UMBC Relationship Violence Prevention Advocates

Position Description (Fall, 2014)

(applications  accepted on a rolling basis from April 18 through August 8)

The UMBC Relationship Violence Prevention Advocates program is a comprehensive program funded by a grant from Verizon.  The program changes each year and is designed to educate the UMBC Community on specific relationship violence prevention information.  The goals of the program are to systematically raise awareness and support for a campus to be without relationship violence and create an environment that encourages healthy relationships and the victims of relationship violence to come forward and receive support. Specific components of the program include:

·         Relationship violence prevention education and training for volunteer students, faculty and staff advocates who represent various groups and populations on campus. These advocates will help to educate the campus community about relationship violence prevention through their networks,  and serve as referral sources for those who have experienced or are impacted by relationship abuse/violence or have questions about unhealthy relationships.

·         Training for UMBC athletic coaches

·         Relationship Skills Program via the UMBC Counseling Center

·         Awareness, Education and Resources through www.umbc.edu/rvap

·         Bystander Intervention training through the Green Dot program (VP Student Affairs Office)

General Description:

UMBC Relationship Violence Prevention Advocates are UMBC community members who: a)  are able to provide relationship/dating/intimate partner violence prevention information to their community networks, b) observe the UMBC community and take notice of and report  community issues related to relationship violence to advocate trainers, c) encourage bystander intervention techniques within UMBC community network, d) make appropriate referrals, e) provide helpful resource information to their UMBC community network and individuals, f) support violence prevention efforts on campus, and g) volunteer to staff events (tabling, etc).

1.      Must attend training on August 25, 2014 from 9:30am-4pm

2.      Must attend the following Friday meetings at noon- August 29; September 5, 12, 19; October 3, 17, 31; November 14; December 5

3.      Must share educational information with your network of friends, colleagues, student organizations, team members, and at campus events

4.      Must utilize skills to make referrals, provide resources, intervene safely when necessary using bystander intervention skills.

UMBC’s ACM student chapter invites nominations from the graduate students in the CSEE department for student officer positions for academic year 2014 – 2015 (Fall 2014 to Spring 2015).

ACM (the Association for Computing Machinery) is a premier organization that promotes computing and technology around the US and the world. On the campus, the ACM student chapter is affiliated and supported by the UMBC graduate students association. The goal of the ACM student chapter is to foster interaction between all students, both graduate and undergraduate, in the CSEE department, provide a forum for student interaction, and opportunities for members to expand their knowledge of computing.

The positions available (and their general responsibilities):

These positions are open to graduate students only. If elected, you would be required to signup as an ACM student member. Membership fee is $19 only.

Please email us the position you would like to run for (there will be elections if we get multiple nominations for a position). Alternatively you can nominate any other person for the positions above. In that case, please send their name, email address and which position you would like to nominate them for.

Please send in your nominations by end of day, Monday, April 14, 2014 to acmofficers at lists dot umbc dot edu. Elections will take place the following week (venue, date and time to be announced later).

UMBC’s ACM student chapter invites nominations from the graduate students in the CSEE department for student officer positions for academic year 2014 – 2015 (Fall 2014 to Spring 2015).

ACM (the Association for Computing Machinery) is a premier organization that promotes computing and technology around the US and the world. On the campus, the ACM student chapter is affiliated and supported by the UMBC graduate students association. The goal of the ACM student chapter is to foster interaction between all students, both graduate and undergraduate, in the CSEE department, provide a forum for student interaction, and opportunities for members to expand their knowledge of computing.

The positions available (and their general responsibilities):

These positions are open to graduate students only. If elected, you would be required to signup as an ACM student member. Membership fee is $19 only.

Please email us the position you would like to run for (there will be elections if we get multiple nominations for a position). Alternatively you can nominate any other person for the positions above. In that case, please send their name, email address and which position you would like to nominate them for.

Please send in your nominations by end of day, Monday, April 14, 2014 to acmofficers at lists dot umbc dot edu. Elections will take place the following week (venue, date and time to be announced later).

A multi-scale approach to analyze large clinical datasets:
Towards the understanding of the complex effects of concussions

Dr. Jesus Caban
National Intrepid Center of Excellence
Walter Reed, Bethesda, MD

Noon Thursday, 10 April 2014, ITE325b

Mild traumatic brain injuries (mTBIs) or concussions are invisible injuries that are poorly understood and their sequelae can be difficult to diagnose. Individuals who have had concussions are at an increased risk of depression, post-traumatic stress disorder (PTSD), headaches, concentration difficulties, and other problems. During the last decade, a significant amount of attention has been given to the acquisition of clinical data from patients suffering from mTBI. Unfortunately, most of the data collection and analysis have focused on individual aspects of the injury, not necessarily on comprehensive and multi-modal analytical techniques to capture the complex biological state of mTBI patients.

This talk will discuss a large-scale informatics database that has been developed to enable interdisciplinary research on mTBI and will introduce a multi-scale approach to mine complex clinical datasets. The millions of multi-modal elements originated from different clinical disciplines are treated as weak features and modeled independently to generate stronger features. Three cases of going from weak to stronger features will be discussed including (a) an inductive/transductive model to extract stable image features from multi-modal MRI scans, (b) a rule-based model used to infer knowledge from blood measurements, and (c) a sentiment analysis-based model to extract behavioral signals from writing samples. Once stronger features are obtained, a relational model is used to integrate the data and extract new knowledge from such a complex dataset.

Dr. Caban is the Acting Chief of Clinical & Research Informatics at the National Intrepid Center of Excellence (NICoE) at Walter Reed Bethesda. He received a Ph.D. in Computer Science from UMBC (2009), his M.S. degree in Computer Science from the University of Kentucky (2005), and his B.S. in Computer Science from the University of Puerto Rico (2002). Over the last eight years Dr. Caban’s research has focused on the design and development of techniques to analyze clinical and imaging data. His research and experience has given him the opportunity to work at top research and healthcare organizations including the National Institutes of Health (NIH), John Hopkins University, the University of Maryland Medical Center, and IBM Research. Dr. Caban is presently an adjunct faculty member at John Hopkins University Applied Physics Lab and a part-time instructor at the Department of Computer Science at UMBC. Recently, he received the 2013-14 JHU/APL Junior faculty award for his commitment to teaching. Currently, he is serving as the Associate Editor of the JAMIA special issue on Visual Analytics in Healthcare and as the contracting officer representative (COR) for the DoD program on “Watson-Like Technologies for TBI/PTSD Clinical Decision Support and Predictive Analytics”.

A multi-scale approach to analyze large clinical datasets:
Towards the understanding of the complex effects of concussions

Dr. Jesus Caban
National Intrepid Center of Excellence
Walter Reed, Bethesda, MD

Noon Thursday, 10 April 2014, ITE325b

Mild traumatic brain injuries (mTBIs) or concussions are invisible injuries that are poorly understood and their sequelae can be difficult to diagnose. Individuals who have had concussions are at an increased risk of depression, post-traumatic stress disorder (PTSD), headaches, concentration difficulties, and other problems. During the last decade, a significant amount of attention has been given to the acquisition of clinical data from patients suffering from mTBI. Unfortunately, most of the data collection and analysis have focused on individual aspects of the injury, not necessarily on comprehensive and multi-modal analytical techniques to capture the complex biological state of mTBI patients.

This talk will discuss a large-scale informatics database that has been developed to enable interdisciplinary research on mTBI and will introduce a multi-scale approach to mine complex clinical datasets. The millions of multi-modal elements originated from different clinical disciplines are treated as weak features and modeled independently to generate stronger features. Three cases of going from weak to stronger features will be discussed including (a) an inductive/transductive model to extract stable image features from multi-modal MRI scans, (b) a rule-based model used to infer knowledge from blood measurements, and (c) a sentiment analysis-based model to extract behavioral signals from writing samples. Once stronger features are obtained, a relational model is used to integrate the data and extract new knowledge from such a complex dataset.

Dr. Caban is the Acting Chief of Clinical & Research Informatics at the National Intrepid Center of Excellence (NICoE) at Walter Reed Bethesda. He received a Ph.D. in Computer Science from UMBC (2009), his M.S. degree in Computer Science from the University of Kentucky (2005), and his B.S. in Computer Science from the University of Puerto Rico (2002). Over the last eight years Dr. Caban’s research has focused on the design and development of techniques to analyze clinical and imaging data. His research and experience has given him the opportunity to work at top research and healthcare organizations including the National Institutes of Health (NIH), John Hopkins University, the University of Maryland Medical Center, and IBM Research. Dr. Caban is presently an adjunct faculty member at John Hopkins University Applied Physics Lab and a part-time instructor at the Department of Computer Science at UMBC. Recently, he received the 2013-14 JHU/APL Junior faculty award for his commitment to teaching. Currently, he is serving as the Associate Editor of the JAMIA special issue on Visual Analytics in Healthcare and as the contracting officer representative (COR) for the DoD program on “Watson-Like Technologies for TBI/PTSD Clinical Decision Support and Predictive Analytics”.

Talking to Robots: Learning to Ground Human
Language in Robotic Perception

Cynthia Matuszek
University of Washington

1:00pm Monday, 7 April 2014, ITE325b, UMBC

Advances in computation, sensing, and hardware are enabling robots to perform an increasing variety of tasks in ever less constrained settings. It is now possible to imagine robots that can operate in traditionally human-centric settings. However, such robots need the flexibility to take instructions and learn about tasks from nonspecialists using language and other natural modalities. At the same time, learning to process natural language about the physical world is difficult without a robot’s sensors and actuators. Combining these areas to create useful robotic systems is a fundamentally multidisciplinary problem, requiring advances in natural language processing, machine learning, robotics, and human-robot interaction. In this talk, I describe my work on learning natural language from end users in a physical context; such language allows a person to communicate their needs in a natural, unscripted way. I demonstrate that this approach can enable a robot to follow directions, learn about novel objects in the world, and perform simple tasks such as navigating an unfamiliar map or putting away objects.

Cynthia Matuszek is a Ph.D. candidate in the University of Washington Computer Science and Engineering department, where she is a member of both the Robotics and State Estimation lab and the Language, Interaction, and Learning group. She earned a B.S. in Computer Science from the University of Texas at Austin, and M.Sc. from the University of Washington. She is published in the areas of artificial intelligence, robotics, ubiquitous computing, and human-robot interaction.

Talking to Robots: Learning to Ground Human
Language in Robotic Perception

Cynthia Matuszek
University of Washington

1:00pm Monday, 7 April 2014, ITE325b, UMBC

Advances in computation, sensing, and hardware are enabling robots to perform an increasing variety of tasks in ever less constrained settings. It is now possible to imagine robots that can operate in traditionally human-centric settings. However, such robots need the flexibility to take instructions and learn about tasks from nonspecialists using language and other natural modalities. At the same time, learning to process natural language about the physical world is difficult without a robot’s sensors and actuators. Combining these areas to create useful robotic systems is a fundamentally multidisciplinary problem, requiring advances in natural language processing, machine learning, robotics, and human-robot interaction. In this talk, I describe my work on learning natural language from end users in a physical context; such language allows a person to communicate their needs in a natural, unscripted way. I demonstrate that this approach can enable a robot to follow directions, learn about novel objects in the world, and perform simple tasks such as navigating an unfamiliar map or putting away objects.

Cynthia Matuszek is a Ph.D. candidate in the University of Washington Computer Science and Engineering department, where she is a member of both the Robotics and State Estimation lab and the Language, Interaction, and Learning group. She earned a B.S. in Computer Science from the University of Texas at Austin, and M.Sc. from the University of Washington. She is published in the areas of artificial intelligence, robotics, ubiquitous computing, and human-robot interaction.