In coordination with the First-Generation Workgroup, Off-Campus Student Services is incredibly excited to announce the First-Generation Peer Mentoring Program (FGPMP). 

FGPMP is designed to support the success of current first-time and full-time first-generation students at UMBC. Students that take part in this program can expect to meet other first-generation students and become a part of a large, supportive community that works to share resources, find lasting friendships and connections, and create an impactful college experience. 

Participants of the FGPMP will also have the opportunity to take part in a series of activities and workshops designed to enrich students both personally and professionally. 

Some additional benefits of taking part in the FGPMP include:

• Developing transferable and marketable skills to enhance professional experience

• Cultivation of leadership, self-management, and organizational skills

• Improved communication and interpersonal skills

• Greater self-efficacy and self-awareness

• An increased network of support

• Expanding your resume

If you would like to be a part of the FGPMP, please click here to fill out the application. The application deadline is Tuesday, September 15, 2020 at 5:00 PM. 

If you have any questions, please contact Dr. M. Antonio Silas at silas@umbc.edu. Thank you and we look forward to connecting with you!

ORIGINAL POST

The VA understands and has prepared for the many impacts COVID-19 can have on students, on-line schooling and institutions of higher learning.  While every student situation is different and there are many unknowns with regards to the pandemic, VA is prepared for any increase in workload for applications and fall enrollments due to the financial and economic impacts of COVID-19 to our beneficiaries.

VA is committed to helping Veterans and their families through this national emergency.  From the beginning of this crisis, we have been working with Congress to preserve GI Bill benefits for students impacted by COVID-19 during this difficult time.  Two public laws were passed in the spring (PL 116-128 and PL 116-140 “Student Veteran Coronavirus Response Act of 2020”) to provide benefits stability for our students whose enrollments have been impacted by COVID-19. 

The new laws allow VA to pay education benefits as if the student were training in-residence regardless of the fact that a program has been converted from resident training to online training.  GI Bill students who are training online in lieu of in-residence will continue to receive housing allowance payments at the resident training until December 21, 2020.  This includes situations where your school is offering hybrid training (combining online and resident training) or where your school is offering the student the option to choose online training in lieu of resident training.  Additionally, VA will afford these same protections to new incoming students.  There is no requirement that the student have a previous history of taking resident courses.  The only requirement is that the student must be enrolled in a “converted course” – a course which your school offered as resident training prior to COVID-19 but is now offering as online training due to COVID-19. 

As a reminder, these laws only protect our students until December 21, 2020.  If a student is enrolled solely online on December 22, 2020, VA will have to reduce their housing to the online rate, regardless of COVID-19.  Also, if a work-study student is unable to work due to COVID-19, the student will no longer receive work-study benefits.

If you have any questions, please contact Tim Rochford (School Certifying Official) at rochford@umbc.edu.

TL;DR: The Women's Center is bringing back our whiteboard Question of the Week... but online!

So, for our first question of the week...

What do you want to see and/or what questions do you want answered on our virtual community whiteboard?

CSEE faculty receive NSF award to help robots learn tasks by interacting naturally with people

UMBC Assistant Professors Cynthia Matuszek (PI) and Francis Ferraro (Co-PI), along with senior staff scientist at JHU-APL John Winder (Co-PI) received a three-year NSF award as part of the National Robotics Initiative on Ubiquitous Collaborative Robots. The award for Semi-Supervised Deep Learning for Domain Adaptation in Robotic Language Acquisition will advance the ability of robots to learn from interactions with people using spoken language and gestures in a variety of situations.

This project will enable robots to learn to perform tasks with human teammates from language and other modalities, and then transfer what they have learned to other robots with different capabilities in order to perform different tasks. This will ultimately allow human-robot teaming in domains where people use varied language and instructions to complete complex tasks. As robots become more capable and ubiquitous, they are increasingly moving into complex, human-centric environments such as workplaces and homes.

Being able to deploy useful robots in settings where human specialists are stretched thin, such as assistive technology, elder care, and education, has the potential to have far-reaching impacts on human quality of life. Achieving this will require the development of robots that learn, from natural interaction, about an end user’s goals and environment.

This work is intended to make robots more accessible and usable for non-specialists. In order to verify success and involve the broader community, tasks will be drawn from and tested in community Makerspaces, which are strongly linked with both education and community involvement. It will address how collaborative learning and successful performance during human-robot interactions can be accomplished by learning from and acting on grounded language. To accomplish this, the project will revolve around learning structured representations of abstract knowledge with goal-directed task completion, grounded in a physical context.

There are three high-level research thrusts: leverage grounded language learning from many sources, capture and represent the expectations implied by language, and use deep hierarchical reinforcement learning to transfer learned knowledge to related tasks and skills. In the first, new perceptual models to learn an alignment among a robot’s multiple, heterogeneous sensor and data streams will be developed. In the second, synchronous grounded language models will be developed to better capture both general linguistic and implicit contextual expectations that are needed for completing shared tasks. In the third, a deep reinforcement learning framework will be developed that can leverage the advances achieved by the first two thrusts, allowing the development of techniques for learning conceptual knowledge. Taken together, these advances will allow an agent to achieve domain adaptation, improve its behaviors in new environments, and transfer conceptual knowledge among robotic agents.

The research award will support both faculty and students working in the Interactive Robotics and Language lab on this task. It includes an education and outreach plan designed to increase participation by and retention of women and underrepresented minorities (URM) in robotics and computing, engaging with UMBC’s large URM population and world-class programs in this area.

The post New NSF grant to improve human-robot interaction appeared first on Department of Computer Science and Electrical Engineering.