earned an M.S.E. in CS from Johns Hopkins University in 2012 and a B.S. in CS from the University of Illinois at Urbana-Champaign in 2009. He has received PhD fellowships from Microsoft Research, the National Science Foundation, and the Johns Hopkins University Whiting School of Engineering. His research focuses on exploratory machine learning and natural language processing for the web and social media, with applications to computational epidemiology and public health informatics.

– more information and directions: http://bit.ly/UMBCtalks –